

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 			a					:


						def __init__(    self  ,   A_  ,   A_=3  ,   A_=32  ,   A_=3  ,   A_=10  ,   A_=[10, 20, 30, 40]  ,   A_=[1, 1, 2, 1]  ,   A_=True  ,   A_=True  ,   A_="relu"  ,   A_=3  ,   A_=None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      parent
										_UpperCAmelCase    :						Tuple							=      batch_size
										_UpperCAmelCase    :						str							=      image_size
										_UpperCAmelCase    :						Optional[int]							=      num_channels
										_UpperCAmelCase    :						List[str]							=      embeddings_size
										_UpperCAmelCase    :						Optional[Any]							=      hidden_sizes
										_UpperCAmelCase    :						Union[str, Any]							=      depths
										_UpperCAmelCase    :						Optional[Any]							=      is_training
										_UpperCAmelCase    :						str							=      use_labels
										_UpperCAmelCase    :						Dict							=      hidden_act
										_UpperCAmelCase    :						str							=      num_labels
										_UpperCAmelCase    :						Tuple							=      scope
										_UpperCAmelCase    :						Dict							=      len(A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

										_UpperCAmelCase    :						int							=      None
										if self.use_labels:
														_UpperCAmelCase    :						Optional[Any]							=      ids_tensor([self.batch_size]  ,   self.num_labels		)

										_UpperCAmelCase    :						int							=      self.get_config()

										return config, pixel_values, labels


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										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     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      TFResNetModel(config=A_		)
										_UpperCAmelCase    :						Dict							=      model(A_		)
										# 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     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.num_labels
										_UpperCAmelCase    :						Any							=      TFResNetForImageClassification(A_		)
										_UpperCAmelCase    :						str							=      model(A_  ,   labels=A_		)
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.num_labels)		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.prepare_config_and_inputs()
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Dict							=      config_and_inputs
										_UpperCAmelCase    :						Union[str, Any]							=      {"pixel_values": pixel_values}
										return config, inputs_dict







@require_tf
class 			a					(		UpperCAmelCase							,	UpperCAmelCase							,	unittest.TestCase			):
						_lowercase        =     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
						_lowercase        =     (
						    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						_lowercase        =     False
						_lowercase        =     False
						_lowercase        =     False
						_lowercase        =     False
						_lowercase        =     False


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      TFResNetModelTester(self		)
										_UpperCAmelCase    :						Optional[Any]							=      ConfigTester(self  ,   config_class=A_  ,   has_text_modality=A_		)


						def     _UpperCAmelCase			(    self		):







										'''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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return


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







										'''simple docstring'''


										pass


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







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, Any]							=      self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_UpperCAmelCase    :						Union[str, Any]							=      model_class(A_		)
														_UpperCAmelCase    :						Any							=      inspect.signature(model.call		)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_UpperCAmelCase    :						Optional[Any]							=      [*signature.parameters.keys()]

														_UpperCAmelCase    :						List[str]							=      ["pixel_values"]
														self.assertListEqual(arg_names[:1]  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										def check_hidden_states_output(A_  ,   A_  ,   A_		):
														_UpperCAmelCase    :						Union[str, Any]							=      model_class(A_		)
														_UpperCAmelCase    :						List[str]							=      model(**self._prepare_for_class(A_  ,   A_		)		)

														_UpperCAmelCase    :						List[str]							=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														_UpperCAmelCase    :						List[Any]							=      self.model_tester.num_stages
														self.assertEqual(len(A_		)  ,   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]  ,   )

										_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCAmelCase    :						List[Any]							=      ["basic", "bottleneck"]
										for model_class in self.all_model_classes:
														for layer_type in layers_type:
																		_UpperCAmelCase    :						Union[str, Any]							=      layer_type
																		_UpperCAmelCase    :						List[str]							=      True
																		check_hidden_states_output(A_  ,   A_  ,   A_		)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		_UpperCAmelCase    :						Union[str, Any]							=      True

																		check_hidden_states_output(A_  ,   A_  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*A_		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						List[str]							=      TFResNetModel.from_pretrained(A_		)
														self.assertIsNotNone(A_		)







def 							__SCREAMING_SNAKE_CASE							(			)				->    Optional[int]:
				_UpperCAmelCase    :						int							=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
				return image





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


						@cached_property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return (
										    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
										    if is_vision_available()
										    else None
										)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)

										_UpperCAmelCase    :						Union[str, Any]							=      self.default_image_processor
										_UpperCAmelCase    :						Dict							=      prepare_img()
										_UpperCAmelCase    :						List[str]							=      image_processor(images=A_  ,   return_tensors="tf"		)

										# forward pass
										_UpperCAmelCase    :						Optional[Any]							=      model(**A_		)

										# verify the logits
										_UpperCAmelCase    :						Any							=      tf.TensorShape((1, 1000)		)
										self.assertEqual(outputs.logits.shape  ,   A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      tf.constant([-11.10_69, -9.78_77, -8.37_77]		)

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





from typing import Any

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      data
										_UpperCAmelCase    :						Any							=      None







class 			a					:


						def __init__(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      None


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.head
										while temp is not None:
														print(temp.data  ,   end=" "		)
														_UpperCAmelCase    :						str							=      temp.next
										print()


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      Node(A_		)
										_UpperCAmelCase    :						Tuple							=      self.head
										_UpperCAmelCase    :						Tuple							=      new_node





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if node_data_a == node_data_a:
														return
										else:
														_UpperCAmelCase    :						int							=      self.head
														while node_a is not None and node_a.data != node_data_a:
																		_UpperCAmelCase    :						Tuple							=      node_a.next

														_UpperCAmelCase    :						Dict							=      self.head
														while node_a is not None and node_a.data != node_data_a:
																		_UpperCAmelCase    :						List[Any]							=      node_a.next

														if node_a is None or node_a is None:
																		return

														_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      node_a.data, node_a.data


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = LinkedList()
					for i in range(5, 0, -1):
										ll.push(i)

					ll.print_list()

					ll.swap_nodes(1, 4)
					print('After swapping')
					ll.print_list()


