



"""simple docstring"""





import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


lowerCamelCase      =		"""\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

lowerCamelCase      =		"""\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

lowerCamelCase      =		"""
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,      _KWARGS_DESCRIPTION   )
class    lowercase__     (					datasets.Metric   ):







       '''simple docstring'''


       def   lowercase__       (							self    :						int					)				->		List[Any]:


             '''simple docstring'''




             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"					),
                     }					)	,       codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"]	,       reference_urls=[
                     "https://en.wikipedia.org/wiki/BLEU",
                     "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
                 ]	,       )







       def   lowercase__       (							self    :						Any	,       _UpperCAmelCase    :						Optional[int]	,       _UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						Tuple=4	,       _UpperCAmelCase    :						Union[str, Any]=False					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							compute_bleu(
                 reference_corpus=_UpperCAmelCase	,       translation_corpus=_UpperCAmelCase	,       max_order=_UpperCAmelCase	,       smooth=_UpperCAmelCase					)
             ((UpperCAmelCase_)				,					(UpperCAmelCase_)				,					(UpperCAmelCase_)				,					(UpperCAmelCase_)				,					(UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							score
             return {
                 "bleu": bleu,
                 "precisions": precisions,
                 "brevity_penalty": bp,
                 "length_ratio": ratio,
                 "translation_length": translation_length,
                 "reference_length": reference_length,
             }









"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """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""",
}
lowerCamelCase      =		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      for attribute in key.split("."   ):
            UpperCAmelCase_    				=							getattr(lowerCAmelCase__			,    lowerCAmelCase__   )

      if weight_type is not None:
            UpperCAmelCase_    				=							getattr(lowerCAmelCase__			,    lowerCAmelCase__   ).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
      elif weight_type == "running_mean":
            UpperCAmelCase_    				=							value
      elif weight_type == "running_var":
            UpperCAmelCase_    				=							value
      elif weight_type == "num_batches_tracked":
            UpperCAmelCase_    				=							value
      elif weight_type == "inv_freq":
            UpperCAmelCase_    				=							value
      else:
            UpperCAmelCase_    				=							value

      logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							fairseq_model.state_dict()

      UpperCAmelCase_    				=							hf_model.wavaveca_conformer.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase_    				=							False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    hf_model.config.feat_extract_norm == "group"			,    )
                  UpperCAmelCase_    				=							True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase_    				=							"wav2vec2_conformer." + 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(lowerCAmelCase__   )[0].split("."   )[-2]
                                    UpperCAmelCase_    				=							mapped_key.replace("*"			,    lowerCAmelCase__   )
                              if "pos_bias_u" in name:
                                    UpperCAmelCase_    				=							None
                              elif "pos_bias_v" in name:
                                    UpperCAmelCase_    				=							None
                              elif "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"
                              elif "running_mean" in name:
                                    UpperCAmelCase_    				=							"running_mean"
                              elif "inv_freq" in name:
                                    UpperCAmelCase_    				=							"inv_freq"
                              elif "running_var" in name:
                                    UpperCAmelCase_    				=							"running_var"
                              elif "num_batches_tracked" in name:
                                    UpperCAmelCase_    				=							"num_batches_tracked"
                              else:
                                    UpperCAmelCase_    				=							None
                              set_recursively(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
                        continue
            if not is_used:
                  unused_weights.append(lowerCAmelCase__   )

      logger.warning(f"""Unused weights: {unused_weights}"""   )
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      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(lowerCAmelCase__   )
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=None			,    lowerCAmelCase__=None			,    lowerCAmelCase__=True   ):
      if config_path is not None:
            UpperCAmelCase_    				=							WavaVecaConformerConfig.from_pretrained(lowerCAmelCase__			,    hidden_act="swish"   )
      else:
            UpperCAmelCase_    				=							WavaVecaConformerConfig()

      if "rope" in checkpoint_path:
            UpperCAmelCase_    				=							"rotary"

      if is_finetuned:
            if dict_path:
                  UpperCAmelCase_    				=							Dictionary.load(lowerCAmelCase__   )

                  # 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(lowerCAmelCase__			,    "vocab.json"   )
                  if not os.path.isdir(lowerCAmelCase__   ):
                        logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowerCAmelCase__   )   )
                        return
                  os.makedirs(lowerCAmelCase__			,    exist_ok=lowerCAmelCase__   )
                  UpperCAmelCase_    				=							target_dict.indices

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

            UpperCAmelCase_    				=							WavaVecaConformerForCTC(lowerCAmelCase__   )
      else:
            UpperCAmelCase_    				=							WavaVecaConformerForPreTraining(lowerCAmelCase__   )

      if is_finetuned:
            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(lowerCAmelCase__   )

            UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,    task=lowerCAmelCase__   )

      UpperCAmelCase_    				=							model[0].eval()

      recursively_load_weights(lowerCAmelCase__			,    lowerCAmelCase__			,    not is_finetuned   )

      hf_wavavec.save_pretrained(lowerCAmelCase__   )


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"""
  )
  lowerCamelCase      =		parser.parse_args()
  convert_wavaveca_conformer_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )




