


def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase	):
							__snake_case		:			int							 =   len(_lowerCamelCase	) + 1
							__snake_case		:			int							 =   len(_lowerCamelCase	) + 1

							# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
							# length i of input_string matches with prefix string of length j of
							# given pattern.
							# "dp" stands for dynamic programming.
							__snake_case		:			List[str]							 =   [[0 for i in range(_lowerCamelCase	)] for j in range(_lowerCamelCase	)]

							# since string of zero length match pattern of zero length
							__snake_case		:			Optional[Any]							 =   1

							# since pattern of zero length will never match with string of non-zero length
							for i in range(1							,	_lowerCamelCase	):
														__snake_case		:			Dict							 =   0

							# since string of zero length will match with pattern where there
							# is at least one * alternatively
							for j in range(1							,	_lowerCamelCase	):
														__snake_case		:			str							 =   dp[0][j - 2] if pattern[j - 1] == "*" else 0

							# now using bottom-up approach to find for all remaining lengths
							for i in range(1							,	_lowerCamelCase	):
														for j in range(1							,	_lowerCamelCase	):
																					if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																												__snake_case		:			Any							 =   dp[i - 1][j - 1]

																					elif pattern[j - 1] == "*":
																												if dp[i][j - 2] == 1:
																																			__snake_case		:			str							 =   1
																												elif pattern[j - 2] in (input_string[i - 1], "."):
																																			__snake_case		:			int							 =   dp[i - 1][j]
																												else:
																																			__snake_case		:			Optional[int]							 =   0
																					else:
																												__snake_case		:			Union[str, Any]							 =   0

							return bool(dp[-1][-1]	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# inputing the strings
			# input_string = input("input a string :")
			# pattern = input("input a pattern :")

			_snake_case     :		List[str]						=				'aab'
			_snake_case     :		Tuple						=				'c*a*b'

			# using function to check whether given string matches the given pattern
			if match_pattern(input_string, pattern):
						print(f'''{input_string} matches the given pattern {pattern}''')
			else:
						print(f'''{input_string} does not match with the given pattern {pattern}''')



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  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:List[Any]     ,   a		:Dict     ,   a		:Any=3     ,   a		:Any=3_2     ,   a		:Optional[Any]=3     ,   a		:str=1_0     ,   a		:Union[str, Any]=[1_0, 2_0, 3_0, 4_0]     ,   a		:Optional[Any]=[1, 1, 2, 1]     ,   a		:Optional[Any]=True     ,   a		:Dict=True     ,   a		:Tuple="relu"     ,   a		:List[str]=3     ,   a		:Tuple=None     ,   )		-> Tuple:
								__UpperCamelCase	:   Optional[Any]			   =					parent
								__UpperCamelCase	:   Dict			   =					batch_size
								__UpperCamelCase	:   int			   =					image_size
								__UpperCamelCase	:   Dict			   =					num_channels
								__UpperCamelCase	:   Optional[int]			   =					embeddings_size
								__UpperCamelCase	:   List[Any]			   =					hidden_sizes
								__UpperCamelCase	:   Optional[Any]			   =					depths
								__UpperCamelCase	:   Optional[int]			   =					is_training
								__UpperCamelCase	:   Union[str, Any]			   =					use_labels
								__UpperCamelCase	:   Optional[int]			   =					hidden_act
								__UpperCamelCase	:   Tuple			   =					num_labels
								__UpperCamelCase	:   Tuple			   =					scope
								__UpperCamelCase	:   Dict			   =					len(a						)







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Any:
								__UpperCamelCase	:   List[str]			   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								__UpperCamelCase	:   List[str]			   =					None
								if self.use_labels:
															__UpperCamelCase	:   Tuple			   =					ids_tensor([self.batch_size]     ,   self.num_labels						)

