


"""simple docstring"""






from typing import Any


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

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

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

												SCREAMING_SNAKE_CASE__     							=				arg_max

    # The final observation
				SCREAMING_SNAKE_CASE__     							=				observations_space[len(lowerCamelCase_  ) - 1]

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

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

				return result


def __lowercase     (      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 __lowercase     (      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 __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      Any  ):
				_validate_list(lowerCamelCase_						,					"observations_space"  )
				_validate_list(lowerCamelCase_						,					"states_space"  )


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


def __lowercase     (      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 __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      str  ):
				_validate_dict(_object						,					lowerCamelCase_						,					lowerCamelCase_  )
				for x in _object.values():
								_validate_dict(lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_  )


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






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_lowerCamelCase     =  datasets.logging.get_logger(__name__)


_lowerCamelCase     =  '\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

_lowerCamelCase     =  '\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

_lowerCamelCase     =  '\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

_lowerCamelCase     =  {
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,  _KWARGS_DESCRIPTION   )
class 			lowerCamelCase_		(			datasets.Metric   ):





				"""simple docstring"""







				def   lowerCAmelCase__		(						self     ):
								return datasets.MetricInfo(
								    description=_DESCRIPTION       ,		citation=_CITATION       ,		homepage="https://github.com/google-research/bleurt"       ,		inputs_description=_KWARGS_DESCRIPTION       ,		features=datasets.Features(
								        {
								            "predictions": datasets.Value("string"       ,		id="sequence"     ),
								            "references": datasets.Value("string"       ,		id="sequence"     ),
								        }     )       ,		codebase_urls=["https://github.com/google-research/bleurt"]       ,		reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]       ,		)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# check that config name specifies a valid BLEURT model
								if self.config_name == "default":
												logger.warning(
												    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
												    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."     )
												SCREAMING_SNAKE_CASE__     							=				"bleurt-base-128"

								if self.config_name.lower() in CHECKPOINT_URLS:
												SCREAMING_SNAKE_CASE__     							=				self.config_name.lower()

								elif self.config_name.upper() in CHECKPOINT_URLS:
												SCREAMING_SNAKE_CASE__     							=				self.config_name.upper()

								else:
												raise KeyError(
												    f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''     )

								# download the model checkpoint specified by self.config_name and set up the scorer
								SCREAMING_SNAKE_CASE__     							=				dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]     )
								SCREAMING_SNAKE_CASE__     							=				score.BleurtScorer(os.path.join(UpperCAmelCase__       ,		UpperCAmelCase__     )     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.scorer.score(references=UpperCAmelCase__       ,		candidates=UpperCAmelCase__     )
								return {"scores": scores}
