






from typing import Any

def _UpperCAmelCase   (	a						:	list  ,					a						:	list  ,					a						:	dict  ,					a						:	dict  ,					a						:	dict  ,					):
							_validation(
							    a  ,					a  ,					a  ,					a  ,					a  ,					)
							# Creates data structures and fill initial step
							snake_case__	  =       {}
							snake_case__	  =       {}
							for state in states_space:
														snake_case__	  =       observations_space[0]
														snake_case__	  =       (
														    initial_probabilities[state] * emission_probabilities[state][observation]
														)
														snake_case__	  =       None

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

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

																					snake_case__	  =       arg_max

    # The final observation
							snake_case__	  =       observations_space[len(a ) - 1]

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

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

							return result

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					):
							_validate_not_empty(
							    a  ,					a  ,					a  ,					a  ,					a  ,					)
							_validate_lists(a  ,					a )
							_validate_dicts(
							    a  ,					a  ,					a )

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

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Any ):
							_validate_list(a  ,					"""observations_space""" )
							_validate_list(a  ,					"""states_space""" )

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

def _UpperCAmelCase   (	a						:	Any  ,					a						:	Any  ,					a						:	Any  ,					):
							_validate_dict(a  ,					"""initial_probabilities"""  ,					a )
							_validate_nested_dict(a  ,					"""transition_probabilities""" )
							_validate_nested_dict(a  ,					"""emission_probabilities""" )

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str ):
							_validate_dict(_object  ,					a  ,					a )
							for x in _object.values():
														_validate_dict(a  ,					a  ,					a  ,					a )

def _UpperCAmelCase   (	a						:	Any  ,					a						:	str  ,					a						:	type  ,					a						:	bool = False ):
							if not isinstance(_object  ,					a ):
														snake_case__	  =       F'''{var_name} must be a dict'''
														raise ValueError(a )
							if not all(isinstance(a  ,					a ) for x in _object ):
														snake_case__	  =       F'''{var_name} all keys must be strings'''
														raise ValueError(a )
							if not all(isinstance(a  ,					a ) for x in _object.values() ):
														snake_case__	  =       """nested dictionary """ if nested else """"""
														snake_case__	  =       F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
														raise ValueError(a )


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

	testmod()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LiltForQuestionAnswering""",
	    """LiltForSequenceClassification""",
	    """LiltForTokenClassification""",
	    """LiltModel""",
	    """LiltPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lilt import (
		    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		    LiltPreTrainedModel,
		)

else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
