






from typing import Any







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int  ,						snake_case_      :							str  ,						snake_case_      :							Any  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							List[str]  ,						):
	_validation(
	    lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)
	# Creates data structures and fill initial step
	snake_case__				:   dict     =						{}
	snake_case__				:   dict     =						{}
	for state in states_space:
		snake_case__				:   Union[str, Any]     =						observations_space[0]
		snake_case__				:   List[Any]     =						(
		    initial_probabilities[state] * emission_probabilities[state][observation]
		)
		snake_case__				:   Any     =						None

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

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

			snake_case__				:   Optional[Any]     =						arg_max

    # The final observation
	snake_case__				:   Dict     =						observations_space[len(lowerCAmelCase_     ) - 1]

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

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

	return result







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							List[str]  ,						snake_case_      :							str  ,						snake_case_      :							int  ,						snake_case_      :							List[str]  ,						):
	_validate_not_empty(
	    lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)
	_validate_lists(lowerCAmelCase_  ,						lowerCAmelCase_     )
	_validate_dicts(
	    lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Dict  ,						snake_case_      :							Optional[int]  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Tuple  ,						snake_case_      :							Any  ,						):


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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str  ,						snake_case_      :							Optional[Any]     ):
	_validate_list(lowerCAmelCase_  ,						"observations_space"     )
	_validate_list(lowerCAmelCase_  ,						"states_space"     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							Dict     ):


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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]  ,						snake_case_      :							Any  ,						snake_case_      :							Dict  ,						):
	_validate_dict(lowerCAmelCase_  ,						"initial_probabilities"  ,						lowerCAmelCase_     )
	_validate_nested_dict(lowerCAmelCase_  ,						"transition_probabilities"     )
	_validate_nested_dict(lowerCAmelCase_  ,						"emission_probabilities"     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, Any]  ,						snake_case_      :							List[str]     ):
	_validate_dict(_object  ,						lowerCAmelCase_  ,						lowerCAmelCase_     )
	for x in _object.values():
		_validate_dict(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]  ,						snake_case_      :							str  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Optional[Any] = False     ):


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


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

	testmod()







from typing import TYPE_CHECKING

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


__lowerCamelCase      :    Tuple   		=   {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Tuple   		=   [
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Union[str, Any]   		=   [
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    List[Any]   		=   [
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    Dict   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



