import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowercase_  : Optional[Any]								=					logging.get_logger('transformers.models.speecht5')

lowercase_  : List[str]								=					{
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
lowercase_  : Union[str, Any]								=					{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
lowercase_  : str								=					{
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
lowercase_  : Optional[Any]								=					{
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
lowercase_  : int								=					{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
lowercase_  : Any								=					{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
lowercase_  : Optional[int]								=					{
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
lowercase_  : Dict								=					{
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
lowercase_  : Any								=					{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowercase_  : Optional[int]								=					{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase_  : Any								=					{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase_  : Optional[int]								=					[]
lowercase_  : List[str]								=					[
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
lowercase_  : List[str]								=					IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
lowercase_  : List[Any]								=					IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
lowercase_  : Any								=					IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]







def       A__   (						snake_case_			:		List[Any]							,					snake_case_			:		Dict							,					snake_case_			:		Any							,					snake_case_			:		Dict							,					snake_case_			:		List[str]   ):
 for attribute in key.split('''.'''   ):
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   getattr(snake_case_							,					snake_case_   )

 if weight_type is not None:
  SCREAMING_SNAKE_CASE__: Tuple=   getattr(snake_case_							,					snake_case_   ).shape
 else:
  SCREAMING_SNAKE_CASE__: int=   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":
  SCREAMING_SNAKE_CASE__: Dict=   value
 elif weight_type == "weight_g":
  SCREAMING_SNAKE_CASE__: int=   value
 elif weight_type == "weight_v":
  SCREAMING_SNAKE_CASE__: Optional[int]=   value
 elif weight_type == "bias":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   value
 elif weight_type == "running_mean":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   value
 elif weight_type == "running_var":
  SCREAMING_SNAKE_CASE__: Tuple=   value
 elif weight_type == "num_batches_tracked":
  SCREAMING_SNAKE_CASE__: Any=   value
 else:
  SCREAMING_SNAKE_CASE__: int=   value

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







def       A__   (						snake_case_			:		str							,					snake_case_			:		Tuple   ):
 for key in ignore_keys:
  if key.endswith('''.*'''   ):
   if name.startswith(key[:-1]   ):
    return True
  elif ".*." in key:
   SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: int=   key.split('''.*.'''   )
   if prefix in name and suffix in name:
    return True
  elif key in name:
   return True
 return False







def       A__   (						snake_case_			:		List[str]							,					snake_case_			:		Tuple							,					snake_case_			:		Union[str, Any]   ):
 SCREAMING_SNAKE_CASE__: Tuple=   []

 if task == "s2t":
  SCREAMING_SNAKE_CASE__: List[str]=   hf_model.speechta.encoder.prenet.feature_encoder
  SCREAMING_SNAKE_CASE__: Optional[Any]=   MAPPING_S2T
  SCREAMING_SNAKE_CASE__: Optional[int]=   IGNORE_KEYS_S2T
 elif task == "t2s":
  SCREAMING_SNAKE_CASE__: Tuple=   None
  SCREAMING_SNAKE_CASE__: Any=   MAPPING_T2S
  SCREAMING_SNAKE_CASE__: Any=   IGNORE_KEYS_T2S
 elif task == "s2s":
  SCREAMING_SNAKE_CASE__: List[Any]=   hf_model.speechta.encoder.prenet.feature_encoder
  SCREAMING_SNAKE_CASE__: int=   MAPPING_S2S
  SCREAMING_SNAKE_CASE__: Optional[Any]=   IGNORE_KEYS_S2S
 else:
  raise ValueError(F'Unsupported task: {task}'   )

 for name, value in fairseq_dict.items():
  if should_ignore(snake_case_							,					snake_case_   ):
   logger.info(F'{name} was ignored'   )
   continue

  SCREAMING_SNAKE_CASE__: Optional[Any]=   False
  if "conv_layers" in name:
   load_conv_layer(
       snake_case_							,					snake_case_							,					snake_case_							,					snake_case_							,					hf_model.config.feat_extract_norm == '''group'''							,					)
   SCREAMING_SNAKE_CASE__: List[Any]=   True
  else:
   for key, mapped_key in MAPPING.items():
    # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

    if "*" in key:
     SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: str=   key.split('''.*.'''   )
     if prefix in name and suffix in name:
      SCREAMING_SNAKE_CASE__: Tuple=   suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
    if key in name:
     SCREAMING_SNAKE_CASE__: Dict=   True
     if "*" in mapped_key:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   name.split(snake_case_   )[0].split('''.'''   )[-2]
      SCREAMING_SNAKE_CASE__: List[str]=   mapped_key.replace('''*'''							,					snake_case_   )
     if "weight_g" in name:
      SCREAMING_SNAKE_CASE__: Tuple=   '''weight_g'''
     elif "weight_v" in name:
      SCREAMING_SNAKE_CASE__: Optional[int]=   '''weight_v'''
     elif "bias" in name:
      SCREAMING_SNAKE_CASE__: List[str]=   '''bias'''
     elif "weight" in name:
      SCREAMING_SNAKE_CASE__: Dict=   '''weight'''
     elif "running_mean" in name:
      SCREAMING_SNAKE_CASE__: int=   '''running_mean'''
     elif "running_var" in name:
      SCREAMING_SNAKE_CASE__: List[str]=   '''running_var'''
     elif "num_batches_tracked" in name:
      SCREAMING_SNAKE_CASE__: Dict=   '''num_batches_tracked'''
     else:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   None
     set_recursively(snake_case_							,					snake_case_							,					snake_case_							,					snake_case_							,					snake_case_   )
    continue
  if not is_used:
   unused_weights.append(snake_case_   )

