






"""simple docstring"""




import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCamelCase							       =    """
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@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\",
}
"""

UpperCamelCase							       =    """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


UpperCamelCase							       =    """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""






def        lowerCAmelCase (      UpperCamelCase_:							str					)       ->							Any:







  '''simple docstring'''







  def remove_articles(UpperCamelCase_:							int					):
    _a							=	re.compile(r"\b(a|an|the)\b"     , re.UNICODE					)
    return re.sub(UpperCamelCase_     , " "     , UpperCamelCase_					)

  def white_space_fix(UpperCamelCase_:							Union[str, Any]					):
    return " ".join(text.split()					)

  def remove_punc(UpperCamelCase_:							List[str]					):
    _a							=	set(string.punctuation					)
    return "".join(ch for ch in text if ch not in exclude					)

  def lower(UpperCamelCase_:							Optional[int]					):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_					)					)					)					)






def        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							List[Any]					)       ->							Optional[int]:







  '''simple docstring'''







  return int(normalize_answer(UpperCamelCase_					) == normalize_answer(UpperCamelCase_					)					)






def        lowerCAmelCase (      UpperCamelCase_:							List[Any]     , UpperCamelCase_:							Any					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	[any(compute_exact(UpperCamelCase_     , UpperCamelCase_					) for ref in refs					) for pred, refs in zip(UpperCamelCase_     , UpperCamelCase_					)]
  return (sum(UpperCamelCase_					) / len(UpperCamelCase_					)) * 100






def        lowerCAmelCase (      UpperCamelCase_:							Tuple     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							str     , UpperCamelCase_:							Union[str, Any]					)       ->							str:







  '''simple docstring'''







  _a							=	[rgram for rgrams in rgramslist for rgram in rgrams]
  _a							=	Counter(UpperCamelCase_					)

  _a							=	Counter(UpperCamelCase_					)
  _a							=	Counter()
  for sgram, scount in sgramcounter.items():
    _a							=	scount * numref

  _a							=	Counter(UpperCamelCase_					)
  _a							=	Counter()
  for cgram, ccount in cgramcounter.items():
    _a							=	ccount * numref

  # KEEP
  _a							=	sgramcounter_rep & cgramcounter_rep
  _a							=	keepgramcounter_rep & rgramcounter
  _a							=	sgramcounter_rep & rgramcounter

  _a							=	0
  _a							=	0
  for keepgram in keepgramcountergood_rep:
    keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
    # Fix an alleged bug [2] in the keep score computation.
    # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
    keeptmpscorea += keepgramcountergood_rep[keepgram]
  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  #      a target exactly.
  _a							=	1
  _a							=	1
  if len(UpperCamelCase_					) > 0:
    _a							=	keeptmpscorea / len(UpperCamelCase_					)
  if len(UpperCamelCase_					) > 0:
    # Fix an alleged bug [2] in the keep score computation.
    # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
    _a							=	keeptmpscorea / sum(keepgramcounterall_rep.values()					)
  _a							=	0
  if keepscore_precision > 0 or keepscore_recall > 0:
    _a							=	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

  # DELETION
  _a							=	sgramcounter_rep - cgramcounter_rep
  _a							=	delgramcounter_rep - rgramcounter
  _a							=	sgramcounter_rep - rgramcounter
  _a							=	0
  _a							=	0
  for delgram in delgramcountergood_rep:
    deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
    deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  # a target exactly.
  _a							=	1
  if len(UpperCamelCase_					) > 0:
    _a							=	deltmpscorea / len(UpperCamelCase_					)

  # ADDITION
  _a							=	set(UpperCamelCase_					) - set(UpperCamelCase_					)
  _a							=	set(UpperCamelCase_					) & set(UpperCamelCase_					)
  _a							=	set(UpperCamelCase_					) - set(UpperCamelCase_					)

  _a							=	0
  for addgram in addgramcountergood:
    addtmpscore += 1

  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  # a target exactly.
  _a							=	1
  _a							=	1
  if len(UpperCamelCase_					) > 0:
    _a							=	addtmpscore / len(UpperCamelCase_					)
  if len(UpperCamelCase_					) > 0:
    _a							=	addtmpscore / len(UpperCamelCase_					)
  _a							=	0
  if addscore_precision > 0 or addscore_recall > 0:
    _a							=	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)






def        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							List[Any]     , UpperCamelCase_:							Any					)       ->							List[str]:







  '''simple docstring'''







  _a							=	len(UpperCamelCase_					)

  _a							=	ssent.split(" "					)
  _a							=	csent.split(" "					)
  _a							=	[]
  _a							=	[]
  _a							=	[]
  _a							=	[]
  _a							=	[]
  _a							=	[]

  _a							=	[]
  _a							=	[]
  _a							=	[]
  _a							=	[]
  for rsent in rsents:
    _a							=	rsent.split(" "					)
    _a							=	[]
    _a							=	[]
    _a							=	[]
    ragramslist.append(UpperCamelCase_					)
    for i in range(0     , len(UpperCamelCase_					) - 1					):
      if i < len(UpperCamelCase_					) - 1:
        _a							=	ragrams[i] + " " + ragrams[i + 1]
        ragrams.append(UpperCamelCase_					)
      if i < len(UpperCamelCase_					) - 2:
        _a							=	ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
        ragrams.append(UpperCamelCase_					)
      if i < len(UpperCamelCase_					) - 3:
        _a							=	ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
        ragrams.append(UpperCamelCase_					)
    ragramslist.append(UpperCamelCase_					)
    ragramslist.append(UpperCamelCase_					)
    ragramslist.append(UpperCamelCase_					)

  for i in range(0     , len(UpperCamelCase_					) - 1					):
    if i < len(UpperCamelCase_					) - 1:
      _a							=	sagrams[i] + " " + sagrams[i + 1]
      sagrams.append(UpperCamelCase_					)
    if i < len(UpperCamelCase_					) - 2:
      _a							=	sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
      sagrams.append(UpperCamelCase_					)
    if i < len(UpperCamelCase_					) - 3:
      _a							=	sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
      sagrams.append(UpperCamelCase_					)

  for i in range(0     , len(UpperCamelCase_					) - 1					):
    if i < len(UpperCamelCase_					) - 1:
      _a							=	cagrams[i] + " " + cagrams[i + 1]
      cagrams.append(UpperCamelCase_					)
    if i < len(UpperCamelCase_					) - 2:
      _a							=	cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
      cagrams.append(UpperCamelCase_					)
    if i < len(UpperCamelCase_					) - 3:
      _a							=	cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
      cagrams.append(UpperCamelCase_					)

  ((_a)			,	(_a)			,	(_a))							=	SARIngram(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
  ((_a)			,	(_a)			,	(_a))							=	SARIngram(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
  ((_a)			,	(_a)			,	(_a))							=	SARIngram(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
  ((_a)			,	(_a)			,	(_a))							=	SARIngram(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
  _a							=	sum([keepascore, keepascore, keepascore, keepascore]					) / 4
  _a							=	sum([delascore, delascore, delascore, delascore]					) / 4
  _a							=	sum([addascore, addascore, addascore, addascore]					) / 4
  _a							=	(avgkeepscore + avgdelscore + avgaddscore) / 3
  return finalscore






def        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							bool = True     , UpperCamelCase_:							str = "13a"     , UpperCamelCase_:							bool = True					)       ->							Union[str, Any]:







  '''simple docstring'''







  if lowercase:
    _a							=	sentence.lower()

  if tokenizer in ["13a", "intl"]:
    if version.parse(sacrebleu.__version__					).major >= 2:
      _a							=	sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase_					)()(UpperCamelCase_					)
    else:
      _a							=	sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase_					)
  elif tokenizer == "moses":
    _a							=	sacremoses.MosesTokenizer().tokenize(UpperCamelCase_     , return_str=UpperCamelCase_     , escape=UpperCamelCase_					)
  elif tokenizer == "penn":
    _a							=	sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase_     , return_str=UpperCamelCase_					)
  else:
    _a							=	sentence

  if not return_str:
    _a							=	normalized_sent.split()

  return normalized_sent






def        lowerCAmelCase (      UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							List[Any]     , UpperCamelCase_:							Optional[int]					)       ->							Tuple:







  '''simple docstring'''







  if not (len(UpperCamelCase_					) == len(UpperCamelCase_					) == len(UpperCamelCase_					)):
    raise ValueError("Sources length must match predictions and references lengths."					)
  _a							=	0
  for src, pred, refs in zip(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					):
    sari_score += SARIsent(normalize(UpperCamelCase_					)     , normalize(UpperCamelCase_					)     , [normalize(UpperCamelCase_					) for sent in refs]					)
  _a							=	sari_score / len(UpperCamelCase_					)
  return 100 * sari_score






def        lowerCAmelCase (      UpperCamelCase_:							Optional[Any]     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Optional[Any]="exp"     , UpperCamelCase_:							str=None     , UpperCamelCase_:							List[Any]=False     , UpperCamelCase_:							str=False     , UpperCamelCase_:							int=False     , )       ->							int:







  '''simple docstring'''







  _a							=	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"					)
  _a							=	[[refs[i] for refs in references] for i in range(UpperCamelCase_					)]
  _a							=	sacrebleu.corpus_bleu(
      UpperCamelCase_     , UpperCamelCase_     , smooth_method=UpperCamelCase_     , smooth_value=UpperCamelCase_     , force=UpperCamelCase_     , lowercase=UpperCamelCase_     , use_effective_order=UpperCamelCase_     , )
  return output.score




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






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        return datasets.MetricInfo(
            description=_DESCRIPTION     ,     citation=_CITATION     ,     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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
                "https://github.com/cocoxu/simplification/blob/master/SARI.py",
                "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
                "https://github.com/mjpost/sacreBLEU",
            ]     ,     reference_urls=[
                "https://www.aclweb.org/anthology/Q16-1029.pdf",
                "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 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_	)	->str:







        '''simple docstring'''


        _a							=	{}
        result.update({"sari": compute_sari(sources=a_     ,     predictions=a_     ,     references=a_	)}	)
        result.update({"sacrebleu": compute_sacrebleu(predictions=a_     ,     references=a_	)}	)
        result.update({"exact": compute_em(predictions=a_     ,     references=a_	)}	)
        return result










"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCamelCase							       =    logging.get_logger(__name__)

UpperCamelCase							       =    {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
UpperCamelCase							       =    [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]






def        lowerCAmelCase (      UpperCamelCase_:							List[str]					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	{}
  with open(UpperCamelCase_     , "r"					) as file:
    for line_number, line in enumerate(UpperCamelCase_					):
      _a							=	line.strip()
      if line:
        _a							=	line.split()
        _a							=	line_number
        _a							=	words[0]
        _a							=	value
  return result






def        lowerCAmelCase (      UpperCamelCase_:							List[str]     , UpperCamelCase_:							Dict     , UpperCamelCase_:							List[str]     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Union[str, Any]					)       ->							Union[str, Any]:







  '''simple docstring'''







  for attribute in key.split("."					):
    _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					)

  _a							=	None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(UpperCamelCase_					):
      _a							=	PARAM_MAPPING[full_name.split("."					)[-1]]
      _a							=	"param"

  if weight_type is not None and weight_type != "param":
    _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					).shape
  elif weight_type is not None and weight_type == "param":
    _a							=	hf_pointer
    for attribute in hf_param_name.split("."					):
      _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					)
    _a							=	shape_pointer.shape

    # let's reduce dimension
    _a							=	value[0]
  else:
    _a							=	hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
        f''' {value.shape} for {full_name}'''					)

  if weight_type == "weight":
    _a							=	value
  elif weight_type == "weight_g":
    _a							=	value
  elif weight_type == "weight_v":
    _a							=	value
  elif weight_type == "bias":
    _a							=	value
  elif weight_type == "param":
    for attribute in hf_param_name.split("."					):
      _a							=	getattr(UpperCamelCase_     , UpperCamelCase_					)
    _a							=	value
  else:
    _a							=	value

  logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''					)






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							Any     , UpperCamelCase_:							Union[str, Any]					)       ->							str:







  '''simple docstring'''







  _a							=	None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(UpperCamelCase_					):
      _a							=	PARAM_MAPPING[full_name.split("."					)[-1]]
      _a							=	"param"

  if weight_type is not None and weight_type != "param":
    _a							=	".".join([key, weight_type]					)
  elif weight_type is not None and weight_type == "param":
    _a							=	".".join([key, hf_param_name]					)
  else:
    _a							=	key

  _a							=	value if "lm_head" in full_key else value[0]


UpperCamelCase							       =    {
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}






def        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							int=None     , UpperCamelCase_:							Optional[int]=None					)       ->							Tuple:







  '''simple docstring'''







  _a							=	False
  for key, mapped_key in MAPPING.items():
    _a							=	"wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split("w2v_model."					)[-1] == name.split("."					)[0]:
      _a							=	True
      if "*" in mapped_key:
        _a							=	name.split(UpperCamelCase_					)[0].split("."					)[-2]
        _a							=	mapped_key.replace("*"     , UpperCamelCase_					)
      if "weight_g" in name:
        _a							=	"weight_g"
      elif "weight_v" in name:
        _a							=	"weight_v"
      elif "bias" in name:
        _a							=	"bias"
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        _a							=	"weight"
      else:
        _a							=	None
      if hf_dict is not None:
        rename_dict(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
      else:
        set_recursively(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
      return is_used
  return is_used






def        lowerCAmelCase (      UpperCamelCase_:							Dict     , UpperCamelCase_:							Dict     , UpperCamelCase_:							Dict					)       ->							Dict:







  '''simple docstring'''







  _a							=	[]
  _a							=	fairseq_model.state_dict()

  _a							=	hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    _a							=	False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , hf_model.config.feat_extract_norm == "group"     , )
      _a							=	True
    else:
      _a							=	load_wavaveca_layer(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
    if not is_used:
      unused_weights.append(UpperCamelCase_					)

  logger.warning(f'''Unused weights: {unused_weights}'''					)






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]     , UpperCamelCase_:							List[Any]     , UpperCamelCase_:							int     , UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							Optional[int]					)       ->							Dict:







  '''simple docstring'''







  _a							=	full_name.split("conv_layers."					)[-1]
  _a							=	name.split("."					)
  _a							=	int(items[0]					)
  _a							=	int(items[1]					)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''					)
      _a							=	value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''					)
      _a							=	value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''					)
      _a							=	value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f'''{full_name} has size {value.shape}, but'''
            f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''					)
      _a							=	value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
  else:
    unused_weights.append(UpperCamelCase_					)






@torch.no_grad()
def        lowerCAmelCase (      UpperCamelCase_:							Tuple     , UpperCamelCase_:							List[str]     , UpperCamelCase_:							int=None     , UpperCamelCase_:							int=None     , UpperCamelCase_:							Optional[Any]=True     , UpperCamelCase_:							Any=False					)       ->							Optional[Any]:







  '''simple docstring'''







  if config_path is not None:
    _a							=	WavaVecaConfig.from_pretrained(UpperCamelCase_					)
  else:
    _a							=	WavaVecaConfig()

  if is_seq_class:
    _a							=	read_txt_into_dict(UpperCamelCase_					)
    _a							=	idalabel
    _a							=	WavaVecaForSequenceClassification(UpperCamelCase_					)
    _a							=	WavaVecaFeatureExtractor(
        feature_size=1     , sampling_rate=16000     , padding_value=0     , do_normalize=UpperCamelCase_     , return_attention_mask=UpperCamelCase_     , )
    feature_extractor.save_pretrained(UpperCamelCase_					)

  elif is_finetuned:
    if dict_path:
      _a							=	Dictionary.load(UpperCamelCase_					)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _a							=	target_dict.pad_index
      _a							=	target_dict.bos_index
      _a							=	target_dict.eos_index
      _a							=	len(target_dict.symbols					)
      _a							=	os.path.join(UpperCamelCase_     , "vocab.json"					)
      if not os.path.isdir(UpperCamelCase_					):
        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCamelCase_					)					)
        return
      os.makedirs(UpperCamelCase_     , exist_ok=UpperCamelCase_					)
      _a							=	target_dict.indices

      # fairseq has the <pad> and <s> switched
      _a							=	0
      _a							=	1
      with open(UpperCamelCase_     , "w"     , encoding="utf-8"					) as vocab_handle:
        json.dump(UpperCamelCase_     , UpperCamelCase_					)
      _a							=	WavaVecaCTCTokenizer(
          UpperCamelCase_     , unk_token=target_dict.unk_word     , pad_token=target_dict.pad_word     , bos_token=target_dict.bos_word     , eos_token=target_dict.eos_word     , word_delimiter_token="|"     , do_lower_case=UpperCamelCase_     , )
      _a							=	True if config.feat_extract_norm == "layer" else False
      _a							=	WavaVecaFeatureExtractor(
          feature_size=1     , sampling_rate=16000     , padding_value=0     , do_normalize=UpperCamelCase_     , return_attention_mask=UpperCamelCase_     , )
      _a							=	WavaVecaProcessor(feature_extractor=UpperCamelCase_     , tokenizer=UpperCamelCase_					)
      processor.save_pretrained(UpperCamelCase_					)

    _a							=	WavaVecaForCTC(UpperCamelCase_					)
  else:
    _a							=	WavaVecaForPreTraining(UpperCamelCase_					)

  if is_finetuned or is_seq_class:
    _a			,	_a			,	_a							=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]     , arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					)}					)
  else:
    _a							=	argparse.Namespace(task="audio_pretraining"					)
    _a							=	fairseq.tasks.setup_task(UpperCamelCase_					)

    _a			,	_a			,	_a							=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     , task=UpperCamelCase_					)

  _a							=	model[0].eval()

  recursively_load_weights(UpperCamelCase_     , UpperCamelCase_     , not is_finetuned					)

  hf_wavavec.save_pretrained(UpperCamelCase_					)


if __name__ == "__main__":
     UpperCamelCase							       =    argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     parser.add_argument(
         """--is_seq_class""",
         action="""store_true""",
         help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
     )
     UpperCamelCase							       =    parser.parse_args()

     UpperCamelCase							       =    not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )


