





import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		from transformers.models.bert.modeling_flax_bert import (
		    FlaxBertForMaskedLM,
		    FlaxBertForMultipleChoice,
		    FlaxBertForNextSentencePrediction,
		    FlaxBertForPreTraining,
		    FlaxBertForQuestionAnswering,
		    FlaxBertForSequenceClassification,
		    FlaxBertForTokenClassification,
		    FlaxBertModel,
		)

class 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




	def __init__(						self							:     Optional[int]					,      __lowerCamelCase							:     List[Any]					,      __lowerCamelCase							:     Any=13					,      __lowerCamelCase							:     Optional[Any]=7					,      __lowerCamelCase							:     List[str]=True					,      __lowerCamelCase							:     Optional[Any]=True					,      __lowerCamelCase							:     Dict=True					,      __lowerCamelCase							:     Dict=True					,      __lowerCamelCase							:     Optional[int]=99					,      __lowerCamelCase							:     Optional[int]=32					,      __lowerCamelCase							:     Optional[int]=5					,      __lowerCamelCase							:     int=4					,      __lowerCamelCase							:     List[Any]=37					,      __lowerCamelCase							:     Union[str, Any]="gelu"					,      __lowerCamelCase							:     Union[str, Any]=0.1					,      __lowerCamelCase							:     List[Any]=0.1					,      __lowerCamelCase							:     str=512					,      __lowerCamelCase							:     Dict=16					,      __lowerCamelCase							:     List[str]=2					,      __lowerCamelCase							:     Any=0.02					,      __lowerCamelCase							:     Optional[Any]=4					,      )       -> int:

			'''simple docstring'''




			__lowercase											=     parent
			__lowercase											=     batch_size
			__lowercase											=     seq_length
			__lowercase											=     is_training
			__lowercase											=     use_attention_mask
			__lowercase											=     use_token_type_ids
			__lowercase											=     use_labels
			__lowercase											=     vocab_size
			__lowercase											=     hidden_size
			__lowercase											=     num_hidden_layers
			__lowercase											=     num_attention_heads
			__lowercase											=     intermediate_size
			__lowercase											=     hidden_act
			__lowercase											=     hidden_dropout_prob
			__lowercase											=     attention_probs_dropout_prob
			__lowercase											=     max_position_embeddings
			__lowercase											=     type_vocab_size
			__lowercase											=     type_sequence_label_size
			__lowercase											=     initializer_range
			__lowercase											=     num_choices




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

			'''simple docstring'''




			__lowercase											=     ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size   )

			__lowercase											=     None
			if self.use_attention_mask:
					__lowercase											=     random_attention_mask([self.batch_size, self.seq_length]   )

			__lowercase											=     None
			if self.use_token_type_ids:
					__lowercase											=     ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size   )

			__lowercase											=     BertConfig(
			    vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      type_vocab_size=self.type_vocab_size					,      is_decoder=__lowerCamelCase					,      initializer_range=self.initializer_range					,      )

			return config, input_ids, token_type_ids, attention_mask




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

			'''simple docstring'''




			__lowercase											=     self.prepare_config_and_inputs()
			__lowercase						,      __lowercase						,      __lowercase						,      __lowercase											=     config_and_inputs
			__lowercase											=     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
			return config, inputs_dict






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

			'''simple docstring'''




			__lowercase											=     self.prepare_config_and_inputs()
			__lowercase						,      __lowercase						,      __lowercase						,      __lowercase											=     config_and_inputs

			__lowercase											=     True
			__lowercase											=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
			__lowercase											=     ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2   )

			return (
			    config,
			    input_ids,
			    attention_mask,
			    encoder_hidden_states,
			    encoder_attention_mask,
			)





@require_flax
class 		snake_case_   (							UpperCAmelCase_     ,     unittest.TestCase						):







	'''simple docstring'''
	__UpperCamelCase   =		True

	__UpperCamelCase   =		(
	    (
	        FlaxBertModel,
	        FlaxBertForPreTraining,
	        FlaxBertForMaskedLM,
	        FlaxBertForMultipleChoice,
	        FlaxBertForQuestionAnswering,
	        FlaxBertForNextSentencePrediction,
	        FlaxBertForSequenceClassification,
	        FlaxBertForTokenClassification,
	        FlaxBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




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

			'''simple docstring'''




			__lowercase											=     FlaxBertModelTester(self   )






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

			'''simple docstring'''




			__lowercase											=     FlaxBertModel.from_pretrained('bert-base-cased'   )
			__lowercase											=     model(np.ones((1, 1)   )   )
			self.assertIsNotNone(__lowerCamelCase   )











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    bool:
		__lowercase											=     len(snake_case							) + 1
		__lowercase											=     len(snake_case							) + 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.
		__lowercase											=     [[0 for i in range(snake_case							)] for j in range(snake_case							)]

		# since string of zero length match pattern of zero length
		__lowercase											=     1

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

		# since string of zero length will match with pattern where there
		# is at least one * alternatively
		for j in range(1						,   snake_case							):
				__lowercase											=     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						,   snake_case							):
				for j in range(1						,   snake_case							):
						if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
								__lowercase											=     dp[i - 1][j - 1]

						elif pattern[j - 1] == "*":
								if dp[i][j - 2] == 1:
										__lowercase											=     1
								elif pattern[j - 2] in (input_string[i - 1], "."):
										__lowercase											=     dp[i - 1][j]
								else:
										__lowercase											=     0
						else:
								__lowercase											=     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 :")

		SCREAMING_SNAKE_CASE_   :      Any			=   '''aab'''
		SCREAMING_SNAKE_CASE_   :      str			=   '''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}""")




