"""
    #################################################################################################################################################################################################
    # 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 math
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ....activations import ACT2FN
from ....file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ....integrations.deepspeed import is_deepspeed_zero3_enabled
from ....modeling_attn_mask_utils import _prepare_4d_attention_mask
from ....modeling_outputs import BaseModelOutput, CausalLMOutput
from ....modeling_utils import (PreTrainedModel, apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer)
from ....utils import logging
from .configuration_mctct import MCTCTConfig
logger = logging.get_logger(__name__)
_HIDDEN_STATES_START_POSITION = 1
_CONFIG_FOR_DOC = "MCTCTConfig"
_CHECKPOINT_FOR_DOC = "speechbrain/m-ctc-t-large"
_EXPECTED_OUTPUT_SHAPE = [1, 195, 1536]
_CTC_EXPECTED_OUTPUT = '"Mr. Quilter is the apostle of the middle classes, and we\'re glad to welcome his gospel."'
_CTC_EXPECTED_LOSS = 1885.65
class MCTCTConv1dSubsampler(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.glu_dim = config.conv_glu_dim
        self.dropout = nn.Dropout(config.conv_dropout)
        self.num_layers = config.num_conv_layers
        self.in_channels = config.input_feat_per_channel * config.input_channels
        if self.num_layers > 1:
            if config.conv_channels is None: raise ValueError("Need to specify `conv_channels` configuration in `MCTCTConfig` to use multiple convolution layers.")
            self.mid_channels = config.conv_channels
        else: self.mid_channels = None
        self.out_channels = config.hidden_size * 2
        self.kernel_size = config.conv_kernel
        self.stride = config.conv_stride
        self.conv_layers = nn.ModuleList(nn.Conv1d(self.in_channels if i == 0 else self.mid_channels[i], self.mid_channels[i] if i < self.num_layers - 1 else self.out_channels, kernel_size=k, stride=self.stride[i], padding="valid") for i, k in enumerate(self.kernel_size))
    def forward(self, input_features):
        padding = sum([size // 2 for size in self.kernel_size])
        input_features = torch.nn.functional.pad(input_features, (0, 0, padding, padding), "constant", 0)
        hidden_states = input_features.transpose(1, 2).contiguous()
        for conv in self.conv_layers:
            hidden_states = conv(hidden_states)
            hidden_states = nn.functional.glu(hidden_states, dim=self.glu_dim)
            hidden_states = self.dropout(hidden_states)
        hidden_states = hidden_states.transpose(1, 2).contiguous()
        return hidden_states
class MCTCTEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.LayerNorm = MCTCTLayerNorm()
        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.register_buffer("token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device), persistent=False)
    def forward(self, input_features=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
        input_shape = input_features.size() if input_features is not None else inputs_embeds.size()[:-1]
        seq_length = input_shape[1]
        if position_ids is None: position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
        if token_type_ids is None:
            if hasattr(self, "token_type_ids"):
                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_features)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
class MCTCTSelfAttention(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 = config.attention_head_dim
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.max_position_embeddings = config.max_position_embeddings
        self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
        self.is_decoder = config.is_decoder
    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 reshape_fortran(self, x, shape):
        if len(x.shape) > 0: x = x.permute(*reversed(range(len(x.shape))))
        return x.reshape(*reversed(shape)).permute(*reversed(range(len(shape))))
    def relative_position_embedding_rotate(self, scores):
        scores = scores.permute(0, 2, 3, 1)
        batch, hidden_state, seq_len, heads = scores.shape
        scores = torch.cat((scores, torch.zeros((batch, seq_len, seq_len, heads), device=scores.device)), dim=1)
        scores = self.reshape_fortran(scores, [batch, (hidden_state + seq_len) * seq_len, 1, heads])
        scores = scores[:, : (seq_len + hidden_state - 1) * seq_len]
        scores = self.reshape_fortran(scores, [batch, hidden_state + seq_len - 1, seq_len, heads])
        halfpoint = hidden_state // 2
        scores = scores[:, halfpoint : halfpoint + seq_len].transpose(1, 2)
        return scores.permute(0, 3, 1, 2)
    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
        mixed_query_layer = self.query(hidden_states)
        mixed_query_layer = mixed_query_layer / math.sqrt(self.attention_head_size)
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        positional_embedding = self.distance_embedding.weight
        relative_position_scores = torch.einsum("lh, bche -> bcle", positional_embedding, query_layer.transpose(2, 3))
        relative_position_scores = self.relative_position_embedding_rotate(relative_position_scores)
        attention_scores = attention_scores + relative_position_scores
        if attention_mask is not None: 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).flatten(start_dim=-2)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
class MCTCTLayerNorm(nn.Module):
    def __init__(self):
        super().__init__()
        self.singleton_weight = nn.Parameter(torch.ones(1))
        self.singleton_bias = nn.Parameter(torch.zeros(1))
    def forward(self, hidden_states): return (hidden_states * self.singleton_weight) + self.singleton_bias
class MCTCTSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
        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, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class MCTCTAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.self = MCTCTSelfAttention(config)
        self.output = MCTCTSelfOutput(config)
        self.pruned_heads = set()
    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, attention_mask=None, head_mask=None, output_attentions=False):
        self_outputs = self.self(hidden_states, attention_mask, head_mask, output_attentions)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class MCTCTIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        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):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states
class MCTCTOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
        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, input_tensor):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class MCTCTLayer(nn.Module):
    def __init__(self, config: MCTCTConfig):
        super().__init__()
        self.seq_len_dim = 1
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.intermediate = MCTCTIntermediate(config)
        self.attention = MCTCTAttention(config)
        self.is_decoder = config.is_decoder
        self.output = MCTCTOutput(config)
    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
        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 MCTCTPreTrainedModel(PreTrainedModel):
    config_class = MCTCTConfig
    base_model_prefix = "mctct"
    main_input_name = "input_features"
    supports_gradient_checkpointing = True
    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            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)
        elif isinstance(module, MCTCTLayerNorm):
            module.singleton_weight.data.fill_(1.0)
            module.singleton_bias.data.zero_()
        if isinstance(module, (nn.Linear, nn.Conv1d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None: module.bias.data.zero_()
    def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
        dilation = 1
        for _, kernel_sz, stride in zip(range(self.config.num_conv_layers), self.config.conv_kernel, self.config.conv_stride):
            padding = kernel_sz // 2
            input_lengths = input_lengths + 2 * padding - dilation * (kernel_sz - 1) - 1
            input_lengths = torch.div(input_lengths, stride, rounding_mode="trunc") + 1
        return input_lengths
    def _get_feature_vector_attention_mask(self, feature_vector_length, attention_mask):
        if len(attention_mask.shape) > 2: attention_mask = attention_mask[:, :, -1]
        subsampled_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1))
        bsz = attention_mask.size()[0]
        attention_mask = torch.zeros((bsz, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device)
        attention_mask[(torch.arange(bsz, device=attention_mask.device), subsampled_lengths - 1)] = 1
        attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).long()
        return attention_mask
MCTCT_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 ([`MCTCTConfig`]): 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.
"""
MCTCT_INPUTS_DOCSTRING = r"""
    Args:
        input_features (`torch.LongTensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`Wav2Vec2CTCTokenizer`]. 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)
        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*.
        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 [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
class MCTCTEncoder(MCTCTPreTrainedModel):
    def __init__(self, config: MCTCTConfig):
        super().__init__(config)
        self.hidden_dropout_prob = config.hidden_dropout_prob
        self.layer_norm = MCTCTLayerNorm()
        self.conv = MCTCTConv1dSubsampler(config)
        self.layers = nn.ModuleList([MCTCTLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False
    def forward(self, input_features: torch.Tensor, attention_mask: torch.Tensor, head_mask: torch.Tensor, output_attentions: bool = False, output_hidden_states: bool = False,
    return_dict: bool = True) -> Union[Tuple, BaseModelOutput]:
        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)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        input_features = self.layer_norm(input_features)
        inputs_embeds = self.conv(input_features)
        if attention_mask is not None: attention_mask = self._get_feature_vector_attention_mask(inputs_embeds.shape[1], attention_mask)
        hidden_states = nn.functional.dropout(inputs_embeds, p=self.hidden_dropout_prob, training=self.training)
        if attention_mask is not None: attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        if head_mask is not None:
            if head_mask.size()[0] != len(self.layers): raise ValueError(f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}.")
        deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()
        for idx, encoder_layer in enumerate(self.layers):
            if output_hidden_states: encoder_states = encoder_states + (hidden_states,)
            dropout_probability = torch.rand([])
            skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
            if not skip_the_layer or deepspeed_zero3_is_enabled:
                if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(encoder_layer.__call__, hidden_states, attention_mask, (head_mask[idx] if head_mask is not None else None), output_attentions)
                else: layer_outputs = encoder_layer(hidden_states=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions)
                hidden_states = layer_outputs[0]
            if skip_the_layer: layer_outputs = (None, None)
            if output_attentions: all_attentions = all_attentions + (layer_outputs[1],)
        if output_hidden_states: encoder_states = encoder_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions)
@add_start_docstrings("The bare M-CTC-T Model transformer outputting raw hidden-states without any specific head on top.", MCTCT_START_DOCSTRING)
class MCTCTModel(MCTCTPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.encoder = MCTCTEncoder(config)
        self.post_init()
    @add_start_docstrings_to_model_forward(MCTCT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_features: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutput]:
        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)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if input_features is None: raise ValueError("You have to specify input_features.")
        encoder_outputs = self.encoder(input_features, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = encoder_outputs[0]
        if not return_dict: return (sequence_output,) + encoder_outputs[1:]
        return BaseModelOutput(last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
@add_start_docstrings("MCTCT Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).", MCTCT_START_DOCSTRING)
class MCTCTForCTC(MCTCTPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.mctct = MCTCTModel(config)
        if config.vocab_size is None: raise ValueError(f"You are trying to instantiate {self.__class__} with a configuration that does not define the vocabulary size of the language model head. Please instantiate the model as follows: `MCTCTForCTC.from_pretrained(..., vocab_size=vocab_size)`. or define `vocab_size` of your model's configuration.")
        output_hidden_size = config.hidden_size
        self.ctc_head = nn.Linear(output_hidden_size, config.vocab_size)
        self.post_init()
    @add_start_docstrings_to_model_forward(MCTCT_INPUTS_DOCSTRING)
    def forward(self, input_features: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.LongTensor] = None) -> Union[Tuple, CausalLMOutput]:
        if labels is not None and labels.max() >= self.config.vocab_size: raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.mctct(input_features, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_states = outputs[0]
        logits = self.ctc_head(hidden_states)
        loss = None
        if labels is not None:
            attention_mask = (attention_mask if attention_mask is not None else torch.ones(input_features.shape[:-1], dtype=torch.long))
            input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
            labels_mask = labels >= 0
            target_lengths = labels_mask.sum(-1)
            flattened_targets = labels.masked_select(labels_mask)
            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
            with torch.backends.cudnn.flags(enabled=False): loss = nn.functional.ctc_loss(log_probs, flattened_targets, input_lengths, target_lengths, blank=self.config.pad_token_id, reduction=self.config.ctc_loss_reduction, zero_infinity=self.config.ctc_zero_infinity)
        if not return_dict:
            output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
            return ((loss,) + output) if loss is not None else output
        return CausalLMOutput(loss=loss, logits=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.                                                                 #
    #################################################################################################################################################################################################
"""
