



import sacrebleu as scb
from packaging import version

import datasets


_lowerCamelCase         =	"""\
@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\",
}
"""

_lowerCamelCase         =	"""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

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

_lowerCamelCase         =	"""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

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`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""

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





     def 						__snake_case     (							self			:				Optional[Any]					)->Union[str, Any]:
      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="https://github.com/mjpost/sacreBLEU"  ,	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"]  ,	reference_urls=[
              "https://github.com/mjpost/sacreBLEU",
              "https://en.wikipedia.org/wiki/BLEU",
              "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
          ]  ,	)






     def 						__snake_case     (							self			:				Dict  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				str="exp"  ,	UpperCamelCase			:				Optional[int]=None  ,	UpperCamelCase			:				Optional[int]=False  ,	UpperCamelCase			:				Optional[int]=False  ,	UpperCamelCase			:				List[Any]=None  ,	UpperCamelCase			:				Tuple=False  ,	)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						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							:     Optional[int]							=						[[refs[i] for refs in references] for i in range(UpperCamelCase					)]
      __SCREAMING_SNAKE_CASE							:     List[str]							=						scb.corpus_bleu(
          UpperCamelCase  ,	UpperCamelCase  ,	smooth_method=UpperCamelCase  ,	smooth_value=UpperCamelCase  ,	force=UpperCamelCase  ,	lowercase=UpperCamelCase  ,	use_effective_order=UpperCamelCase  ,	**({"tokenize": tokenize} if tokenize else {})  ,	)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						{
          "score": output.score,
          "counts": output.counts,
          "totals": output.totals,
          "precisions": output.precisions,
          "bp": output.bp,
          "sys_len": output.sys_len,
          "ref_len": output.ref_len,
      }
      return output_dict











import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


_lowerCamelCase         =	logging.get_logger(__name__)  # pylint: disable=invalid-name


_lowerCamelCase         =	"""lfs-multipart-upload"""

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     @staticmethod
     def 						__snake_case     (							UpperCamelCase			:				ArgumentParser					)->Any:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						parser.add_parser(
          "lfs-enable-largefiles"  ,	help=(
              "Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."
          )  ,	)
      enable_parser.add_argument("path"  ,	type=UpperCamelCase  ,	help="Local path to repository you want to configure."					)
      enable_parser.set_defaults(func=lambda UpperCamelCase					: LfsEnableCommand(UpperCamelCase					)					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						parser.add_parser(
          UpperCamelCase  ,	help=(
              "Deprecated: use `huggingface-cli` instead. "
              "Command will get called by git-lfs, do not call it directly."
          )  ,	)
      upload_parser.set_defaults(func=lambda UpperCamelCase					: LfsUploadCommand(UpperCamelCase					)					)






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				Union[str, Any]  ,	UpperCamelCase			:				List[Any]					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     str							=						args






     def 						__snake_case     (							self			:				Union[str, Any]					)->List[Any]:
      warnings.warn(
          "Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						os.path.abspath(self.args.path					)
      if not os.path.isdir(UpperCamelCase					):
       print("This does not look like a valid git repo."					)
       exit(1					)
      subprocess.run(
          "git config lfs.customtransfer.multipart.path transformers-cli".split()  ,	check=UpperCamelCase  ,	cwd=UpperCamelCase					)
      subprocess.run(
          F"""git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}""".split()  ,	check=UpperCamelCase  ,	cwd=UpperCamelCase  ,	)
      print("Local repo set up for largefiles"					)






def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[str]							=						json.dumps(__lowerCamelCase  ) + "\n"
 sys.stdout.write(__lowerCamelCase  )
 sys.stdout.flush()






