




__a          =			'Tobias Carryer'

from time import time

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=int(time()  )  )			->		Optional[Any]:  # noqa: B008
         UpperCAmelCase_      :							Union[str, Any]       =      multiplier
         UpperCAmelCase_      :							Any       =      increment
         UpperCAmelCase_      :							List[str]       =      modulo
         UpperCAmelCase_      :							List[Any]       =      seed





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Union[str, Any]       =      (self.multiplier * self.seed + self.increment) % self.modulo
         return self.seed


if __name__ == "__main__":
     # Show the LCG in action.
     __a          =			LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
     while True:
          print(lcg.next_number())





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:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=19       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=None       ,)			->		Dict:
         UpperCAmelCase_      :							Optional[Any]       =      parent
         UpperCAmelCase_      :							Dict       =      batch_size
         UpperCAmelCase_      :							Optional[int]       =      seq_length
         UpperCAmelCase_      :							Union[str, Any]       =      is_training
         UpperCAmelCase_      :							Any       =      use_input_mask
         UpperCAmelCase_      :							Tuple       =      use_token_type_ids
         UpperCAmelCase_      :							Optional[int]       =      use_labels
         UpperCAmelCase_      :							List[Any]       =      vocab_size
         UpperCAmelCase_      :							List[Any]       =      hidden_size
         UpperCAmelCase_      :							str       =      num_hidden_layers
         UpperCAmelCase_      :							Dict       =      num_attention_heads
         UpperCAmelCase_      :							Optional[Any]       =      intermediate_size
         UpperCAmelCase_      :							str       =      hidden_act
         UpperCAmelCase_      :							int       =      hidden_dropout_prob
         UpperCAmelCase_      :							List[Any]       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Optional[int]       =      max_position_embeddings
         UpperCAmelCase_      :							Optional[int]       =      type_vocab_size
         UpperCAmelCase_      :							Any       =      type_sequence_label_size
         UpperCAmelCase_      :							int       =      initializer_range
         UpperCAmelCase_      :							Any       =      num_labels
         UpperCAmelCase_      :							Optional[Any]       =      num_choices
         UpperCAmelCase_      :							List[str]       =      scope





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size  )

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

         UpperCAmelCase_      :							Union[str, Any]       =      None
         UpperCAmelCase_      :							List[str]       =      None
         UpperCAmelCase_      :							Optional[Any]       =      None
         if self.use_labels:
              UpperCAmelCase_      :							Optional[int]       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )
              UpperCAmelCase_      :							List[str]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.num_labels  )
              UpperCAmelCase_      :							Optional[int]       =      ids_tensor([self.batch_size]       ,self.num_choices  )

         UpperCAmelCase_      :							Optional[Any]       =      self.get_config()

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





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Union[str, Any]       =      EsmConfig(
             vocab_size=33       ,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=_SCREAMING_SNAKE_CASE       ,esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}       ,)
         return config





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							Optional[int]       =      EsmForProteinFolding(config=_SCREAMING_SNAKE_CASE  ).float()
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Union[str, Any]       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      model(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      model(_SCREAMING_SNAKE_CASE  )

         self.parent.assertEqual(result.positions.shape       ,(8, self.batch_size, self.seq_length, 14, 3)  )
         self.parent.assertEqual(result.angles.shape       ,(8, self.batch_size, self.seq_length, 7, 2)  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							int       =      self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   
         )      :							Optional[Any]       =      config_and_inputs
         UpperCAmelCase_      :							int       =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				False

    lowerCAmelCase        =				(EsmForProteinFolding,) if is_torch_available() else ()
    lowerCAmelCase        =				()
    lowerCAmelCase        =				{} if is_torch_available() else {}
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Optional[int]       =      EsmFoldModelTester(self  )
         UpperCAmelCase_      :							int       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self  )			->		Tuple:
         self.config_tester.run_common_tests()





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Optional[Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    @unittest.skip('''Does not support attention outputs'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''Esm does not support embedding resizing'''  )
    def 							a__				(				self  )			->		Union[str, Any]:
         pass





    @unittest.skip('''Esm does not support embedding resizing'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





    @unittest.skip('''ESMFold does not support passing input embeds!'''  )
    def 							a__				(				self  )			->		Any:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





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





    @unittest.skip('''ESMFold does not output hidden states in the normal way.'''  )
    def 							a__				(				self  )			->		str:
         pass





    @unittest.skip('''ESMfold does not output hidden states in the normal way.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold only has one output format.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality'''  )
    def 							a__				(				self  )			->		int:
         pass





    @unittest.skip('''ESMFold does not support input chunking.'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





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





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





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		List[str]:
         pass





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		Tuple:
         pass





    @unittest.skip('''ESMFold doesn\'t support data parallel.'''  )
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass


@require_torch
class   __a(					_a       ):






    """simple docstring"""





    @slow
    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1'''  ).float()
         model.eval()
         UpperCAmelCase_      :							str       =      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]  )
         UpperCAmelCase_      :							Optional[int]       =      model(_SCREAMING_SNAKE_CASE  )['''positions''']
         UpperCAmelCase_      :							List[str]       =      torch.tensor([2.58_28, 0.79_93, -10.93_34]       ,dtype=torch.floataa  )
         self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )