"""
    #################################################################################################################################################################################################
    # 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 collections.abc
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 ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import ImageClassifierOutputWithNoAttention, ModelOutput
from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import logging
from .configuration_cvt import CvtConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "CvtConfig"
_CHECKPOINT_FOR_DOC = "microsoft/cvt-13"
_EXPECTED_OUTPUT_SHAPE = [1, 384, 14, 14]
_IMAGE_CLASS_CHECKPOINT = "microsoft/cvt-13"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
@dataclass
class BaseModelOutputWithCLSToken(ModelOutput):
    """Args:"""
    last_hidden_state: torch.FloatTensor = None
    cls_token_value: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
    if drop_prob == 0.0 or not training: return input
    keep_prob = 1 - drop_prob
    shape = (input.shape[0],) + (1,) * (input.ndim - 1)
    random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
    random_tensor.floor_()
    output = input.div(keep_prob) * random_tensor
    return output
class CvtDropPath(nn.Module):
    def __init__(self, drop_prob: Optional[float] = None) -> None:
        super().__init__()
        self.drop_prob = drop_prob
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: return drop_path(hidden_states, self.drop_prob, self.training)
    def extra_repr(self) -> str: return "p={}".format(self.drop_prob)
class CvtEmbeddings(nn.Module):
    def __init__(self, patch_size, num_channels, embed_dim, stride, padding, dropout_rate):
        super().__init__()
        self.convolution_embeddings = CvtConvEmbeddings(patch_size=patch_size, num_channels=num_channels, embed_dim=embed_dim, stride=stride, padding=padding)
        self.dropout = nn.Dropout(dropout_rate)
    def forward(self, pixel_values):
        hidden_state = self.convolution_embeddings(pixel_values)
        hidden_state = self.dropout(hidden_state)
        return hidden_state
class CvtConvEmbeddings(nn.Module):
    def __init__(self, patch_size, num_channels, embed_dim, stride, padding):
        super().__init__()
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        self.patch_size = patch_size
        self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=stride, padding=padding)
        self.normalization = nn.LayerNorm(embed_dim)
    def forward(self, pixel_values):
        pixel_values = self.projection(pixel_values)
        batch_size, num_channels, height, width = pixel_values.shape
        hidden_size = height * width
        pixel_values = pixel_values.view(batch_size, num_channels, hidden_size).permute(0, 2, 1)
        if self.normalization: pixel_values = self.normalization(pixel_values)
        pixel_values = pixel_values.permute(0, 2, 1).view(batch_size, num_channels, height, width)
        return pixel_values
class CvtSelfAttentionConvProjection(nn.Module):
    def __init__(self, embed_dim, kernel_size, padding, stride):
        super().__init__()
        self.convolution = nn.Conv2d(embed_dim, embed_dim, kernel_size=kernel_size, padding=padding, stride=stride, bias=False, groups=embed_dim)
        self.normalization = nn.BatchNorm2d(embed_dim)
    def forward(self, hidden_state):
        hidden_state = self.convolution(hidden_state)
        hidden_state = self.normalization(hidden_state)
        return hidden_state
class CvtSelfAttentionLinearProjection(nn.Module):
    def forward(self, hidden_state):
        batch_size, num_channels, height, width = hidden_state.shape
        hidden_size = height * width
        hidden_state = hidden_state.view(batch_size, num_channels, hidden_size).permute(0, 2, 1)
        return hidden_state
class CvtSelfAttentionProjection(nn.Module):
    def __init__(self, embed_dim, kernel_size, padding, stride, projection_method="dw_bn"):
        super().__init__()
        if projection_method == "dw_bn": self.convolution_projection = CvtSelfAttentionConvProjection(embed_dim, kernel_size, padding, stride)
        self.linear_projection = CvtSelfAttentionLinearProjection()
    def forward(self, hidden_state):
        hidden_state = self.convolution_projection(hidden_state)
