





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowercase  :   Optional[Any]						      =logging.get_logger(__name__)

__lowercase  :   Optional[Any]						      ={
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   for attribute in key.split("."  ):
      UpperCAmelCase_											=getattr(lowercase__  ,    lowercase__  )

   if weight_type is not None:
      UpperCAmelCase_											=getattr(lowercase__  ,    lowercase__  ).shape
   else:
      UpperCAmelCase_											=hf_pointer.shape

   assert hf_shape == value.shape, (
       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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=[]
   UpperCAmelCase_											=fairseq_model.state_dict()

   UpperCAmelCase_											=hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      UpperCAmelCase_											=False
      if "conv_layers" in name:
         load_conv_layer(
             lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    hf_model.config.feat_extract_norm == "group"  ,    )
         UpperCAmelCase_											=True
      else:
         for key, mapped_key in MAPPING.items():
            UpperCAmelCase_											="sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") 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(lowercase__  )[0].split("."  )[-2]
                  UpperCAmelCase_											=mapped_key.replace("*"  ,    lowercase__  )
               if "weight_g" in name:
                  UpperCAmelCase_											="weight_g"
               elif "weight_v" in name:
                  UpperCAmelCase_											="weight_v"
               elif "weight" in name:
                  UpperCAmelCase_											="weight"
               elif "bias" in name:
                  UpperCAmelCase_											="bias"
               else:
                  UpperCAmelCase_											=None
               set_recursively(lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  )
            continue
      if not is_used:
         unused_weights.append(lowercase__  )

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




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''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:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             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:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             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:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F'{full_name} has size {value.shape}, but {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:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             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(lowercase__  )




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=SEWConfig()
   if is_finetuned:
      UpperCAmelCase_											=model.wav_encoder.wav_model.cfg
   else:
      UpperCAmelCase_											=model.cfg

   UpperCAmelCase_											=fs_config.conv_bias
   UpperCAmelCase_											=eval(fs_config.conv_feature_layers  )
   UpperCAmelCase_											=[x[0] for x in conv_layers]
   UpperCAmelCase_											=[x[1] for x in conv_layers]
   UpperCAmelCase_											=[x[2] for x in conv_layers]
   UpperCAmelCase_											="gelu"
   UpperCAmelCase_											="layer" if fs_config.extractor_mode == "layer_norm" else "group"
   UpperCAmelCase_											=0.0
   UpperCAmelCase_											=fs_config.activation_fn.name
   UpperCAmelCase_											=fs_config.encoder_embed_dim
   UpperCAmelCase_											=0.02
   UpperCAmelCase_											=fs_config.encoder_ffn_embed_dim
   UpperCAmelCase_											=1E-5
   UpperCAmelCase_											=fs_config.encoder_layerdrop
   UpperCAmelCase_											=fs_config.encoder_attention_heads
   UpperCAmelCase_											=fs_config.conv_pos_groups
   UpperCAmelCase_											=fs_config.conv_pos
   UpperCAmelCase_											=len(lowercase__  )
   UpperCAmelCase_											=fs_config.encoder_layers
   UpperCAmelCase_											=fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      UpperCAmelCase_											=model.cfg
      UpperCAmelCase_											=fs_config.final_dropout
      UpperCAmelCase_											=fs_config.layerdrop
   UpperCAmelCase_											=fs_config.activation_dropout
   UpperCAmelCase_											=fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   UpperCAmelCase_											=fs_config.attention_dropout
   UpperCAmelCase_											=fs_config.dropout_input
   UpperCAmelCase_											=fs_config.dropout
   UpperCAmelCase_											=fs_config.mask_channel_length
   UpperCAmelCase_											=fs_config.mask_channel_prob
   UpperCAmelCase_											=fs_config.mask_length
   UpperCAmelCase_											=fs_config.mask_prob

   UpperCAmelCase_											="Wav2Vec2FeatureExtractor"
   UpperCAmelCase_											="Wav2Vec2CTCTokenizer"

   return config




@torch.no_grad()
def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__=None  ,    lowercase__=None  ,    lowercase__=True  ):






   '''simple docstring'''






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

   if config_path is not None:
      UpperCAmelCase_											=SEWConfig.from_pretrained(lowercase__  )
   else:
      UpperCAmelCase_											=convert_config(model[0]  ,    lowercase__  )
   UpperCAmelCase_											=model[0].eval()

   UpperCAmelCase_											=True if config.feat_extract_norm == "layer" else False
   UpperCAmelCase_											=WavaVecaFeatureExtractor(
       feature_size=1  ,    sampling_rate=1_6_0_0_0  ,    padding_value=0  ,    do_normalize=lowercase__  ,    return_attention_mask=lowercase__  ,    )

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

         # 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.pad_index
         UpperCAmelCase_											=target_dict.bos_index
         UpperCAmelCase_											=target_dict.eos_index
         UpperCAmelCase_											=len(target_dict.symbols  )
         UpperCAmelCase_											=os.path.join(lowercase__  ,    "vocab.json"  )
         if not os.path.isdir(lowercase__  ):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowercase__  )  )
            return
         os.makedirs(lowercase__  ,    exist_ok=lowercase__  )
         with open(lowercase__  ,    "w"  ,    encoding="utf-8"  ) as vocab_handle:
            json.dump(target_dict.indices  ,    lowercase__  )
         UpperCAmelCase_											=WavaVecaCTCTokenizer(
             lowercase__  ,    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=lowercase__  ,    )
         UpperCAmelCase_											=WavaVecaProcessor(feature_extractor=lowercase__  ,    tokenizer=lowercase__  )
         processor.save_pretrained(lowercase__  )

      UpperCAmelCase_											=SEWForCTC(lowercase__  )
   else:
      UpperCAmelCase_											=SEWModel(lowercase__  )
      feature_extractor.save_pretrained(lowercase__  )

   recursively_load_weights(lowercase__  ,    lowercase__  ,    lowercase__  )

   hf_model.save_pretrained(lowercase__  )


if __name__ == "__main__":
     __lowercase  :   Optional[Any]						      =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(
         """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     __lowercase  :   Any						      =parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )











import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__lowercase  :   int						      ="""\
@article{hendrycksmath2021,
  title={Measuring Mathematical Problem Solving With the MATH Dataset},
  author={Dan Hendrycks
    and Collin Burns
    and Saurav Kadavath
    and Akul Arora
    and Steven Basart
    and Eric Tang
    and Dawn Song
    and Jacob Steinhardt},
  journal={arXiv preprint arXiv:2103.03874},
  year={2021}
}
"""


__lowercase  :   List[Any]						      ="""\
This metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.
It first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.
"""


__lowercase  :   str						      =R"""
Calculates accuracy after canonicalizing inputs.

Args:
    predictions: list of predictions to score. Each prediction
        is a string that contains natural language and LaTex.
    references: list of reference for each prediction. Each
        reference is a string that contains natural language
        and LaTex.
Returns:
    accuracy: accuracy after canonicalizing inputs
        (e.g., converting \"1/2\" to \"\\frac{1}{2}\")

Examples:
    >>> metric = datasets.load_metric(\"competition_math\")
    >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])
    >>> print(results)
    {'accuracy': 1.0}
"""

@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION		,      _KWARGS_DESCRIPTION						)
class      A       (	datasets.Metric						):



      def 	lowerCAmelCase__    (	self:						Any       )						->    Optional[Any]:
         '''simple docstring'''

         return datasets.MetricInfo(
             description=_DESCRIPTION		,  citation=_CITATION		,  inputs_description=_KWARGS_DESCRIPTION		,  features=datasets.Features(
                 {
                     "predictions": datasets.Value("string"       ),
                     "references": datasets.Value("string"       ),
                 }       )		,  homepage="https://github.com/hendrycks/math"		,  codebase_urls=["https://github.com/hendrycks/math"]		,  )






      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Union[str, Any]       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=0.0
         for i, j in zip(_lowerCAmelCase		,  _lowerCAmelCase       ):
            n_correct += 1.0 if math_equivalence.is_equiv(_lowerCAmelCase		,  _lowerCAmelCase       ) else 0.0
         UpperCAmelCase_											=n_correct / len(_lowerCAmelCase       )
         return {
             "accuracy": accuracy,
         }




