





from typing import Any
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       str	,	):





  """simple docstring"""



  _validation(
      SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	)
  # Creates data structures and fill initial step
  a__     : Tuple  ={}
  a__     : Optional[Any]  ={}
  for state in states_space:
    a__     : List[str]  =observations_space[0]
    a__     : List[str]  =(
        initial_probabilities[state] * emission_probabilities[state][observation]
    )
    a__     : Any  =None

  # Fills the data structure with the probabilities of
  # different transitions and pointers to previous states
  for o in range(1	,	len(SCREAMING_SNAKE_CASE						)						):
    a__     : List[str]  =observations_space[o]
    a__     : Any  =observations_space[o - 1]
    for state in states_space:
      # Calculates the argmax for probability function
      a__     : Union[str, Any]  =""
      a__     : Dict  =-1
      for k_state in states_space:
        a__     : int  =(
            probabilities[(k_state, prior_observation)]
            * transition_probabilities[k_state][state]
            * emission_probabilities[state][observation]
        )
        if probability > max_probability:
          a__     : Tuple  =probability
          a__     : Any  =k_state

            # Update probabilities and pointers dicts
      a__     : List[Any]  =(
          probabilities[(arg_max, prior_observation)]
          * transition_probabilities[arg_max][state]
          * emission_probabilities[state][observation]
      )

      a__     : List[Any]  =arg_max

    # The final observation
  a__     : Optional[Any]  =observations_space[len(SCREAMING_SNAKE_CASE						) - 1]

  # argmax for given final observation
  a__     : Tuple  =""
  a__     : str  =-1
  for k_state in states_space:
    a__     : Union[str, Any]  =probabilities[(k_state, final_observation)]
    if probability > max_probability:
      a__     : List[Any]  =probability
      a__     : str  =k_state
  a__     : List[str]  =arg_max

  # Process pointers backwards
  a__     : Tuple  =last_state
  a__     : Optional[int]  =[]
  for o in range(len(SCREAMING_SNAKE_CASE						) - 1	,	-1	,	-1						):
    result.append(SCREAMING_SNAKE_CASE						)
    a__     : int  =pointers[previous, observations_space[o]]
  result.reverse()

  return result
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       str	,	):





  """simple docstring"""



  _validate_not_empty(
      SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	)
  _validate_lists(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  _validate_dicts(
      SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       Optional[Any]	,	):





  """simple docstring"""



  if not all(
      [
          observations_space,
          states_space,
          initial_probabilities,
          transition_probabilities,
          emission_probabilities,
      ]						):
    raise ValueError("There\'s an empty parameter"						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]						):





  """simple docstring"""



  _validate_list(SCREAMING_SNAKE_CASE	,	"observations_space"						)
  _validate_list(SCREAMING_SNAKE_CASE	,	"states_space"						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  if not isinstance(_object	,	SCREAMING_SNAKE_CASE						):
    a__     : Any  =f'''{var_name} must be a list'''
    raise ValueError(SCREAMING_SNAKE_CASE						)
  else:
    for x in _object:
      if not isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
        a__     : int  =f'''{var_name} must be a list of strings'''
        raise ValueError(SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Tuple	,	):





  """simple docstring"""



  _validate_dict(SCREAMING_SNAKE_CASE	,	"initial_probabilities"	,	SCREAMING_SNAKE_CASE						)
  _validate_nested_dict(SCREAMING_SNAKE_CASE	,	"transition_probabilities"						)
  _validate_nested_dict(SCREAMING_SNAKE_CASE	,	"emission_probabilities"						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



  _validate_dict(_object	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  for x in _object.values():
    _validate_dict(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Optional[Any] = False						):





  """simple docstring"""



  if not isinstance(_object	,	SCREAMING_SNAKE_CASE						):
    a__     : List[str]  =f'''{var_name} must be a dict'''
    raise ValueError(SCREAMING_SNAKE_CASE						)
  if not all(isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						) for x in _object						):
    a__     : str  =f'''{var_name} all keys must be strings'''
    raise ValueError(SCREAMING_SNAKE_CASE						)
  if not all(isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						) for x in _object.values()						):
    a__     : Dict  ="nested dictionary " if nested else ""
    a__     : Tuple  =f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
    raise ValueError(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     from doctest import testmod

     testmod()













import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import 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 import (
         NystromformerForMaskedLM,
         NystromformerForMultipleChoice,
         NystromformerForQuestionAnswering,
         NystromformerForSequenceClassification,
         NystromformerForTokenClassification,
         NystromformerModel,
     )
     from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=1_3		,				lowerCAmelCase__=7		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				lowerCAmelCase__=9_9		,				lowerCAmelCase__=3_2		,				lowerCAmelCase__=5		,				lowerCAmelCase__=4		,				lowerCAmelCase__=3_7		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=5_1_2		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=2		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=3		,				lowerCAmelCase__=4		,				lowerCAmelCase__=None		,				)       ->						List[Any]:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        a__     : List[Any]  =ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size							)

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

        a__     : str  =None
        if self.use_token_type_ids:
          a__     : List[str]  =ids_tensor([self.batch_size, self.seq_length]		,				self.type_vocab_size							)

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

        a__     : Tuple  =self.get_config()

