

from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


__snake_case				:      int   		=			logging.get_logger(__name__)



@dataclass
class        A__(_UpperCamelCase							):
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			[
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]





						def __init__(    self  ,      **_lowercase    )     -> Tuple:





									for deprecated_arg in self.deprecated_args:
												if deprecated_arg in kwargs:
															a_						:						Tuple 							= deprecated_arg[3:]
															a_						:						int 							= not kwargs.pop(_lowercase    )
															logger.warning(
															    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
															    F''' {positive_arg}={kwargs[positive_arg]}'''    )
									a_						:						Optional[int] 							= kwargs.pop("""tpu_name"""  ,      self.tpu_name    )
									a_						:						Optional[int] 							= kwargs.pop("""device_idx"""  ,      self.device_idx    )
									a_						:						str 							= kwargs.pop("""eager_mode"""  ,      self.eager_mode    )
									a_						:						Optional[Any] 							= kwargs.pop("""use_xla"""  ,      self.use_xla    )
									super().__init__(**_lowercase    )

						_A    :				str       					=			field(
						    default=_UpperCamelCase,  metadata={'''help''': '''Name of TPU'''},  )
						_A    :				int       					=			field(
						    default=0,  metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''},  )
						_A    :				bool       					=			field(default=_UpperCamelCase,  metadata={'''help''': '''Benchmark models in eager model.'''}							)
						_A    :				bool       					=			field(
						    default=_UpperCamelCase,  metadata={
						        '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
						    },  )





						@cached_property
						def 			UpperCamelCase__					(    self    )     -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
									requires_backends(self  ,      ["""tf"""]    )
									a_						:						int 							= None
									if self.tpu:
												try:
															if self.tpu_name:
																		a_						:						Optional[Any] 							= tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name    )
															else:
																		a_						:						Union[str, Any] 							= tf.distribute.cluster_resolver.TPUClusterResolver()
												except ValueError:
															a_						:						Dict 							= None
									return tpu





						@cached_property
						def 			UpperCamelCase__					(    self    )     -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
									requires_backends(self  ,      ["""tf"""]    )
									if self.is_tpu:
												tf.config.experimental_connect_to_cluster(self._setup_tpu    )
												tf.tpu.experimental.initialize_tpu_system(self._setup_tpu    )

												a_						:						Dict 							= tf.distribute.TPUStrategy(self._setup_tpu    )
									else:
												# currently no multi gpu is allowed
												if self.is_gpu:
															# TODO: Currently only single GPU is supported
															tf.config.set_visible_devices(self.gpu_list[self.device_idx]  ,      """GPU"""    )
															a_						:						int 							= tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''    )
												else:
															tf.config.set_visible_devices([]  ,      """GPU"""    )  # disable GPU
															a_						:						Optional[Any] 							= tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''    )

									return strategy





						@property
						def 			UpperCamelCase__					(    self    )     -> bool:
									requires_backends(self  ,      ["""tf"""]    )
									return self._setup_tpu is not None





						@property
						def 			UpperCamelCase__					(    self    )     -> "tf.distribute.Strategy":
									requires_backends(self  ,      ["""tf"""]    )
									return self._setup_strategy





						@property
						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									requires_backends(self  ,      ["""tf"""]    )
									return tf.config.list_physical_devices("""GPU"""    )





						@property
						def 			UpperCamelCase__					(    self    )     -> int:
									requires_backends(self  ,      ["""tf"""]    )
									if self.cuda:
												return len(self.gpu_list    )
									return 0





						@property
						def 			UpperCamelCase__					(    self    )     -> bool:
									return self.n_gpu > 0









import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


SCREAMING_SNAKE_CASE__							:      List[Any]         =    """\
@inproceedings{snover-etal-2006-study,
    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
    author = \"Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John\",
    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
    month = aug # \" 8-12\",
    year = \"2006\",
    address = \"Cambridge, Massachusetts, USA\",
    publisher = \"Association for Machine Translation in the Americas\",
    url = \"https://aclanthology.org/2006.amta-papers.25\",
    pages = \"223--231\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

SCREAMING_SNAKE_CASE__							:      int         =    """
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""







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





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      if version.parse(scb.__version__   ) < version.parse("1.4.12"   ):
         raise ImportWarning(
             "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
             "You can install it with `pip install \"sacrebleu>=1.4.12\"`."   )
      return datasets.MetricInfo(
          description=_DESCRIPTION       ,      citation=_CITATION       ,      homepage="http://www.cs.umd.edu/~snover/tercom/"       ,      inputs_description=_KWARGS_DESCRIPTION       ,      features=datasets.Features(
              {
                  "predictions": datasets.Value("string"       ,      id="sequence"   ),
                  "references": datasets.Sequence(datasets.Value("string"       ,      id="sequence"   )       ,      id="references"   ),
              }   )       ,      codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]       ,      reference_urls=[
              "https://github.com/jhclark/tercom",
          ]       ,      )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = False       ,      snake_case = False       ,      snake_case = False       ,      snake_case = False       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    int   		=	len(references[0]   )
      if any(len(snake_case   ) != references_per_prediction for refs in references   ):
         raise ValueError("Sacrebleu requires the same number of references for each prediction"   )
      a__						:    int   		=	[[refs[i] for refs in references] for i in range(snake_case   )]

      a__						:    Optional[Any]   		=	TER(
          normalized=snake_case       ,      no_punct=snake_case       ,      asian_support=snake_case       ,      case_sensitive=snake_case       ,      )
      a__						:    Union[str, Any]   		=	sb_ter.corpus_score(snake_case       ,      snake_case   )

      return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}

