"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import copy
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (BaseModelOutput, ModelOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings)
from .configuration_canine import CanineConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/canine-s"
_CONFIG_FOR_DOC = "CanineConfig"
_PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223]
@dataclass
class CanineModelOutputWithPooling(ModelOutput):
    """Args:"""
    last_hidden_state: torch.FloatTensor = None
    pooler_output: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
def load_tf_weights_in_canine(model, config, tf_checkpoint_path):
    try:
        import re
        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error("Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.")
        raise
    tf_path = os.path.abspath(tf_checkpoint_path)
    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array)
    for name, array in zip(names, arrays):
        name = name.split("/")
        if any(n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step", "cls", "autoregressive_decoder", "char_output_weights"] for n in name):
            logger.info(f"Skipping {'/'.join(name)}")
            continue
        if name[0] == "bert": name[0] = "encoder"
        elif name[1] == "embeddings": name.remove(name[1])
        elif name[1] == "segment_embeddings": name[1] = "token_type_embeddings"
        elif name[1] == "initial_char_encoder": name = ["chars_to_molecules"] + name[-2:]
        elif name[0] == "final_char_encoder" and name[1] in ["LayerNorm", "conv"]: name = ["projection"] + name[1:]
        pointer = model
        for m_name in name:
            if (re.fullmatch(r"[A-Za-z]+_\d+", m_name)) and "Embedder" not in m_name: scope_names = re.split(r"_(\d+)", m_name)
            else: scope_names = [m_name]
            if scope_names[0] == "kernel" or scope_names[0] == "gamma": pointer = getattr(pointer, "weight")
            elif scope_names[0] == "output_bias" or scope_names[0] == "beta": pointer = getattr(pointer, "bias")
            elif scope_names[0] == "output_weights": pointer = getattr(pointer, "weight")
            else:
                try: pointer = getattr(pointer, scope_names[0])
                except AttributeError:
                    logger.info(f"Skipping {'/'.join(name)}")
                    continue
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]
        if m_name[-11:] == "_embeddings": pointer = getattr(pointer, "weight")
        elif m_name[-10:] in [f"Embedder_{i}" for i in range(8)]: pointer = getattr(pointer, "weight")
        elif m_name == "kernel": array = np.transpose(array)
        if pointer.shape != array.shape: raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
        logger.info(f"Initialize PyTorch weight {name}")
        pointer.data = torch.from_numpy(array)
    return model
class CanineEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        shard_embedding_size = config.hidden_size // config.num_hash_functions
        for i in range(config.num_hash_functions):
            name = f"HashBucketCodepointEmbedder_{i}"
            setattr(self, name, nn.Embedding(config.num_hash_buckets, shard_embedding_size))
        self.char_position_embeddings = nn.Embedding(config.num_hash_buckets, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
    def _hash_bucket_tensors(self, input_ids, num_hashes: int, num_buckets: int):
        if num_hashes > len(_PRIMES): raise ValueError(f"`num_hashes` must be <= {len(_PRIMES)}")
        primes = _PRIMES[:num_hashes]
        result_tensors = []
        for prime in primes:
            hashed = ((input_ids + 1) * prime) % num_buckets
            result_tensors.append(hashed)
        return result_tensors
    def _embed_hash_buckets(self, input_ids, embedding_size: int, num_hashes: int, num_buckets: int):
        if embedding_size % num_hashes != 0: raise ValueError(f"Expected `embedding_size` ({embedding_size}) % `num_hashes` ({num_hashes}) == 0")
        hash_bucket_tensors = self._hash_bucket_tensors(input_ids, num_hashes=num_hashes, num_buckets=num_buckets)
        embedding_shards = []
        for i, hash_bucket_ids in enumerate(hash_bucket_tensors):
            name = f"HashBucketCodepointEmbedder_{i}"
            shard_embeddings = getattr(self, name)(hash_bucket_ids)
            embedding_shards.append(shard_embeddings)
        return torch.cat(embedding_shards, dim=-1)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        seq_length = input_shape[1]
        if position_ids is None: position_ids = self.position_ids[:, :seq_length]
        if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        if inputs_embeds is None: inputs_embeds = self._embed_hash_buckets(input_ids, self.config.hidden_size, self.config.num_hash_functions, self.config.num_hash_buckets)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + token_type_embeddings
        if self.position_embedding_type == "absolute":
            position_embeddings = self.char_position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
class CharactersToMolecules(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.conv = nn.Conv1d(in_channels=config.hidden_size, out_channels=config.hidden_size, kernel_size=config.downsampling_rate, stride=config.downsampling_rate)
        self.activation = ACT2FN[config.hidden_act]
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
    def forward(self, char_encoding: torch.Tensor) -> torch.Tensor:
        cls_encoding = char_encoding[:, 0:1, :]
        char_encoding = torch.transpose(char_encoding, 1, 2)
        downsampled = self.conv(char_encoding)
        downsampled = torch.transpose(downsampled, 1, 2)
        downsampled = self.activation(downsampled)
        downsampled_truncated = downsampled[:, 0:-1, :]
        result = torch.cat([cls_encoding, downsampled_truncated], dim=1)
        result = self.LayerNorm(result)
        return result
class ConvProjection(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.conv = nn.Conv1d(in_channels=config.hidden_size * 2, out_channels=config.hidden_size, kernel_size=config.upsampling_kernel_size, stride=1)
        self.activation = ACT2FN[config.hidden_act]
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, inputs: torch.Tensor, final_seq_char_positions: Optional[torch.Tensor] = None) -> torch.Tensor:
        inputs = torch.transpose(inputs, 1, 2)
        pad_total = self.config.upsampling_kernel_size - 1
        pad_beg = pad_total // 2
        pad_end = pad_total - pad_beg
        pad = nn.ConstantPad1d((pad_beg, pad_end), 0)
        result = self.conv(pad(inputs))
        result = torch.transpose(result, 1, 2)
        result = self.activation(result)
        result = self.LayerNorm(result)
        result = self.dropout(result)
        final_char_seq = result
        if final_seq_char_positions is not None: raise NotImplementedError("CanineForMaskedLM is currently not supported")
        else: query_seq = final_char_seq
        return query_seq
class CanineSelfAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})")
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            self.max_position_embeddings = config.max_position_embeddings
            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        x = x.view(*new_x_shape)
        return x.permute(0, 2, 1, 3)
    def forward(self, from_tensor: torch.Tensor, to_tensor: torch.Tensor, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    output_attentions: Optional[bool] = False) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        mixed_query_layer = self.query(from_tensor)
        key_layer = self.transpose_for_scores(self.key(to_tensor))
        value_layer = self.transpose_for_scores(self.value(to_tensor))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
            seq_length = from_tensor.size()[1]
            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(-1, 1)
            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(1, -1)
            distance = position_ids_l - position_ids_r
            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)
            if self.position_embedding_type == "relative_key":
                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores
            elif self.position_embedding_type == "relative_key_query":
                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            if attention_mask.ndim == 3:
                attention_mask = torch.unsqueeze(attention_mask, dim=1)
                attention_mask = (1.0 - attention_mask.float()) * torch.finfo(attention_scores.dtype).min
            attention_scores = attention_scores + attention_mask
        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
        attention_probs = self.dropout(attention_probs)
        if head_mask is not None: attention_probs = attention_probs * head_mask
        context_layer = torch.matmul(attention_probs, value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
class CanineSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class CanineAttention(nn.Module):
    def __init__(self, config, local=False, always_attend_to_first_position: bool = False, first_position_attends_to_all: bool = False, attend_from_chunk_width: int = 128,
    attend_from_chunk_stride: int = 128, attend_to_chunk_width: int = 128, attend_to_chunk_stride: int = 128):
        super().__init__()
        self.self = CanineSelfAttention(config)
        self.output = CanineSelfOutput(config)
        self.pruned_heads = set()
        self.local = local
        if attend_from_chunk_width < attend_from_chunk_stride: raise ValueError("`attend_from_chunk_width` < `attend_from_chunk_stride` would cause sequence positions to get skipped.")
        if attend_to_chunk_width < attend_to_chunk_stride: raise ValueError("`attend_to_chunk_width` < `attend_to_chunk_stride`would cause sequence positions to get skipped.")
        self.always_attend_to_first_position = always_attend_to_first_position
        self.first_position_attends_to_all = first_position_attends_to_all
        self.attend_from_chunk_width = attend_from_chunk_width
        self.attend_from_chunk_stride = attend_from_chunk_stride
        self.attend_to_chunk_width = attend_to_chunk_width
        self.attend_to_chunk_stride = attend_to_chunk_stride
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads)
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    output_attentions: Optional[bool] = False) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
        if not self.local:
            self_outputs = self.self(hidden_states, hidden_states, attention_mask, head_mask, output_attentions)
            attention_output = self_outputs[0]
        else:
            from_seq_length = to_seq_length = hidden_states.shape[1]
            from_tensor = to_tensor = hidden_states
            from_chunks = []
            if self.first_position_attends_to_all:
                from_chunks.append((0, 1))
                from_start = 1
            else: from_start = 0
            for chunk_start in range(from_start, from_seq_length, self.attend_from_chunk_stride):
                chunk_end = min(from_seq_length, chunk_start + self.attend_from_chunk_width)
                from_chunks.append((chunk_start, chunk_end))
            to_chunks = []
            if self.first_position_attends_to_all: to_chunks.append((0, to_seq_length))
            for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride):
                chunk_end = min(to_seq_length, chunk_start + self.attend_to_chunk_width)
                to_chunks.append((chunk_start, chunk_end))
            if len(from_chunks) != len(to_chunks): raise ValueError(f"Expected to have same number of `from_chunks` ({from_chunks}) and `to_chunks` ({from_chunks}). Check strides.")
            attention_output_chunks = []
            attention_probs_chunks = []
            for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks):
                from_tensor_chunk = from_tensor[:, from_start:from_end, :]
                to_tensor_chunk = to_tensor[:, to_start:to_end, :]
                attention_mask_chunk = attention_mask[:, from_start:from_end, to_start:to_end]
                if self.always_attend_to_first_position:
                    cls_attention_mask = attention_mask[:, from_start:from_end, 0:1]
                    attention_mask_chunk = torch.cat([cls_attention_mask, attention_mask_chunk], dim=2)
                    cls_position = to_tensor[:, 0:1, :]
                    to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1)
                attention_outputs_chunk = self.self(from_tensor_chunk, to_tensor_chunk, attention_mask_chunk, head_mask, output_attentions)
                attention_output_chunks.append(attention_outputs_chunk[0])
                if output_attentions: attention_probs_chunks.append(attention_outputs_chunk[1])
            attention_output = torch.cat(attention_output_chunks, dim=1)
        attention_output = self.output(attention_output, hidden_states)
        outputs = (attention_output,)
        if not self.local: outputs = outputs + self_outputs[1:]
        else: outputs = outputs + tuple(attention_probs_chunks)
        return outputs
class CanineIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else: self.intermediate_act_fn = config.hidden_act
    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states
class CanineOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor) -> torch.FloatTensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class CanineLayer(nn.Module):
    def __init__(self, config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = CanineAttention(config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride)
        self.intermediate = CanineIntermediate(config)
        self.output = CanineOutput(config)
    def forward(self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    output_attentions: Optional[bool] = False) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
        self_attention_outputs = self.attention(hidden_states, attention_mask, head_mask, output_attentions=output_attentions)
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]
        layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output)
        outputs = (layer_output,) + outputs
        return outputs
    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output
class CanineEncoder(nn.Module):
    def __init__(self, config, local=False, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=128, attend_from_chunk_stride=128, attend_to_chunk_width=128, attend_to_chunk_stride=128):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([CanineLayer(config, local, always_attend_to_first_position, first_position_attends_to_all, attend_from_chunk_width, attend_from_chunk_stride, attend_to_chunk_width, attend_to_chunk_stride) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False
    def forward(self, hidden_states: Tuple[torch.FloatTensor], attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = False,
