



'''simple docstring'''


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_lowerCamelCase	:       Any      	=	logging.get_logger(__name__)

_lowerCamelCase	:       int      	=	{
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
_lowerCamelCase	:       List[Any]      	=	[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    )       ->	List[Any]:






   """simple docstring"""
   for attribute in key.split('.'    ):
      UpperCamelCase					=  getattr(A__    ,						A__    )

   if weight_type is not None:
      UpperCamelCase					=  getattr(A__    ,						A__    ).shape
   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
   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__    )       ->	Dict:






   """simple docstring"""
   UpperCamelCase					=  []
   UpperCamelCase					=  fairseq_model.state_dict()

   UpperCamelCase					=  hf_model.unispeech_sat.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:
         for key, mapped_key in MAPPING.items():
            UpperCamelCase					=  'unispeech_sat.' + 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]:
               if "layer_norm_for_extract" in name and (".".join(name.split('.'    )[:-1]    ) != key):
                  # special case since naming is very similar
                  continue
               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
               set_recursively(A__    ,						A__    ,						A__    ,						A__    ,						A__    )
            continue
      if not is_used:
         unused_weights.append(A__    )

   logger.warning(F"""Unused weights: {unused_weights}"""    )







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    )       ->	Dict:






   """simple docstring"""
   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[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[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    )       ->	int:






   """simple docstring"""
   if config_path is not None:
      UpperCamelCase					=  UniSpeechSatConfig.from_pretrained(A__    )
   else:
      UpperCamelCase					=  UniSpeechSatConfig()

   UpperCamelCase					=  ''

   if is_finetuned:
      UpperCamelCase					=  UniSpeechSatForCTC(A__    )
   else:
      UpperCamelCase					=  UniSpeechSatForPreTraining(A__    )

   UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
       [checkpoint_path]    ,						arg_overrides={'data': '/'.join(dict_path.split('/'    )[:-1]    )}    )
   UpperCamelCase					=  model[0].eval()

   recursively_load_weights(A__    ,						A__    )

   hf_wavavec.save_pretrained(A__    )


if __name__ == "__main__":
   _lowerCamelCase	:       Tuple      	=	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"
   )
   _lowerCamelCase	:       Any      	=	parser.parse_args()
   convert_unispeech_sat_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )






'''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


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

_lowerCamelCase	:       Optional[int]      	=	"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

_lowerCamelCase	:       str      	=	"\nCalculates average rouge scores for a list of hypotheses and 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    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    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))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"

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






    """simple docstring"""





    def  A       (  self      :					Union[str, Any] ):


       """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  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[Any]=False ):


       """simple docstring"""







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

       UpperCamelCase					=  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase__  ,						use_stemmer=UpperCamelCase__ )
       if use_aggregator:
          UpperCamelCase					=  scoring.BootstrapAggregator()
       else:
          UpperCamelCase					=  []

       for ref, pred in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
          UpperCamelCase					=  scorer.score(UpperCamelCase__  ,						UpperCamelCase__ )
          if use_aggregator:
             aggregator.add_scores(UpperCamelCase__ )
          else:
             scores.append(UpperCamelCase__ )

       if use_aggregator:
          UpperCamelCase					=  aggregator.aggregate()
       else:
          UpperCamelCase					=  {}
          for key in scores[0]:
             UpperCamelCase					=  [score[key] for score in scores]

       return result

