



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 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase=12		,__lowerCamelCase=7		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=True		,__lowerCamelCase=99		,__lowerCamelCase=32		,__lowerCamelCase=32		,__lowerCamelCase=2		,__lowerCamelCase=4		,__lowerCamelCase=37		,__lowerCamelCase=0.1		,__lowerCamelCase=0.1		,__lowerCamelCase=5_12		,__lowerCamelCase=0.02		,__lowerCamelCase=0		,__lowerCamelCase=None		,)					->      Optional[Any]:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






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

						lowerCAmelCase__					:    List[str]												=					None
						if self.use_input_mask:
										lowerCAmelCase__					:    Any												=					random_attention_mask([self.batch_size, self.seq_length]			)

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

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

						return config, input_ids, tf.convert_to_tensor(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""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 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					TFBlipTextModel(config=__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					model(__lowerCamelCase		,attention_mask=__lowerCamelCase		,training=__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					model(__lowerCamelCase		,training=__lowerCamelCase			)
						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 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					self.prepare_config_and_inputs()
						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[int]												=					config_and_inputs
						lowerCAmelCase__					:    Optional[int]												=					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
						return config, inputs_dict



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




		'''simple docstring'''



		snake_case_     =(TFBlipTextModel,) if is_tf_available() else ()
		snake_case_     =False
		snake_case_     =False
		snake_case_     =False



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					BlipTextModelTester(self			)
						lowerCAmelCase__					:    int												=					ConfigTester(self		,config_class=__lowerCamelCase		,hidden_size=37			)



		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						pass



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''Blip does not use inputs_embeds'''			)
		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''			)
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						pass



		@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''			)
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						pass



		@slow
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__					:    Optional[Any]												=					TFBlipTextModel.from_pretrained(__lowerCamelCase			)
										self.assertIsNotNone(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=True			)					->      Any:

						"""simple docstring"""






						super().test_pt_tf_model_equivalence(allow_missing_keys=__lowerCamelCase			)






def      lowerCAmelCase__		(   lowerCamelCase_		:		str	,lowerCamelCase_		:		str):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[Any]												=					len(lowerCamelCase_)
				lowerCAmelCase__					:    str												=					len(lowerCamelCase_)
				lowerCAmelCase__					:    Union[str, Any]												=					[[False for _ in range(m + 1)] for _ in range(n + 1)]
				lowerCAmelCase__					:    List[Any]												=					True
				for i in range(lowerCamelCase_):
								for j in range(m + 1):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				lowerCAmelCase__					:    int												=					True
																if a[i].islower():
																				lowerCAmelCase__					:    int												=					True
				return dp[n][m]


if __name__ == "__main__":
							import doctest

							doctest.testmod()

