





import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
     import tensorflow as tf
class      UpperCamelCase_						(					enum.Enum   ):
     lowerCAmelCase_							=		0
     lowerCAmelCase_							=		1
     lowerCAmelCase_							=		2





@add_end_docstrings(_lowerCamelCase   )
class      UpperCamelCase_						(					_lowerCamelCase   ):
     lowerCAmelCase_							=		'''
    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
    begging for his blessing. <eod> </s> <eos>
    '''




     def __init__(						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							Any:
          super().__init__(*lowerCAmelCase_			,  **lowerCAmelCase_					)
          self.check_model_type(
              TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == 'tf' else MODEL_FOR_CAUSAL_LM_MAPPING					)
          if "prefix" not in self._preprocess_params:
               # This is very specific. The logic is quite complex and needs to be done
               # as a "default".
               # It also defines both some preprocess_kwargs and generate_kwargs
               # which is why we cannot put them in their respective methods.
               _snake_case													=			None
               if self.model.config.prefix is not None:
                    _snake_case													=			self.model.config.prefix
               if prefix is None and self.model.__class__.__name__ in [
                   "XLNetLMHeadModel",
                   "TransfoXLLMHeadModel",
                   "TFXLNetLMHeadModel",
                   "TFTransfoXLLMHeadModel",
               ]:
                    # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
                    _snake_case													=			self.XL_PREFIX
               if prefix is not None:
                    # Recalculate some generate_kwargs linked to prefix.
                    _snake_case							,       _snake_case							,       _snake_case													=			self._sanitize_parameters(prefix=lowerCAmelCase_			,  **self._forward_params					)
                    _snake_case													=			{**self._preprocess_params, **preprocess_params}
                    _snake_case													=			{**self._forward_params, **forward_params}




     def 		lowerCAmelCase     (						self			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  **lowerCAmelCase_			,  )						->							Tuple:
          _snake_case													=			{}
          if prefix is not None:
               _snake_case													=			prefix
          if prefix:
               _snake_case													=			self.tokenizer(
                   lowerCAmelCase_			,  padding=lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_			,  return_tensors=self.framework					)
               _snake_case													=			prefix_inputs['input_ids'].shape[-1]

          if handle_long_generation is not None:
               if handle_long_generation not in {"hole"}:
                    raise ValueError(
                        F'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
                        ' [None, \'hole\']'					)
               _snake_case													=			handle_long_generation

          preprocess_params.update(lowerCAmelCase_					)
          _snake_case													=			generate_kwargs

          _snake_case													=			{}
          if return_full_text is not None and return_type is None:
               if return_text is not None:
                    raise ValueError('`return_text` is mutually exclusive with `return_full_text`'					)
               if return_tensors is not None:
                    raise ValueError('`return_full_text` is mutually exclusive with `return_tensors`'					)
               _snake_case													=			ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
          if return_tensors is not None and return_type is None:
               if return_text is not None:
                    raise ValueError('`return_text` is mutually exclusive with `return_tensors`'					)
               _snake_case													=			ReturnType.TENSORS
          if return_type is not None:
               _snake_case													=			return_type
          if clean_up_tokenization_spaces is not None:
               _snake_case													=			clean_up_tokenization_spaces

          if stop_sequence is not None:
               _snake_case													=			self.tokenizer.encode(lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_					)
               if len(lowerCAmelCase_					) > 1:
                    warnings.warn(
                        'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
                        ' the stop sequence will be used as the stop sequence string in the interim.'					)
               _snake_case													=			stop_sequence_ids[0]

          return preprocess_params, forward_params, postprocess_params




     def 		lowerCAmelCase     (						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							List[str]:
          # Parse arguments
          if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
               kwargs.update({'add_space_before_punct_symbol': True}					)

          return super()._parse_and_tokenize(*lowerCAmelCase_			,  **lowerCAmelCase_					)




