

from __future__ import annotations





def      snake_case_				(__A					:						int | float | str ,       __A					:						int | float | str					)      ->     List[Any]:






   """simple docstring"""




   if nth_term == "":
      return [""]
   __lowerCAmelCase  :      Optional[Any]    =						int(_a					)
   __lowerCAmelCase  :      Dict    =						int(_a					)
   __lowerCAmelCase  :      Any    =						[]
   for temp in range(int(_a					)					):
      series.append(f'''1 / {pow(temp + 1 ,       int(_a					)					)}''' if series else """1"""					)
   return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   __UpperCAmelCase								=			int(input("""Enter the last number (nth term) of the P-Series"""))
   __UpperCAmelCase								=			int(input("""Enter the power for  P-Series"""))
   print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
   print(p_series(nth_term, power))





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 SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[Any]=13	,							lowerCAmelCase			:   List[str]=7	,							lowerCAmelCase			:   int=False	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   List[Any]=False	,							lowerCAmelCase			:   List[str]=False	,							lowerCAmelCase			:   Optional[int]=19	,							lowerCAmelCase			:   Any=32	,							lowerCAmelCase			:   int=5	,							lowerCAmelCase			:   Optional[Any]=4	,							lowerCAmelCase			:   Optional[Any]=37	,							lowerCAmelCase			:   Dict="gelu"	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   List[str]=5_12	,							lowerCAmelCase			:   Dict=16	,							lowerCAmelCase			:   List[Any]=2	,							lowerCAmelCase			:   Union[str, Any]=0.02	,							lowerCAmelCase			:   Dict=3	,							lowerCAmelCase			:   List[Any]=4	,							lowerCAmelCase			:   Union[str, Any]=None	,							)							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						parent
     __lowerCAmelCase  :      Tuple    =						batch_size
     __lowerCAmelCase  :      Union[str, Any]    =						seq_length
     __lowerCAmelCase  :      int    =						is_training
     __lowerCAmelCase  :      Dict    =						use_input_mask
     __lowerCAmelCase  :      Dict    =						use_token_type_ids
     __lowerCAmelCase  :      Optional[int]    =						use_labels
     __lowerCAmelCase  :      str    =						vocab_size
     __lowerCAmelCase  :      Optional[int]    =						hidden_size
     __lowerCAmelCase  :      Union[str, Any]    =						num_hidden_layers
     __lowerCAmelCase  :      Union[str, Any]    =						num_attention_heads
     __lowerCAmelCase  :      Tuple    =						intermediate_size
     __lowerCAmelCase  :      List[str]    =						hidden_act
     __lowerCAmelCase  :      Optional[int]    =						hidden_dropout_prob
     __lowerCAmelCase  :      Any    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      List[str]    =						max_position_embeddings
     __lowerCAmelCase  :      Any    =						type_vocab_size
     __lowerCAmelCase  :      Union[str, Any]    =						type_sequence_label_size
     __lowerCAmelCase  :      List[str]    =						initializer_range
     __lowerCAmelCase  :      int    =						num_labels
     __lowerCAmelCase  :      Tuple    =						num_choices
     __lowerCAmelCase  :      str    =						scope







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

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

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

     __lowerCAmelCase  :      Tuple    =						self.get_config()

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						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=lowerCAmelCase	,							esmfold_config={"""trunk""": {"""num_blocks""": 2}, """fp16_esm""": False}	,							)
     return config







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Dict   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						EsmForProteinFolding(config=lowerCAmelCase   ).float()
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Any    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						model(lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[int]    =						model(lowerCAmelCase   )

     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			:   Union[str, Any]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.prepare_config_and_inputs()
     (
         (
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,
     )  :      Optional[int]    =						config_and_inputs
     __lowerCAmelCase  :      int    =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
     return config, inputs_dict






@require_torch
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Tuple										=False

  lowerCamelCase   :      int										=(EsmForProteinFolding,) if is_torch_available() else ()
  lowerCamelCase   :      int										=()
  lowerCamelCase   :      Dict										={} if is_torch_available() else {}
  lowerCamelCase   :      str										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						EsmFoldModelTester(self   )
     __lowerCAmelCase  :      Optional[Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase	,							hidden_size=37   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     int:






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase   )







  @unittest.skip("""Does not support attention outputs"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[Any]:






     """simple docstring"""







     pass







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






     """simple docstring"""







     pass







  @unittest.skip("""Esm does not support embedding resizing"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Tuple:






     """simple docstring"""







     pass







  @unittest.skip("""Esm does not support embedding resizing"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Union[str, Any]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support passing input embeds!"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support head pruning."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[int]:






     """simple docstring"""







     pass







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






     """simple docstring"""







     pass







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






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support head pruning."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     str:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support head pruning."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Dict:






     """simple docstring"""







     pass







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






     """simple docstring"""







     pass







  @unittest.skip("""ESMfold does not output hidden states in the normal way."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     pass







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






     """simple docstring"""







     pass







  @unittest.skip("""This test doesn't work for ESMFold and doesn't test core functionality"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold does not support input chunking."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     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			:   List[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     pass







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






     """simple docstring"""







     pass







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






     """simple docstring"""







     pass







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






     """simple docstring"""







     pass







  @unittest.skip("""ESMFold doesn't support data parallel."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     int:






     """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			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     pass






@require_torch
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[str]:






     """simple docstring"""







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


