




from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
		from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							def  _UpperCAmelCase  (       self							:						Tuple      )      ->       int:
										SCREAMING_SNAKE_CASE     =       self.config_class(**self.inputs_dict      )
										self.parent.assertTrue(hasattr(a							,  """hidden_sizes"""      )      )
										self.parent.assertTrue(hasattr(a							,  """neck_hidden_sizes"""      )      )
										self.parent.assertTrue(hasattr(a							,  """num_attention_heads"""      )      )





class       UpperCAmelCase_   :




							'''simple docstring'''



							def __init__(       self							:						Optional[Any]							,  a							:						Dict							,  a							:						Optional[int]=13							,  a							:						Optional[int]=32							,  a							:						List[str]=2							,  a							:						Optional[int]=3							,  a							:						str=640							,  a							:						str=4							,  a							:						Optional[int]="silu"							,  a							:						Union[str, Any]=3							,  a							:						Optional[int]=32							,  a							:						Optional[Any]=0.1							,  a							:						Optional[int]=0.1							,  a							:						int=0.1							,  a							:						List[str]=0.02							,  a							:						Tuple=True							,  a							:						Optional[Any]=True							,  a							:						List[Any]=10							,  a							:						List[Any]=None							,  )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       parent
										SCREAMING_SNAKE_CASE     =       batch_size
										SCREAMING_SNAKE_CASE     =       image_size
										SCREAMING_SNAKE_CASE     =       patch_size
										SCREAMING_SNAKE_CASE     =       num_channels
										SCREAMING_SNAKE_CASE     =       last_hidden_size
										SCREAMING_SNAKE_CASE     =       num_attention_heads
										SCREAMING_SNAKE_CASE     =       hidden_act
										SCREAMING_SNAKE_CASE     =       conv_kernel_size
										SCREAMING_SNAKE_CASE     =       output_stride
										SCREAMING_SNAKE_CASE     =       hidden_dropout_prob
										SCREAMING_SNAKE_CASE     =       attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE     =       classifier_dropout_prob
										SCREAMING_SNAKE_CASE     =       use_labels
										SCREAMING_SNAKE_CASE     =       is_training
										SCREAMING_SNAKE_CASE     =       num_labels
										SCREAMING_SNAKE_CASE     =       initializer_range
										SCREAMING_SNAKE_CASE     =       scope





							def  _UpperCAmelCase  (       self							:						Tuple      )      ->       List[Any]:
										SCREAMING_SNAKE_CASE     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

										SCREAMING_SNAKE_CASE     =       self.get_config()

										return config, pixel_values, labels, pixel_labels





							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       Union[str, Any]:
										return MobileViTConfig(
										    image_size=self.image_size							,  patch_size=self.patch_size							,  num_channels=self.num_channels							,  num_attention_heads=self.num_attention_heads							,  hidden_act=self.hidden_act							,  conv_kernel_size=self.conv_kernel_size							,  output_stride=self.output_stride							,  hidden_dropout_prob=self.hidden_dropout_prob							,  attention_probs_dropout_prob=self.attention_probs_dropout_prob							,  classifier_dropout_prob=self.classifier_dropout_prob							,  initializer_range=self.initializer_range							,  )





							def  _UpperCAmelCase  (       self							:						List[str]							,  a							:						Union[str, Any]							,  a							:						Tuple							,  a							:						Optional[int]							,  a							:						str      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       TFMobileViTModel(config=a      )
										SCREAMING_SNAKE_CASE     =       model(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       SCREAMING_SNAKE_CASE     =       self.image_size // self.output_stride
										self.parent.assertEqual(
										    result.last_hidden_state.shape							,  (self.batch_size, self.last_hidden_size, expected_height, expected_width)      )





							def  _UpperCAmelCase  (       self							:						Any							,  a							:						Dict							,  a							:						Optional[Any]							,  a							:						List[Any]							,  a							:						List[str]      )      ->       Any:
										SCREAMING_SNAKE_CASE     =       self.num_labels
										SCREAMING_SNAKE_CASE     =       TFMobileViTForImageClassification(a      )
										SCREAMING_SNAKE_CASE     =       model(a							,  labels=a							,  training=a      )
										self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.num_labels)      )





							def  _UpperCAmelCase  (       self							:						Dict							,  a							:						List[str]							,  a							:						Union[str, Any]							,  a							:						List[Any]							,  a							:						Union[str, Any]      )      ->       Optional[int]:
										SCREAMING_SNAKE_CASE     =       self.num_labels
										SCREAMING_SNAKE_CASE     =       TFMobileViTForSemanticSegmentation(a      )
										SCREAMING_SNAKE_CASE     =       SCREAMING_SNAKE_CASE     =       self.image_size // self.output_stride

