



"""simple docstring"""

from typing import Any





def       __magic_name__    (   _lowerCamelCase				: list  ,       _lowerCamelCase				: list  ,       _lowerCamelCase				: dict  ,       _lowerCamelCase				: dict  ,       _lowerCamelCase				: dict  ,       ):
							_validation(
							    _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       )
							# Creates data structures and fill initial step
							__a		:		dict									=       {}
							__a		:		dict									=       {}
							for state in states_space:
														__a		:		List[str]									=       observations_space[0]
														__a		:		Tuple									=       (
														    initial_probabilities[state] * emission_probabilities[state][observation]
														)
														__a		:		Optional[Any]									=       None

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

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

																					__a		:		Tuple									=       arg_max

    # The final observation
							__a		:		Optional[int]									=       observations_space[len(_lowerCamelCase					) - 1]

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

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

							return result





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       ):
							_validate_not_empty(
							    _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       )
							_validate_lists(_lowerCamelCase  ,       _lowerCamelCase					)
							_validate_dicts(
							    _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





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





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any					):
							_validate_list(_lowerCamelCase  ,       """observations_space"""					)
							_validate_list(_lowerCamelCase  ,       """states_space"""					)





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





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       ):
							_validate_dict(_lowerCamelCase  ,       """initial_probabilities"""  ,       _lowerCamelCase					)
							_validate_nested_dict(_lowerCamelCase  ,       """transition_probabilities"""					)
							_validate_nested_dict(_lowerCamelCase  ,       """emission_probabilities"""					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: str					):
							_validate_dict(_object  ,       _lowerCamelCase  ,       _lowerCamelCase					)
							for x in _object.values():
														_validate_dict(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: type  ,       _lowerCamelCase				: bool = False					):
							if not isinstance(_object  ,       _lowerCamelCase					):
														__a		:		Dict									=       F'''{var_name} must be a dict'''
														raise ValueError(_lowerCamelCase					)
							if not all(isinstance(_lowerCamelCase  ,       _lowerCamelCase					) for x in _object					):
														__a		:		Optional[int]									=       F'''{var_name} all keys must be strings'''
														raise ValueError(_lowerCamelCase					)
							if not all(isinstance(_lowerCamelCase  ,       _lowerCamelCase					) for x in _object.values()					):
														__a		:		Optional[Any]									=       """nested dictionary """ if nested else """"""
														__a		:		Tuple									=       F'''{var_name} {nested_text}all values must be {value_type.__name__}'''
														raise ValueError(_lowerCamelCase					)


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

	testmod()






"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__			   =  {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  ["LlamaTokenizer"]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  ["LlamaTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  [
	    "LlamaForCausalLM",
	    "LlamaModel",
	    "LlamaPreTrainedModel",
	    "LlamaForSequenceClassification",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

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

