'''simple docstring'''




import os

def _UpperCamelCase   (       ):
     with open(os.path.dirname(SCREAMING_SNAKE_CASE_							) + '/p022_names.txt'							) as file:
          lowercase_					:       Dict     				=					str(file.readlines()[0]							)
          lowercase_					:       int     				=					names.replace('"'  ,       ''							).split(','							)

     names.sort()

     lowercase_					:       Dict     				=					0
     lowercase_					:       Optional[Any]     				=					0

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

          total_score += (i + 1) * name_score
          lowercase_					:       Dict     				=					0
     return total_score


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





'''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       UpperCAmelCase__  :






   """simple docstring"""
   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       ,				)    ->     Optional[Any]:
        lowercase_					:       List[Any]     				=					parent
        lowercase_					:       List[Any]     				=					batch_size
        lowercase_					:       Optional[Any]     				=					seq_length
        lowercase_					:       Optional[Any]     				=					is_training
        lowercase_					:       Optional[int]     				=					use_input_mask
        lowercase_					:       Any     				=					use_labels
        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       Union[str, Any]     				=					hidden_size
        lowercase_					:       str     				=					projection_dim
        lowercase_					:       str     				=					num_hidden_layers
        lowercase_					:       List[str]     				=					num_attention_heads
        lowercase_					:       List[str]     				=					intermediate_size
        lowercase_					:       Optional[Any]     				=					dropout
        lowercase_					:       Tuple     				=					attention_dropout
        lowercase_					:       Optional[Any]     				=					max_position_embeddings
        lowercase_					:       Any     				=					initializer_range
        lowercase_					:       List[str]     				=					scope
        lowercase_					:       Optional[Any]     				=					bos_token_id
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Optional[Any]     				=					ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size				)

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

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

        lowercase_					:       List[Any]     				=					self.get_config()

        return config, input_ids, tf.convert_to_tensor(_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        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       ,				_a       ,				_a       ,				_a				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					TFBlipTextModel(config=_a				)
        lowercase_					:       List[str]     				=					model(_a       ,				attention_mask=_a       ,				training=_a				)
        lowercase_					:       Union[str, Any]     				=					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 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Optional[Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Dict     				=					config_and_inputs
        lowercase_					:       Optional[int]     				=					{'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict



@require_tf
class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Any     =    (TFBlipTextModel,) if is_tf_available() else ()
   A							:       Union[str, Any]     =    False
   A							:       int     =    False
   A							:       str     =    False
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Tuple     				=					BlipTextModelTester(self				)
        lowercase_					:       str     				=					ConfigTester(self       ,				config_class=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        self.config_tester.run_common_tests()
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       str     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        pass
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @unittest.skip(reason='Blip does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Any:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @slow
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       List[str]     				=					TFBlipTextModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)





   def 		_lowerCamelCase (self       ,				_a=True				)    ->     Optional[int]:
        super().test_pt_tf_model_equivalence(allow_missing_keys=_a				)




