

'''simple docstring'''





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class        A  :





						def __init__(	self	,    snake_case_	,    snake_case_=1_3	,    snake_case_=7	,    snake_case_=False	,    snake_case_=True	,    snake_case_=False	,    snake_case_=False	,    snake_case_=1_9	,    snake_case_=3_2	,    snake_case_=5	,    snake_case_=4	,    snake_case_=3_7	,    snake_case_="gelu"	,    snake_case_=0.1	,    snake_case_=0.1	,    snake_case_=5_1_2	,    snake_case_=1_6	,    snake_case_=2	,    snake_case_=0.02	,    snake_case_=3	,    snake_case_=4	,    snake_case_=None	,    )			->							List[Any]:
										_a        =   parent
										_a        =   batch_size
										_a        =   seq_length
										_a        =   is_training
										_a        =   use_input_mask
										_a        =   use_token_type_ids
										_a        =   use_labels
										_a        =   vocab_size
										_a        =   hidden_size
										_a        =   num_hidden_layers
										_a        =   num_attention_heads
										_a        =   intermediate_size
										_a        =   hidden_act
										_a        =   hidden_dropout_prob
										_a        =   attention_probs_dropout_prob
										_a        =   max_position_embeddings
										_a        =   type_vocab_size
										_a        =   type_sequence_label_size
										_a        =   initializer_range
										_a        =   num_labels
										_a        =   num_choices
										_a        =   scope





						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										_a        =   ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size			)

										_a        =   None
										if self.use_input_mask:
														_a        =   random_attention_mask([self.batch_size, self.seq_length]			)

										_a        =   None
										_a        =   None
										_a        =   None
										if self.use_labels:
														_a        =   ids_tensor([self.batch_size]	,    self.type_sequence_label_size			)
														_a        =   ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels			)
														_a        =   ids_tensor([self.batch_size]	,    self.num_choices			)

										_a        =   self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										_a        =   EsmConfig(
										    vocab_size=3_3	,    hidden_size=self.hidden_size	,    pad_token_id=1	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    initializer_range=self.initializer_range	,    is_folding_model=snake_case__	,    esmfold_config={"trunk": {"num_blocks": 2}, "fp16_esm": False}	,    )
										return config





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_			)			->							Union[str, Any]:
										_a        =   EsmForProteinFolding(config=snake_case__			).float()
										model.to(snake_case__			)
										model.eval()
										_a        =   model(snake_case__	,    attention_mask=snake_case__			)
										_a        =   model(snake_case__			)
										_a        =   model(snake_case__			)

										self.parent.assertEqual(result.positions.shape	,    (8, self.batch_size, self.seq_length, 1_4, 3)			)
										self.parent.assertEqual(result.angles.shape	,    (8, self.batch_size, self.seq_length, 7, 2)			)





						def __lowerCAmelCase   (	self			)			->							str:
										_a        =   self.prepare_config_and_inputs()
										(
										    _a				
										)        =   config_and_inputs
										_a        =   {"input_ids": input_ids, "attention_mask": input_mask}
										return config, inputs_dict



@require_torch
class        A  (	a  ,    a  ,    unittest.TestCase ):
						__UpperCAmelCase				:	Any   =   False

						__UpperCAmelCase				:	Any   =   (EsmForProteinFolding,) if is_torch_available() else ()
						__UpperCAmelCase				:	int   =   ()
						__UpperCAmelCase				:	Tuple   =   {} if is_torch_available() else {}
						__UpperCAmelCase				:	int   =   False





						def __lowerCAmelCase   (	self			)			->							Tuple:
										_a        =   EsmFoldModelTester(self			)
										_a        =   ConfigTester(self	,    config_class=snake_case__	,    hidden_size=3_7			)





						def __lowerCAmelCase   (	self			)			->							int:
										self.config_tester.run_common_tests()





						def __lowerCAmelCase   (	self			)			->							int:
										_a        =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__			)





						@unittest.skip("Does not support attention outputs"			)
						def __lowerCAmelCase   (	self			)			->							Dict:
										pass





						@unittest.skip
						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										pass





						@unittest.skip("Esm does not support embedding resizing"			)
						def __lowerCAmelCase   (	self			)			->							Dict:
										pass





						@unittest.skip("Esm does not support embedding resizing"			)
						def __lowerCAmelCase   (	self			)			->							Dict:
										pass





						@unittest.skip("ESMFold does not support passing input embeds!"			)
						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										pass





						@unittest.skip("ESMFold does not support head pruning."			)
						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										pass





						@unittest.skip("ESMFold does not support head pruning."			)
						def __lowerCAmelCase   (	self			)			->							Tuple:
										pass





						@unittest.skip("ESMFold does not support head pruning."			)
						def __lowerCAmelCase   (	self			)			->							Dict:
										pass





						@unittest.skip("ESMFold does not support head pruning."			)
						def __lowerCAmelCase   (	self			)			->							List[str]:
										pass





						@unittest.skip("ESMFold does not support head pruning."			)
						def __lowerCAmelCase   (	self			)			->							Tuple:
										pass





						@unittest.skip("ESMFold does not output hidden states in the normal way."			)
						def __lowerCAmelCase   (	self			)			->							List[Any]:
										pass





						@unittest.skip("ESMfold does not output hidden states in the normal way."			)
						def __lowerCAmelCase   (	self			)			->							int:
										pass





						@unittest.skip("ESMFold only has one output format."			)
						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										pass





						@unittest.skip("This test doesn't work for ESMFold and doesn't test core functionality"			)
						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										pass





						@unittest.skip("ESMFold does not support input chunking."			)
						def __lowerCAmelCase   (	self			)			->							Dict:
										pass





						@unittest.skip("ESMFold doesn't respect you and it certainly doesn't respect your initialization arguments."			)
						def __lowerCAmelCase   (	self			)			->							Tuple:
										pass





						@unittest.skip("ESMFold doesn't support torchscript compilation."			)
						def __lowerCAmelCase   (	self			)			->							int:
										pass





						@unittest.skip("ESMFold doesn't support torchscript compilation."			)
						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										pass





						@unittest.skip("ESMFold doesn't support torchscript compilation."			)
						def __lowerCAmelCase   (	self			)			->							List[Any]:
										pass





						@unittest.skip("ESMFold doesn't support data parallel."			)
						def __lowerCAmelCase   (	self			)			->							Any:
										pass





						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."			)
						def __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										pass



@require_torch
class        A  (	a ):





						@slow
						def __lowerCAmelCase   (	self			)			->							List[Any]:
										_a        =   EsmForProteinFolding.from_pretrained("facebook/esmfold_v1"			).float()
										model.eval()
										_a        =   torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]			)
										_a        =   model(snake_case__			)["positions"]
										_a        =   torch.tensor([2.5_828, 0.7_993, -10.9_334]	,    dtype=torch.floataa			)
										self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]	,    snake_case__	,    atol=1E-4			)			)













"""simple docstring"""



import os


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



     with open(os.path.dirname(snake_case				) + "/p022_names.txt"				) as file:
          UpperCAmelCase__						:    Tuple							=							str(file.readlines()[0]				)
          UpperCAmelCase__						:    str							=							names.replace("\""			,					""				).split(","				)

     names.sort()

     UpperCAmelCase__						:    Optional[int]							=							0
     UpperCAmelCase__						:    List[str]							=							0

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

          total_score += (i + 1) * name_score
          UpperCAmelCase__						:    Optional[int]							=							0
     return total_score


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