								__UpperCamelCase	:   List[Any]			   =					self.get_config()

								return config, pixel_values, labels







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> int:
								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		:List[Any]     ,   a		:Dict     ,   a		:int     ,   a		:Optional[Any]						)		-> Tuple:
								__UpperCamelCase	:   str			   =					TFResNetModel(config=a						)
								__UpperCamelCase	:   Union[str, Any]			   =					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 // 3_2, self.image_size // 3_2)     ,   )







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:Optional[int]     ,   a		:List[str]     ,   a		:Optional[Any]						)		-> Any:
								__UpperCamelCase	:   str			   =					self.num_labels
								__UpperCamelCase	:   Optional[int]			   =					TFResNetForImageClassification(a						)
								__UpperCamelCase	:   List[str]			   =					model(a     ,   labels=a						)
								self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)						)







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> List[str]:
								__UpperCamelCase	:   Optional[int]			   =					self.prepare_config_and_inputs()
								__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					config_and_inputs
								__UpperCamelCase	:   List[str]			   =					{"pixel_values": pixel_values}
								return config, inputs_dict




@require_tf
class  lowerCamelCase__							(				__lowercase     ,						__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
	_A								=     (
	    {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
	    if is_tf_available()
	    else {}
	)

	_A								=     False
	_A								=     False
	_A								=     False
	_A								=     False
	_A								=     False







	def    _lowerCamelCase				(					self		:int						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					TFResNetModelTester(self						)
								__UpperCamelCase	:   List[Any]			   =					ConfigTester(self     ,   config_class=a     ,   has_text_modality=a						)







	def    _lowerCamelCase				(					self		:int						)		-> Dict:
								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						)		-> Optional[Any]:
								return







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







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







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Tuple:
								__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					self.model_tester.prepare_config_and_inputs_for_common()

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

															__UpperCamelCase	:   Union[str, Any]			   =					["pixel_values"]
															self.assertListEqual(arg_names[:1]     ,   a						)







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







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Tuple:
								def check_hidden_states_output(a		:Optional[Any]     ,   a		:Optional[int]     ,   a		:List[str]						):
															__UpperCamelCase	:   int			   =					model_class(a						)
															__UpperCamelCase	:   int			   =					model(**self._prepare_for_class(a     ,   a						)						)

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

															__UpperCamelCase	:   Optional[int]			   =					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	:   Optional[Any]			   =					self.model_tester.prepare_config_and_inputs_for_common()
								__UpperCamelCase	:   str			   =					["basic", "bottleneck"]
								for model_class in self.all_model_classes:
															for layer_type in layers_type:
																						__UpperCamelCase	:   int			   =					layer_type
																						__UpperCamelCase	:   int			   =					True
																						check_hidden_states_output(a     ,   a     ,   a						)

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

																						check_hidden_states_output(a     ,   a     ,   a						)







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Dict:
								__UpperCamelCase	:   int			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a						)







	@slow
	def    _lowerCamelCase				(					self		:Dict						)		-> Dict:
								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__UpperCamelCase	:   Optional[Any]			   =					TFResNetModel.from_pretrained(a						)
															self.assertIsNotNone(a						)




def 	_SCREAMING_SNAKE_CASE					(		)					->    int:






							'''simple docstring'''





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




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




	'''simple docstring'''




	@cached_property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Tuple:
								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
								    if is_vision_available()
								    else None
								)







	@slow
	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Optional[int]:
								__UpperCamelCase	:   int			   =					TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

								__UpperCamelCase	:   List[Any]			   =					self.default_image_processor
								__UpperCamelCase	:   List[str]			   =					prepare_img()
								__UpperCamelCase	:   List[str]			   =					image_processor(images=a     ,   return_tensors="tf"						)

								# forward pass
								__UpperCamelCase	:   Dict			   =					model(**a						)

								# verify the logits
								__UpperCamelCase	:   Dict			   =					tf.TensorShape((1, 1_0_0_0)						)
								self.assertEqual(outputs.logits.shape     ,   a						)

								__UpperCamelCase	:   Union[str, Any]			   =					tf.constant([-11.1069, -9.7877, -8.3777]						)

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