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







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        return NystromformerConfig(
            vocab_size=self.vocab_size		,				hidden_size=self.hidden_size		,				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		,				is_decoder=lowerCAmelCase__		,				initializer_range=self.initializer_range		,				)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Tuple  =NystromformerModel(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : Optional[Any]  =model(lowerCAmelCase__		,				attention_mask=lowerCAmelCase__		,				token_type_ids=lowerCAmelCase__							)
        a__     : str  =model(lowerCAmelCase__		,				token_type_ids=lowerCAmelCase__							)
        a__     : Optional[int]  =model(lowerCAmelCase__							)
        self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : int  =NystromformerForMaskedLM(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : Dict  =model(lowerCAmelCase__		,				attention_mask=lowerCAmelCase__		,				token_type_ids=lowerCAmelCase__		,				labels=lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.vocab_size)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Any:



        '''simple docstring'''




        a__     : Optional[int]  =NystromformerForQuestionAnswering(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : str  =model(
            lowerCAmelCase__		,				attention_mask=lowerCAmelCase__		,				token_type_ids=lowerCAmelCase__		,				start_positions=lowerCAmelCase__		,				end_positions=lowerCAmelCase__		,				)
        self.parent.assertEqual(result.start_logits.shape		,				(self.batch_size, self.seq_length)							)
        self.parent.assertEqual(result.end_logits.shape		,				(self.batch_size, self.seq_length)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Optional[Any]  =self.num_labels
        a__     : Dict  =NystromformerForSequenceClassification(lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : List[str]  =model(lowerCAmelCase__		,				attention_mask=lowerCAmelCase__		,				token_type_ids=lowerCAmelCase__		,				labels=lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        a__     : Tuple  =self.num_labels
        a__     : List[str]  =NystromformerForTokenClassification(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : List[Any]  =model(lowerCAmelCase__		,				attention_mask=lowerCAmelCase__		,				token_type_ids=lowerCAmelCase__		,				labels=lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length, self.num_labels)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        a__     : List[Any]  =self.num_choices
        a__     : Optional[Any]  =NystromformerForMultipleChoice(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__     : List[str]  =input_ids.unsqueeze(1							).expand(-1		,				self.num_choices		,				-1							).contiguous()
        a__     : List[Any]  =token_type_ids.unsqueeze(1							).expand(-1		,				self.num_choices		,				-1							).contiguous()
        a__     : List[Any]  =input_mask.unsqueeze(1							).expand(-1		,				self.num_choices		,				-1							).contiguous()
        a__     : Dict  =model(
            lowerCAmelCase__		,				attention_mask=lowerCAmelCase__		,				token_type_ids=lowerCAmelCase__		,				labels=lowerCAmelCase__		,				)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_choices)							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : Optional[Any]  =self.prepare_config_and_inputs()
        (
            (
            a__
        ) ,							(
            a__
        ) ,							(
            a__
        ) ,							(
            a__
        ) ,							(
            a__
        ) ,							(
            a__
        ) ,							(
            a__
        ) ,							
        )     : List[str]  =config_and_inputs
        a__     : str  ={"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict







@require_torch
class     __lowerCAmelCase	(   UpperCamelCase__						,    UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : int            =      (
          (
              NystromformerModel,
              NystromformerForMaskedLM,
              NystromformerForMultipleChoice,
              NystromformerForQuestionAnswering,
              NystromformerForSequenceClassification,
              NystromformerForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      _lowercase    : Union[str, Any]            =      (
          {
              """feature-extraction""": NystromformerModel,
              """fill-mask""": NystromformerForMaskedLM,
              """question-answering""": NystromformerForQuestionAnswering,
              """text-classification""": NystromformerForSequenceClassification,
              """token-classification""": NystromformerForTokenClassification,
              """zero-shot""": NystromformerForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _lowercase    : Union[str, Any]            =      False
      _lowercase    : Union[str, Any]            =      False







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        a__     : Optional[int]  =NystromformerModelTester(self							)
        a__     : Optional[int]  =ConfigTester(self		,				config_class=lowerCAmelCase__		,				hidden_size=3_7							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        self.config_tester.run_common_tests()







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : List[str]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Tuple  =self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
          a__     : int  =type
          self.model_tester.create_and_check_model(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Optional[Any]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : str  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        a__     : Tuple  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Tuple  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : int  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__							)







      @slow
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__     : int  =NystromformerModel.from_pretrained(lowerCAmelCase__							)
          self.assertIsNotNone(lowerCAmelCase__							)







@require_torch
class     __lowerCAmelCase	(   unittest.TestCase):







      @slow
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : str  =NystromformerModel.from_pretrained("uw-madison/nystromformer-512"							)
        a__     : int  =torch.tensor([[0, 1, 2, 3, 4, 5]]							)

        with torch.no_grad():
          a__     : Tuple  =model(lowerCAmelCase__							)[0]

        a__     : List[str]  =torch.Size((1, 6, 7_6_8)							)
        self.assertEqual(output.shape		,				lowerCAmelCase__							)

        a__     : int  =torch.tensor(
            [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]]							)

        self.assertTrue(torch.allclose(output[:, :3, :3]		,				lowerCAmelCase__		,				atol=1E-4							)							)







      @slow
      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Union[str, Any]  ="the [MASK] of Belgium is Brussels"

        a__     : str  =AutoTokenizer.from_pretrained("uw-madison/nystromformer-512"							)
        a__     : int  =NystromformerForMaskedLM.from_pretrained("uw-madison/nystromformer-512"							)

        a__     : List[Any]  =tokenizer(lowerCAmelCase__		,				return_tensors="pt"							)

        with torch.no_grad():
          a__     : str  =model(encoding.input_ids							).logits

        a__     : List[str]  =token_logits[:, 2, :].argmax(-1							)[0]

        self.assertEqual(tokenizer.decode(lowerCAmelCase__							)		,				"capital"							)






