




import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import EfficientNetForImageClassification, EfficientNetModel
   from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor




class 			a__       :







     def __init__(	self  ,		A  ,		A=13  ,		A=32  ,		A=3  ,		A=[3, 3, 5]  ,		A=[32, 16, 24]  ,		A=[16, 24, 40]  ,		A=[1, 1, 2]  ,		A=[1, 1, 2]  ,		A=[1, 6, 6]  ,		A=True  ,		A=True  ,		A=37  ,		A="gelu"  ,		A=10  ,		)  ->    List[Any]:



          '''simple docstring'''



          a      =      parent
          a      =      batch_size
          a      =      image_size
          a      =      num_channels
          a      =      kernel_sizes
          a      =      in_channels
          a      =      out_channels
          a      =      strides
          a      =      num_block_repeats
          a      =      expand_ratios
          a      =      is_training
          a      =      hidden_act
          a      =      num_labels
          a      =      use_labels







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          a      =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

          a      =      None
          if self.use_labels:
               a      =      ids_tensor([self.batch_size]  ,		self.num_labels )

          a      =      self.get_config()
          return config, pixel_values, labels







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          return EfficientNetConfig(
              num_channels=self.num_channels  ,		kernel_sizes=self.kernel_sizes  ,		in_channels=self.in_channels  ,		out_channels=self.out_channels  ,		strides=self.strides  ,		num_block_repeats=self.num_block_repeats  ,		expand_ratios=self.expand_ratios  ,		hidden_act=self.hidden_act  ,		num_labels=self.num_labels  ,		)







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A )  ->    Optional[int]:



          '''simple docstring'''



          a      =      EfficientNetModel(config=A )
          model.to(A )
          model.eval()
          a      =      model(A )
          # expected last hidden states: B, C, H // 4, W // 4
          self.parent.assertEqual(
              result.last_hidden_state.shape  ,		(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)  ,		)







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		A )  ->    List[Any]:



          '''simple docstring'''



          a      =      EfficientNetForImageClassification(A )
          model.to(A )
          model.eval()
          a      =      model(A  ,		labels=A )
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_labels) )







     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''simple docstring'''



          a      =      self.prepare_config_and_inputs()
          a  ,      a  ,      a      =      config_and_inputs
          a      =      {"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class 			a__       (					UpperCamelCase__   ,					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			List[str]							=	(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
     a		:			Dict							=	(
         {"""feature-extraction""": EfficientNetModel, """image-classification""": EfficientNetForImageClassification}
         if is_torch_available()
         else {}
     )

     a		:			int							=	False
     a		:			Union[str, Any]							=	False
     a		:			Optional[Any]							=	False
     a		:			Dict							=	False
     a		:			Any							=	False







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



          '''simple docstring'''



          a      =      EfficientNetModelTester(self )
          a      =      ConfigTester(
              self  ,		config_class=A  ,		has_text_modality=A  ,		hidden_size=37 )







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          return







     @unittest.skip(reason="EfficientNet does not use inputs_embeds" )
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          pass







     @unittest.skip(reason="EfficientNet does not support input and output embeddings" )
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          pass







     @unittest.skip(reason="EfficientNet does not use feedforward chunking" )
     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          pass







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a  ,      a      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               a      =      model_class(A )
               a      =      inspect.signature(model.forward )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               a      =      [*signature.parameters.keys()]

               a      =      ["pixel_values"]
               self.assertListEqual(arg_names[:1]  ,		A )







     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''simple docstring'''



          a      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          def check_hidden_states_output(A  ,		A  ,		A ):
               a      =      model_class(A )
               model.to(A )
               model.eval()

               with torch.no_grad():
                    a      =      model(**self._prepare_for_class(A  ,		A ) )

               a      =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
               a      =      sum(config.num_block_repeats ) * 4
               self.assertEqual(len(A )  ,		A )

               # EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:] )  ,		[self.model_tester.image_size // 2, self.model_tester.image_size // 2]  ,		)

          a  ,      a      =      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               a      =      True
               check_hidden_states_output(A  ,		A  ,		A )

