





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		)






class 						UpperCamelCase_  (  unittest.TestCase							):



					def __init__(		self     :     Dict       ,      lowerCamelCase     :     Union[str, Any]       ,      lowerCamelCase     :     Dict=13       ,      lowerCamelCase     :     Optional[Any]=7       ,      lowerCamelCase     :     int=True       ,      lowerCamelCase     :     str=True       ,      lowerCamelCase     :     int=True       ,      lowerCamelCase     :     Optional[int]=True       ,      lowerCamelCase     :     Optional[int]=99       ,      lowerCamelCase     :     List[Any]=32       ,      lowerCamelCase     :     Any=5       ,      lowerCamelCase     :     Dict=4       ,      lowerCamelCase     :     int=37       ,      lowerCamelCase     :     int="gelu"       ,      lowerCamelCase     :     Tuple=0.1       ,      lowerCamelCase     :     List[str]=0.1       ,      lowerCamelCase     :     Optional[int]=5_12       ,      lowerCamelCase     :     str=16       ,      lowerCamelCase     :     List[str]=2       ,      lowerCamelCase     :     Dict=0.02       ,      lowerCamelCase     :     int=4       ,      ):
							lowerCamelCase_	:     Tuple 						=      parent
							lowerCamelCase_	:     int 						=      batch_size
							lowerCamelCase_	:     Any 						=      seq_length
							lowerCamelCase_	:     Optional[Any] 						=      is_training
							lowerCamelCase_	:     List[str] 						=      use_attention_mask
							lowerCamelCase_	:     Any 						=      use_token_type_ids
							lowerCamelCase_	:     Any 						=      use_labels
							lowerCamelCase_	:     Tuple 						=      vocab_size
							lowerCamelCase_	:     Union[str, Any] 						=      hidden_size
							lowerCamelCase_	:     str 						=      num_hidden_layers
							lowerCamelCase_	:     Dict 						=      num_attention_heads
							lowerCamelCase_	:     int 						=      intermediate_size
							lowerCamelCase_	:     str 						=      hidden_act
							lowerCamelCase_	:     Dict 						=      hidden_dropout_prob
							lowerCamelCase_	:     Optional[int] 						=      attention_probs_dropout_prob
							lowerCamelCase_	:     str 						=      max_position_embeddings
							lowerCamelCase_	:     Optional[Any] 						=      type_vocab_size
							lowerCamelCase_	:     Optional[Any] 						=      type_sequence_label_size
							lowerCamelCase_	:     str 						=      initializer_range
							lowerCamelCase_	:     List[Any] 						=      num_choices



					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     str 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size						)

							lowerCamelCase_	:     Any 						=      None
							if self.use_attention_mask:
									lowerCamelCase_	:     Tuple 						=      random_attention_mask([self.batch_size, self.seq_length]						)

							lowerCamelCase_	:     Tuple 						=      None
							if self.use_token_type_ids:
									lowerCamelCase_	:     Union[str, Any] 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.type_vocab_size						)

							lowerCamelCase_	:     str 						=      RobertaPreLayerNormConfig(
							    vocab_size=self.vocab_size       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      num_attention_heads=self.num_attention_heads       ,      intermediate_size=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      hidden_dropout_prob=self.hidden_dropout_prob       ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      type_vocab_size=self.type_vocab_size       ,      is_decoder=a__       ,      initializer_range=self.initializer_range       ,      )

							return config, input_ids, token_type_ids, attention_mask



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     str 						=      self.prepare_config_and_inputs()
							lowerCamelCase_	:     Optional[int] 						=      config_and_inputs
							lowerCamelCase_	:     List[Any] 						=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
							return config, inputs_dict





					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     Union[str, Any] 						=      self.prepare_config_and_inputs()
							lowerCamelCase_	:     int 						=      config_and_inputs

							lowerCamelCase_	:     Optional[Any] 						=      True
							lowerCamelCase_	:     List[Any] 						=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
							lowerCamelCase_	:     List[str] 						=      ids_tensor([self.batch_size, self.seq_length]       ,      vocab_size=2						)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 						UpperCamelCase_  (  lowercase_  ,     unittest.TestCase							):
					_a      :						Tuple						    =					True

					_a      :						Optional[int]						    =					(
					    (
					        FlaxRobertaPreLayerNormModel,
					        FlaxRobertaPreLayerNormForCausalLM,
					        FlaxRobertaPreLayerNormForMaskedLM,
					        FlaxRobertaPreLayerNormForSequenceClassification,
					        FlaxRobertaPreLayerNormForTokenClassification,
					        FlaxRobertaPreLayerNormForMultipleChoice,
					        FlaxRobertaPreLayerNormForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     Dict 						=      FlaxRobertaPreLayerNormModelTester(self						)





					@slow
					def   __a     (		self     :     List[Any]						):





							for model_class_name in self.all_model_classes:
									lowerCamelCase_	:     List[str] 						=      model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'       ,      from_pt=a__						)
									lowerCamelCase_	:     Union[str, Any] 						=      model(np.ones((1, 1)						)						)
									self.assertIsNotNone(a__						)






@require_flax
class 						UpperCamelCase_  (  unittest.TestCase							):



					@slow
					def   __a     (		self     :     int						):
							lowerCamelCase_	:     Dict 						=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'       ,      from_pt=a__						)

							lowerCamelCase_	:     List[Any] 						=      np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]       ,      dtype=jnp.intaa						)
							lowerCamelCase_	:     int 						=      model(a__						)[0]
							lowerCamelCase_	:     str 						=      [1, 11, 5_02_65]
							self.assertEqual(list(output.shape						)       ,      a__						)
							# compare the actual values for a slice.
							lowerCamelCase_	:     Optional[Any] 						=      np.array(
							    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]       ,      dtype=np.floataa						)
							self.assertTrue(np.allclose(output[:, :3, :3]       ,      a__       ,      atol=1E-4						)						)





					@slow
					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     List[Any] 						=      FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'       ,      from_pt=a__						)

							lowerCamelCase_	:     Tuple 						=      np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]       ,      dtype=jnp.intaa						)
							lowerCamelCase_	:     Any 						=      model(a__						)[0]
							# compare the actual values for a slice.
							lowerCamelCase_	:     str 						=      np.array(
							    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]       ,      dtype=np.floataa						)
							self.assertTrue(np.allclose(output[:, :3, :3]       ,      a__       ,      atol=1E-4						)						)








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__      ,    )


