






'''simple docstring'''





from typing import TYPE_CHECKING

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


__UpperCAmelCase							:   List[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:
	__UpperCAmelCase							:   Dict							=  ["DistilBertTokenizerFast"]

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

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCAmelCase							:   Dict							=  [
	    "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:
	__UpperCAmelCase							:   Union[str, 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

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







from typing import Any





def 					lowercase_ (    __snake_case     :							list	,	__snake_case     :							list	,	__snake_case     :							dict	,	__snake_case     :							dict	,	__snake_case     :							dict	,	)     -> list:





				'''simple docstring'''


				_validation(
				    __snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	)
				# Creates data structures and fill initial step
				snake_case__      :dict         =   {}
				snake_case__      :dict         =   {}
				for state in states_space:
								snake_case__      :List[Any]         =   observations_space[0]
								snake_case__      :str         =   (
								    initial_probabilities[state] * emission_probabilities[state][observation]
								)
								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(__snake_case      )      ):
								snake_case__      :Any         =   observations_space[o]
								snake_case__      :Tuple         =   observations_space[o - 1]
								for state in states_space:
												# Calculates the argmax for probability function
												snake_case__      :Tuple         =   ""
												snake_case__      :Union[str, Any]         =   -1
												for k_state in states_space:
																snake_case__      :int         =   (
																    probabilities[(k_state, prior_observation)]
																    * transition_probabilities[k_state][state]
																    * emission_probabilities[state][observation]
																)
																if probability > max_probability:
																				snake_case__      :str         =   probability
																				snake_case__      :Tuple         =   k_state

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

												snake_case__      :List[str]         =   arg_max

    # The final observation
				snake_case__      :str         =   observations_space[len(__snake_case      ) - 1]

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

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

				return result





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	)     -> None:





				'''simple docstring'''


				_validate_not_empty(
				    __snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	)
				_validate_lists(__snake_case	,	__snake_case      )
				_validate_dicts(
				    __snake_case	,	__snake_case	,	__snake_case      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	)     -> None:





				'''simple docstring'''


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





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any      )     -> None:





				'''simple docstring'''


				_validate_list(__snake_case	,	"observations_space"      )
				_validate_list(__snake_case	,	"states_space"      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							str      )     -> None:





				'''simple docstring'''


				if not isinstance(_object	,	__snake_case      ):
								snake_case__      :Optional[int]         =   F'{var_name} must be a list'
								raise ValueError(__snake_case      )
				else:
								for x in _object:
												if not isinstance(__snake_case	,	__snake_case      ):
																snake_case__      :Any         =   F'{var_name} must be a list of strings'
																raise ValueError(__snake_case      )





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any	,	__snake_case     :							Any	,	)     -> None:





				'''simple docstring'''


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





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							str      )     -> None:





				'''simple docstring'''


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





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							str	,	__snake_case     :							type	,	__snake_case     :							bool = False      )     -> None:





				'''simple docstring'''


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


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

	testmod()