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               a      =      True

               check_hidden_states_output(A  ,		A  ,		A )







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          a      =      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A )







     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          pass







     @slow
     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               a      =      EfficientNetModel.from_pretrained(A )
               self.assertIsNotNone(A )







     @is_pipeline_test
     @require_vision
     @slow
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          super().test_pipeline_image_classification()







def 					SCREAMING_SNAKE_CASE							(   )			->     str:
     a      =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     return image





@require_torch
@require_vision
class 			a__       (					unittest.TestCase      ):







     @cached_property
     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          return AutoImageProcessor.from_pretrained("google/efficientnet-b7" ) if is_vision_available() else None







     @slow
     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          a      =      EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7" ).to(A )

          a      =      self.default_image_processor
          a      =      prepare_img()
          a      =      image_processor(images=A  ,		return_tensors="pt" ).to(A )

          # forward pass
          with torch.no_grad():
               a      =      model(**A )

          # verify the logits
          a      =      torch.Size((1, 1000) )
          self.assertEqual(outputs.logits.shape  ,		A )

          a      =      torch.tensor([0.0_0_0_1, 0.0_0_0_2, 0.0_0_0_2] ).to(A )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,		A  ,		atol=1e-4 ) )







from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


lowercase__      :			int           =					logging.get_logger(__name__)

# General docstring
lowercase__      :			str           =					"ResNetConfig"

# Base docstring
lowercase__      :			Dict           =					"microsoft/resnet-50"
lowercase__      :			Dict           =					[1, 2_048, 7, 7]

# Image classification docstring
lowercase__      :			Optional[Any]           =					"microsoft/resnet-50"
lowercase__      :			str           =					"tiger cat"

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




class 			a__       (					tf.keras.layers.Layer      ):







     def __init__(	self  ,		A  ,		A = 3  ,		A = 1  ,		A = "relu"  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          a      =      kernel_size // 2
          a      =      tf.keras.layers.ConvaD(
              A  ,		kernel_size=A  ,		strides=A  ,		padding="valid"  ,		use_bias=A  ,		name="convolution" )
          # Use same default momentum and epsilon as PyTorch equivalent
          a      =      tf.keras.layers.BatchNormalization(epsilon=1e-5  ,		momentum=0.9  ,		name="normalization" )
          a      =      ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear" )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      a      =      (self.pad_value, self.pad_value)
          a      =      tf.pad(A  ,		[(0, 0), height_pad, width_pad, (0, 0)] )
          a      =      self.conv(A )
          return hidden_state







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      self.convolution(A )
          a      =      self.normalization(A  ,		training=A )
          a      =      self.activation(A )
          return hidden_state







class 			a__       (					tf.keras.layers.Layer      ):







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          a      =      TFResNetConvLayer(
              config.embedding_size  ,		kernel_size=7  ,		stride=2  ,		activation=config.hidden_act  ,		name="embedder"  ,		)
          a      =      tf.keras.layers.MaxPoolaD(pool_size=3  ,		strides=2  ,		padding="valid"  ,		name="pooler" )
          a      =      config.num_channels







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a  ,      a  ,      a  ,      a      =      shape_list(A )
          if tf.executing_eagerly() and 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." )
          a      =      pixel_values
          a      =      self.embedder(A )
          a      =      tf.pad(A  ,		[[0, 0], [1, 1], [1, 1], [0, 0]] )
          a      =      self.pooler(A )
          return hidden_state







class 			a__       (					tf.keras.layers.Layer      ):







     def __init__(	self  ,		A  ,		A = 2  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          a      =      tf.keras.layers.ConvaD(
              A  ,		kernel_size=1  ,		strides=A  ,		use_bias=A  ,		name="convolution" )
          # Use same default momentum and epsilon as PyTorch equivalent
          a      =      tf.keras.layers.BatchNormalization(epsilon=1e-5  ,		momentum=0.9  ,		name="normalization" )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      x
          a      =      self.convolution(A )
          a      =      self.normalization(A  ,		training=A )
          return hidden_state







class 			a__       (					tf.keras.layers.Layer      ):







     def __init__(	self  ,		A  ,		A  ,		A = 1  ,		A = "relu"  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          a      =      in_channels != out_channels or stride != 1
          a      =      TFResNetConvLayer(A  ,		stride=A  ,		name="layer.0" )
          a      =      TFResNetConvLayer(A  ,		activation=A  ,		name="layer.1" )
          a      =      (
              TFResNetShortCut(A  ,		stride=A  ,		name="shortcut" )
              if should_apply_shortcut
              else tf.keras.layers.Activation("linear"  ,		name="shortcut" )
          )
          a      =      ACTaFN[activation]







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      hidden_state
          a      =      self.conva(A  ,		training=A )
          a      =      self.conva(A  ,		training=A )
          a      =      self.shortcut(A  ,		training=A )
          hidden_state += residual
          a      =      self.activation(A )
          return hidden_state







class 			a__       (					tf.keras.layers.Layer      ):







     def __init__(	self  ,		A  ,		A  ,		A = 1  ,		A = "relu"  ,		A = 4  ,		**A  ,		)  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          a      =      in_channels != out_channels or stride != 1
          a      =      out_channels // reduction
          a      =      TFResNetConvLayer(A  ,		kernel_size=1  ,		name="layer.0" )
          a      =      TFResNetConvLayer(A  ,		stride=A  ,		name="layer.1" )
          a      =      TFResNetConvLayer(A  ,		kernel_size=1  ,		activation=A  ,		name="layer.2" )
          a      =      (
              TFResNetShortCut(A  ,		stride=A  ,		name="shortcut" )
              if should_apply_shortcut
              else tf.keras.layers.Activation("linear"  ,		name="shortcut" )
          )
          a      =      ACTaFN[activation]







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      hidden_state
          a      =      self.conva(A  ,		training=A )
          a      =      self.conva(A  ,		training=A )
          a      =      self.conva(A  ,		training=A )
          a      =      self.shortcut(A  ,		training=A )
          hidden_state += residual
          a      =      self.activation(A )
          return hidden_state







class 			a__       (					tf.keras.layers.Layer      ):







     def __init__(	self  ,		A  ,		A  ,		A  ,		A = 2  ,		A = 2  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )

          a      =      TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

          a      =      [layer(A  ,		A  ,		stride=A  ,		activation=config.hidden_act  ,		name="layers.0" )]
          layers += [
              layer(A  ,		A  ,		activation=config.hidden_act  ,		name=F'''layers.{i + 1}''' )
              for i in range(depth - 1 )
          ]
          a      =      layers







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False )  ->    tf.Tensor:



          '''simple docstring'''



          for layer in self.stage_layers:
               a      =      layer(A  ,		training=A )
          return hidden_state







class 			a__       (					tf.keras.layers.Layer      ):







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
          a      =      [
              TFResNetStage(
                  A  ,		config.embedding_size  ,		config.hidden_sizes[0]  ,		stride=2 if config.downsample_in_first_stage else 1  ,		depth=config.depths[0]  ,		name="stages.0"  ,		)
          ]
          for i, (in_channels, out_channels, depth) in enumerate(
              zip(config.hidden_sizes  ,		config.hidden_sizes[1:]  ,		config.depths[1:] ) ):
               self.stages.append(TFResNetStage(A  ,		A  ,		A  ,		depth=A  ,		name=F'''stages.{i + 1}''' ) )







     def 			lowerCAmelCase_				(	self  ,		A  ,		A = False  ,		A = True  ,		A = False  ,		)  ->    TFBaseModelOutputWithNoAttention:



          '''simple docstring'''



          a      =      () if output_hidden_states else None

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

               a      =      stage_module(A  ,		training=A )

          if output_hidden_states:
               a      =      hidden_states + (hidden_state,)

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

          return TFBaseModelOutputWithNoAttention(last_hidden_state=A  ,		hidden_states=A )







class 			a__       (					UpperCamelCase__      ):
     a		:			List[Any]							=	ResNetConfig
     a		:			str							=	"""resnet"""
     a		:			List[str]							=	"""pixel_values"""







     @property
     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)  ,		dtype=tf.floataa )}







lowercase__      :			Any           =					R"\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"


lowercase__      :			Dict           =					R"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"




@keras_serializable
class 			a__       (					tf.keras.layers.Layer      ):
     a		:			List[str]							=	ResNetConfig







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(**A )
          a      =      config
          a      =      TFResNetEmbeddings(A  ,		name="embedder" )
          a      =      TFResNetEncoder(A  ,		name="encoder" )
          a      =      tf.keras.layers.GlobalAveragePoolingaD(keepdims=A )







     @unpack_inputs
     def 			lowerCAmelCase_				(	self  ,		A  ,		A = None  ,		A = None  ,		A = False  ,		)  ->    Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:



          '''simple docstring'''



          a      =      (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          a      =      return_dict if return_dict is not None else self.config.use_return_dict

          # TF 2.0 image layers can't use NCHW format when running on CPU.
          # We transpose to NHWC format and then transpose back after the full forward pass.
          # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
          a      =      tf.transpose(A  ,		perm=[0, 2, 3, 1] )
          a      =      self.embedder(A  ,		training=A )

          a      =      self.encoder(
              A  ,		output_hidden_states=A  ,		return_dict=A  ,		training=A )

          a      =      encoder_outputs[0]

          a      =      self.pooler(A )

          # Transpose all the outputs to the NCHW format
          # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
          a      =      tf.transpose(A  ,		(0, 3, 1, 2) )
          a      =      tf.transpose(A  ,		(0, 3, 1, 2) )
          a      =      ()
          for hidden_state in encoder_outputs[1:]:
               a      =      hidden_states + tuple(tf.transpose(A  ,		(0, 3, 1, 2) ) for h in hidden_state )

          if not return_dict:
               return (last_hidden_state, pooled_output) + hidden_states

          a      =      hidden_states if output_hidden_states else None

          return TFBaseModelOutputWithPoolingAndNoAttention(
              last_hidden_state=A  ,		pooler_output=A  ,		hidden_states=A  ,		)







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







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(A  ,		**A )
          a      =      TFResNetMainLayer(config=A  ,		name="resnet" )







     @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  ,		)
     @unpack_inputs
     def 			lowerCAmelCase_				(	self  ,		A  ,		A = None  ,		A = None  ,		A = False  ,		)  ->    Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:



          '''simple docstring'''



          a      =      (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          a      =      return_dict if return_dict is not None else self.config.use_return_dict

          a      =      self.resnet(
              pixel_values=A  ,		output_hidden_states=A  ,		return_dict=A  ,		training=A  ,		)
          return resnet_outputs







@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.
    """   ,					UpperCamelCase__   ,					)
class 			a__       (					UpperCamelCase__   ,					UpperCamelCase__      ):







     def __init__(	self  ,		A  ,		**A )  ->    None:



          '''simple docstring'''



          super().__init__(A  ,		**A )
          a      =      config.num_labels
          a      =      TFResNetMainLayer(A  ,		name="resnet" )
          # classification head
          a      =      (
              tf.keras.layers.Dense(config.num_labels  ,		name="classifier.1" )
              if config.num_labels > 0
              else tf.keras.layers.Activation("linear"  ,		name="classifier.1" )
          )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    tf.Tensor:



          '''simple docstring'''



          a      =      tf.keras.layers.Flatten()(A )
          a      =      self.classifier_layer(A )
          return logits







     @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  ,		)
     @unpack_inputs
     def 			lowerCAmelCase_				(	self  ,		A = None  ,		A = None  ,		A = None  ,		A = None  ,		A = False  ,		)  ->    Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:



          '''simple docstring'''



          a      =      return_dict if return_dict is not None else self.config.use_return_dict

          a      =      self.resnet(
              A  ,		output_hidden_states=A  ,		return_dict=A  ,		training=A )

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

          a      =      self.classifier(A )

          a      =      None if labels is None else self.hf_compute_loss(A  ,		A )

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

          return TFImageClassifierOutputWithNoAttention(loss=A  ,		logits=A  ,		hidden_states=outputs.hidden_states )