										SCREAMING_SNAKE_CASE     =       model(a							,  training=a      )
										self.parent.assertEqual(
										    result.logits.shape							,  (self.batch_size, self.num_labels, expected_height, expected_width)      )

										SCREAMING_SNAKE_CASE     =       model(a							,  labels=a							,  training=a      )
										self.parent.assertEqual(
										    result.logits.shape							,  (self.batch_size, self.num_labels, expected_height, expected_width)      )







							def  _UpperCAmelCase  (       self							:						Any      )      ->       Optional[int]:
										SCREAMING_SNAKE_CASE     =       self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       config_and_inputs
										SCREAMING_SNAKE_CASE     =       {"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_tf
class       UpperCAmelCase_   ( A		,   A		,   unittest.TestCase							):




							'''simple docstring'''



							a__    	=  (
							    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
							    if is_tf_available()
							    else ()
							)
							a__    	=  (
							    {'''feature-extraction''': TFMobileViTModel, '''image-classification''': TFMobileViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							a__    	=  False
							a__    	=  False
							a__    	=  False
							a__    	=  False
							a__    	=  False





							def  _UpperCAmelCase  (       self							:						str      )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       TFMobileViTModelTester(self      )
										SCREAMING_SNAKE_CASE     =       TFMobileViTConfigTester(self							,  config_class=a							,  has_text_modality=a      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       Dict:
										self.config_tester.run_common_tests()





							@unittest.skip(reason="""MobileViT does not use inputs_embeds"""      )
							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Tuple:
										pass





							@unittest.skip(reason="""MobileViT does not support input and output embeddings"""      )
							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       Tuple:
										pass





							@unittest.skip(reason="""MobileViT does not output attentions"""      )
							def  _UpperCAmelCase  (       self							:						str      )      ->       List[Any]:
										pass





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       List[Any]:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs_for_common()

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

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





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       List[Any]:
										SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a      )





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Union[str, Any]:
										def check_hidden_states_output(a							:						Dict							,  a							:						Union[str, Any]							,  a							:						Optional[Any]      ):
													SCREAMING_SNAKE_CASE     =       model_class(a      )

													SCREAMING_SNAKE_CASE     =       model(**self._prepare_for_class(a							,  a      )      )

													SCREAMING_SNAKE_CASE     =       outputs.hidden_states

													SCREAMING_SNAKE_CASE     =       5
													self.assertEqual(len(a      )							,  a      )

													# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
													# with the width and height being successively divided by 2.
													SCREAMING_SNAKE_CASE     =       2
													for i in range(len(a      )      ):
																self.assertListEqual(
																    list(hidden_states[i].shape[-2:]      )							,  [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]							,  )
																divisor *= 2

													self.assertEqual(self.model_tester.output_stride							,  divisor // 2      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs_for_common()

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

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

													check_hidden_states_output(a							,  a							,  a      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*a      )





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Tuple:
										SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*a      )





							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""      )      ) == 0							,  reason="""TF does not support backprop for grouped convolutions on CPU."""							,  )
							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Any:
										super().test_dataset_conversion()





							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						List[str]							,  a							:						Optional[int]							,  a							:						Any=2E-1							,  a							:						List[str]=2E-1      )      ->       Dict:
										self.assertTrue(np.allclose(a							,  a							,  atol=a							,  rtol=a      )      )