        hidden_state = self.linear_projection(hidden_state)
        return hidden_state
class CvtSelfAttention(nn.Module):
    def __init__(self, num_heads, embed_dim, kernel_size, padding_q, padding_kv, stride_q, stride_kv, qkv_projection_method, qkv_bias, attention_drop_rate, with_cls_token=True, **kwargs):
        super().__init__()
        self.scale = embed_dim**-0.5
        self.with_cls_token = with_cls_token
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.convolution_projection_query = CvtSelfAttentionProjection(embed_dim, kernel_size, padding_q, stride_q, projection_method="linear" if qkv_projection_method == "avg" else qkv_projection_method)
        self.convolution_projection_key = CvtSelfAttentionProjection(embed_dim, kernel_size, padding_kv, stride_kv, projection_method=qkv_projection_method)
        self.convolution_projection_value = CvtSelfAttentionProjection(embed_dim, kernel_size, padding_kv, stride_kv, projection_method=qkv_projection_method)
        self.projection_query = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
        self.projection_key = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
        self.projection_value = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
        self.dropout = nn.Dropout(attention_drop_rate)
    def rearrange_for_multi_head_attention(self, hidden_state):
        batch_size, hidden_size, _ = hidden_state.shape
        head_dim = self.embed_dim // self.num_heads
        return hidden_state.view(batch_size, hidden_size, self.num_heads, head_dim).permute(0, 2, 1, 3)
    def forward(self, hidden_state, height, width):
        if self.with_cls_token: cls_token, hidden_state = torch.split(hidden_state, [1, height * width], 1)
        batch_size, hidden_size, num_channels = hidden_state.shape
        hidden_state = hidden_state.permute(0, 2, 1).view(batch_size, num_channels, height, width)
        key = self.convolution_projection_key(hidden_state)
        query = self.convolution_projection_query(hidden_state)
        value = self.convolution_projection_value(hidden_state)
        if self.with_cls_token:
            query = torch.cat((cls_token, query), dim=1)
            key = torch.cat((cls_token, key), dim=1)
            value = torch.cat((cls_token, value), dim=1)
        head_dim = self.embed_dim // self.num_heads
        query = self.rearrange_for_multi_head_attention(self.projection_query(query))
        key = self.rearrange_for_multi_head_attention(self.projection_key(key))
        value = self.rearrange_for_multi_head_attention(self.projection_value(value))
        attention_score = torch.einsum("bhlk,bhtk->bhlt", [query, key]) * self.scale
        attention_probs = torch.nn.functional.softmax(attention_score, dim=-1)
        attention_probs = self.dropout(attention_probs)
        context = torch.einsum("bhlt,bhtv->bhlv", [attention_probs, value])
        _, _, hidden_size, _ = context.shape
        context = context.permute(0, 2, 1, 3).contiguous().view(batch_size, hidden_size, self.num_heads * head_dim)
        return context
class CvtSelfOutput(nn.Module):
    def __init__(self, embed_dim, drop_rate):
        super().__init__()
        self.dense = nn.Linear(embed_dim, embed_dim)
        self.dropout = nn.Dropout(drop_rate)
    def forward(self, hidden_state, input_tensor):
        hidden_state = self.dense(hidden_state)
        hidden_state = self.dropout(hidden_state)
        return hidden_state
class CvtAttention(nn.Module):
    def __init__(self, num_heads, embed_dim, kernel_size, padding_q, padding_kv, stride_q, stride_kv, qkv_projection_method, qkv_bias, attention_drop_rate, drop_rate, with_cls_token=True):
        super().__init__()
        self.attention = CvtSelfAttention(num_heads, embed_dim, kernel_size, padding_q, padding_kv, stride_q, stride_kv, qkv_projection_method, qkv_bias, attention_drop_rate, with_cls_token)
        self.output = CvtSelfOutput(embed_dim, drop_rate)
        self.pruned_heads = set()
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads)
        self.attention.query = prune_linear_layer(self.attention.query, index)
        self.attention.key = prune_linear_layer(self.attention.key, index)
        self.attention.value = prune_linear_layer(self.attention.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, hidden_state, height, width):
        self_output = self.attention(hidden_state, height, width)