    output_hidden_states: Optional[bool] = False, return_dict: Optional[bool] = True) -> Union[Tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_head_mask = head_mask[i] if head_mask is not None else None
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, attention_mask, layer_head_mask, output_attentions)
            else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions)
class CaninePooler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = nn.Tanh()
    def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor:
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output
class CaninePredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act]
        else: self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
    def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states
class CanineLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = CaninePredictionHeadTransform(config)
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        self.decoder.bias = self.bias
    def forward(self, hidden_states: Tuple[torch.FloatTensor]) -> torch.FloatTensor:
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
class CanineOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = CanineLMPredictionHead(config)
    def forward(self, sequence_output: Tuple[torch.Tensor]) -> Tuple[torch.Tensor]:
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores
class CaninePreTrainedModel(PreTrainedModel):
    config_class = CanineConfig
    load_tf_weights = load_tf_weights_in_canine
    base_model_prefix = "canine"
    supports_gradient_checkpointing = True
    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Conv1d)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
CANINE_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
    Parameters:
        config ([`CanineConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
CANINE_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `(0)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        inputs_embeds (`torch.FloatTensor` of shape `(0, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
            model's internal embedding lookup matrix.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings("The bare CANINE Model transformer outputting raw hidden-states without any specific head on top.", CANINE_START_DOCSTRING)
class CanineModel(CaninePreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        shallow_config = copy.deepcopy(config)
        shallow_config.num_hidden_layers = 1
        self.char_embeddings = CanineEmbeddings(config)
        self.initial_char_encoder = CanineEncoder(shallow_config, local=True, always_attend_to_first_position=False, first_position_attends_to_all=False, attend_from_chunk_width=config.local_transformer_stride,
        attend_from_chunk_stride=config.local_transformer_stride, attend_to_chunk_width=config.local_transformer_stride, attend_to_chunk_stride=config.local_transformer_stride)
        self.chars_to_molecules = CharactersToMolecules(config)
        self.encoder = CanineEncoder(config)
        self.projection = ConvProjection(config)
        self.final_char_encoder = CanineEncoder(shallow_config)
        self.pooler = CaninePooler(config) if add_pooling_layer else None
        self.post_init()
    def _prune_heads(self, heads_to_prune):
        for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
    def _create_3d_attention_mask_from_input_mask(self, from_tensor, to_mask):
        batch_size, from_seq_length = from_tensor.shape[0], from_tensor.shape[1]
        to_seq_length = to_mask.shape[1]
        to_mask = torch.reshape(to_mask, (batch_size, 1, to_seq_length)).float()
        broadcast_ones = torch.ones(size=(batch_size, from_seq_length, 1), dtype=torch.float32, device=to_mask.device)
        mask = broadcast_ones * to_mask
        return mask
    def _downsample_attention_mask(self, char_attention_mask: torch.Tensor, downsampling_rate: int):
        batch_size, char_seq_len = char_attention_mask.shape
        poolable_char_mask = torch.reshape(char_attention_mask, (batch_size, 1, char_seq_len))
        pooled_molecule_mask = torch.nn.MaxPool1d(kernel_size=downsampling_rate, stride=downsampling_rate)(poolable_char_mask.float())
        molecule_attention_mask = torch.squeeze(pooled_molecule_mask, dim=-1)
        return molecule_attention_mask
    def _repeat_molecules(self, molecules: torch.Tensor, char_seq_length: torch.Tensor) -> torch.Tensor:
        rate = self.config.downsampling_rate
        molecules_without_extra_cls = molecules[:, 1:, :]
        repeated = torch.repeat_interleave(molecules_without_extra_cls, repeats=rate, dim=-2)
        last_molecule = molecules[:, -1:, :]
        remainder_length = torch.fmod(torch.tensor(char_seq_length), torch.tensor(rate)).item()
        remainder_repeated = torch.repeat_interleave(last_molecule, repeats=remainder_length + rate, dim=-2)
        return torch.cat([repeated, remainder_repeated], dim=-2)
    @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, CanineModelOutputWithPooling]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device)
        if token_type_ids is None: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
        molecule_attention_mask = self._downsample_attention_mask(attention_mask, downsampling_rate=self.config.downsampling_rate)
        extended_molecule_attention_mask: torch.Tensor = self.get_extended_attention_mask(molecule_attention_mask, (batch_size, molecule_attention_mask.shape[-1]))
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        input_char_embeddings = self.char_embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
        char_attention_mask = self._create_3d_attention_mask_from_input_mask(input_ids if input_ids is not None else inputs_embeds, attention_mask)
        init_chars_encoder_outputs = self.initial_char_encoder(input_char_embeddings, attention_mask=char_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states)
        input_char_encoding = init_chars_encoder_outputs.last_hidden_state
        init_molecule_encoding = self.chars_to_molecules(input_char_encoding)
        encoder_outputs = self.encoder(init_molecule_encoding, attention_mask=extended_molecule_attention_mask, head_mask=head_mask, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        molecule_sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(molecule_sequence_output) if self.pooler is not None else None
        repeated_molecules = self._repeat_molecules(molecule_sequence_output, char_seq_length=input_shape[-1])
        concat = torch.cat([input_char_encoding, repeated_molecules], dim=-1)
        sequence_output = self.projection(concat)
        final_chars_encoder_outputs = self.final_char_encoder(sequence_output, attention_mask=extended_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states)
        sequence_output = final_chars_encoder_outputs.last_hidden_state
        if output_hidden_states:
            deep_encoder_hidden_states = encoder_outputs.hidden_states if return_dict else encoder_outputs[1]
            all_hidden_states = (all_hidden_states + init_chars_encoder_outputs.hidden_states + deep_encoder_hidden_states + final_chars_encoder_outputs.hidden_states)
        if output_attentions:
            deep_encoder_self_attentions = encoder_outputs.attentions if return_dict else encoder_outputs[-1]
            all_self_attentions = (all_self_attentions + init_chars_encoder_outputs.attentions + deep_encoder_self_attentions + final_chars_encoder_outputs.attentions)
        if not return_dict:
            output = (sequence_output, pooled_output)
            output += tuple(v for v in [all_hidden_states, all_self_attentions] if v is not None)
            return output
        return CanineModelOutputWithPooling(last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=all_hidden_states, attentions=all_self_attentions)
@add_start_docstrings("CANINE Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", CANINE_START_DOCSTRING)
class CanineForSequenceClassification(CaninePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.canine = CanineModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, SequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.canine(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1: self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("CANINE Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.", CANINE_START_DOCSTRING)
class CanineForMultipleChoice(CaninePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.canine = CanineModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, 1)
        self.post_init()
    @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, MultipleChoiceModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
        inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None)
        outputs = self.canine(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)
        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return MultipleChoiceModelOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("CANINE Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.", CANINE_START_DOCSTRING)
class CanineForTokenClassification(CaninePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.canine = CanineModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, TokenClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.canine(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output
        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("CANINE Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).", CANINE_START_DOCSTRING)
class CanineForQuestionAnswering(CaninePreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.canine = CanineModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(CANINE_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, start_positions: Optional[torch.LongTensor] = None,
    end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, QuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.canine(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)
        total_loss = None
        if start_positions is not None and end_positions is not None:
            if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1)
            ignored_index = start_logits.size(1)
            start_positions.clamp_(0, ignored_index)
            end_positions.clamp_(0, ignored_index)
            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2
        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output
        return QuestionAnsweringModelOutput(loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
