

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()
lowerCAmelCase__       = logging.get_logger(__name__)

lowerCAmelCase__       = {
    "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",
}
lowerCAmelCase__       = [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  {}
  with open(__A   ,       "r"			) as file:
    for line_number, line in enumerate(__A			):
      UpperCAmelCase					      =  line.strip()
      if line:
        UpperCAmelCase					      =  line.split()
        UpperCAmelCase					      =  line_number
        UpperCAmelCase					      =  words[0]
        UpperCAmelCase					      =  value
  return result
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  for attribute in key.split("."			):
    UpperCAmelCase					      =  getattr(__A   ,       __A			)

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

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

    # let's reduce dimension
    UpperCAmelCase					      =  value[0]
  else:
    UpperCAmelCase					      =  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":
    UpperCAmelCase					      =  value
  elif weight_type == "weight_g":
    UpperCAmelCase					      =  value
  elif weight_type == "weight_v":
    UpperCAmelCase					      =  value
  elif weight_type == "bias":
    UpperCAmelCase					      =  value
  elif weight_type == "param":
    for attribute in hf_param_name.split("."			):
      UpperCAmelCase					      =  getattr(__A   ,       __A			)
    UpperCAmelCase					      =  value
  else:
    UpperCAmelCase					      =  value

  logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  UpperCAmelCase					      =  None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(__A			):
      UpperCAmelCase					      =  PARAM_MAPPING[full_name.split("."			)[-1]]
      UpperCAmelCase					      =  "param"

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

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


lowerCAmelCase__       = {
    "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(	__A   ,       __A   ,       __A=None   ,       __A=None			):
  UpperCAmelCase					      =  False
  for key, mapped_key in MAPPING.items():
    UpperCAmelCase					      =  "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]:
      UpperCAmelCase					      =  True
      if "*" in mapped_key:
        UpperCAmelCase					      =  name.split(__A			)[0].split("."			)[-2]
        UpperCAmelCase					      =  mapped_key.replace("*"   ,       __A			)
      if "weight_g" in name:
        UpperCAmelCase					      =  "weight_g"
      elif "weight_v" in name:
        UpperCAmelCase					      =  "weight_v"
      elif "bias" in name:
        UpperCAmelCase					      =  "bias"
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        UpperCAmelCase					      =  "weight"
      else:
        UpperCAmelCase					      =  None
      if hf_dict is not None:
        rename_dict(__A   ,       __A   ,       __A   ,       __A   ,       __A			)
      else:
        set_recursively(__A   ,       __A   ,       __A   ,       __A   ,       __A			)
      return is_used
  return is_used
def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  UpperCAmelCase					      =  []
  UpperCAmelCase					      =  fairseq_model.state_dict()

  UpperCAmelCase					      =  hf_model.wavaveca.feature_extractor

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

  logger.warning(F"Unused weights: {unused_weights}"			)
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  UpperCAmelCase					      =  full_name.split("conv_layers."			)[-1]
  UpperCAmelCase					      =  name.split("."			)
  UpperCAmelCase					      =  int(items[0]			)
  UpperCAmelCase					      =  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."			)
      UpperCAmelCase					      =  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."			)
      UpperCAmelCase					      =  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."			)
      UpperCAmelCase					      =  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."			)
      UpperCAmelCase					      =  value
      logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
  else:
    unused_weights.append(__A			)
@torch.no_grad()
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=None   ,       __A=None   ,       __A=True   ,       __A=False			):
  if config_path is not None:
    UpperCAmelCase					      =  WavaVecaConfig.from_pretrained(__A			)
  else:
    UpperCAmelCase					      =  WavaVecaConfig()

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

  elif is_finetuned:
    if dict_path:
      UpperCAmelCase					      =  Dictionary.load(__A			)

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

      # fairseq has the <pad> and <s> switched
      UpperCAmelCase					      =  0
      UpperCAmelCase					      =  1
      with open(__A   ,       "w"   ,       encoding="utf-8"			) as vocab_handle:
        json.dump(__A   ,       __A			)
      UpperCAmelCase					      =  WavaVecaCTCTokenizer(
          __A   ,       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=__A   ,       )
      UpperCAmelCase					      =  True if config.feat_extract_norm == "layer" else False
      UpperCAmelCase					      =  WavaVecaFeatureExtractor(
          feature_size=1   ,       sampling_rate=16000   ,       padding_value=0   ,       do_normalize=__A   ,       return_attention_mask=__A   ,       )
      UpperCAmelCase					      =  WavaVecaProcessor(feature_extractor=__A   ,       tokenizer=__A			)
      processor.save_pretrained(__A			)

    UpperCAmelCase					      =  WavaVecaForCTC(__A			)
  else:
    UpperCAmelCase					      =  WavaVecaForPreTraining(__A			)

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

    UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase					      =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,       task=__A			)

  UpperCAmelCase					      =  model[0].eval()

  recursively_load_weights(__A   ,       __A   ,       not is_finetuned			)

  hf_wavavec.save_pretrained(__A			)


if __name__ == "__main__":
      lowerCAmelCase__       = 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",
      )
      lowerCAmelCase__       = parser.parse_args()

      lowerCAmelCase__       = 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,
      )






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase__       = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
      from nltk import word_tokenize


lowerCAmelCase__       = "\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"

lowerCAmelCase__       = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"

lowerCAmelCase__       = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    'meteor': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric('meteor')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"






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


 def _UpperCamelCase	(						self		: int    )    ->      str:
   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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"]			,						reference_urls=[
           "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
           "https://en.wikipedia.org/wiki/METEOR",
       ]			,						)


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: List[Any]    )    ->      Dict:
   import nltk

   nltk.download("wordnet"    )
   if NLTK_VERSION >= version.Version("3.6.5"    ):
     nltk.download("punkt"    )
   if NLTK_VERSION >= version.Version("3.6.6"    ):
     nltk.download("omw-1.4"    )


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Dict			,						lowerCAmelCase__		: Any=0.9			,						lowerCAmelCase__		: Tuple=3			,						lowerCAmelCase__		: Optional[int]=0.5    )    ->      Any:
   if NLTK_VERSION >= version.Version("3.6.5"    ):
     UpperCAmelCase					      =  [
         meteor_score.single_meteor_score(
             word_tokenize(lowerCAmelCase__    )			,						word_tokenize(lowerCAmelCase__    )			,						alpha=lowerCAmelCase__			,						beta=lowerCAmelCase__			,						gamma=lowerCAmelCase__    )
         for ref, pred in zip(lowerCAmelCase__			,						lowerCAmelCase__    )
     ]
   else:
     UpperCAmelCase					      =  [
         meteor_score.single_meteor_score(lowerCAmelCase__			,						lowerCAmelCase__			,						alpha=lowerCAmelCase__			,						beta=lowerCAmelCase__			,						gamma=lowerCAmelCase__    )
         for ref, pred in zip(lowerCAmelCase__			,						lowerCAmelCase__    )
     ]

   return {"meteor": np.mean(lowerCAmelCase__    )}



