






from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
     from transformers import (
         AutoConfig,
         BertConfig,
         GPTaConfig,
         TaConfig,
         TFAutoModel,
         TFAutoModelForCausalLM,
         TFAutoModelForMaskedLM,
         TFAutoModelForPreTraining,
         TFAutoModelForQuestionAnswering,
         TFAutoModelForSeqaSeqLM,
         TFAutoModelForSequenceClassification,
         TFAutoModelWithLMHead,
         TFBertForMaskedLM,
         TFBertForPreTraining,
         TFBertForQuestionAnswering,
         TFBertForSequenceClassification,
         TFBertModel,
         TFGPTaLMHeadModel,
         TFRobertaForMaskedLM,
         TFTaForConditionalGeneration,
     )
     from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
     from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
     from transformers import (
         AutoModel,
         AutoModelForCausalLM,
         AutoModelForMaskedLM,
         AutoModelForPreTraining,
         AutoModelForQuestionAnswering,
         AutoModelForSeqaSeqLM,
         AutoModelForSequenceClassification,
         AutoModelWithLMHead,
         BertForMaskedLM,
         BertForPreTraining,
         BertForQuestionAnswering,
         BertForSequenceClassification,
         BertModel,
         GPTaLMHeadModel,
         RobertaForMaskedLM,
         TaForConditionalGeneration,
     )




@is_pt_tf_cross_test
class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       @slow
       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            for model_name in ["bert-base-uncased"]:
                 __snake_case      :Any 							=    AutoConfig.from_pretrained(a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Tuple 							=    TFAutoModel.from_pretrained(a__      ,    from_pt=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Optional[int] 							=    AutoModel.from_pretrained(a__      ,    from_tf=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )



       @slow
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            for model_name in ["bert-base-uncased"]:
                 __snake_case      :Union[str, Any] 							=    AutoConfig.from_pretrained(a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Tuple 							=    TFAutoModelForPreTraining.from_pretrained(a__      ,    from_pt=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Any 							=    AutoModelForPreTraining.from_pretrained(a__      ,    from_tf=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )



       @slow
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case      :str 							=    AutoConfig.from_pretrained(a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :str 							=    TFAutoModelForCausalLM.from_pretrained(a__      ,    from_pt=a__     )
                 __snake_case							,			__snake_case      :str 							=    TFAutoModelForCausalLM.from_pretrained(
                     a__      ,    output_loading_info=a__      ,    from_pt=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Dict 							=    AutoModelForCausalLM.from_pretrained(a__      ,    from_tf=a__     )
                 __snake_case							,			__snake_case      :Optional[int] 							=    AutoModelForCausalLM.from_pretrained(
                     a__      ,    output_loading_info=a__      ,    from_tf=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )



       @slow
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case      :List[str] 							=    AutoConfig.from_pretrained(a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Optional[int] 							=    TFAutoModelWithLMHead.from_pretrained(a__      ,    from_pt=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :int 							=    AutoModelWithLMHead.from_pretrained(a__      ,    from_tf=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )



       @slow
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case      :Tuple 							=    AutoConfig.from_pretrained(a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :List[str] 							=    TFAutoModelForMaskedLM.from_pretrained(a__      ,    from_pt=a__     )
                 __snake_case							,			__snake_case      :Union[str, Any] 							=    TFAutoModelForMaskedLM.from_pretrained(
                     a__      ,    output_loading_info=a__      ,    from_pt=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Union[str, Any] 							=    AutoModelForMaskedLM.from_pretrained(a__      ,    from_tf=a__     )
                 __snake_case							,			__snake_case      :Optional[int] 							=    AutoModelForMaskedLM.from_pretrained(
                     a__      ,    output_loading_info=a__      ,    from_tf=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )



       @slow
       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case      :List[str] 							=    AutoConfig.from_pretrained(a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Any 							=    TFAutoModelForSeqaSeqLM.from_pretrained(a__      ,    from_pt=a__     )
                 __snake_case							,			__snake_case      :Optional[Any] 							=    TFAutoModelForSeqaSeqLM.from_pretrained(
                     a__      ,    output_loading_info=a__      ,    from_pt=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Optional[int] 							=    AutoModelForSeqaSeqLM.from_pretrained(a__      ,    from_tf=a__     )
                 __snake_case							,			__snake_case      :Any 							=    AutoModelForSeqaSeqLM.from_pretrained(
                     a__      ,    output_loading_info=a__      ,    from_tf=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )



       @slow
       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            for model_name in ["bert-base-uncased"]:
                 __snake_case      :List[str] 							=    AutoConfig.from_pretrained(a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :str 							=    TFAutoModelForSequenceClassification.from_pretrained(a__      ,    from_pt=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Dict 							=    AutoModelForSequenceClassification.from_pretrained(a__      ,    from_tf=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )



       @slow
       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            for model_name in ["bert-base-uncased"]:
                 __snake_case      :List[str] 							=    AutoConfig.from_pretrained(a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :Union[str, Any] 							=    TFAutoModelForQuestionAnswering.from_pretrained(a__      ,    from_pt=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )

                 __snake_case      :str 							=    AutoModelForQuestionAnswering.from_pretrained(a__      ,    from_tf=a__     )
                 self.assertIsNotNone(a__     )
                 self.assertIsInstance(a__      ,    a__     )



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :str 							=    TFAutoModelWithLMHead.from_pretrained(a__      ,    from_pt=a__     )
            self.assertIsInstance(a__      ,    a__     )
            self.assertEqual(model.num_parameters()      ,    1_44_10     )
            self.assertEqual(model.num_parameters(only_trainable=a__     )      ,    1_44_10     )

            __snake_case      :int 							=    AutoModelWithLMHead.from_pretrained(a__      ,    from_tf=a__     )
            self.assertIsInstance(a__      ,    a__     )
            self.assertEqual(model.num_parameters()      ,    1_44_10     )
            self.assertEqual(model.num_parameters(only_trainable=a__     )      ,    1_44_10     )



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






            '''simple docstring'''





            __snake_case      :List[Any] 							=    TFAutoModelWithLMHead.from_pretrained(a__      ,    from_pt=a__     )
            self.assertIsInstance(a__      ,    a__     )
            self.assertEqual(model.num_parameters()      ,    1_44_10     )
            self.assertEqual(model.num_parameters(only_trainable=a__     )      ,    1_44_10     )

            __snake_case      :int 							=    AutoModelWithLMHead.from_pretrained(a__      ,    from_tf=a__     )
            self.assertIsInstance(a__      ,    a__     )
            self.assertEqual(model.num_parameters()      ,    1_44_10     )
            self.assertEqual(model.num_parameters(only_trainable=a__     )      ,    1_44_10     )










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__      ,    )


