






from typing import Any

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       )			->							list:
  _validation(
      UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       )
  # Creates data structures and fill initial step
  UpperCamelCase_:   dict        =							{}
  UpperCamelCase_:   dict        =							{}
  for state in states_space:
    UpperCamelCase_:   Any        =							observations_space[0]
    UpperCamelCase_:   Tuple        =							(
        initial_probabilities[state] * emission_probabilities[state][observation]
    )
    UpperCamelCase_:   List[str]        =							None

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

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

      UpperCamelCase_:   Dict        =							arg_max

    # The final observation
  UpperCamelCase_:   Dict        =							observations_space[len(UpperCAmelCase__    ) - 1]

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

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

  return result

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       )			->							None:
  _validate_not_empty(
      UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       )
  _validate_lists(UpperCAmelCase__      ,       UpperCAmelCase__    )
  _validate_dicts(
      UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       )			->							None:
  if not all(
      [
          observations_space,
          states_space,
          initial_probabilities,
          transition_probabilities,
          emission_probabilities,
      ]    ):
    raise ValueError('There\'s an empty parameter'    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							None:
  _validate_list(UpperCAmelCase__      ,       'observations_space'    )
  _validate_list(UpperCAmelCase__      ,       'states_space'    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							None:
  if not isinstance(_object      ,       UpperCAmelCase__    ):
    UpperCamelCase_:   Optional[Any]        =							F'''{var_name} must be a list'''
    raise ValueError(UpperCAmelCase__    )
  else:
    for x in _object:
      if not isinstance(UpperCAmelCase__      ,       UpperCAmelCase__    ):
        UpperCamelCase_:   List[str]        =							F'''{var_name} must be a list of strings'''
        raise ValueError(UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       )			->							None:
  _validate_dict(UpperCAmelCase__      ,       'initial_probabilities'      ,       UpperCAmelCase__    )
  _validate_nested_dict(UpperCAmelCase__      ,       'transition_probabilities'    )
  _validate_nested_dict(UpperCAmelCase__      ,       'emission_probabilities'    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							None:
  _validate_dict(_object      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )
  for x in _object.values():
    _validate_dict(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__ = False    )			->							None:
  if not isinstance(_object      ,       UpperCAmelCase__    ):
    UpperCamelCase_:   Union[str, Any]        =							F'''{var_name} must be a dict'''
    raise ValueError(UpperCAmelCase__    )
  if not all(isinstance(UpperCAmelCase__      ,       UpperCAmelCase__    ) for x in _object    ):
    UpperCamelCase_:   int        =							F'''{var_name} all keys must be strings'''
    raise ValueError(UpperCAmelCase__    )
  if not all(isinstance(UpperCAmelCase__      ,       UpperCAmelCase__    ) for x in _object.values()    ):
    UpperCamelCase_:   str        =							'nested dictionary ' if nested else ''
    UpperCamelCase_:   Any        =							F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
    raise ValueError(UpperCAmelCase__    )


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

   testmod()

'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

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






				"""simple docstring"""







				snake_case  :       Any     			=						TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''      )

				snake_case  :       List[Any]     			=						tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,	dtype=tf.intaa  ,	)  # J'aime le camembert !"

				snake_case  :       Any     			=						model(UpperCamelCase__      )['''last_hidden_state''']
				snake_case  :       int     			=						tf.TensorShape((1, 10, 768)      )
				self.assertEqual(output.shape  ,	UpperCamelCase__      )
				# compare the actual values for a slice.
				snake_case  :       Dict     			=						tf.convert_to_tensor(
				    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,	dtype=tf.floataa  ,	)
				# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
				# camembert.eval()
				# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

				self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,	expected_slice.numpy()  ,	atol=1e-4      )      )


