

from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


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

lowerCAmelCase__       = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

lowerCAmelCase__       = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"






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


	def _UpperCamelCase	(						self		: Optional[Any]    )    ->      MetricInfo:
			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 _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: List[List[List[str]]]			,						lowerCAmelCase__		: List[List[str]]			,						lowerCAmelCase__		: int = 1			,						lowerCAmelCase__		: int = 4			,						)    ->      Dict[str, float]:
			return {
			    "google_bleu": gleu_score.corpus_gleu(
			        list_of_references=lowerCAmelCase__			,						hypotheses=lowerCAmelCase__			,						min_len=lowerCAmelCase__			,						max_len=lowerCAmelCase__    )
			}






import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase__       = "src/diffusers"

# Matches is_xxx_available()
lowerCAmelCase__       = re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
lowerCAmelCase__       = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


lowerCAmelCase__       = "\n{0} = None\n"

lowerCAmelCase__       = "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


lowerCAmelCase__       = "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  _re_backend.findall(__A			)
  if len(__A			) == 0:
    return None

  return "_and_".join(__A			)
def      _lowerCAmelCase(	):
  with open(os.path.join(__A   ,       "__init__.py"			)   ,       "r"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
    UpperCAmelCase					      =  f.readlines()

  # Get to the point we do the actual imports for type checking
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  {}
  # Go through the end of the file
  while line_index < len(__A			):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    UpperCAmelCase					      =  find_backend(lines[line_index]			)
    if backend is not None:
      while not lines[line_index].startswith("else:"			):
        line_index += 1
      line_index += 1
      UpperCAmelCase					      =  []
      # Until we unindent, add backend objects to the list
      while line_index < len(__A			) and len(lines[line_index]			) > 1:
        UpperCAmelCase					      =  lines[line_index]
        UpperCAmelCase					      =  _re_single_line_import.search(__A			)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(", "			)			)
        elif line.startswith(" " * 8			):
          objects.append(line[8:-2]			)
        line_index += 1

      if len(__A			) > 0:
        UpperCAmelCase					      =  objects
    else:
      line_index += 1

  return backend_specific_objects
def      _lowerCAmelCase(	__A   ,       __A			):
  if name.isupper():
    return DUMMY_CONSTANT.format(__A			)
  elif name.islower():
    return DUMMY_FUNCTION.format(__A   ,       __A			)
  else:
    return DUMMY_CLASS.format(__A   ,       __A			)
def      _lowerCAmelCase(	__A=None			):
  if backend_specific_objects is None:
    UpperCAmelCase					      =  read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  UpperCAmelCase					      =  {}

  for backend, objects in backend_specific_objects.items():
    UpperCAmelCase					      =  "[" + ", ".join(F"\"{b}\"" for b in backend.split("_and_"			)			) + "]"
    UpperCAmelCase					      =  "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(__A   ,       __A			) for o in objects]			)
    UpperCAmelCase					      =  dummy_file

  return dummy_files
def      _lowerCAmelCase(	__A=False			):
  UpperCAmelCase					      =  create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  UpperCAmelCase					      =  {"torch": "pt"}

  # Locate actual dummy modules and read their content.
  UpperCAmelCase					      =  os.path.join(__A   ,       "utils"			)
  UpperCAmelCase					      =  {
      backend: os.path.join(__A   ,       F"dummy_{short_names.get(__A   ,       __A			)}_objects.py"			)
      for backend in dummy_files.keys()
  }

  UpperCAmelCase					      =  {}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(__A			):
      with open(__A   ,       "r"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
        UpperCAmelCase					      =  f.read()
    else:
      UpperCAmelCase					      =  ""

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            F"Updating diffusers.utils.dummy_{short_names.get(__A   ,       __A			)}_objects.py as the main "
            "__init__ has new objects."			)
        with open(dummy_file_paths[backend]   ,       "w"   ,       encoding="utf-8"   ,       newline="\n"			) as f:
          f.write(dummy_files[backend]			)
      else:
        raise ValueError(
            "The main __init__ has objects that are not present in "
            F"diffusers.utils.dummy_{short_names.get(__A   ,       __A			)}_objects.py. Run `make fix-copies` "
            "to fix this."			)


if __name__ == "__main__":
      lowerCAmelCase__       = argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      lowerCAmelCase__       = parser.parse_args()

      check_dummies(args.fix_and_overwrite)



