"""simple docstring"""


from typing import Any







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A    ,				):






     '''simple docstring'''



     _validation(
         A    ,				A    ,				A    ,				A    ,				A    ,				)
     # Creates data structures and fill initial step
     lowercase__				 =							{}
     lowercase__				 =							{}
     for state in states_space:
          lowercase__				 =							observations_space[0]
          lowercase__				 =							(
              initial_probabilities[state] * emission_probabilities[state][observation]
          )
          lowercase__				 =							None

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

            # Update probabilities and pointers dicts
               lowercase__				 =							(
                   probabilities[(arg_max, prior_observation)]
                   * transition_probabilities[arg_max][state]
                   * emission_probabilities[state][observation]
               )

               lowercase__				 =							arg_max

    # The final observation
     lowercase__				 =							observations_space[len(A      ) - 1]

     # argmax for given final observation
     lowercase__				 =							""
     lowercase__				 =							-1
     for k_state in states_space:
          lowercase__				 =							probabilities[(k_state, final_observation)]
          if probability > max_probability:
               lowercase__				 =							probability
               lowercase__				 =							k_state
     lowercase__				 =							arg_max

     # Process pointers backwards
     lowercase__				 =							last_state
     lowercase__				 =							[]
     for o in range(len(A      ) - 1    ,				-1    ,				-1      ):
          result.append(A      )
          lowercase__				 =							pointers[previous, observations_space[o]]
     result.reverse()

     return result







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A    ,				):






     '''simple docstring'''



     _validate_not_empty(
         A    ,				A    ,				A    ,				A    ,				A    ,				)
     _validate_lists(A    ,				A      )
     _validate_dicts(
         A    ,				A    ,				A      )







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A    ,				):






     '''simple docstring'''



     if not all(
         [
             observations_space,
             states_space,
             initial_probabilities,
             transition_probabilities,
             emission_probabilities,
         ]      ):
          raise ValueError("There's an empty parameter"      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     _validate_list(A    ,				"observations_space"      )
     _validate_list(A    ,				"states_space"      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     if not isinstance(_object    ,				A      ):
          lowercase__				 =							f'''{var_name} must be a list'''
          raise ValueError(A      )
     else:
          for x in _object:
               if not isinstance(A    ,				A      ):
                    lowercase__				 =							f'''{var_name} must be a list of strings'''
                    raise ValueError(A      )







def 					__a   (		A    ,				A    ,				A    ,				):






     '''simple docstring'''



     _validate_dict(A    ,				"initial_probabilities"    ,				A      )
     _validate_nested_dict(A    ,				"transition_probabilities"      )
     _validate_nested_dict(A    ,				"emission_probabilities"      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     _validate_dict(_object    ,				A    ,				A      )
     for x in _object.values():
          _validate_dict(A    ,				A    ,				A    ,				A      )







def 					__a   (		A    ,				A    ,				A    ,				A = False      ):






     '''simple docstring'''



     if not isinstance(_object    ,				A      ):
          lowercase__				 =							f'''{var_name} must be a dict'''
          raise ValueError(A      )
     if not all(isinstance(A    ,				A      ) for x in _object      ):
          lowercase__				 =							f'''{var_name} all keys must be strings'''
          raise ValueError(A      )
     if not all(isinstance(A    ,				A      ) for x in _object.values()      ):
          lowercase__				 =							"nested dictionary " if nested else ""
          lowercase__				 =							f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
          raise ValueError(A      )


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

 testmod()





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_:					Union[str, Any]     =					{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Union[str, Any]     =					["DistilBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Any     =					[
     "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DistilBertForMaskedLM",
     "DistilBertForMultipleChoice",
     "DistilBertForQuestionAnswering",
     "DistilBertForSequenceClassification",
     "DistilBertForTokenClassification",
     "DistilBertModel",
     "DistilBertPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Tuple     =					[
     "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDistilBertForMaskedLM",
     "TFDistilBertForMultipleChoice",
     "TFDistilBertForQuestionAnswering",
     "TFDistilBertForSequenceClassification",
     "TFDistilBertForTokenClassification",
     "TFDistilBertMainLayer",
     "TFDistilBertModel",
     "TFDistilBertPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					Optional[Any]     =					[
     "FlaxDistilBertForMaskedLM",
     "FlaxDistilBertForMultipleChoice",
     "FlaxDistilBertForQuestionAnswering",
     "FlaxDistilBertForSequenceClassification",
     "FlaxDistilBertForTokenClassification",
     "FlaxDistilBertModel",
     "FlaxDistilBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase_:					Optional[int]     =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




