

from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
					import tensorflow as tf

					from transformers import TFBlipTextModel
					from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 			a					:


						def __init__(    self  ,   A_  ,   A_=12  ,   A_=7  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_=99  ,   A_=32  ,   A_=32  ,   A_=2  ,   A_=4  ,   A_=37  ,   A_=0.1  ,   A_=0.1  ,   A_=512  ,   A_=0.02  ,   A_=0  ,   A_=None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      parent
										_UpperCAmelCase    :						List[str]							=      batch_size
										_UpperCAmelCase    :						Any							=      seq_length
										_UpperCAmelCase    :						Optional[Any]							=      is_training
										_UpperCAmelCase    :						Any							=      use_input_mask
										_UpperCAmelCase    :						Optional[int]							=      use_labels
										_UpperCAmelCase    :						Union[str, Any]							=      vocab_size
										_UpperCAmelCase    :						int							=      hidden_size
										_UpperCAmelCase    :						Optional[Any]							=      projection_dim
										_UpperCAmelCase    :						List[str]							=      num_hidden_layers
										_UpperCAmelCase    :						Any							=      num_attention_heads
										_UpperCAmelCase    :						int							=      intermediate_size
										_UpperCAmelCase    :						List[str]							=      dropout
										_UpperCAmelCase    :						Optional[Any]							=      attention_dropout
										_UpperCAmelCase    :						int							=      max_position_embeddings
										_UpperCAmelCase    :						Optional[int]							=      initializer_range
										_UpperCAmelCase    :						Union[str, Any]							=      scope
										_UpperCAmelCase    :						Optional[Any]							=      bos_token_id


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      ids_tensor([self.batch_size, self.seq_length]  ,   self.vocab_size		)

										_UpperCAmelCase    :						Dict							=      None
										if self.use_input_mask:
														_UpperCAmelCase    :						Optional[Any]							=      random_attention_mask([self.batch_size, self.seq_length]		)

										if input_mask is not None:
														_UpperCAmelCase    :						Dict							=      input_mask.numpy()
														_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      input_mask.shape
														_UpperCAmelCase    :						Dict							=      np.random.randint(1  ,   seq_length - 1  ,   size=(batch_size,)		)
														for batch_idx, start_index in enumerate(A_		):
																		_UpperCAmelCase    :						Optional[int]							=      1
																		_UpperCAmelCase    :						Optional[int]							=      0

										_UpperCAmelCase    :						Optional[int]							=      self.get_config()

										return config, input_ids, tf.convert_to_tensor(A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return BlipTextConfig(
										    vocab_size=self.vocab_size  ,   hidden_size=self.hidden_size  ,   projection_dim=self.projection_dim  ,   num_hidden_layers=self.num_hidden_layers  ,   num_attention_heads=self.num_attention_heads  ,   intermediate_size=self.intermediate_size  ,   dropout=self.dropout  ,   attention_dropout=self.attention_dropout  ,   max_position_embeddings=self.max_position_embeddings  ,   initializer_range=self.initializer_range  ,   bos_token_id=self.bos_token_id  ,   )


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      TFBlipTextModel(config=A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      model(A_  ,   attention_mask=A_  ,   training=A_		)
										_UpperCAmelCase    :						List[str]							=      model(A_  ,   training=A_		)
										self.parent.assertEqual(result.last_hidden_state.shape  ,   (self.batch_size, self.seq_length, self.hidden_size)		)
										self.parent.assertEqual(result.pooler_output.shape  ,   (self.batch_size, self.hidden_size)		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.prepare_config_and_inputs()
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      config_and_inputs
										_UpperCAmelCase    :						Any							=      {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict







@require_tf
class 			a					(		UpperCAmelCase							,	unittest.TestCase			):
						_lowercase        =     (TFBlipTextModel,) if is_tf_available() else ()
						_lowercase        =     False
						_lowercase        =     False
						_lowercase        =     False


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      BlipTextModelTester(self		)
										_UpperCAmelCase    :						Optional[Any]							=      ConfigTester(self  ,   config_class=A_  ,   hidden_size=37		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


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







										'''simple docstring'''


										pass


						@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase    :						Tuple							=      TFBlipTextModel.from_pretrained(A_		)
														self.assertIsNotNone(A_		)





						def     _UpperCAmelCase			(    self  ,   A_=True		):







										'''simple docstring'''


										super().test_pt_tf_model_equivalence(allow_missing_keys=A_		)





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					str	)				->    bool:
				_UpperCAmelCase    :						Optional[Any]							=      len(lowerCAmelCase	) + 1
				_UpperCAmelCase    :						Optional[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.
				_UpperCAmelCase    :						List[str]							=      [[0 for i in range(lowerCAmelCase	)] for j in range(lowerCAmelCase	)]

				# since string of zero length match pattern of zero length
				_UpperCAmelCase    :						List[Any]							=      1

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

				# since string of zero length will match with pattern where there
				# is at least one * alternatively
				for j in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Tuple							=      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] == ".":
																_UpperCAmelCase    :						Optional[Any]							=      dp[i - 1][j - 1]

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

					SCREAMING_SNAKE_CASE_            = 'aab'
					SCREAMING_SNAKE_CASE_            = '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}''')


