






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

import datasets


snake_case    				=		"""\
@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\",
}
"""

snake_case    				=		"""\
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.
"""

snake_case    				=		"""
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  SCREAMING_SNAKE_CASE			(			datasets.Metric					):
     '''simple docstring'''


     def 				_A       (				self       :	Optional[int]      ):
       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 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	bool = False					, UpperCAmelCase_       :	bool = False					, UpperCAmelCase_       :	bool = False					, UpperCAmelCase_       :	bool = False					, ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    len(references[0]      )
       if any(len(UpperCAmelCase_      ) != references_per_prediction for refs in references      ):
         raise ValueError("Sacrebleu requires the same number of references for each prediction"      )
       SCREAMING_SNAKE_CASE       :      Dict							=    [[refs[i] for refs in references] for i in range(UpperCAmelCase_      )]

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    TER(
           normalized=UpperCAmelCase_					, no_punct=UpperCAmelCase_					, asian_support=UpperCAmelCase_					, case_sensitive=UpperCAmelCase_					, )
       SCREAMING_SNAKE_CASE       :      int							=    sb_ter.corpus_score(UpperCAmelCase_					, UpperCAmelCase_      )

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














# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


snake_case    				=		"""Create a default config file for Accelerate with only a few flags set."""


def   lowerCamelCase__			(     lowercase="no"						,     lowercase = default_json_config_file						,     lowercase = False				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Tuple							=    Path(lowercase				)
  path.parent.mkdir(parents=lowercase						,     exist_ok=lowercase				)
  if path.exists():
    print(
        F'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
    return False
  SCREAMING_SNAKE_CASE       :      int							=    mixed_precision.lower()
  if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
    raise ValueError(
        F'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {
      "compute_environment": "LOCAL_MACHINE",
      "mixed_precision": mixed_precision,
  }
  if torch.cuda.is_available():
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.cuda.device_count()
    SCREAMING_SNAKE_CASE       :      int							=    num_gpus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_gpus > 1:
      SCREAMING_SNAKE_CASE       :      Tuple							=    "MULTI_GPU"
    else:
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    "NO"
  elif is_xpu_available() and use_xpu:
    SCREAMING_SNAKE_CASE       :      List[str]							=    torch.xpu.device_count()
    SCREAMING_SNAKE_CASE       :      str							=    num_xpus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_xpus > 1:
      SCREAMING_SNAKE_CASE       :      Any							=    "MULTI_XPU"
    else:
      SCREAMING_SNAKE_CASE       :      str							=    "NO"
  elif is_npu_available():
    SCREAMING_SNAKE_CASE       :      List[Any]							=    torch.npu.device_count()
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    num_npus
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    False
    if num_npus > 1:
      SCREAMING_SNAKE_CASE       :      str							=    "MULTI_NPU"
    else:
      SCREAMING_SNAKE_CASE       :      int							=    "NO"
  else:
    SCREAMING_SNAKE_CASE       :      Dict							=    0
    SCREAMING_SNAKE_CASE       :      List[Any]							=    True
    SCREAMING_SNAKE_CASE       :      Dict							=    1
    SCREAMING_SNAKE_CASE       :      int							=    "NO"
  SCREAMING_SNAKE_CASE       :      Dict							=    ClusterConfig(**lowercase				)
  config.to_json_file(lowercase				)
  return path


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    parser.add_parser("default"						,     parents=lowercase						,     help=lowercase						,     formatter_class=lowercase				)
  parser.add_argument(
      "--config_file"						,     default=lowercase						,     help=(
          "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
          "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
          "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
          "with 'huggingface'."
      )						,     dest="save_location"						,     )

  parser.add_argument(
      "--mixed_precision"						,     choices=["no", "fp16", "bf16"]						,     type=lowercase						,     help="Whether or not to use mixed precision training. "
      "Choose between FP16 and BF16 (bfloat16) training. "
      "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."						,     default="no"						,     )
  parser.set_defaults(func=lowercase				)
  return parser


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[int]							=    write_basic_config(args.mixed_precision						,     args.save_location				)
  if config_file:
    print(F'''accelerate configuration saved at {config_file}'''				)






