






import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
     raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
     raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  """Hello, World!"""
lowerCamelCase__       		=  """en_XX"""






def 	UpperCamelCase	(							snake_case__  : str						,snake_case__  : str						,snake_case__  : bool	):




     '''simple docstring'''






     __snake_case      :int 							=    Path("""data_bin"""	)
     __snake_case      :Dict 							=    FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(snake_case__	).parent	)						,checkpoint_file=Path(snake_case__	).name						,_name="""xmod_base"""						,arch="""xmod_base"""						,task="""multilingual_masked_lm"""						,data_name_or_path=str(snake_case__	)						,bpe="""sentencepiece"""						,sentencepiece_model=str(Path(snake_case__	).parent / """sentencepiece.bpe.model"""	)						,src_dict=str(data_dir / """dict.txt"""	)						,)
     xmod.eval()  # disable dropout
     print(snake_case__	)

     __snake_case      :Union[str, Any] 							=    xmod.model.encoder.sentence_encoder
     __snake_case      :Union[str, Any] 							=    XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings						,hidden_size=xmod.cfg.model.encoder_embed_dim						,num_hidden_layers=xmod.cfg.model.encoder_layers						,num_attention_heads=xmod.cfg.model.encoder_attention_heads						,intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim						,max_position_embeddings=514						,type_vocab_size=1						,layer_norm_eps=1e-5						,pre_norm=xmod.cfg.model.encoder_normalize_before						,adapter_reduction_factor=getattr(xmod.cfg.model						,"""bottleneck"""						,2	)						,adapter_layer_norm=xmod.cfg.model.adapter_layer_norm						,adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm						,ln_before_adapter=xmod.cfg.model.ln_before_adapter						,languages=xmod.cfg.model.languages						,)
     if classification_head:
          __snake_case      :Union[str, Any] 							=    xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

     print("""Our X-MOD config:"""						,snake_case__	)

     __snake_case      :Union[str, Any] 							=    XmodForSequenceClassification(snake_case__	) if classification_head else XmodForMaskedLM(snake_case__	)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __snake_case      :Optional[int] 							=    xmod_sent_encoder.embed_tokens.weight
     __snake_case      :Union[str, Any] 							=    xmod_sent_encoder.embed_positions.weight
     __snake_case      :Any 							=    torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

     __snake_case      :Optional[Any] 							=    xmod_sent_encoder.layernorm_embedding.weight
     __snake_case      :List[str] 							=    xmod_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers	):
          # Encoder: start of layer
          __snake_case      :Union[str, Any] 							=    model.roberta.encoder.layer[i]
          __snake_case      :Dict 							=    xmod_sent_encoder.layers[i]

          # self attention
          __snake_case      :Optional[Any] 							=    layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)	)
          ):
               raise AssertionError("""Dimensions of self-attention weights do not match."""	)

          __snake_case      :str 							=    xmod_layer.self_attn.q_proj.weight
          __snake_case      :Optional[int] 							=    xmod_layer.self_attn.q_proj.bias
          __snake_case      :int 							=    xmod_layer.self_attn.k_proj.weight
          __snake_case      :List[Any] 							=    xmod_layer.self_attn.k_proj.bias
          __snake_case      :List[Any] 							=    xmod_layer.self_attn.v_proj.weight
          __snake_case      :Union[str, Any] 							=    xmod_layer.self_attn.v_proj.bias

          # self-attention output
          __snake_case      :Union[str, Any] 							=    layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError("""Dimensions of self-attention output weights do not match."""	)
          __snake_case      :List[Any] 							=    xmod_layer.self_attn.out_proj.weight
          __snake_case      :str 							=    xmod_layer.self_attn.out_proj.bias
          __snake_case      :List[Any] 							=    xmod_layer.self_attn_layer_norm.weight
          __snake_case      :Optional[Any] 							=    xmod_layer.self_attn_layer_norm.bias

          # intermediate
          __snake_case      :Dict 							=    layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of intermediate weights do not match."""	)
          __snake_case      :Any 							=    xmod_layer.fca.weight
          __snake_case      :List[Any] 							=    xmod_layer.fca.bias

          # output
          __snake_case      :List[Any] 							=    layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of feed-forward weights do not match."""	)
          __snake_case      :Optional[int] 							=    xmod_layer.fca.weight
          __snake_case      :Any 							=    xmod_layer.fca.bias
          __snake_case      :Dict 							=    xmod_layer.final_layer_norm.weight
          __snake_case      :Tuple 							=    xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               __snake_case      :Tuple 							=    xmod_layer.adapter_layer_norm.weight
               __snake_case      :Any 							=    xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
               raise AssertionError("""Lists of language adapters do not match."""	)
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               __snake_case      :Optional[int] 							=    bert_output.adapter_modules[lang_code]
               __snake_case      :List[str] 							=    xmod_layer.adapter_modules[lang_code]
               __snake_case      :str 							=    from_adapter.fca.weight
               __snake_case      :Any 							=    from_adapter.fca.bias
               __snake_case      :List[Any] 							=    from_adapter.fca.weight
               __snake_case      :List[str] 							=    from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          __snake_case      :str 							=    xmod_sent_encoder.layer_norm.weight
          __snake_case      :Any 							=    xmod_sent_encoder.layer_norm.bias

     if classification_head:
          __snake_case      :Optional[Any] 							=    xmod.model.classification_heads["""mnli"""].dense.weight
          __snake_case      :List[str] 							=    xmod.model.classification_heads["""mnli"""].dense.bias
          __snake_case      :List[Any] 							=    xmod.model.classification_heads["""mnli"""].out_proj.weight
          __snake_case      :List[str] 							=    xmod.model.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          __snake_case      :int 							=    xmod.model.encoder.lm_head.dense.weight
          __snake_case      :int 							=    xmod.model.encoder.lm_head.dense.bias
          __snake_case      :Tuple 							=    xmod.model.encoder.lm_head.layer_norm.weight
          __snake_case      :Dict 							=    xmod.model.encoder.lm_head.layer_norm.bias
          __snake_case      :Union[str, Any] 							=    xmod.model.encoder.lm_head.weight
          __snake_case      :List[Any] 							=    xmod.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     __snake_case      :List[str] 							=    xmod.encode(snake_case__	).unsqueeze(0	)  # batch of size 1
     model.roberta.set_default_language(snake_case__	)

     __snake_case      :str 							=    model(snake_case__	)[0]
     if classification_head:
          __snake_case      :Dict 							=    xmod.model.classification_heads["""mnli"""](xmod.extract_features(snake_case__	)	)
     else:
          __snake_case      :Optional[Any] 							=    xmod.model(snake_case__						,lang_id=[SAMPLE_LANGUAGE]	)[0]
     print(our_output.shape						,their_output.shape	)
     __snake_case      :Union[str, Any] 							=    torch.max(torch.abs(our_output - their_output	)	).item()
     print(f'''max_absolute_diff = {max_absolute_diff}'''	)  # ~ 1e-7
     __snake_case      :Optional[Any] 							=    torch.allclose(snake_case__						,snake_case__						,atol=1e-3	)
     print("""Do both models output the same tensors?"""						,"""🔥""" if success else """💩"""	)
     if not success:
          raise Exception("""Something went wRoNg"""	)

     Path(snake_case__	).mkdir(parents=snake_case__						,exist_ok=snake_case__	)
     print(f'''Saving model to {pytorch_dump_folder_path}'''	)
     model.save_pretrained(snake_case__	)


if __name__ == "__main__":
     lowerCamelCase__       		=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
     )
     lowerCamelCase__       		=  parser.parse_args()
     convert_xmod_checkpoint_to_pytorch(
         args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )










from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


lowerCamelCase__       		=  logging.get_logger(__name__)

# General docstring
lowerCamelCase__       		=  """ResNetConfig"""

# Base docstring
lowerCamelCase__       		=  """microsoft/resnet-50"""
lowerCamelCase__       		=  [1, 2048, 7, 7]

# Image classification docstring
lowerCamelCase__       		=  """microsoft/resnet-50"""
lowerCamelCase__       		=  """tiger cat"""

lowerCamelCase__       		=  [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__ = 3      ,    a__ = 1      ,    a__ = "relu"     )			->   Optional[Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Dict 							=    nn.Convad(
                a__      ,    a__      ,    kernel_size=a__      ,    stride=a__      ,    padding=kernel_size // 2      ,    bias=a__     )
            __snake_case      :str 							=    nn.BatchNormad(a__     )
            __snake_case      :List[Any] 							=    ACTaFN[activation] if activation is not None else nn.Identity()



       def        __lowercase    (				self      ,    a__     )			->   Tensor:






            '''simple docstring'''





            __snake_case      :int 							=    self.convolution(a__     )
            __snake_case      :Any 							=    self.normalization(a__     )
            __snake_case      :Optional[int] 							=    self.activation(a__     )
            return hidden_state




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   str:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Dict 							=    ResNetConvLayer(
                config.num_channels      ,    config.embedding_size      ,    kernel_size=7      ,    stride=2      ,    activation=config.hidden_act     )
            __snake_case      :List[Any] 							=    nn.MaxPoolad(kernel_size=3      ,    stride=2      ,    padding=1     )
            __snake_case      :Tuple 							=    config.num_channels



       def        __lowercase    (				self      ,    a__     )			->   Tensor:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    pixel_values.shape[1]
            if num_channels != self.num_channels:
                 raise ValueError(
                     """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""     )
            __snake_case      :Optional[int] 							=    self.embedder(a__     )
            __snake_case      :int 							=    self.pooler(a__     )
            return embedding




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__ = 2     )			->   Optional[Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Optional[Any] 							=    nn.Convad(a__      ,    a__      ,    kernel_size=1      ,    stride=a__      ,    bias=a__     )
            __snake_case      :Tuple 							=    nn.BatchNormad(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Tensor:






            '''simple docstring'''





            __snake_case      :Any 							=    self.convolution(a__     )
            __snake_case      :str 							=    self.normalization(a__     )
            return hidden_state




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = "relu"     )			->   List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :int 							=    in_channels != out_channels or stride != 1
            __snake_case      :Tuple 							=    (
                ResNetShortCut(a__      ,    a__      ,    stride=a__     ) if should_apply_shortcut else nn.Identity()
            )
            __snake_case      :Optional[int] 							=    nn.Sequential(
                ResNetConvLayer(a__      ,    a__      ,    stride=a__     )      ,    ResNetConvLayer(a__      ,    a__      ,    activation=a__     )      ,    )
            __snake_case      :Union[str, Any] 							=    ACTaFN[activation]



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :int 							=    hidden_state
            __snake_case      :Dict 							=    self.layer(a__     )
            __snake_case      :Any 							=    self.shortcut(a__     )
            hidden_state += residual
            __snake_case      :List[Any] 							=    self.activation(a__     )
            return hidden_state




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__ = 1      ,    a__ = "relu"      ,    a__ = 4     )			->   List[Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Optional[int] 							=    in_channels != out_channels or stride != 1
            __snake_case      :List[Any] 							=    out_channels // reduction
            __snake_case      :List[str] 							=    (
                ResNetShortCut(a__      ,    a__      ,    stride=a__     ) if should_apply_shortcut else nn.Identity()
            )
            __snake_case      :int 							=    nn.Sequential(
                ResNetConvLayer(a__      ,    a__      ,    kernel_size=1     )      ,    ResNetConvLayer(a__      ,    a__      ,    stride=a__     )      ,    ResNetConvLayer(a__      ,    a__      ,    kernel_size=1      ,    activation=a__     )      ,    )
            __snake_case      :Dict 							=    ACTaFN[activation]



       def        __lowercase    (				self      ,    a__     )			->   Any:






            '''simple docstring'''





            __snake_case      :List[str] 							=    hidden_state
            __snake_case      :List[Any] 							=    self.layer(a__     )
            __snake_case      :List[Any] 							=    self.shortcut(a__     )
            hidden_state += residual
            __snake_case      :Optional[Any] 							=    self.activation(a__     )
            return hidden_state




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__      ,    a__      ,    a__      ,    a__ = 2      ,    a__ = 2      ,    )			->   Any:






            '''simple docstring'''





            super().__init__()

            __snake_case      :Optional[int] 							=    ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

            __snake_case      :Tuple 							=    nn.Sequential(
                # downsampling is done in the first layer with stride of 2
                layer(a__      ,    a__      ,    stride=a__      ,    activation=config.hidden_act     )      ,    *[layer(a__      ,    a__      ,    activation=config.hidden_act     ) for _ in range(depth - 1     )]      ,    )



       def        __lowercase    (				self      ,    a__     )			->   Tensor:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    input
            for layer in self.layers:
                 __snake_case      :str 							=    layer(a__     )
            return hidden_state




class       snake_case__ (				nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Optional[int] 							=    nn.ModuleList([]     )
            # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
            self.stages.append(
                ResNetStage(
                    a__      ,    config.embedding_size      ,    config.hidden_sizes[0]      ,    stride=2 if config.downsample_in_first_stage else 1      ,    depth=config.depths[0]      ,    )     )
            __snake_case      :Tuple 							=    zip(config.hidden_sizes      ,    config.hidden_sizes[1:]     )
            for (in_channels, out_channels), depth in zip(a__      ,    config.depths[1:]     ):
                 self.stages.append(ResNetStage(a__      ,    a__      ,    a__      ,    depth=a__     )     )



       def        __lowercase    (				self      ,    a__      ,    a__ = False      ,    a__ = True     )			->   BaseModelOutputWithNoAttention:






            '''simple docstring'''





            __snake_case      :Dict 							=    () if output_hidden_states else None

            for stage_module in self.stages:
                 if output_hidden_states:
                      __snake_case      :Optional[int] 							=    hidden_states + (hidden_state,)

                 __snake_case      :Tuple 							=    stage_module(a__     )

            if output_hidden_states:
                 __snake_case      :Optional[Any] 							=    hidden_states + (hidden_state,)

            if not return_dict:
                 return tuple(v for v in [hidden_state, hidden_states] if v is not None     )

            return BaseModelOutputWithNoAttention(
                last_hidden_state=a__      ,    hidden_states=a__      ,    )




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					List[Any]								=			ResNetConfig
       lowerCamelCase				:					Optional[Any]								=			"resnet"
       lowerCamelCase				:					str								=			"pixel_values"
       lowerCamelCase				:					Optional[int]								=			True



       def        __lowercase    (				self      ,    a__     )			->   Dict:






            '''simple docstring'''





            if isinstance(a__      ,    nn.Convad     ):
                 nn.init.kaiming_normal_(module.weight      ,    mode="""fan_out"""      ,    nonlinearity="""relu"""     )
            elif isinstance(a__      ,    (nn.BatchNormad, nn.GroupNorm)     ):
                 nn.init.constant_(module.weight      ,    1     )
                 nn.init.constant_(module.bias      ,    0     )



       def        __lowercase    (				self      ,    a__      ,    a__=False     )			->   Optional[int]:






            '''simple docstring'''





            if isinstance(a__      ,    a__     ):
                 __snake_case      :Union[str, Any] 							=    value
lowerCamelCase__       		=  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 ([`ResNetConfig`]): 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.
"""

lowerCamelCase__       		=  R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__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 [`~utils.ModelOutput`] instead of a plain tuple.
"""




@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."						,     lowercase_						,     )
class       snake_case__ (				lowercase_):

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   Tuple:






            '''simple docstring'''





            super().__init__(a__     )
            __snake_case      :int 							=    config
            __snake_case      :Any 							=    ResNetEmbeddings(a__     )
            __snake_case      :Dict 							=    ResNetEncoder(a__     )
            __snake_case      :Dict 							=    nn.AdaptiveAvgPoolad((1, 1)     )
            # Initialize weights and apply final processing
            self.post_init()



       @add_start_docstrings_to_model_forward(a__     )
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC      ,    output_type=a__      ,    config_class=_CONFIG_FOR_DOC      ,    modality="""vision"""      ,    expected_output=_EXPECTED_OUTPUT_SHAPE      ,    )
       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None     )			->   BaseModelOutputWithPoolingAndNoAttention:






            '''simple docstring'''





            __snake_case      :List[str] 							=    (
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            __snake_case      :List[str] 							=    return_dict if return_dict is not None else self.config.use_return_dict

            __snake_case      :int 							=    self.embedder(a__     )

            __snake_case      :Any 							=    self.encoder(
                a__      ,    output_hidden_states=a__      ,    return_dict=a__     )

            __snake_case      :Any 							=    encoder_outputs[0]

            __snake_case      :int 							=    self.pooler(a__     )

            if not return_dict:
                 return (last_hidden_state, pooled_output) + encoder_outputs[1:]

            return BaseModelOutputWithPoolingAndNoAttention(
                last_hidden_state=a__      ,    pooler_output=a__      ,    hidden_states=encoder_outputs.hidden_states      ,    )




@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "						,     lowercase_						,     )
class       snake_case__ (				lowercase_):

