





"""simple docstring"""






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 			__A					(	a_  :int      ,				a_  :List[str]      ,				a_  :str      ,				a_  :Any      ,				)						->						list[float]:
				__a       :  str					=     coefficient_matrix.shape
				__a       :  Any					=     constant_matrix.shape

				if rowsa != colsa:
								__a       :  Union[str, Any]					=     F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
								raise ValueError(__lowerCamelCase)

				if colsa != 1:
								__a       :  List[Any]					=     F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
								raise ValueError(__lowerCamelCase)

				if rowsa != rowsa:
								__a       :  Dict					=     (
								    '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
								    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
								)
								raise ValueError(__lowerCamelCase)

				if len(__lowerCamelCase) != rowsa:
								__a       :  List[Any]					=     (
								    '''Number of initial values must be equal to number of rows in coefficient '''
								    F"""matrix but received {len(__lowerCamelCase)} and {rowsa}"""
								)
								raise ValueError(__lowerCamelCase)

				if iterations <= 0:
								raise ValueError('''Iterations must be at least 1''')

				__a       :  NDArray[floataa]					=     np.concatenate(
				    (coefficient_matrix, constant_matrix)      ,				axis=1)

				__a       :  Dict					=     table.shape

				strictly_diagonally_dominant(__lowerCamelCase)

				# Iterates the whole matrix for given number of times
				for _ in range(__lowerCamelCase):
								__a       :  Optional[Any]					=     []
								for row in range(__lowerCamelCase):
												__a       :  Optional[int]					=     0
												for col in range(__lowerCamelCase):
																if col == row:
																				__a       :  List[Any]					=     table[row][col]
																elif col == cols - 1:
																				__a       :  List[Any]					=     table[row][col]
																else:
																				temp += (-1) * table[row][col] * init_val[col]
												__a       :  Any					=     (temp + val) / denom
												new_val.append(__lowerCamelCase)
								__a       :  int					=     new_val

				return [float(__lowerCamelCase) for i in new_val]

def 			__A					(	a_  :List[Any])						->						bool:
				__a       :  Tuple					=     table.shape

				__a       :  Dict					=     True

				for i in range(0      ,				__lowerCamelCase):
								__a       :  List[str]					=     0
								for j in range(0      ,				cols - 1):
												if i == j:
																continue
												else:
																total += table[i][j]

								if table[i][i] <= total:
												raise ValueError('''Coefficient matrix is not strictly diagonally dominant''')

				return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()




"""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        __A       :







		'''simple docstring'''





		def __init__(	self						:  str			,_snake_case						:  List[Any]			,_snake_case						:  Optional[int]=3			,_snake_case						:  Optional[int]=32			,_snake_case						:  Union[str, Any]=3			,_snake_case						:  int=10			,_snake_case						:  List[str]=[10, 20, 30, 40]			,_snake_case						:  Any=[1, 1, 2, 1]			,_snake_case						:  int=True			,_snake_case						:  Optional[Any]=True			,_snake_case						:  Union[str, Any]="relu"			,_snake_case						:  Dict=3			,_snake_case						:  Any=None			,)					->		str:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Tuple:






				"""simple docstring"""



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

				lowercase__   :    Tuple				=			None
				if self.use_labels:
						lowercase__   :    Dict				=			ids_tensor([self.batch_size]			,self.num_labels						)

				lowercase__   :    int				=			self.get_config()

				return config, pixel_values, labels







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		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 					UpperCAmelCase    (	self						:  List[str]			,_snake_case						:  Optional[int]			,_snake_case						:  int			,_snake_case						:  Tuple						)					->		List[Any]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			TFResNetModel(config=_snake_case						)
				lowercase__   :    List[str]				=			model(_snake_case						)
				# 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						:  Optional[int]			,_snake_case						:  Optional[Any]			,_snake_case						:  int			,_snake_case						:  Any						)					->		Tuple:






				"""simple docstring"""



				lowercase__   :    Tuple				=			self.num_labels
				lowercase__   :    Union[str, Any]				=			TFResNetForImageClassification(_snake_case						)
				lowercase__   :    List[str]				=			model(_snake_case			,labels=_snake_case						)
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		str:






				"""simple docstring"""



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





@require_tf
class        __A       (	A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Optional[int]   	=				(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		lowerCAmelCase			:					Any   	=				(
		    {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		lowerCAmelCase			:					List[Any]   	=				False
		lowerCAmelCase			:					List[Any]   	=				False
		lowerCAmelCase			:					int   	=				False
		lowerCAmelCase			:					Union[str, Any]   	=				False
		lowerCAmelCase			:					List[str]   	=				False







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			TFResNetModelTester(self						)
				lowercase__   :    int				=			ConfigTester(self			,config_class=_snake_case			,has_text_modality=_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		str:






				"""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						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				return







		@unittest.skip(reason='''ResNet does not use inputs_embeds'''						)
		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Dict:






				"""simple docstring"""



				pass







		@unittest.skip(reason='''ResNet does not support input and output embeddings'''						)
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				pass







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






				"""simple docstring"""



				lowercase__		,			lowercase__   :    Dict				=			self.model_tester.prepare_config_and_inputs_for_common()

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

						lowercase__   :    Any				=			['''pixel_values''']
						self.assertListEqual(arg_names[:1]			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		List[str]:






				"""simple docstring"""



				def check_hidden_states_output(_snake_case						:  Optional[int]			,_snake_case						:  List[str]			,_snake_case						:  Optional[Any]						):
						lowercase__   :    str				=			model_class(_snake_case						)
						lowercase__   :    Union[str, Any]				=			model(**self._prepare_for_class(_snake_case			,_snake_case						)						)

						lowercase__   :    List[Any]				=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						lowercase__   :    Tuple				=			self.model_tester.num_stages
						self.assertEqual(len(_snake_case						)			,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]			,)

				lowercase__		,			lowercase__   :    int				=			self.model_tester.prepare_config_and_inputs_for_common()
				lowercase__   :    List[Any]				=			['''basic''', '''bottleneck''']
				for model_class in self.all_model_classes:
						for layer_type in layers_type:
								lowercase__   :    List[Any]				=			layer_type
								lowercase__   :    Dict				=			True
								check_hidden_states_output(_snake_case			,_snake_case			,_snake_case						)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								lowercase__   :    Dict				=			True

								check_hidden_states_output(_snake_case			,_snake_case			,_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		int:






				"""simple docstring"""



				for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Optional[Any]				=			TFResNetModel.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)





def      __UpperCAmelCase      (    )							->      Dict:
		lowercase__   :    List[str]				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image





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







		'''simple docstring'''





		@cached_property
		def 					UpperCAmelCase    (	self						:  str						)					->		Any:






				"""simple docstring"""



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







		@slow
		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Tuple				=			TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

				lowercase__   :    Any				=			self.default_image_processor
				lowercase__   :    int				=			prepare_img()
				lowercase__   :    Tuple				=			image_processor(images=_snake_case			,return_tensors='''tf'''						)

				# forward pass
				lowercase__   :    Dict				=			model(**_snake_case						)

				# verify the logits
				lowercase__   :    List[str]				=			tf.TensorShape((1, 1_000)						)
				self.assertEqual(outputs.logits.shape			,_snake_case						)

				lowercase__   :    Any				=			tf.constant([-11.1069, -9.7877, -8.3777]						)

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