							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""      )      ) == 0							,  reason="""TF does not support backprop for grouped convolutions on CPU."""							,  )
							@slow
							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       str:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
													if model_class.__name__ != "TFMobileViTModel":
																SCREAMING_SNAKE_CASE     =       model_class(a      )
																if getattr(a							,  """hf_compute_loss"""							,  a      ):
																			super().test_keras_fit()





							def  _UpperCAmelCase  (       self							:						int      )      ->       Optional[Any]:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
													# set an ignore index to correctly test the masked loss used in
													# `TFMobileViTForSemanticSegmentation`.
													if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
																SCREAMING_SNAKE_CASE     =       5

													SCREAMING_SNAKE_CASE     =       model_class(a      )
													if getattr(a							,  """hf_compute_loss"""							,  a      ):
																# The number of elements in the loss should be the same as the number of elements in the label
																SCREAMING_SNAKE_CASE     =       self._prepare_for_class(inputs_dict.copy()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       prepared_for_class[
																    sorted(prepared_for_class.keys() - inputs_dict.keys()							,  reverse=a      )[0]
																]
																SCREAMING_SNAKE_CASE     =       added_label.shape.as_list()[:1]

																# Test that model correctly compute the loss with kwargs
																SCREAMING_SNAKE_CASE     =       self._prepare_for_class(inputs_dict.copy()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       {"""input_ids""", """pixel_values""", """input_features"""}
																SCREAMING_SNAKE_CASE     =       possible_input_names.intersection(set(a      )      ).pop()
																SCREAMING_SNAKE_CASE     =       prepared_for_class.pop(a      )

																SCREAMING_SNAKE_CASE     =       model(a							,  **a      )[0]
																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

																# Test that model correctly compute the loss when we mask some positions
																SCREAMING_SNAKE_CASE     =       self._prepare_for_class(inputs_dict.copy()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       {"""input_ids""", """pixel_values""", """input_features"""}
																SCREAMING_SNAKE_CASE     =       possible_input_names.intersection(set(a      )      ).pop()
																SCREAMING_SNAKE_CASE     =       prepared_for_class.pop(a      )
																if "labels" in prepared_for_class:
																			SCREAMING_SNAKE_CASE     =       prepared_for_class["""labels"""].numpy()
																			if len(labels.shape      ) > 1 and labels.shape[1] != 1:
																						# labels[0] = -100
																						SCREAMING_SNAKE_CASE     =       tf.convert_to_tensor(a      )
																						SCREAMING_SNAKE_CASE     =       model(a							,  **a      )[0]
																						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )
																						self.assertTrue(not np.any(np.isnan(loss.numpy()      )      )      )

                # Test that model correctly compute the loss with a dict
																SCREAMING_SNAKE_CASE     =       self._prepare_for_class(inputs_dict.copy()							,  a							,  return_labels=a      )
																SCREAMING_SNAKE_CASE     =       model(a      )[0]
																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

																# Test that model correctly compute the loss with a tuple
																SCREAMING_SNAKE_CASE     =       self._prepare_for_class(inputs_dict.copy()							,  a							,  return_labels=a      )

																# Get keys that were added with the _prepare_for_class function
																SCREAMING_SNAKE_CASE     =       prepared_for_class.keys() - inputs_dict.keys()
																SCREAMING_SNAKE_CASE     =       inspect.signature(model.call      ).parameters
																SCREAMING_SNAKE_CASE     =       list(signature.keys()      )

																# Create a dictionary holding the location of the tensors in the tuple
																SCREAMING_SNAKE_CASE     =       {0: input_name}
																for label_key in label_keys:
																			SCREAMING_SNAKE_CASE     =       signature_names.index(a      )
																			SCREAMING_SNAKE_CASE     =       label_key
																SCREAMING_SNAKE_CASE     =       sorted(tuple_index_mapping.items()      )
																# Initialize a list with their default values, update the values and convert to a tuple
																SCREAMING_SNAKE_CASE     =       []

																for name in signature_names:
																			if name != "kwargs":
																						list_input.append(signature[name].default      )

																for index, value in sorted_tuple_index_mapping:
																			SCREAMING_SNAKE_CASE     =       prepared_for_class[value]

																SCREAMING_SNAKE_CASE     =       tuple(a      )

																# Send to model
																SCREAMING_SNAKE_CASE     =       model(tuple_input[:-1]      )[0]

																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )







							@slow
							def  _UpperCAmelCase  (       self							:						str      )      ->       List[Any]:
										for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE     =       TFMobileViTModel.from_pretrained(a      )
													self.assertIsNotNone(a      )





def        lowerCamelCase_     (     ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
			return image




@require_tf
class       UpperCAmelCase_   ( unittest.TestCase							):




							'''simple docstring'''



							@slow
							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""      )

										SCREAMING_SNAKE_CASE     =       MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""      )
										SCREAMING_SNAKE_CASE     =       prepare_img()
										SCREAMING_SNAKE_CASE     =       image_processor(images=a							,  return_tensors="""tf"""      )

										# forward pass
										SCREAMING_SNAKE_CASE     =       model(**a							,  training=a      )

										# verify the logits
										SCREAMING_SNAKE_CASE     =       tf.TensorShape((1, 1_000)      )
										self.assertEqual(outputs.logits.shape							,  a      )

										SCREAMING_SNAKE_CASE     =       tf.constant([-1.9364, -1.2327, -0.4653]      )

										tf.debugging.assert_near(outputs.logits[0, :3]							,  a							,  atol=1E-4							,  rtol=1E-0_4      )







							@slow
							def  _UpperCAmelCase  (       self							:						int      )      ->       Tuple:
										# `from_pt` will be removed
										SCREAMING_SNAKE_CASE     =       TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""      )

										SCREAMING_SNAKE_CASE     =       MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""      )

										SCREAMING_SNAKE_CASE     =       prepare_img()
										SCREAMING_SNAKE_CASE     =       image_processor(images=a							,  return_tensors="""tf"""      )

										# forward pass
										SCREAMING_SNAKE_CASE     =       model(inputs.pixel_values							,  training=a      )
										SCREAMING_SNAKE_CASE     =       outputs.logits

										# verify the logits
										SCREAMING_SNAKE_CASE     =       tf.TensorShape((1, 21, 32, 32)      )
										self.assertEqual(logits.shape							,  a      )

										SCREAMING_SNAKE_CASE     =       tf.constant(
										    [
										        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
										        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
										        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
										    ]      )

										tf.debugging.assert_near(logits[0, :3, :3, :3]							,  a							,  rtol=1E-4							,  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


__A    :				int    						=	logging.get_logger(__name__)

# General docstring
__A    :				str    						=	"""ResNetConfig"""

# Base docstring
__A    :				Optional[Any]    						=	"""microsoft/resnet-50"""
__A    :				Tuple    						=	[1, 2_0_4_8, 7, 7]

# Image classification docstring
__A    :				Tuple    						=	"""microsoft/resnet-50"""
__A    :				List[str]    						=	"""tiger cat"""

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




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




							'''simple docstring'''



							def __init__(       self							:						List[str]							,  a							:						int							,  a							:						int = 3							,  a							:						int = 1							,  a							:						str = "relu"							,  **a							:						Dict      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       kernel_size // 2
										SCREAMING_SNAKE_CASE     =       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
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.BatchNormalization(epsilon=1E-5							,  momentum=0.9							,  name="""normalization"""      )
										SCREAMING_SNAKE_CASE     =       ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("""linear"""      )





							def  _UpperCAmelCase  (       self							:						str							,  a							:						tf.Tensor      )      ->       tf.Tensor:
										# Pad to match that done in the PyTorch Conv2D model
										SCREAMING_SNAKE_CASE     =       SCREAMING_SNAKE_CASE     =       (self.pad_value, self.pad_value)
										SCREAMING_SNAKE_CASE     =       tf.pad(a							,  [(0, 0), height_pad, width_pad, (0, 0)]      )
										SCREAMING_SNAKE_CASE     =       self.conv(a      )
										return hidden_state







							def  _UpperCAmelCase  (       self							:						int							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       self.convolution(a      )
										SCREAMING_SNAKE_CASE     =       self.normalization(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.activation(a      )
										return hidden_state





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




							'''simple docstring'''



							def __init__(       self							:						Optional[Any]							,  a							:						ResNetConfig							,  **a							:						Dict      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(
										    config.embedding_size							,  kernel_size=7							,  stride=2							,  activation=config.hidden_act							,  name="""embedder"""							,  )
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.MaxPoolaD(pool_size=3							,  strides=2							,  padding="""valid"""							,  name="""pooler"""      )
										SCREAMING_SNAKE_CASE     =       config.num_channels