        attention_output = self.output(self_output, hidden_state)
        return attention_output
class CvtIntermediate(nn.Module):
    def __init__(self, embed_dim, mlp_ratio):
        super().__init__()
        self.dense = nn.Linear(embed_dim, int(embed_dim * mlp_ratio))
        self.activation = nn.GELU()
    def forward(self, hidden_state):
        hidden_state = self.dense(hidden_state)
        hidden_state = self.activation(hidden_state)
        return hidden_state
class CvtOutput(nn.Module):
    def __init__(self, embed_dim, mlp_ratio, drop_rate):
        super().__init__()
        self.dense = nn.Linear(int(embed_dim * mlp_ratio), embed_dim)
        self.dropout = nn.Dropout(drop_rate)
    def forward(self, hidden_state, input_tensor):
        hidden_state = self.dense(hidden_state)
        hidden_state = self.dropout(hidden_state)
        hidden_state = hidden_state + input_tensor
        return hidden_state
class CvtLayer(nn.Module):
    def __init__(self, num_heads, embed_dim, kernel_size, padding_q, padding_kv, stride_q, stride_kv, qkv_projection_method, qkv_bias, attention_drop_rate,
    drop_rate, mlp_ratio, drop_path_rate, with_cls_token=True):
        super().__init__()
        self.attention = CvtAttention(num_heads, embed_dim, kernel_size, padding_q, padding_kv, stride_q, stride_kv, qkv_projection_method, qkv_bias, attention_drop_rate, drop_rate, with_cls_token)
        self.intermediate = CvtIntermediate(embed_dim, mlp_ratio)
        self.output = CvtOutput(embed_dim, mlp_ratio, drop_rate)
        self.drop_path = CvtDropPath(drop_prob=drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
        self.layernorm_before = nn.LayerNorm(embed_dim)
        self.layernorm_after = nn.LayerNorm(embed_dim)
    def forward(self, hidden_state, height, width):
        self_attention_output = self.attention(self.layernorm_before(hidden_state), height, width)
        attention_output = self_attention_output
        attention_output = self.drop_path(attention_output)
        hidden_state = attention_output + hidden_state
        layer_output = self.layernorm_after(hidden_state)
        layer_output = self.intermediate(layer_output)
        layer_output = self.output(layer_output, hidden_state)
        layer_output = self.drop_path(layer_output)
        return layer_output
class CvtStage(nn.Module):
    def __init__(self, config, stage):
        super().__init__()
        self.config = config
        self.stage = stage
        if self.config.cls_token[self.stage]: self.cls_token = nn.Parameter(torch.randn(1, 1, self.config.embed_dim[-1]))
        self.embedding = CvtEmbeddings(patch_size=config.patch_sizes[self.stage], stride=config.patch_stride[self.stage], num_channels=config.num_channels if self.stage == 0 else config.embed_dim[self.stage - 1],
        embed_dim=config.embed_dim[self.stage], padding=config.patch_padding[self.stage], dropout_rate=config.drop_rate[self.stage])
        drop_path_rates = [x.item() for x in torch.linspace(0, config.drop_path_rate[self.stage], config.depth[stage])]
        self.layers = nn.Sequential(*[CvtLayer(num_heads=config.num_heads[self.stage], embed_dim=config.embed_dim[self.stage], kernel_size=config.kernel_qkv[self.stage], padding_q=config.padding_q[self.stage], padding_kv=config.padding_kv[self.stage], stride_kv=config.stride_kv[self.stage],
        stride_q=config.stride_q[self.stage], qkv_projection_method=config.qkv_projection_method[self.stage], qkv_bias=config.qkv_bias[self.stage], attention_drop_rate=config.attention_drop_rate[self.stage],
        drop_rate=config.drop_rate[self.stage], drop_path_rate=drop_path_rates[self.stage], mlp_ratio=config.mlp_ratio[self.stage], with_cls_token=config.cls_token[self.stage]) for _ in range(config.depth[self.stage])])
    def forward(self, hidden_state):
        cls_token = None
        hidden_state = self.embedding(hidden_state)
        batch_size, num_channels, height, width = hidden_state.shape
        hidden_state = hidden_state.view(batch_size, num_channels, height * width).permute(0, 2, 1)
        if self.config.cls_token[self.stage]:
            cls_token = self.cls_token.expand(batch_size, -1, -1)
            hidden_state = torch.cat((cls_token, hidden_state), dim=1)
        for layer in self.layers:
            layer_outputs = layer(hidden_state, height, width)
            hidden_state = layer_outputs
        if self.config.cls_token[self.stage]: cls_token, hidden_state = torch.split(hidden_state, [1, height * width], 1)
        hidden_state = hidden_state.permute(0, 2, 1).view(batch_size, num_channels, height, width)
        return hidden_state, cls_token
class CvtEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.stages = nn.ModuleList([])
        for stage_idx in range(len(config.depth)): self.stages.append(CvtStage(config, stage_idx))
    def forward(self, pixel_values, output_hidden_states=False, return_dict=True):
        all_hidden_states = () if output_hidden_states else None
        hidden_state = pixel_values
        cls_token = None
        for _, (stage_module) in enumerate(self.stages):
            hidden_state, cls_token = stage_module(hidden_state)
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_state,)
        if not return_dict: return tuple(v for v in [hidden_state, cls_token, all_hidden_states] if v is not None)
        return BaseModelOutputWithCLSToken(last_hidden_state=hidden_state, cls_token_value=cls_token, hidden_states=all_hidden_states)
class CvtPreTrainedModel(PreTrainedModel):
    config_class = CvtConfig
    base_model_prefix = "cvt"
    main_input_name = "pixel_values"
    _no_split_modules = ["CvtLayer"]
    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data = nn.init.trunc_normal_(module.weight.data, mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
        elif isinstance(module, CvtStage):
            if self.config.cls_token[module.stage]: module.cls_token.data = nn.init.trunc_normal_(torch.zeros(1, 1, self.config.embed_dim[-1]), mean=0.0, std=self.config.initializer_range)
CVT_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
    Parameters:
        config ([`CvtConfig`]): 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.
"""
CVT_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CvtImageProcessor.__call__`]
            for details.
        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.
"""
@add_start_docstrings("The bare Cvt Model transformer outputting raw hidden-states without any specific head on top.", CVT_START_DOCSTRING)
class CvtModel(CvtPreTrainedModel):
    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config
        self.encoder = CvtEncoder(config)
        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)
    @add_start_docstrings_to_model_forward(CVT_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutputWithCLSToken]:
        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 pixel_values is None: raise ValueError("You have to specify pixel_values")
        encoder_outputs = self.encoder(pixel_values, 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 BaseModelOutputWithCLSToken(last_hidden_state=sequence_output, cls_token_value=encoder_outputs.cls_token_value, hidden_states=encoder_outputs.hidden_states)
@add_start_docstrings("Cvt Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.", CVT_START_DOCSTRING)
class CvtForImageClassification(CvtPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.cvt = CvtModel(config, add_pooling_layer=False)
        self.layernorm = nn.LayerNorm(config.embed_dim[-1])
        self.classifier = (nn.Linear(config.embed_dim[-1], config.num_labels) if config.num_labels > 0 else nn.Identity())
        self.post_init()
    @add_start_docstrings_to_model_forward(CVT_INPUTS_DOCSTRING)
    def forward(self, pixel_values: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.cvt(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        cls_token = outputs[1]
        if self.config.cls_token[-1]: sequence_output = self.layernorm(cls_token)
        else:
            batch_size, num_channels, height, width = sequence_output.shape
            sequence_output = sequence_output.view(batch_size, num_channels, height * width).permute(0, 2, 1)
            sequence_output = self.layernorm(sequence_output)
        sequence_output_mean = sequence_output.mean(dim=1)
        logits = self.classifier(sequence_output_mean)
        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.config.num_labels == 1: self.config.problem_type = "regression"
                elif self.config.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.config.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.config.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 ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
