





from typing import Any






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							):
     _validation(
         UpperCAmelCase_,							UpperCAmelCase_,							UpperCAmelCase_,							UpperCAmelCase_,							UpperCAmelCase_,							)
     # Creates data structures and fill initial step
     _SCREAMING_SNAKE_CASE							:						dict         =				{}
     _SCREAMING_SNAKE_CASE							:						dict         =				{}
     for state in states_space:
          _SCREAMING_SNAKE_CASE							:						List[Any]         =				observations_space[0]
          _SCREAMING_SNAKE_CASE							:						Any         =				(
              initial_probabilities[state] * emission_probabilities[state][observation]
          )
          _SCREAMING_SNAKE_CASE							:						str         =				None

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

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

               _SCREAMING_SNAKE_CASE							:						str         =				arg_max

    # The final observation
     _SCREAMING_SNAKE_CASE							:						int         =				observations_space[len(UpperCAmelCase_						) - 1]

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

     # Process pointers backwards
     _SCREAMING_SNAKE_CASE							:						List[Any]         =				last_state
     _SCREAMING_SNAKE_CASE							:						Optional[int]         =				[]
     for o in range(len(UpperCAmelCase_						) - 1,							-1,							-1						):
          result.append(UpperCAmelCase_						)
          _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				pointers[previous, observations_space[o]]
     result.reverse()

     return result






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							):
     _validate_not_empty(
         UpperCAmelCase_,							UpperCAmelCase_,							UpperCAmelCase_,							UpperCAmelCase_,							UpperCAmelCase_,							)
     _validate_lists(UpperCAmelCase_,							UpperCAmelCase_						)
     _validate_dicts(
         UpperCAmelCase_,							UpperCAmelCase_,							UpperCAmelCase_						)






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							):





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






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase						):
     _validate_list(UpperCAmelCase_,							"observations_space"						)
     _validate_list(UpperCAmelCase_,							"states_space"						)






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase						):





     if not isinstance(_object,							UpperCAmelCase_						):
          _SCREAMING_SNAKE_CASE							:						int         =				f"""{var_name} must be a list"""
          raise ValueError(UpperCAmelCase_						)
     else:
          for x in _object:
               if not isinstance(UpperCAmelCase_,							UpperCAmelCase_						):
                    _SCREAMING_SNAKE_CASE							:						str         =				f"""{var_name} must be a list of strings"""
                    raise ValueError(UpperCAmelCase_						)






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							):
     _validate_dict(UpperCAmelCase_,							"initial_probabilities",							UpperCAmelCase_						)
     _validate_nested_dict(UpperCAmelCase_,							"transition_probabilities"						)
     _validate_nested_dict(UpperCAmelCase_,							"emission_probabilities"						)






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase						):
     _validate_dict(_object,							UpperCAmelCase_,							UpperCAmelCase_						)
     for x in _object.values():
          _validate_dict(UpperCAmelCase_,							UpperCAmelCase_,							UpperCAmelCase_,							UpperCAmelCase_						)






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase,							__lowerCamelCase = False						):





     if not isinstance(_object,							UpperCAmelCase_						):
          _SCREAMING_SNAKE_CASE							:						Any         =				f"""{var_name} must be a dict"""
          raise ValueError(UpperCAmelCase_						)
     if not all(isinstance(UpperCAmelCase_,							UpperCAmelCase_						) for x in _object						):
          _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				f"""{var_name} all keys must be strings"""
          raise ValueError(UpperCAmelCase_						)
     if not all(isinstance(UpperCAmelCase_,							UpperCAmelCase_						) for x in _object.values()						):
          _SCREAMING_SNAKE_CASE							:						Optional[int]         =				'''nested dictionary ''' if nested else ''''''
          _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				f"""{var_name} {nested_text}all values must be {value_type.__name__}"""
          raise ValueError(UpperCAmelCase_						)


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

       testmod()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


snake_case							:       Any										=	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       List[Any]										=	[
         '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTNeoForCausalLM''',
         '''GPTNeoForQuestionAnswering''',
         '''GPTNeoForSequenceClassification''',
         '''GPTNeoForTokenClassification''',
         '''GPTNeoModel''',
         '''GPTNeoPreTrainedModel''',
         '''load_tf_weights_in_gpt_neo''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case							:       Any										=	[
         '''FlaxGPTNeoForCausalLM''',
         '''FlaxGPTNeoModel''',
         '''FlaxGPTNeoPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_neo import (
              GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTNeoForCausalLM,
              GPTNeoForQuestionAnswering,
              GPTNeoForSequenceClassification,
              GPTNeoForTokenClassification,
              GPTNeoModel,
              GPTNeoPreTrainedModel,
              load_tf_weights_in_gpt_neo,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
     import sys

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





