



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


   def 				lowercase (							self					:							Union[str, Any]	)			->				Union[str, Any]:
         lowercase     :					int 		=      ids_tensor([self.batch_size, self.seq_length],   self.vocab_size	)

         lowercase     :					Union[str, Any] 		=      None
         if self.use_input_mask:
               lowercase     :					List[str] 		=      random_attention_mask([self.batch_size, self.seq_length]	)

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

         lowercase     :					int 		=      self.get_config()

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


   def 				lowercase (							self					:							Optional[int]	)			->				Optional[Any]:
         lowercase     :					Optional[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=UpperCAmelCase_,   esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False},   )
         return config


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

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




@require_torch
class      a__						(  _UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase   ):
   _lowerCamelCase    							= False

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


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


   def 				lowercase (							self					:							List[str]	)			->				List[Any]:
         self.config_tester.run_common_tests()


   def 				lowercase (							self					:							Tuple	)			->				Dict:
         lowercase     :					List[str] 		=      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCAmelCase_	)


   @unittest.skip('Does not support attention outputs'	)
   def 				lowercase (							self					:							Tuple	)			->				Any:
         pass


   @unittest.skip
   def 				lowercase (							self					:							Any	)			->				List[Any]:
         pass


   @unittest.skip('Esm does not support embedding resizing'	)
   def 				lowercase (							self					:							str	)			->				Optional[int]:
         pass


   @unittest.skip('Esm does not support embedding resizing'	)
   def 				lowercase (							self					:							int	)			->				Dict:
         pass


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


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


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


   @unittest.skip('ESMFold does not support head pruning.'	)
   def 				lowercase (							self					:							str	)			->				Any:
         pass


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


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


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


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


   @unittest.skip('ESMFold only has one output format.'	)
   def 				lowercase (							self					:							int	)			->				str:
         pass


   @unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality'	)
   def 				lowercase (							self					:							Optional[Any]	)			->				List[str]:
         pass


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


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


   @unittest.skip('ESMFold doesn\'t support torchscript compilation.'	)
   def 				lowercase (							self					:							Dict	)			->				Union[str, Any]:
         pass


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


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


   @unittest.skip('ESMFold doesn\'t support data parallel.'	)
   def 				lowercase (							self					:							Optional[Any]	)			->				str:
         pass






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




@require_torch
class      a__						(  _UpperCAmelCase   ):


   @slow
   def 				lowercase (							self					:							str	)			->				str:
         lowercase     :					List[str] 		=      EsmForProteinFolding.from_pretrained('facebook/esmfold_v1'	).float()
         model.eval()
         lowercase     :					Union[str, Any] 		=      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]	)
         lowercase     :					Dict 		=      model(UpperCAmelCase_	)['positions']
         lowercase     :					List[str] 		=      torch.tensor([2.5828, 0.7993, -10.9334],   dtype=torch.floataa	)
         self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0],   UpperCAmelCase_,   atol=1e-4	)	)


def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		str:
					'''simple docstring'''


					if not isinstance(UpperCamelCase_	,	UpperCamelCase_		):
										raise ValueError('iterations must be defined as integers'		)
					if not isinstance(UpperCamelCase_	,	UpperCamelCase_		) or not number >= 1:
										raise ValueError(
										    'starting number must be\n                         and integer and be more than 0'		)
					if not iterations >= 1:
										raise ValueError('Iterations must be done more than 0 times to play FizzBuzz'		)

					SCREAMING_SNAKE_CASE__													= ''
					while number <= iterations:
										if number % 3 == 0:
															out += "Fizz"
										if number % 5 == 0:
															out += "Buzz"
										if 0 not in (number % 3, number % 5):
															out += str(UpperCamelCase_		)

										# print(out)
										number += 1
										out += " "
					return out


if __name__ == "__main__":
				import doctest

				doctest.testmod()