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   List[Any]:






            '''simple docstring'''





            super().__init__(a__     )
            __snake_case      :Union[str, Any] 							=    config.num_labels
            __snake_case      :Optional[int] 							=    ResNetModel(a__     )
            # classification head
            __snake_case      :List[str] 							=    nn.Sequential(
                nn.Flatten()      ,    nn.Linear(config.hidden_sizes[-1]      ,    config.num_labels     ) if config.num_labels > 0 else nn.Identity()      ,    )
            # initialize weights and apply final processing
            self.post_init()



       @add_start_docstrings_to_model_forward(a__     )
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT      ,    output_type=a__      ,    config_class=_CONFIG_FOR_DOC      ,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,    )
       def        __lowercase    (				self      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    a__ = None      ,    )			->   ImageClassifierOutputWithNoAttention:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    return_dict if return_dict is not None else self.config.use_return_dict

            __snake_case      :Tuple 							=    self.resnet(a__      ,    output_hidden_states=a__      ,    return_dict=a__     )

            __snake_case      :Optional[Any] 							=    outputs.pooler_output if return_dict else outputs[1]

            __snake_case      :Optional[Any] 							=    self.classifier(a__     )

            __snake_case      :Any 							=    None

            if labels is not None:
                 if self.config.problem_type is None:
                      if self.num_labels == 1:
                           __snake_case      :List[Any] 							=    """regression"""
                      elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                           __snake_case      :List[Any] 							=    """single_label_classification"""
                      else:
                           __snake_case      :Union[str, Any] 							=    """multi_label_classification"""
                 if self.config.problem_type == "regression":
                      __snake_case      :Any 							=    MSELoss()
                      if self.num_labels == 1:
                           __snake_case      :Optional[Any] 							=    loss_fct(logits.squeeze()      ,    labels.squeeze()     )
                      else:
                           __snake_case      :Any 							=    loss_fct(a__      ,    a__     )
                 elif self.config.problem_type == "single_label_classification":
                      __snake_case      :int 							=    CrossEntropyLoss()
                      __snake_case      :List[Any] 							=    loss_fct(logits.view(-1      ,    self.num_labels     )      ,    labels.view(-1     )     )
                 elif self.config.problem_type == "multi_label_classification":
                      __snake_case      :List[str] 							=    BCEWithLogitsLoss()
                      __snake_case      :int 							=    loss_fct(a__      ,    a__     )

            if not return_dict:
                 __snake_case      :int 							=    (logits,) + outputs[2:]
                 return (loss,) + output if loss is not None else output

            return ImageClassifierOutputWithNoAttention(loss=a__      ,    logits=a__      ,    hidden_states=outputs.hidden_states     )




@add_start_docstrings(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    "						,     lowercase_						,     )
class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   int:






            '''simple docstring'''





            super().__init__(a__     )
            super()._init_backbone(a__     )

            __snake_case      :Optional[int] 							=    [config.embedding_size] + config.hidden_sizes
            __snake_case      :str 							=    ResNetEmbeddings(a__     )
            __snake_case      :Any 							=    ResNetEncoder(a__     )

            # initialize weights and apply final processing
            self.post_init()



       @add_start_docstrings_to_model_forward(a__     )
       @replace_return_docstrings(output_type=a__      ,    config_class=_CONFIG_FOR_DOC     )
       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None     )			->   BackboneOutput:






            '''simple docstring'''





            __snake_case      :int 							=    return_dict if return_dict is not None else self.config.use_return_dict
            __snake_case      :Union[str, Any] 							=    (
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )

            __snake_case      :List[str] 							=    self.embedder(a__     )

            __snake_case      :List[Any] 							=    self.encoder(a__      ,    output_hidden_states=a__      ,    return_dict=a__     )

            __snake_case      :Optional[int] 							=    outputs.hidden_states

            __snake_case      :Tuple 							=    ()
            for idx, stage in enumerate(self.stage_names     ):
                 if stage in self.out_features:
                      feature_maps += (hidden_states[idx],)

            if not return_dict:
                 __snake_case      :Union[str, Any] 							=    (feature_maps,)
                 if output_hidden_states:
                      output += (outputs.hidden_states,)
                 return output

            return BackboneOutput(
                feature_maps=a__      ,    hidden_states=outputs.hidden_states if output_hidden_states else None      ,    attentions=a__      ,    )


