'''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		:		List[str]  ,				a		:		Optional[int]  ,				a		:		Any=13  ,				a		:		Optional[int]=7  ,				a		:		Optional[Any]=False  ,				a		:		Tuple=True  ,				a		:		int=False  ,				a		:		str=False  ,				a		:		List[Any]=19  ,				a		:		Union[str, Any]=32  ,				a		:		Optional[int]=5  ,				a		:		Tuple=4  ,				a		:		Optional[int]=37  ,				a		:		int="gelu"  ,				a		:		Optional[int]=0.1  ,				a		:		Union[str, Any]=0.1  ,				a		:		List[Any]=5_12  ,				a		:		Dict=16  ,				a		:		List[str]=2  ,				a		:		Optional[Any]=0.02  ,				a		:		int=3  ,				a		:		Optional[Any]=4  ,				a		:		int=None  ,				):





         """simple docstring"""



         __lowerCamelCase		=  parent
         __lowerCamelCase		=  batch_size
         __lowerCamelCase		=  seq_length
         __lowerCamelCase		=  is_training
         __lowerCamelCase		=  use_input_mask
         __lowerCamelCase		=  use_token_type_ids
         __lowerCamelCase		=  use_labels
         __lowerCamelCase		=  vocab_size
         __lowerCamelCase		=  hidden_size
         __lowerCamelCase		=  num_hidden_layers
         __lowerCamelCase		=  num_attention_heads
         __lowerCamelCase		=  intermediate_size
         __lowerCamelCase		=  hidden_act
         __lowerCamelCase		=  hidden_dropout_prob
         __lowerCamelCase		=  attention_probs_dropout_prob
         __lowerCamelCase		=  max_position_embeddings
         __lowerCamelCase		=  type_vocab_size
         __lowerCamelCase		=  type_sequence_label_size
         __lowerCamelCase		=  initializer_range
         __lowerCamelCase		=  num_labels
         __lowerCamelCase		=  num_choices
         __lowerCamelCase		=  scope






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size    )

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

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

         __lowerCamelCase		=  self.get_config()

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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  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=a  ,				esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}  ,				)
         return config






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		Any  ,				a		:		Dict  ,				a		:		str  ,				a		:		List[Any]  ,				a		:		Union[str, Any]  ,				a		:		int    ):





         """simple docstring"""



         __lowerCamelCase		=  EsmForProteinFolding(config=a    ).float()
         model.to(a    )
         model.eval()
         __lowerCamelCase		=  model(a  ,				attention_mask=a    )
         __lowerCamelCase		=  model(a    )
         __lowerCamelCase		=  model(a    )

         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    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  self.prepare_config_and_inputs()
         (
             (
             __lowerCamelCase
         )	,  (
             __lowerCamelCase
         )	,  (
             __lowerCamelCase
         )	,  (
             __lowerCamelCase
         )	,  (
             __lowerCamelCase
         )	,  (
             __lowerCamelCase
         )	,  
         )		=  config_and_inputs
         __lowerCamelCase		=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict




@require_torch
class     a__      (   UpperCAmelCase__							,   UpperCAmelCase__							,   unittest.TestCase ):
     lowerCamelCase  :     List[Any]							=False

     lowerCamelCase  :     List[Any]							=(EsmForProteinFolding,) if is_torch_available() else ()
     lowerCamelCase  :     Tuple							=()
     lowerCamelCase  :     Any							={} if is_torch_available() else {}
     lowerCamelCase  :     Any							=False






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[str]    ):





         """simple docstring"""



         __lowerCamelCase		=  EsmFoldModelTester(self    )
         __lowerCamelCase		=  ConfigTester(self  ,				config_class=a  ,				hidden_size=37    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         self.config_tester.run_common_tests()






     def    SCREAMING_SNAKE_CASE__   ( self		:		str    ):





         """simple docstring"""



         __lowerCamelCase		=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a    )






     @unittest.skip('''Does not support attention outputs'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         pass






     @unittest.skip
     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         pass






     @unittest.skip('''Esm does not support embedding resizing'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         pass






     @unittest.skip('''Esm does not support embedding resizing'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold does not support passing input embeds!'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold does not support head pruning.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold does not support head pruning.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold does not support head pruning.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		List[str]    ):





         """simple docstring"""



         pass






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





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold does not support head pruning.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold does not output hidden states in the normal way.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         pass






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





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold only has one output format.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         pass






     @unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         pass






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





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]    ):





         """simple docstring"""



         pass






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





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		str    ):





         """simple docstring"""



         pass






     @unittest.skip('''ESMFold doesn\'t support data parallel.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		str    ):





         """simple docstring"""



         pass






     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         pass




@require_torch
class     a__      (   UpperCAmelCase__ ):






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		str    ):





         """simple docstring"""



         __lowerCamelCase		=  EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1'''    ).float()
         model.eval()
         __lowerCamelCase		=  torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]    )
         __lowerCamelCase		=  model(a    )['''positions''']
         __lowerCamelCase		=  torch.tensor([2.58_28, 0.79_93, -10.93_34]  ,				dtype=torch.floataa    )
         self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]  ,				a  ,				atol=1e-4    )    )





import random






def     a(      A       :    Optional[Any] ,     A       :    Optional[Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





					a					     =   a[left_index]
					a					     =   left_index + 1
					for j in range(left_index + 1 ,     A       ):
										if a[j] < pivot:
															a     ,				a					     =   a[i], a[j]
															i += 1
					a     ,				a					     =   a[i - 1], a[left_index]
					return i - 1






def     a(      A       :    List[Any] ,     A       :    List[Any] ,     A       :    Union[str, Any]       )		->       List[Any]:
					"""simple docstring"""





					if left < right:
										a					     =   random.randint(A ,     right - 1       )
										a     ,				a					     =   (
										    a[left],
										    a[pivot],
										)  # switches the pivot with the left most bound
										a					     =   partition(A ,     A ,     A       )
										quick_sort_random(
										    A ,     A ,     A       )  # recursive quicksort to the left of the pivot point
										quick_sort_random(
										    A ,     pivot_index + 1 ,     A       )  # recursive quicksort to the right of the pivot point






def     a(      )		->       Any:
					"""simple docstring"""





					a					     =   input("Enter numbers separated by a comma:\n"       ).strip()
					a					     =   [int(A       ) for item in user_input.split(","       )]

					quick_sort_random(A ,     0 ,     len(A       )       )

					print(A       )


if __name__ == "__main__":
				main()
