



import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


__A       =						logging.get_logger(__name__)






class 		lowercase						(			snake_case__):






 """simple docstring"""


 a__				: str		       =							"AutoTokenizer"
 a__				: Dict		       =							["tokenizer"]

 a__				: Optional[int]		       =							{
     "semantic_prompt": 1,
     "coarse_prompt": 2,
     "fine_prompt": 2,
 }




 def __init__(     self     :						Optional[int]						,  __UpperCAmelCase     :						Any						,  __UpperCAmelCase     :						Tuple=None					)      ->			Optional[int]:
       super().__init__(__UpperCAmelCase					)

       UpperCAmelCase_=       speaker_embeddings




 @classmethod
 def   _SCREAMING_SNAKE_CASE		(     cls     :						Optional[int]						,  __UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						str="speaker_embeddings_path.json"						,  **__UpperCAmelCase     :						Optional[int]					)      ->			int:

       if speaker_embeddings_dict_path is not None:
             UpperCAmelCase_=       get_file_from_repo(
                 __UpperCAmelCase						,  __UpperCAmelCase						,  subfolder=kwargs.pop("""subfolder"""						,  __UpperCAmelCase					)						,  cache_dir=kwargs.pop("""cache_dir"""						,  __UpperCAmelCase					)						,  force_download=kwargs.pop("""force_download"""						,  __UpperCAmelCase					)						,  proxies=kwargs.pop("""proxies"""						,  __UpperCAmelCase					)						,  resume_download=kwargs.pop("""resume_download"""						,  __UpperCAmelCase					)						,  local_files_only=kwargs.pop("""local_files_only"""						,  __UpperCAmelCase					)						,  use_auth_token=kwargs.pop("""use_auth_token"""						,  __UpperCAmelCase					)						,  revision=kwargs.pop("""revision"""						,  __UpperCAmelCase					)						,  )
             if speaker_embeddings_path is None:
                   logger.warning(
                       F"""`{os.path.join(__UpperCAmelCase						,  __UpperCAmelCase					)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""					)
                   UpperCAmelCase_=       None
             else:
                   with open(__UpperCAmelCase					) as speaker_embeddings_json:
                         UpperCAmelCase_=       json.load(__UpperCAmelCase					)
       else:
             UpperCAmelCase_=       None

       UpperCAmelCase_=       AutoTokenizer.from_pretrained(__UpperCAmelCase						,  **__UpperCAmelCase					)

       return cls(tokenizer=__UpperCAmelCase						,  speaker_embeddings=__UpperCAmelCase					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						Optional[int]						,  __UpperCAmelCase     :						Optional[Any]="speaker_embeddings_path.json"						,  __UpperCAmelCase     :						int="speaker_embeddings"						,  __UpperCAmelCase     :						bool = False						,  **__UpperCAmelCase     :						Union[str, Any]						,  )      ->			Dict:
       if self.speaker_embeddings is not None:
             os.makedirs(os.path.join(__UpperCAmelCase						,  __UpperCAmelCase						,  """v2"""					)						,  exist_ok=__UpperCAmelCase					)

             UpperCAmelCase_=       {}

             UpperCAmelCase_=       save_directory

             for prompt_key in self.speaker_embeddings:
                   if prompt_key != "repo_or_path":
                         UpperCAmelCase_=       self._load_voice_preset(__UpperCAmelCase					)

                         UpperCAmelCase_=       {}
                         for key in self.speaker_embeddings[prompt_key]:
                               np.save(
                                   os.path.join(
                                       embeddings_dict["""repo_or_path"""]						,  __UpperCAmelCase						,  F"""{prompt_key}_{key}"""					)						,  voice_preset[key]						,  allow_pickle=__UpperCAmelCase						,  )
                               UpperCAmelCase_=       os.path.join(__UpperCAmelCase						,  F"""{prompt_key}_{key}.npy"""					)

                         UpperCAmelCase_=       tmp_dict

             with open(os.path.join(__UpperCAmelCase						,  __UpperCAmelCase					)						,  """w"""					) as fp:
                   json.dump(__UpperCAmelCase						,  __UpperCAmelCase					)

       super().save_pretrained(__UpperCAmelCase						,  __UpperCAmelCase						,  **__UpperCAmelCase					)




 def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						str = None						,  **__UpperCAmelCase     :						List[str]					)      ->			List[Any]:
       UpperCAmelCase_=       self.speaker_embeddings[voice_preset]

       UpperCAmelCase_=       {}
       for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
             if key not in voice_preset_paths:
                   raise ValueError(
                       F"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."""					)

             UpperCAmelCase_=       get_file_from_repo(
                 self.speaker_embeddings.get("""repo_or_path"""						,  """/"""					)						,  voice_preset_paths[key]						,  subfolder=kwargs.pop("""subfolder"""						,  __UpperCAmelCase					)						,  cache_dir=kwargs.pop("""cache_dir"""						,  __UpperCAmelCase					)						,  force_download=kwargs.pop("""force_download"""						,  __UpperCAmelCase					)						,  proxies=kwargs.pop("""proxies"""						,  __UpperCAmelCase					)						,  resume_download=kwargs.pop("""resume_download"""						,  __UpperCAmelCase					)						,  local_files_only=kwargs.pop("""local_files_only"""						,  __UpperCAmelCase					)						,  use_auth_token=kwargs.pop("""use_auth_token"""						,  __UpperCAmelCase					)						,  revision=kwargs.pop("""revision"""						,  __UpperCAmelCase					)						,  )
             if path is None:
                   raise ValueError(
                       F"""`{os.path.join(self.speaker_embeddings.get("repo_or_path"						,  "/"					)						,  voice_preset_paths[key]					)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""					)

             UpperCAmelCase_=       np.load(__UpperCAmelCase					)

       return voice_preset_dict




 def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Optional[dict] = None					)      ->			Optional[int]:
       for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
             if key not in voice_preset:
                   raise ValueError(F"""Voice preset unrecognized, missing {key} as a key."""					)

             if not isinstance(voice_preset[key]						,  np.ndarray					):
                   raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]					)}D ndarray."""					)

             if len(voice_preset[key].shape					) != self.preset_shape[key]:
                   raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]					)}D ndarray."""					)




 def __call__(     self     :						str						,  __UpperCAmelCase     :						int=None						,  __UpperCAmelCase     :						Optional[int]=None						,  __UpperCAmelCase     :						str="pt"						,  __UpperCAmelCase     :						List[Any]=256						,  __UpperCAmelCase     :						Optional[int]=False						,  __UpperCAmelCase     :						Tuple=True						,  __UpperCAmelCase     :						Dict=False						,  **__UpperCAmelCase     :						List[Any]						,  )      ->			Dict:
       if voice_preset is not None and not isinstance(__UpperCAmelCase						,  __UpperCAmelCase					):
             if (
                 isinstance(__UpperCAmelCase						,  __UpperCAmelCase					)
                 and self.speaker_embeddings is not None
                 and voice_preset in self.speaker_embeddings
             ):
                   UpperCAmelCase_=       self._load_voice_preset(__UpperCAmelCase					)

             else:
                   if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) and not voice_preset.endswith(""".npz"""					):
                         UpperCAmelCase_=       voice_preset + """.npz"""

                   UpperCAmelCase_=       np.load(__UpperCAmelCase					)

       if voice_preset is not None:
             self._validate_voice_preset_dict(__UpperCAmelCase						,  **__UpperCAmelCase					)
             UpperCAmelCase_=       BatchFeature(data=__UpperCAmelCase						,  tensor_type=__UpperCAmelCase					)

       UpperCAmelCase_=       self.tokenizer(
           __UpperCAmelCase						,  return_tensors=__UpperCAmelCase						,  padding="""max_length"""						,  max_length=__UpperCAmelCase						,  return_attention_mask=__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase						,  **__UpperCAmelCase						,  )

       if voice_preset is not None:
             UpperCAmelCase_=       voice_preset

       return encoded_text









from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__A       =						'''\
@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}
}
'''

__A       =						'''\
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.
'''

__A       =						'''\
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 		lowercase						(			datasets.Metric):






	"""simple docstring"""


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




