






"""simple docstring"""



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        lowercase__      :


			def __init__(				self					:  Dict       ,	snake_case__					:  str       ,	snake_case__					:  Dict=12       ,	snake_case__					:  Union[str, Any]=7       ,	snake_case__					:  int=True       ,	snake_case__					:  str=True       ,	snake_case__					:  str=True       ,	snake_case__					:  Any=99       ,	snake_case__					:  Dict=32       ,	snake_case__					:  Tuple=32       ,	snake_case__					:  Optional[Any]=2       ,	snake_case__					:  Optional[Any]=4       ,	snake_case__					:  Tuple=37       ,	snake_case__					:  Union[str, Any]=0.1       ,	snake_case__					:  Optional[int]=0.1       ,	snake_case__					:  Optional[int]=512       ,	snake_case__					:  Union[str, Any]=0.02       ,	snake_case__					:  Optional[int]=0       ,	snake_case__					:  Optional[int]=None       ,	):
										lowerCamelCase_	:							List[str]									=parent
										lowerCamelCase_	:							List[str]									=batch_size
										lowerCamelCase_	:							Any									=seq_length
										lowerCamelCase_	:							List[Any]									=is_training
										lowerCamelCase_	:							List[str]									=use_input_mask
										lowerCamelCase_	:							int									=use_labels
										lowerCamelCase_	:							int									=vocab_size
										lowerCamelCase_	:							List[str]									=hidden_size
										lowerCamelCase_	:							str									=projection_dim
										lowerCamelCase_	:							Any									=num_hidden_layers
										lowerCamelCase_	:							Dict									=num_attention_heads
										lowerCamelCase_	:							Any									=intermediate_size
										lowerCamelCase_	:							int									=dropout
										lowerCamelCase_	:							Tuple									=attention_dropout
										lowerCamelCase_	:							Tuple									=max_position_embeddings
										lowerCamelCase_	:							List[str]									=initializer_range
										lowerCamelCase_	:							Optional[Any]									=scope
										lowerCamelCase_	:							List[Any]									=bos_token_id


			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							Any									=ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size )

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

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

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

										return config, input_ids, tf.convert_to_tensor(snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										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					:  Optional[Any]       ,	snake_case__					:  int       ,	snake_case__					:  int       ,	snake_case__					:  Dict ):
										lowerCamelCase_	:							Any									=TFBlipTextModel(config=snake_case__ )
										lowerCamelCase_	:							str									=model(snake_case__       ,	attention_mask=snake_case__       ,	training=snake_case__ )
										lowerCamelCase_	:							int									=model(snake_case__       ,	training=snake_case__ )
										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					:  str ):
										lowerCamelCase_	:							Union[str, Any]									=self.prepare_config_and_inputs()
										lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Optional[Any]									=config_and_inputs
										lowerCamelCase_	:							int									={"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict







@require_tf
class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:List[str]             =			(TFBlipTextModel,) if is_tf_available() else ()
			_UpperCAmelCase		:List[str]             =			False
			_UpperCAmelCase		:List[str]             =			False
			_UpperCAmelCase		:Union[str, Any]             =			False


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							int									=BlipTextModelTester(self )
										lowerCamelCase_	:							Optional[Any]									=ConfigTester(self       ,	config_class=snake_case__       ,	hidden_size=37 )


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										self.config_tester.run_common_tests()


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							str									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__ )


			def      UpperCAmelCase__						(				self					:  int ):
										pass


			def      UpperCAmelCase__						(				self					:  List[str] ):
										pass


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


			@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING" )
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										pass


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


			@slow
			def      UpperCAmelCase__						(				self					:  Tuple ):
										for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCamelCase_	:							Tuple									=TFBlipTextModel.from_pretrained(snake_case__ )
																	self.assertIsNotNone(snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  int=True ):
										super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case__ )











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   int  )    ->     int:

							if not isinstance(lowerCamelCase__    ,						lowerCamelCase__  ):
														raise TypeError("only integers accepted as input"  )
							else:
														lowerCamelCase_	:							str									=str(abs(lowerCamelCase__  )  )
														lowerCamelCase_	:							Tuple									=[list(lowerCamelCase__  ) for char in range(len(lowerCamelCase__  )  )]
														for index in range(len(lowerCamelCase__  )  ):
																					num_transpositions[index].pop(lowerCamelCase__  )
														return max(
														    int("".join(list(lowerCamelCase__  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
						__import__('doctest').testmod()



