



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 tensorflow as tf

						from transformers import TFResNetForImageClassification, TFResNetModel
						from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__=3			,	UpperCAmelCase__=32			,	UpperCAmelCase__=3			,	UpperCAmelCase__=10			,	UpperCAmelCase__=[10, 20, 30, 40]			,	UpperCAmelCase__=[1, 1, 2, 1]			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__="relu"			,	UpperCAmelCase__=3			,	UpperCAmelCase__=None			,	):
							A__							=       parent
							A__							=       batch_size
							A__							=       image_size
							A__							=       num_channels
							A__							=       embeddings_size
							A__							=       hidden_sizes
							A__							=       depths
							A__							=       is_training
							A__							=       use_labels
							A__							=       hidden_act
							A__							=       num_labels
							A__							=       scope
							A__							=       len(UpperCAmelCase__		)



	def 			__A							(       self		):
							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 			__A							(       self		):
							return ResNetConfig(
							    num_channels=self.num_channels			,	embeddings_size=self.embeddings_size			,	hidden_sizes=self.hidden_sizes			,	depths=self.depths			,	hidden_act=self.hidden_act			,	num_labels=self.num_labels			,	image_size=self.image_size			,	)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       TFResNetModel(config=UpperCAmelCase__		)
							A__							=       model(UpperCAmelCase__		)
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape			,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,	)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       self.num_labels
							A__							=       TFResNetForImageClassification(UpperCAmelCase__		)
							A__							=       model(UpperCAmelCase__			,	labels=UpperCAmelCase__		)
							self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels)		)



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






@require_tf
class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase       ,    unittest.TestCase     ):
	lowerCAmelCase	:   str				=	(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
	lowerCAmelCase	:   Optional[int]				=	(
	    {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	lowerCAmelCase	:   List[Any]				=	False
	lowerCAmelCase	:   Dict				=	False
	lowerCAmelCase	:   int				=	False
	lowerCAmelCase	:   int				=	False
	lowerCAmelCase	:   Any				=	False



	def 			__A							(       self		):
							A__							=       TFResNetModelTester(self		)
							A__							=       ConfigTester(self			,	config_class=UpperCAmelCase__			,	has_text_modality=UpperCAmelCase__		)



	def 			__A							(       self		):
							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 			__A							(       self		):
							return



	@unittest.skip(reason="ResNet does not use inputs_embeds"		)
	def 			__A							(       self		):
							pass



	@unittest.skip(reason="ResNet does not support input and output embeddings"		)
	def 			__A							(       self		):
							pass



	def 			__A							(       self		):
							A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()

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

													A__							=       ["pixel_values"]
													self.assertListEqual(arg_names[:1]			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase__		)



	def 			__A							(       self		):
							def check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
													A__							=       model_class(UpperCAmelCase__		)
													A__							=       model(**self._prepare_for_class(UpperCAmelCase__			,	UpperCAmelCase__		)		)

													A__							=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													A__							=       self.model_tester.num_stages
													self.assertEqual(len(UpperCAmelCase__		)			,	expected_num_stages + 1		)

													# ResNet'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 // 4, self.model_tester.image_size // 4]			,	)

							A__						,       A__							=       self.model_tester.prepare_config_and_inputs_for_common()
							A__							=       ["basic", "bottleneck"]
							for model_class in self.all_model_classes:
													for layer_type in layers_type:
																			A__							=       layer_type
																			A__							=       True
																			check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)

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

																			check_hidden_states_output(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__		)



	@slow
	def 			__A							(       self		):
							for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A__							=       TFResNetModel.from_pretrained(UpperCAmelCase__		)
													self.assertIsNotNone(UpperCAmelCase__		)





def  UpperCamelCase						(       )->       List[str]:





						"""simple docstring"""





						A__							=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
						return image






@require_tf
@require_vision
class      UpperCamelCase    (						unittest.TestCase     ):



	@cached_property
	def 			__A							(       self		):
							return (
							    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
							    if is_vision_available()
							    else None
							)



	@slow
	def 			__A							(       self		):
							A__							=       TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)

							A__							=       self.default_image_processor
							A__							=       prepare_img()
							A__							=       image_processor(images=UpperCAmelCase__			,	return_tensors="tf"		)

							# forward pass
							A__							=       model(**UpperCAmelCase__		)

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

							A__							=       tf.constant([-11.1_069, -9.7_877, -8.3_777]		)

							self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()			,	UpperCAmelCase__			,	atol=1e-4		)		)










from __future__ import annotations






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__=None		):
							A__							=       data
							A__							=       None



	def __repr__(       self		):
							A__							=       []
							A__							=       self
							while temp:
													string_rep.append(F"""{temp.data}"""		)
													A__							=       temp.next
							return "->".join(UpperCAmelCase__		)





def  UpperCamelCase						(       _A			:				list    )->       Dict:





						"""simple docstring"""





						if not elements_list:
												raise Exception("The Elements List is empty"    )

						A__							=       A__							=       Node(elements_list[0]    )
						for i in range(1			,							len(_A    )    ):
												A__							=       Node(elements_list[i]    )
												A__							=       current.next
						return head
def  UpperCamelCase						(       _A			:				Node    )->       None:





						"""simple docstring"""





						if head_node is not None and isinstance(_A			,							_A    ):
												print_reverse(head_node.next    )
												print(head_node.data    )
def  UpperCamelCase						(       )->       Tuple:





						"""simple docstring"""





						from doctest import testmod

						testmod()

						A__							=       make_linked_list([14, 52, 14, 12, 43]    )
						print("Linked List:"    )
						print(_A    )
						print("Elements in Reverse:"    )
						print_reverse(_A    )


if __name__ == "__main__":
						main()





