



"""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        __A       :







		'''simple docstring'''





		def __init__(	self						:  int			,_snake_case						:  Union[str, Any]			,_snake_case						:  Tuple=12			,_snake_case						:  Optional[int]=7			,_snake_case						:  List[Any]=True			,_snake_case						:  int=True			,_snake_case						:  Optional[int]=True			,_snake_case						:  Union[str, Any]=99			,_snake_case						:  int=32			,_snake_case						:  int=32			,_snake_case						:  List[Any]=2			,_snake_case						:  int=4			,_snake_case						:  Union[str, Any]=37			,_snake_case						:  Dict=0.1			,_snake_case						:  int=0.1			,_snake_case						:  int=512			,_snake_case						:  Tuple=0.02			,_snake_case						:  Optional[Any]=0			,_snake_case						:  Union[str, Any]=None			,)					->		Optional[int]:






				"""simple docstring"""



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







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






				"""simple docstring"""



				lowercase__   :    int				=			ids_tensor([self.batch_size, self.seq_length]			,self.vocab_size						)

				lowercase__   :    Any				=			None
				if self.use_input_mask:
						lowercase__   :    int				=			random_attention_mask([self.batch_size, self.seq_length]						)

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

				lowercase__   :    str				=			self.get_config()

				return config, input_ids, tf.convert_to_tensor(_snake_case						)







		def 					UpperCAmelCase    (	self						:  Tuple						)					->		str:






				"""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						:  List[Any]			,_snake_case						:  Dict			,_snake_case						:  List[Any]			,_snake_case						:  List[Any]						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			TFBlipTextModel(config=_snake_case						)
				lowercase__   :    List[Any]				=			model(_snake_case			,attention_mask=_snake_case			,training=_snake_case						)
				lowercase__   :    List[Any]				=			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						:  Optional[Any]						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.prepare_config_and_inputs()
				lowercase__		,			lowercase__		,			lowercase__   :    Any				=			config_and_inputs
				lowercase__   :    List[Any]				=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
				return config, inputs_dict





@require_tf
class        __A       (	A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					int   	=				(TFBlipTextModel,) if is_tf_available() else ()
		lowerCAmelCase			:					Optional[Any]   	=				False
		lowerCAmelCase			:					List[Any]   	=				False
		lowerCAmelCase			:					Union[str, Any]   	=				False







		def 					UpperCAmelCase    (	self						:  Any						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			BlipTextModelTester(self						)
				lowercase__   :    List[Any]				=			ConfigTester(self			,config_class=_snake_case			,hidden_size=37						)







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






				"""simple docstring"""



				self.config_tester.run_common_tests()







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		int:






				"""simple docstring"""



				lowercase__   :    int				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case						)







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Tuple:






				"""simple docstring"""



				pass







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Tuple:






				"""simple docstring"""



				pass







		@unittest.skip(reason='''Blip does not use inputs_embeds'''						)
		def 					UpperCAmelCase    (	self						:  int						)					->		str:






				"""simple docstring"""



				pass







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






				"""simple docstring"""



				pass







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






				"""simple docstring"""



				pass







		@slow
		def 					UpperCAmelCase    (	self						:  int						)					->		List[Any]:






				"""simple docstring"""



				for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    str				=			TFBlipTextModel.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  List[str]=True						)					->		Optional[int]:






				"""simple docstring"""



				super().test_pt_tf_model_equivalence(allow_missing_keys=_snake_case						)





"""simple docstring"""







import os



def      __UpperCAmelCase      (    )							->      int:
		with open(os.path.dirname(__lowerCamelCase	) + '''/p022_names.txt'''	) as file:
				lowercase__   :    List[Any]				=			str(file.readlines()[0]	)
				lowercase__   :    Dict				=			names.replace('''"'''						,     ''''''	).split(''','''	)

		names.sort()

		lowercase__   :    int				=			0
		lowercase__   :    Optional[Any]				=			0

		for i, name in enumerate(__lowerCamelCase	):
				for letter in name:
						name_score += ord(__lowerCamelCase	) - 64

				total_score += (i + 1) * name_score
				lowercase__   :    List[str]				=			0
		return total_score


if __name__ == "__main__":
							print(solution())