     def __call__(						self			,  lowerCAmelCase_			,  **lowerCAmelCase_					)						->							List[Any]:
          return super().__call__(lowerCAmelCase_			,  **lowerCAmelCase_					)




     def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=""			,  lowerCAmelCase_=None			,  **lowerCAmelCase_					)						->							Any:
          _snake_case													=			self.tokenizer(
              prefix + prompt_text			,  padding=lowerCAmelCase_			,  add_special_tokens=lowerCAmelCase_			,  return_tensors=self.framework					)
          _snake_case													=			prompt_text

          if handle_long_generation == "hole":
               _snake_case													=			inputs['input_ids'].shape[-1]
               if "max_new_tokens" in generate_kwargs:
                    _snake_case													=			generate_kwargs['max_new_tokens']
               else:
                    _snake_case													=			generate_kwargs.get('max_length'			,  self.model.config.max_length					) - cur_len
                    if new_tokens < 0:
                         raise ValueError('We cannot infer how many new tokens are expected'					)
               if cur_len + new_tokens > self.tokenizer.model_max_length:
                    _snake_case													=			self.tokenizer.model_max_length - new_tokens
                    if keep_length <= 0:
                         raise ValueError(
                             'We cannot use `hole` to handle this generation the number of desired tokens exceeds the'
                             ' models max length'					)

                    _snake_case													=			inputs['input_ids'][:, -keep_length:]
                    if "attention_mask" in inputs:
                         _snake_case													=			inputs['attention_mask'][:, -keep_length:]

          return inputs




     def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  **lowerCAmelCase_					)						->							Optional[Any]:
          _snake_case													=			model_inputs['input_ids']
          _snake_case													=			model_inputs.get('attention_mask'			,  lowerCAmelCase_					)
          # Allow empty prompts
          if input_ids.shape[1] == 0:
               _snake_case													=			None
               _snake_case													=			None
               _snake_case													=			1
          else:
               _snake_case													=			input_ids.shape[0]
          _snake_case													=			model_inputs.pop('prompt_text'					)

          # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
          # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
          _snake_case													=			generate_kwargs.pop('prefix_length'			,  0					)
          if prefix_length > 0:
               _snake_case													=			'max_new_tokens' in generate_kwargs or (
                   'generation_config' in generate_kwargs
                   and generate_kwargs['generation_config'].max_new_tokens is not None
               )
               if not has_max_new_tokens:
                    _snake_case													=			generate_kwargs.get('max_length'					) or self.model.config.max_length
                    generate_kwargs["max_length"] += prefix_length
               _snake_case													=			'min_new_tokens' in generate_kwargs or (
                   'generation_config' in generate_kwargs
                   and generate_kwargs['generation_config'].min_new_tokens is not None
               )
               if not has_min_new_tokens and "min_length" in generate_kwargs:
                    generate_kwargs["min_length"] += prefix_length

        # BS x SL
          _snake_case													=			self.model.generate(input_ids=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  **lowerCAmelCase_					)
          _snake_case													=			generated_sequence.shape[0]
          if self.framework == "pt":
               _snake_case													=			generated_sequence.reshape(lowerCAmelCase_			,  out_b // in_b			,  *generated_sequence.shape[1:]					)
          elif self.framework == "tf":
               _snake_case													=			tf.reshape(lowerCAmelCase_			,  (in_b, out_b // in_b, *generated_sequence.shape[1:])					)
          return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}






     def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=ReturnType.FULL_TEXT			,  lowerCAmelCase_=True					)						->							int:
          _snake_case													=			model_outputs['generated_sequence'][0]
          _snake_case													=			model_outputs['input_ids']
          _snake_case													=			model_outputs['prompt_text']
          _snake_case													=			generated_sequence.numpy().tolist()
          _snake_case													=			[]
          for sequence in generated_sequence:
               if return_type == ReturnType.TENSORS:
                    _snake_case													=			{'generated_token_ids': sequence}
               elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                    # Decode text
                    _snake_case													=			self.tokenizer.decode(
                        lowerCAmelCase_			,  skip_special_tokens=lowerCAmelCase_			,  clean_up_tokenization_spaces=lowerCAmelCase_			,  )

