


"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''mctct'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=8065      ,   SCREAMING_SNAKE_CASE=1536      ,   SCREAMING_SNAKE_CASE=36      ,   SCREAMING_SNAKE_CASE=6144      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=384      ,   SCREAMING_SNAKE_CASE=920      ,   SCREAMING_SNAKE_CASE=1e-5      ,   SCREAMING_SNAKE_CASE=0.3      ,   SCREAMING_SNAKE_CASE="relu"      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=0.3      ,   SCREAMING_SNAKE_CASE=0.3      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=0      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=0.3      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=(7,)      ,   SCREAMING_SNAKE_CASE=(3,)      ,   SCREAMING_SNAKE_CASE=80      ,   SCREAMING_SNAKE_CASE=1      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE="sum"      ,   SCREAMING_SNAKE_CASE=False      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Dict:
      super().__init__(**SCREAMING_SNAKE_CASE      ,   pad_token_id=SCREAMING_SNAKE_CASE      ,   bos_token_id=SCREAMING_SNAKE_CASE      ,   eos_token_id=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		vocab_size
      _lowerCamelCase	:					Optional[Any]   =		hidden_size
      _lowerCamelCase	:					int   =		num_hidden_layers
      _lowerCamelCase	:					Tuple   =		intermediate_size
      _lowerCamelCase	:					Any   =		num_attention_heads
      _lowerCamelCase	:					List[str]   =		attention_head_dim
      _lowerCamelCase	:					Any   =		max_position_embeddings
      _lowerCamelCase	:					int   =		layer_norm_eps
      _lowerCamelCase	:					str   =		layerdrop
      _lowerCamelCase	:					Optional[Any]   =		hidden_act
      _lowerCamelCase	:					int   =		initializer_range
      _lowerCamelCase	:					List[str]   =		hidden_dropout_prob
      _lowerCamelCase	:					Union[str, Any]   =		attention_probs_dropout_prob
      _lowerCamelCase	:					Optional[Any]   =		pad_token_id
      _lowerCamelCase	:					List[str]   =		bos_token_id
      _lowerCamelCase	:					int   =		eos_token_id
      _lowerCamelCase	:					int   =		conv_glu_dim
      _lowerCamelCase	:					Any   =		conv_dropout
      _lowerCamelCase	:					int   =		num_conv_layers
      _lowerCamelCase	:					str   =		input_feat_per_channel
      _lowerCamelCase	:					int   =		input_channels
      _lowerCamelCase	:					List[Any]   =		conv_channels
      _lowerCamelCase	:					Optional[int]   =		ctc_loss_reduction
      _lowerCamelCase	:					Union[str, Any]   =		ctc_zero_infinity

      # prevents config testing fail with exporting to json
      _lowerCamelCase	:					List[Any]   =		list(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		list(SCREAMING_SNAKE_CASE)

      if len(self.conv_kernel) != self.num_conv_layers:
         raise ValueError(
             """Configuration for convolutional module is incorrect. """
             """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
             F'but is `len(config.conv_kernel) = {len(self.conv_kernel)}`, '
             F'`config.num_conv_layers = {self.num_conv_layers}`.')





"""simple docstring"""
from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


UpperCAmelCase				=						"""\
@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}
}
"""

UpperCAmelCase				=						"""\
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.
"""

UpperCAmelCase				=						"""\
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				):





   def 		UpperCamelCase_  (							self)  ->  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      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = 1      ,   SCREAMING_SNAKE_CASE = 4      ,   )  ->  Dict[str, float]:
      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)
      }

