
from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowerCamelCase__     :		Tuple        = """\
@misc{wu2016googles,
      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

lowerCamelCase__     :		Union[str, Any]        = """\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

lowerCamelCase__     :		Optional[int]        = """\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""



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


 def   lowercase__  (       self):


   '''simple docstring'''




   return datasets.MetricInfo(
       description=_DESCRIPTION       ,	citation=_CITATION       ,	inputs_description=_KWARGS_DESCRIPTION       ,	features=datasets.Features(
           {
               """predictions""": datasets.Sequence(datasets.Value("""string"""       ,	id="""token""")       ,	id="""sequence"""),
               """references""": datasets.Sequence(
                   datasets.Sequence(datasets.Value("""string"""       ,	id="""token""")       ,	id="""sequence""")       ,	id="""references"""),
           })       ,	)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = 1       ,	SCREAMING_SNAKE_CASE_ = 4       ,	):


   '''simple docstring'''




   return {
       "google_bleu": gleu_score.corpus_gleu(
           list_of_references=SCREAMING_SNAKE_CASE_       ,	hypotheses=SCREAMING_SNAKE_CASE_       ,	min_len=SCREAMING_SNAKE_CASE_       ,	max_len=SCREAMING_SNAKE_CASE_)
   }








# 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


lowerCamelCase__     :		Any        = """Create a default config file for Accelerate with only a few flags set."""

def 		UpperCamelCase						(   lowercase_="no"			,			lowercase_ = default_json_config_file			,			lowercase_ = False )		->						Any:


  '''simple docstring'''






  lowercase__	: Any     		=   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
  lowercase__	: 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}' )
  lowercase__	: Dict     		=   {
      """compute_environment""": """LOCAL_MACHINE""",
      """mixed_precision""": mixed_precision,
  }
  if torch.cuda.is_available():
    lowercase__	: Any     		=   torch.cuda.device_count()
    lowercase__	: Any     		=   num_gpus
    lowercase__	: Optional[int]     		=   False
    if num_gpus > 1:
      lowercase__	: Tuple     		=   """MULTI_GPU"""
    else:
      lowercase__	: Optional[Any]     		=   """NO"""
  elif is_xpu_available() and use_xpu:
    lowercase__	: Union[str, Any]     		=   torch.xpu.device_count()
    lowercase__	: str     		=   num_xpus
    lowercase__	: List[Any]     		=   False
    if num_xpus > 1:
      lowercase__	: str     		=   """MULTI_XPU"""
    else:
      lowercase__	: Optional[Any]     		=   """NO"""
  elif is_npu_available():
    lowercase__	: Tuple     		=   torch.npu.device_count()
    lowercase__	: Union[str, Any]     		=   num_npus
    lowercase__	: Union[str, Any]     		=   False
    if num_npus > 1:
      lowercase__	: List[Any]     		=   """MULTI_NPU"""
    else:
      lowercase__	: int     		=   """NO"""
  else:
    lowercase__	: Union[str, Any]     		=   0
    lowercase__	: str     		=   True
    lowercase__	: Union[str, Any]     		=   1
    lowercase__	: int     		=   """NO"""
  lowercase__	: Tuple     		=   ClusterConfig(**lowercase_ )
  config.to_json_file(lowercase_ )
  return path

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: List[str]     		=   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 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






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