							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       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."""      )
										SCREAMING_SNAKE_CASE     =       pixel_values
										SCREAMING_SNAKE_CASE     =       self.embedder(a      )
										SCREAMING_SNAKE_CASE     =       tf.pad(a							,  [[0, 0], [1, 1], [1, 1], [0, 0]]      )
										SCREAMING_SNAKE_CASE     =       self.pooler(a      )
										return hidden_state





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




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						int							,  a							:						int = 2							,  **a							:						Optional[int]      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.ConvaD(
										    a							,  kernel_size=1							,  strides=a							,  use_bias=a							,  name="""convolution"""      )
										# Use same default momentum and epsilon as PyTorch equivalent
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.BatchNormalization(epsilon=1E-5							,  momentum=0.9							,  name="""normalization"""      )







							def  _UpperCAmelCase  (       self							:						str							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       x
										SCREAMING_SNAKE_CASE     =       self.convolution(a      )
										SCREAMING_SNAKE_CASE     =       self.normalization(a							,  training=a      )
										return hidden_state





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




							'''simple docstring'''



							def __init__(       self							:						Optional[Any]							,  a							:						int							,  a							:						int							,  a							:						int = 1							,  a							:						str = "relu"							,  **a							:						List[Any]      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       in_channels != out_channels or stride != 1
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(a							,  stride=a							,  name="""layer.0"""      )
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(a							,  activation=a							,  name="""layer.1"""      )
										SCREAMING_SNAKE_CASE     =       (
										    TFResNetShortCut(a							,  stride=a							,  name="""shortcut"""      )
										    if should_apply_shortcut
										    else tf.keras.layers.Activation("""linear"""							,  name="""shortcut"""      )
										)
										SCREAMING_SNAKE_CASE     =       ACTaFN[activation]







							def  _UpperCAmelCase  (       self							:						int							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       hidden_state
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.shortcut(a							,  training=a      )
										hidden_state += residual
										SCREAMING_SNAKE_CASE     =       self.activation(a      )
										return hidden_state





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




							'''simple docstring'''



							def __init__(       self							:						List[Any]							,  a							:						int							,  a							:						int							,  a							:						int = 1							,  a							:						str = "relu"							,  a							:						int = 4							,  **a							:						List[Any]							,  )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       in_channels != out_channels or stride != 1
										SCREAMING_SNAKE_CASE     =       out_channels // reduction
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(a							,  kernel_size=1							,  name="""layer.0"""      )
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(a							,  stride=a							,  name="""layer.1"""      )
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(a							,  kernel_size=1							,  activation=a							,  name="""layer.2"""      )
										SCREAMING_SNAKE_CASE     =       (
										    TFResNetShortCut(a							,  stride=a							,  name="""shortcut"""      )
										    if should_apply_shortcut
										    else tf.keras.layers.Activation("""linear"""							,  name="""shortcut"""      )
										)
										SCREAMING_SNAKE_CASE     =       ACTaFN[activation]







							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       hidden_state
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.shortcut(a							,  training=a      )
										hidden_state += residual
										SCREAMING_SNAKE_CASE     =       self.activation(a      )
										return hidden_state





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




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						ResNetConfig							,  a							:						int							,  a							:						int							,  a							:						int = 2							,  a							:						int = 2							,  **a							:						Union[str, Any]      )      ->       None:
										super().__init__(**a      )

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

										SCREAMING_SNAKE_CASE     =       [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      )
										]
										SCREAMING_SNAKE_CASE     =       layers







							def  _UpperCAmelCase  (       self							:						Optional[int]							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										for layer in self.stage_layers:
													SCREAMING_SNAKE_CASE     =       layer(a							,  training=a      )
										return hidden_state