                    # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
                    if input_ids is None:
                         _snake_case													=			0
                    else:
                         _snake_case													=			len(
                             self.tokenizer.decode(
                                 input_ids[0]			,  skip_special_tokens=lowerCAmelCase_			,  clean_up_tokenization_spaces=lowerCAmelCase_			,  )					)

                    if return_type == ReturnType.FULL_TEXT:
                         _snake_case													=			prompt_text + text[prompt_length:]
                    else:
                         _snake_case													=			text[prompt_length:]

                    _snake_case													=			{'generated_text': all_text}
               records.append(lowerCAmelCase_					)

          return records










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


UpperCAmelCase_ 							=       logging.get_logger(__name__)

# General docstring
UpperCAmelCase_ 							=       """ResNetConfig"""

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

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

UpperCAmelCase_ 							=       [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]
class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 3			,  lowerCAmelCase_ = 1			,  lowerCAmelCase_ = "relu"					)						->							Union[str, Any]:
										super().__init__()
										_snake_case													=			nn.Convad(
										    lowerCAmelCase_			,  lowerCAmelCase_			,  kernel_size=lowerCAmelCase_			,  stride=lowerCAmelCase_			,  padding=kernel_size // 2			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.BatchNormad(lowerCAmelCase_					)
										_snake_case													=			ACTaFN[activation] if activation is not None else nn.Identity()






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			self.convolution(lowerCAmelCase_					)
										_snake_case													=			self.normalization(lowerCAmelCase_					)
										_snake_case													=			self.activation(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							Dict:
										super().__init__()
										_snake_case													=			ResNetConvLayer(
										    config.num_channels			,  config.embedding_size			,  kernel_size=7			,  stride=2			,  activation=config.hidden_act					)
										_snake_case													=			nn.MaxPoolad(kernel_size=3			,  stride=2			,  padding=1					)
										_snake_case													=			config.num_channels






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			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													=			self.embedder(lowerCAmelCase_					)
										_snake_case													=			self.pooler(lowerCAmelCase_					)
										return embedding





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 2					)						->							List[Any]:
										super().__init__()
										_snake_case													=			nn.Convad(lowerCAmelCase_			,  lowerCAmelCase_			,  kernel_size=1			,  stride=lowerCAmelCase_			,  bias=lowerCAmelCase_					)
										_snake_case													=			nn.BatchNormad(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			self.convolution(lowerCAmelCase_					)
										_snake_case													=			self.normalization(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 1			,  lowerCAmelCase_ = "relu"					)						->							Any:
										super().__init__()
										_snake_case													=			in_channels != out_channels or stride != 1
										_snake_case													=			(
										    ResNetShortCut(lowerCAmelCase_			,  lowerCAmelCase_			,  stride=lowerCAmelCase_					) if should_apply_shortcut else nn.Identity()
										)
										_snake_case													=			nn.Sequential(
										    ResNetConvLayer(lowerCAmelCase_			,  lowerCAmelCase_			,  stride=lowerCAmelCase_					)			,  ResNetConvLayer(lowerCAmelCase_			,  lowerCAmelCase_			,  activation=lowerCAmelCase_					)			,  )
										_snake_case													=			ACTaFN[activation]