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







def       A__   (						snake_case_			:		Optional[Any]							,					snake_case_			:		Any							,					snake_case_			:		str							,					snake_case_			:		Optional[int]							,					snake_case_			:		List[Any]   ):
 SCREAMING_SNAKE_CASE__: Any=   full_name.split('''conv_layers.'''   )[-1]
 SCREAMING_SNAKE_CASE__: Optional[Any]=   name.split('''.'''   )
 SCREAMING_SNAKE_CASE__: Optional[int]=   int(items[0]   )
 SCREAMING_SNAKE_CASE__: Dict=   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.'   )
   SCREAMING_SNAKE_CASE__: str=   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.'   )
   SCREAMING_SNAKE_CASE__: Optional[Any]=   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.'   )
   SCREAMING_SNAKE_CASE__: str=   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.'   )
   SCREAMING_SNAKE_CASE__: Tuple=   value
   logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
 else:
  unused_weights.append(snake_case_   )







@torch.no_grad()
def       A__   (						snake_case_			:		Dict							,					snake_case_			:		Tuple							,					snake_case_			:		Tuple							,					snake_case_			:		int=None							,					snake_case_			:		Any=None							,					snake_case_			:		str=None							,					):
 if config_path is not None:
  SCREAMING_SNAKE_CASE__: Optional[int]=   SpeechTaConfig.from_pretrained(snake_case_   )
 else:
  SCREAMING_SNAKE_CASE__: Tuple=   SpeechTaConfig()

 if task == "s2t":
  SCREAMING_SNAKE_CASE__: List[Any]=   config.max_text_positions
  SCREAMING_SNAKE_CASE__: List[str]=   SpeechTaForSpeechToText(snake_case_   )
 elif task == "t2s":
  SCREAMING_SNAKE_CASE__: List[str]=   1_876
  SCREAMING_SNAKE_CASE__: str=   600
  SCREAMING_SNAKE_CASE__: Tuple=   config.max_speech_positions
  SCREAMING_SNAKE_CASE__: str=   SpeechTaForTextToSpeech(snake_case_   )
 elif task == "s2s":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   1_876
  SCREAMING_SNAKE_CASE__: Optional[int]=   config.max_speech_positions
  SCREAMING_SNAKE_CASE__: Tuple=   SpeechTaForSpeechToSpeech(snake_case_   )
 else:
  raise ValueError(F'Unknown task name: {task}'   )

 if vocab_path:
  SCREAMING_SNAKE_CASE__: List[str]=   SpeechTaTokenizer(snake_case_							,					model_max_length=config.max_text_positions   )

  # Mask token behaves like a normal word, i.e. include the space before it
  SCREAMING_SNAKE_CASE__: List[Any]=   AddedToken('''<mask>'''							,					lstrip=snake_case_							,					rstrip=snake_case_   )
  SCREAMING_SNAKE_CASE__: Dict=   mask_token
  tokenizer.add_special_tokens({'''mask_token''': mask_token}   )
  tokenizer.add_tokens(['''<ctc_blank>''']   )

 SCREAMING_SNAKE_CASE__: Union[str, Any]=   SpeechTaFeatureExtractor()
 SCREAMING_SNAKE_CASE__: List[Any]=   SpeechTaProcessor(tokenizer=snake_case_							,					feature_extractor=snake_case_   )
 processor.save_pretrained(snake_case_   )

 SCREAMING_SNAKE_CASE__: List[str]=   torch.load(snake_case_   )
 recursively_load_weights(fairseq_checkpoint['''model''']							,					snake_case_							,					snake_case_   )

 model.save_pretrained(snake_case_   )

 if repo_id:
  print('''Pushing to the hub...'''   )
  processor.push_to_hub(snake_case_   )
  model.push_to_hub(snake_case_   )


if __name__ == "__main__":
      lowercase_  : Any								=					argparse.ArgumentParser()
      parser.add_argument(
          '--task',
          default='s2t',
          type=str,
          help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
      )
      parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
      parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
      parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
      parser.add_argument(
          '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
      )
      parser.add_argument(
          '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
      )

      lowercase_  : Any								=					parser.parse_args()
      convert_speechta_checkpoint(
          args.task,
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.vocab_path,
          args.push_to_hub,
      )



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

import datasets


__lowerCamelCase				=			"""\
@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}
}
"""


__lowerCamelCase				=			"""\
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.
"""


__lowerCamelCase				=			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     UpperCAmelCase						(		datasets.Metric					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]					)						->      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 							_SCREAMING_SNAKE_CASE  (self						:   Optional[int]       ,		snake_case__						:   str       ,		snake_case__						:   List[Any]					)						->      Optional[Any]:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= 0.0
          for i, j in zip(snake_case__       ,		snake_case__					):
                 n_correct += 1.0 if math_equivalence.is_equiv(snake_case__       ,		snake_case__					) else 0.0
          snake_case			:  Optional[int]     							= n_correct / len(snake_case__					)
          return {
              "accuracy": accuracy,
          }





