

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase     : Dict       =						logging.get_logger(__name__)

__lowercase     : Dict       =						{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}



class 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      Optional[Any]					=   '''sew'''





  def __init__(  self   ,SCREAMING_SNAKE_CASE_=32   ,SCREAMING_SNAKE_CASE_=768   ,SCREAMING_SNAKE_CASE_=12   ,SCREAMING_SNAKE_CASE_=12   ,SCREAMING_SNAKE_CASE_=3072   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_="gelu"   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=0.0   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=0.1   ,SCREAMING_SNAKE_CASE_=0.02   ,SCREAMING_SNAKE_CASE_=1E-5   ,SCREAMING_SNAKE_CASE_="group"   ,SCREAMING_SNAKE_CASE_="gelu"   ,SCREAMING_SNAKE_CASE_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,SCREAMING_SNAKE_CASE_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,SCREAMING_SNAKE_CASE_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,SCREAMING_SNAKE_CASE_=False   ,SCREAMING_SNAKE_CASE_=128   ,SCREAMING_SNAKE_CASE_=16   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_=0.05   ,SCREAMING_SNAKE_CASE_=10   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=0.0   ,SCREAMING_SNAKE_CASE_=10   ,SCREAMING_SNAKE_CASE_=0   ,SCREAMING_SNAKE_CASE_="mean"   ,SCREAMING_SNAKE_CASE_=False   ,SCREAMING_SNAKE_CASE_=False   ,SCREAMING_SNAKE_CASE_=256   ,SCREAMING_SNAKE_CASE_=0   ,SCREAMING_SNAKE_CASE_=1   ,SCREAMING_SNAKE_CASE_=2   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      super().__init__(**SCREAMING_SNAKE_CASE_   ,pad_token_id=SCREAMING_SNAKE_CASE_   ,bos_token_id=SCREAMING_SNAKE_CASE_   ,eos_token_id=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[Any]        =		hidden_size
      snake_case							:		List[Any]        =		feat_extract_norm
      snake_case							:		List[str]        =		feat_extract_activation
      snake_case							:		int        =		list(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Tuple        =		list(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		list(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		conv_bias
      snake_case							:		Any        =		num_conv_pos_embeddings
      snake_case							:		List[str]        =		num_conv_pos_embedding_groups
      snake_case							:		Union[str, Any]        =		len(self.conv_dim    )
      snake_case							:		Optional[Any]        =		num_hidden_layers
      snake_case							:		List[str]        =		intermediate_size
      snake_case							:		List[Any]        =		squeeze_factor
      snake_case							:		Dict        =		hidden_act
      snake_case							:		Tuple        =		num_attention_heads
      snake_case							:		int        =		hidden_dropout
      snake_case							:		Tuple        =		attention_dropout
      snake_case							:		Tuple        =		activation_dropout
      snake_case							:		List[str]        =		feat_proj_dropout
      snake_case							:		Tuple        =		final_dropout
      snake_case							:		Tuple        =		layerdrop
      snake_case							:		Any        =		layer_norm_eps
      snake_case							:		Union[str, Any]        =		initializer_range
      snake_case							:		Optional[int]        =		vocab_size

      if (
          (len(self.conv_stride    ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
          or (len(self.conv_dim    ) != self.num_feat_extract_layers)
      ):
          raise ValueError(
              """Configuration for convolutional layers is incorrect."""
              """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
              F"""but is `len(config.conv_dim) = {len(self.conv_dim    )}`, `len(config.conv_stride)"""
              F"""= {len(self.conv_stride    )}`, `len(config.conv_kernel) = {len(self.conv_kernel    )}`."""    )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      snake_case							:		int        =		apply_spec_augment
      snake_case							:		Any        =		mask_time_prob
      snake_case							:		int        =		mask_time_length
      snake_case							:		Any        =		mask_time_min_masks
      snake_case							:		List[Any]        =		mask_feature_prob
      snake_case							:		Dict        =		mask_feature_length
      snake_case							:		Any        =		mask_feature_min_masks

      # ctc loss
      snake_case							:		List[str]        =		ctc_loss_reduction
      snake_case							:		Union[str, Any]        =		ctc_zero_infinity

      # sequence classification
      snake_case							:		int        =		use_weighted_layer_sum
      snake_case							:		List[str]        =		classifier_proj_size





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return functools.reduce(operator.mul   ,self.conv_stride   ,1    )










"""simple docstring"""



import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_					=     """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_					=     """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_					=     """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""






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





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return datasets.MetricInfo(
         description=_DESCRIPTION   ,		citation=_CITATION   ,		inputs_description=_KWARGS_DESCRIPTION   ,		features=datasets.Features(
             {
                 '''predictions''': datasets.Value('''string'''   ,		id='''sequence'''    ),
                 '''references''': datasets.Value('''string'''   ,		id='''sequence'''    ),
             }    )   ,		codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge''']   ,		reference_urls=[
             '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
             '''https://github.com/google-research/google-research/tree/master/rouge''',
         ]   ,		)







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




     if rouge_types is None:
         __A       :		Dict							=	['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

     __A       :		Dict							=	rouge_scorer.RougeScorer(rouge_types=__lowerCamelCase   ,		use_stemmer=__lowerCamelCase    )
     if use_aggregator:
         __A       :		Dict							=	scoring.BootstrapAggregator()
     else:
         __A       :		Optional[Any]							=	[]

     for ref, pred in zip(__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		List[Any]							=	scorer.score(__lowerCamelCase   ,		__lowerCamelCase    )
         if use_aggregator:
             aggregator.add_scores(__lowerCamelCase    )
         else:
             scores.append(__lowerCamelCase    )

     if use_aggregator:
         __A       :		Optional[Any]							=	aggregator.aggregate()
     else:
         __A       :		List[Any]							=	{}
         for key in scores[0]:
             __A       :		Tuple							=	[score[key] for score in scores]

     return result




