

"""simple docstring"""






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       snake_case__  :







			def __init__(			self			: Tuple			,  lowercase			: Tuple			,  lowercase			: Optional[int]=3			,  lowercase			: Any=32			,  lowercase			: Optional[Any]=3			,  lowercase			: Optional[Any]=10			,  lowercase			: Tuple=[10, 20, 30, 40]			,  lowercase			: Optional[int]=[1, 1, 2, 1]			,  lowercase			: List[Any]=True			,  lowercase			: Dict=True			,  lowercase			: Dict="relu"			,  lowercase			: Tuple=3			,  lowercase			: Optional[Any]=None			,  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								UpperCAmelCase			:   Optional[Any]      =				None
								if self.use_labels:
													UpperCAmelCase			:   List[str]      =				ids_tensor([self.batch_size]			,  self.num_labels  )

								UpperCAmelCase			:   str      =				self.get_config()

								return config, pixel_values, labels







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''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 			__lowerCAmelCase				(			self			: Any			,  lowercase			: Optional[int]			,  lowercase			: Optional[int]			,  lowercase			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				TFResNetModel(config=lowercase  )
								UpperCAmelCase			:   Dict      =				model(lowercase  )
								# 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 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: Optional[Any]			,  lowercase			: List[str]			,  lowercase			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.num_labels
								UpperCAmelCase			:   str      =				TFResNetForImageClassification(lowercase  )
								UpperCAmelCase			:   Any      =				model(lowercase			,  labels=lowercase  )
								self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)  )







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.prepare_config_and_inputs()
								UpperCAmelCase			:   Optional[int]      =				config_and_inputs
								UpperCAmelCase			:   str      =				{"pixel_values": pixel_values}
								return config, inputs_dict







@require_tf
class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
			SCREAMING_SNAKE_CASE__     				=						(
			    {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
			    if is_tf_available()
			    else {}
			)

			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						False







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				TFResNetModelTester(self  )
								UpperCAmelCase			:   int      =				ConfigTester(self			,  config_class=lowercase			,  has_text_modality=lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''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			: str  ):





								'''simple docstring'''



								return







			@unittest.skip(reason="ResNet does not use inputs_embeds"  )
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								pass







			@unittest.skip(reason="ResNet does not support input and output embeddings"  )
			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								pass







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.model_tester.prepare_config_and_inputs_for_common()

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

													UpperCAmelCase			:   Optional[Any]      =				["pixel_values"]
													self.assertListEqual(arg_names[:1]			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowercase  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								def check_hidden_states_output(lowercase			: Optional[int]			,  lowercase			: Tuple			,  lowercase			: Optional[Any]  ):
													UpperCAmelCase			:   Dict      =				model_class(lowercase  )
													UpperCAmelCase			:   List[Any]      =				model(**self._prepare_for_class(lowercase			,  lowercase  )  )

													UpperCAmelCase			:   Optional[Any]      =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													UpperCAmelCase			:   Union[str, Any]      =				self.model_tester.num_stages
													self.assertEqual(len(lowercase  )			,  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			:   List[str]      =				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[Any]      =				True
																		check_hidden_states_output(lowercase			,  lowercase			,  lowercase  )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		UpperCAmelCase			:   Optional[Any]      =				True

																		check_hidden_states_output(lowercase			,  lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowercase  )







			@slow
			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



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



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Optional[Any]      =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
					return image







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







			@cached_property
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



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







			@slow
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )

								UpperCAmelCase			:   int      =				self.default_image_processor
								UpperCAmelCase			:   Tuple      =				prepare_img()
								UpperCAmelCase			:   Dict      =				image_processor(images=lowercase			,  return_tensors="tf"  )

								# forward pass
								UpperCAmelCase			:   Any      =				model(**lowercase  )

								# verify the logits
								UpperCAmelCase			:   Optional[int]      =				tf.TensorShape((1, 10_00)  )
								self.assertEqual(outputs.logits.shape			,  lowercase  )

								UpperCAmelCase			:   Union[str, Any]      =				tf.constant([-11.10_69, -9.7_8_7_7, -8.3_7_7_7]  )

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





"""simple docstring"""






from __future__ import annotations

from collections import Counter
from random import random







class       snake_case__  :







			def __init__(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{}







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				{}







			def 			__lowerCAmelCase				(			self			: Optional[int]			,  lowercase			: str			,  lowercase			: str			,  lowercase			: float  ):





								'''simple docstring'''



								if nodea not in self.connections:
													self.add_node(lowercase  )
								if nodea not in self.connections:
													self.add_node(lowercase  )
								UpperCAmelCase			:   int      =				probability







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								return list(self.connections  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				0
								UpperCAmelCase			:   Union[str, Any]      =				random()

								for dest in self.connections[node]:
													current_probability += self.connections[node][dest]
													if current_probability > random_value:
																		return dest
								return ""



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		list[tuple[str, str, float]]							,      _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				MarkovChainGraphUndirectedUnweighted()

					for nodea, nodea, probability in transitions:
										graph.add_transition_probability(_lowercase							,      _lowercase							,      _lowercase	)

					UpperCAmelCase			:   List[Any]      =				Counter(graph.get_nodes()	)
					UpperCAmelCase			:   List[Any]      =				start

					for _ in range(_lowercase	):
										UpperCAmelCase			:   Optional[Any]      =				graph.transition(_lowercase	)
										visited[node] += 1

					return visited


if __name__ == "__main__":
	import doctest

	doctest.testmod()