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




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						ResNetConfig							,  **a							:						Tuple      )      ->       None:
										super().__init__(**a      )
										# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
										SCREAMING_SNAKE_CASE     =       [
										    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  _UpperCAmelCase  (       self							:						Any							,  a							:						tf.Tensor							,  a							:						bool = False							,  a							:						bool = True							,  a							:						bool = False							,  )      ->       TFBaseModelOutputWithNoAttention:
										SCREAMING_SNAKE_CASE     =       () if output_hidden_states else None

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

													SCREAMING_SNAKE_CASE     =       stage_module(a							,  training=a      )

										if output_hidden_states:
													SCREAMING_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 TFBaseModelOutputWithNoAttention(last_hidden_state=a							,  hidden_states=a      )





class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							a__    	=  ResNetConfig
							a__    	=  '''resnet'''
							a__    	=  '''pixel_values'''





							@property
							def  _UpperCAmelCase  (       self							:						Any      )      ->       Union[str, Any]:
										return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)							,  dtype=tf.floataa      )}





__A    :				Any    						=	r"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""


__A    :				Optional[Any]    						=	r"""
    Args:
        pixel_values (`tf.Tensor` 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.
"""




@keras_serializable
class       UpperCAmelCase_   ( tf.keras.layers.Layer							):




							'''simple docstring'''



							a__    	=  ResNetConfig





							def __init__(       self							:						Optional[Any]							,  a							:						ResNetConfig							,  **a							:						List[Any]      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       config
										SCREAMING_SNAKE_CASE     =       TFResNetEmbeddings(a							,  name="""embedder"""      )
										SCREAMING_SNAKE_CASE     =       TFResNetEncoder(a							,  name="""encoder"""      )
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.GlobalAveragePoolingaD(keepdims=a      )







							@unpack_inputs
							def  _UpperCAmelCase  (       self							:						List[str]							,  a							:						tf.Tensor							,  a							:						Optional[bool] = None							,  a							:						Optional[bool] = None							,  a							:						bool = False							,  )      ->       Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
										SCREAMING_SNAKE_CASE     =       (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										SCREAMING_SNAKE_CASE     =       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)
										SCREAMING_SNAKE_CASE     =       tf.transpose(a							,  perm=[0, 2, 3, 1]      )
										SCREAMING_SNAKE_CASE     =       self.embedder(a							,  training=a      )

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

										SCREAMING_SNAKE_CASE     =       encoder_outputs[0]

										SCREAMING_SNAKE_CASE     =       self.pooler(a      )

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

										SCREAMING_SNAKE_CASE     =       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.'''		,   A		,   )
class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							def __init__(       self							:						List[Any]							,  a							:						ResNetConfig							,  **a							:						str      )      ->       None:
										super().__init__(a							,  **a      )
										SCREAMING_SNAKE_CASE     =       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  _UpperCAmelCase  (       self							:						List[Any]							,  a							:						tf.Tensor							,  a							:						Optional[bool] = None							,  a							:						Optional[bool] = None							,  a							:						bool = False							,  )      ->       Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
										SCREAMING_SNAKE_CASE     =       (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										SCREAMING_SNAKE_CASE     =       return_dict if return_dict is not None else self.config.use_return_dict

										SCREAMING_SNAKE_CASE     =       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.
    '''		,   A		,   )
class       UpperCAmelCase_   ( A		,   A							):




							'''simple docstring'''



							def __init__(       self							:						Union[str, Any]							,  a							:						ResNetConfig							,  **a							:						Optional[Any]      )      ->       None:
										super().__init__(a							,  **a      )
										SCREAMING_SNAKE_CASE     =       config.num_labels
										SCREAMING_SNAKE_CASE     =       TFResNetMainLayer(a							,  name="""resnet"""      )
										# classification head
										SCREAMING_SNAKE_CASE     =       (
										    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  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						tf.Tensor      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.Flatten()(a      )
										SCREAMING_SNAKE_CASE     =       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  _UpperCAmelCase  (       self							:						Any							,  a							:						tf.Tensor = None							,  a							:						tf.Tensor = None							,  a							:						bool = None							,  a							:						bool = None							,  a							:						bool = False							,  )      ->       Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
										SCREAMING_SNAKE_CASE     =       return_dict if return_dict is not None else self.config.use_return_dict

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

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

										SCREAMING_SNAKE_CASE     =       self.classifier(a      )

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

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

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