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							str:
										_snake_case													=			hidden_state
										_snake_case													=			self.layer(lowerCAmelCase_					)
										_snake_case													=			self.shortcut(lowerCAmelCase_					)
										hidden_state += residual
										_snake_case													=			self.activation(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 1			,  lowerCAmelCase_ = "relu"			,  lowerCAmelCase_ = 4					)						->							List[str]:
										super().__init__()
										_snake_case													=			in_channels != out_channels or stride != 1
										_snake_case													=			out_channels // reduction
										_snake_case													=			(
										    ResNetShortCut(lowerCAmelCase_			,  lowerCAmelCase_			,  stride=lowerCAmelCase_					) if should_apply_shortcut else nn.Identity()
										)
										_snake_case													=			nn.Sequential(
										    ResNetConvLayer(lowerCAmelCase_			,  lowerCAmelCase_			,  kernel_size=1					)			,  ResNetConvLayer(lowerCAmelCase_			,  lowerCAmelCase_			,  stride=lowerCAmelCase_					)			,  ResNetConvLayer(lowerCAmelCase_			,  lowerCAmelCase_			,  kernel_size=1			,  activation=lowerCAmelCase_					)			,  )
										_snake_case													=			ACTaFN[activation]






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case													=			hidden_state
										_snake_case													=			self.layer(lowerCAmelCase_					)
										_snake_case													=			self.shortcut(lowerCAmelCase_					)
										hidden_state += residual
										_snake_case													=			self.activation(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = 2			,  lowerCAmelCase_ = 2			,  )						->							Tuple:
										super().__init__()

										_snake_case													=			ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tensor:
										_snake_case													=			input
										for layer in self.layers:
															_snake_case													=			layer(lowerCAmelCase_					)
										return hidden_state





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							int:
										super().__init__()
										_snake_case													=			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(
										        lowerCAmelCase_			,  config.embedding_size			,  config.hidden_sizes[0]			,  stride=2 if config.downsample_in_first_stage else 1			,  depth=config.depths[0]			,  )					)
										_snake_case													=			zip(config.hidden_sizes			,  config.hidden_sizes[1:]					)
										for (in_channels, out_channels), depth in zip(lowerCAmelCase_			,  config.depths[1:]					):
															self.stages.append(ResNetStage(lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  depth=lowerCAmelCase_					)					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = False			,  lowerCAmelCase_ = True					)						->							BaseModelOutputWithNoAttention:
										_snake_case													=			() if output_hidden_states else None

										for stage_module in self.stages:
															if output_hidden_states:
																				_snake_case													=			hidden_states + (hidden_state,)

															_snake_case													=			stage_module(lowerCAmelCase_					)

										if output_hidden_states:
															_snake_case													=			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=lowerCAmelCase_			,  hidden_states=lowerCAmelCase_			,  )





class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		ResNetConfig
					lowerCAmelCase_							=		'''resnet'''
					lowerCAmelCase_							=		'''pixel_values'''
					lowerCAmelCase_							=		True




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Dict:
										if isinstance(lowerCAmelCase_			,  nn.Convad					):
															nn.init.kaiming_normal_(module.weight			,  mode='fan_out'			,  nonlinearity='relu'					)
										elif isinstance(lowerCAmelCase_			,  (nn.BatchNormad, nn.GroupNorm)					):
															nn.init.constant_(module.weight			,  1					)
															nn.init.constant_(module.bias			,  0					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=False					)						->							List[Any]:
										if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			value





UpperCAmelCase_ 							=       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.
"""

UpperCAmelCase_ 							=       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.'''							,					_lowerCamelCase							,					)
class      UpperCamelCase_						(					_lowerCamelCase   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							int:
										super().__init__(lowerCAmelCase_					)
										_snake_case													=			config
										_snake_case													=			ResNetEmbeddings(lowerCAmelCase_					)
										_snake_case													=			ResNetEncoder(lowerCAmelCase_					)
										_snake_case													=			nn.AdaptiveAvgPoolad((1, 1)					)
										# Initialize weights and apply final processing
										self.post_init()






					@add_start_docstrings_to_model_forward(lowerCAmelCase_					)
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC			,  output_type=lowerCAmelCase_			,  config_class=_CONFIG_FOR_DOC			,  modality='vision'			,  expected_output=_EXPECTED_OUTPUT_SHAPE			,  )
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None					)						->							BaseModelOutputWithPoolingAndNoAttention:
										_snake_case													=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_snake_case													=			return_dict if return_dict is not None else self.config.use_return_dict

										_snake_case													=			self.embedder(lowerCAmelCase_					)

										_snake_case													=			self.encoder(
										    lowerCAmelCase_			,  output_hidden_states=lowerCAmelCase_			,  return_dict=lowerCAmelCase_					)

										_snake_case													=			encoder_outputs[0]

										_snake_case													=			self.pooler(lowerCAmelCase_					)

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

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





@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''							,					_lowerCamelCase							,					)
class      UpperCamelCase_						(					_lowerCamelCase   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							Union[str, Any]:
										super().__init__(lowerCAmelCase_					)
										_snake_case													=			config.num_labels
										_snake_case													=			ResNetModel(lowerCAmelCase_					)
										# classification head
										_snake_case													=			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(lowerCAmelCase_					)
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT			,  output_type=lowerCAmelCase_			,  config_class=_CONFIG_FOR_DOC			,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT			,  )
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None			,  )						->							ImageClassifierOutputWithNoAttention:
										_snake_case													=			return_dict if return_dict is not None else self.config.use_return_dict

										_snake_case													=			self.resnet(lowerCAmelCase_			,  output_hidden_states=lowerCAmelCase_			,  return_dict=lowerCAmelCase_					)

										_snake_case													=			outputs.pooler_output if return_dict else outputs[1]

										_snake_case													=			self.classifier(lowerCAmelCase_					)

										_snake_case													=			None

										if labels is not None:
															if self.config.problem_type is None:
																				if self.num_labels == 1:
																									_snake_case													=			'regression'
																				elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																									_snake_case													=			'single_label_classification'
																				else:
																									_snake_case													=			'multi_label_classification'
															if self.config.problem_type == "regression":
																				_snake_case													=			MSELoss()
																				if self.num_labels == 1:
																									_snake_case													=			loss_fct(logits.squeeze()			,  labels.squeeze()					)
																				else:
																									_snake_case													=			loss_fct(lowerCAmelCase_			,  lowerCAmelCase_					)
															elif self.config.problem_type == "single_label_classification":
																				_snake_case													=			CrossEntropyLoss()
																				_snake_case													=			loss_fct(logits.view(-1			,  self.num_labels					)			,  labels.view(-1					)					)
															elif self.config.problem_type == "multi_label_classification":
																				_snake_case													=			BCEWithLogitsLoss()
																				_snake_case													=			loss_fct(lowerCAmelCase_			,  lowerCAmelCase_					)

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

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





@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''							,					_lowerCamelCase							,					)
class      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase   ):




					def __init__(						self			,  lowerCAmelCase_					)						->							List[Any]:
										super().__init__(lowerCAmelCase_					)
										super()._init_backbone(lowerCAmelCase_					)

										_snake_case													=			[config.embedding_size] + config.hidden_sizes
										_snake_case													=			ResNetEmbeddings(lowerCAmelCase_					)
										_snake_case													=			ResNetEncoder(lowerCAmelCase_					)

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






					@add_start_docstrings_to_model_forward(lowerCAmelCase_					)
					@replace_return_docstrings(output_type=lowerCAmelCase_			,  config_class=_CONFIG_FOR_DOC					)
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = None					)						->							BackboneOutput:
										_snake_case													=			return_dict if return_dict is not None else self.config.use_return_dict
										_snake_case													=			(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)

										_snake_case													=			self.embedder(lowerCAmelCase_					)

										_snake_case													=			self.encoder(lowerCAmelCase_			,  output_hidden_states=lowerCAmelCase_			,  return_dict=lowerCAmelCase_					)

										_snake_case													=			outputs.hidden_states

										_snake_case													=			()
										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													=			(feature_maps,)
															if output_hidden_states:
																				output += (outputs.hidden_states,)
															return output

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