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						json.loads(sys.stdin.readline().strip()  )

 if "terminate" in (msg.get("type"  ), msg.get("event"  )):
  # terminate message received
  return None

 if msg.get("event"  ) not in ("download", "upload"):
  logger.critical("Received unexpected message"  )
  sys.exit(1  )

 return msg

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				int					)->int:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						filepath
      __SCREAMING_SNAKE_CASE							:     List[str]							=						seek_from
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						read_limit
      __SCREAMING_SNAKE_CASE							:     int							=						0





     def __enter__(							self			:				Optional[Any]					)->int:
      __SCREAMING_SNAKE_CASE							:     int							=						open(self.filepath  ,	"rb"					)
      self.f.seek(self.seek_from					)
      return self





     def __len__(							self			:				int					)->Dict:
      __SCREAMING_SNAKE_CASE							:     str							=						os.fstat(self.f.fileno()					).st_size
      return min(self.read_limit  ,	total_length - self.seek_from					)





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				List[str]=-1					)->Optional[Any]:
      if self.n_seen >= self.read_limit:
       return b""
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.read_limit - self.n_seen
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.f.read(remaining_amount if n < 0 else min(UpperCamelCase  ,	UpperCamelCase					)					)
      self.n_seen += len(UpperCamelCase					)
      return data





     def __iter__(							self			:				Optional[Any]					)->Any:
      yield self.read(n=4 * 1_0_2_4 * 1_0_2_4					)






     def __exit__(							self			:				Tuple  ,	*UpperCamelCase			:				int					)->str:
      self.f.close()






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				List[str]  ,	UpperCamelCase			:				Optional[int]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Any							=						args






     def 						__snake_case     (							self			:				int					)->int:
      # Immediately after invoking a custom transfer process, git-lfs
      # sends initiation data to the process over stdin.
      # This tells the process useful information about the configuration.
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						json.loads(sys.stdin.readline().strip()					)
      if not (init_msg.get("event"					) == "init" and init_msg.get("operation"					) == "upload"):
       write_msg({"error": {"code": 3_2, "message": "Wrong lfs init operation"}}					)
       sys.exit(1					)

      # The transfer process should use the information it needs from the
      # initiation structure, and also perform any one-off setup tasks it
      # needs to do. It should then respond on stdout with a simple empty
      # confirmation structure, as follows:
      write_msg({}					)

      # After the initiation exchange, git-lfs will send any number of
      # transfer requests to the stdin of the transfer process, in a serial sequence.
      while True:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						read_msg()
       if msg is None:
        # When all transfers have been processed, git-lfs will send
        # a terminate event to the stdin of the transfer process.
        # On receiving this message the transfer process should
        # clean up and terminate. No response is expected.
        sys.exit(0					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						msg["oid"]
       __SCREAMING_SNAKE_CASE							:     List[str]							=						msg["path"]
       __SCREAMING_SNAKE_CASE							:     Tuple							=						msg["action"]["href"]
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						msg["action"]["header"]
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						int(header.pop("chunk_size"					)					)
       __SCREAMING_SNAKE_CASE							:     List[str]							=						list(header.values()					)

       __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
       for i, presigned_url in enumerate(UpperCamelCase					):
        with FileSlice(UpperCamelCase  ,	seek_from=i * chunk_size  ,	read_limit=UpperCamelCase					) as data:
         __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						requests.put(UpperCamelCase  ,	data=UpperCamelCase					)
         r.raise_for_status()
         parts.append(
             {
                 "etag": r.headers.get("etag"					),
                 "partNumber": i + 1,
             }					)
         # In order to support progress reporting while data is uploading / downloading,
         # the transfer process should post messages to stdout
         write_msg(
             {
                 "event": "progress",
                 "oid": oid,
                 "bytesSoFar": (i + 1) * chunk_size,
                 "bytesSinceLast": chunk_size,
             }					)
         # Not precise but that's ok.

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						requests.post(
           UpperCamelCase  ,	json={
               "oid": oid,
               "parts": parts,
           }  ,	)
       r.raise_for_status()

       write_msg({"event": "complete", "oid": oid}					)






