






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()
__lowerCamelCase	:		str										=   logging.get_logger("""transformers.models.speecht5""")

__lowerCamelCase	:		Any										=   {
    """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""",
}
__lowerCamelCase	:		Dict										=   {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
__lowerCamelCase	:		Dict										=   {
    """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""",
}
__lowerCamelCase	:		int										=   {
    """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""",
}
__lowerCamelCase	:		Dict										=   {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
__lowerCamelCase	:		Union[str, Any]										=   {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
__lowerCamelCase	:		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""",
}
__lowerCamelCase	:		List[Any]										=   {
    """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""",
}
__lowerCamelCase	:		Optional[Any]										=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowerCamelCase	:		Tuple										=   {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase	:		int										=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase	:		Optional[Any]										=   []
__lowerCamelCase	:		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""",
]
__lowerCamelCase	:		Any										=   IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
__lowerCamelCase	:		str										=   IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
__lowerCamelCase	:		Any										=   IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]







def 			A__							(      _a  :					Tuple		,  _a  :					int		,  _a  :					List[str]		,  _a  :					Optional[Any]		,  _a  :					List[Any]	):





 '''simple docstring'''







 for attribute in key.split("""."""	):
  snake_case__  :    Optional[int]							       =getattr(_a		,  _a	)

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

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







def 			A__							(      _a  :					Optional[int]		,  _a  :					Any	):





 '''simple docstring'''







 for key in ignore_keys:
  if key.endswith(""".*"""	):
   if name.startswith(key[:-1]	):
    return True
  elif ".*." in key:
   snake_case__       ,   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__							(      _a  :					int		,  _a  :					Optional[int]		,  _a  :					Optional[Any]	):





 '''simple docstring'''







 snake_case__  :    Dict							       =[]

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

 for name, value in fairseq_dict.items():
  if should_ignore(_a		,  _a	):
   logger.info(f"{name} was ignored"	)
   continue

  snake_case__  :    List[Any]							       =False
  if "conv_layers" in name:
   load_conv_layer(
       _a		,  _a		,  _a		,  _a		,  hf_model.config.feat_extract_norm == """group"""		,  )
   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:
     snake_case__       ,   snake_case__  :    int							       =key.split(""".*."""	)
     if prefix in name and suffix in name:
      snake_case__  :    int							       =suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
    if key in name:
     snake_case__  :    str							       =True
     if "*" in mapped_key:
      snake_case__  :    Any							       =name.split(_a	)[0].split("""."""	)[-2]
      snake_case__  :    List[str]							       =mapped_key.replace("""*"""		,  _a	)
     if "weight_g" in name:
      snake_case__  :    Dict							       ="""weight_g"""
     elif "weight_v" in name:
      snake_case__  :    Union[str, Any]							       ="""weight_v"""
     elif "bias" in name:
      snake_case__  :    Dict							       ="""bias"""
     elif "weight" in name:
      snake_case__  :    Optional[Any]							       ="""weight"""
     elif "running_mean" in name:
      snake_case__  :    Union[str, Any]							       ="""running_mean"""
     elif "running_var" in name:
      snake_case__  :    str							       ="""running_var"""
     elif "num_batches_tracked" in name:
      snake_case__  :    List[Any]							       ="""num_batches_tracked"""
     else:
      snake_case__  :    int							       =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 			A__							(      _a  :					List[str]		,  _a  :					Dict		,  _a  :					Optional[int]		,  _a  :					List[str]		,  _a  :					Optional[int]	):





 '''simple docstring'''







 snake_case__  :    Optional[int]							       =full_name.split("""conv_layers."""	)[-1]
 snake_case__  :    int							       =name.split("""."""	)
 snake_case__  :    Optional[Any]							       =int(items[0]	)
 snake_case__  :    str							       =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."	)
   snake_case__  :    int							       =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."	)
   snake_case__  :    List[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."	)
   snake_case__  :    Union[str, Any]							       =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."	)
   snake_case__  :    List[Any]							       =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 			A__							(      _a  :					str		,  _a  :					Optional[Any]		,  _a  :					Optional[Any]		,  _a  :					str=None		,  _a  :					Dict=None		,  _a  :					Tuple=None		,  ):





 '''simple docstring'''







 if config_path is not None:
  snake_case__  :    Tuple							       =SpeechTaConfig.from_pretrained(_a	)
 else:
  snake_case__  :    Any							       =SpeechTaConfig()

 if task == "s2t":
  snake_case__  :    List[Any]							       =config.max_text_positions
  snake_case__  :    int							       =SpeechTaForSpeechToText(_a	)
 elif task == "t2s":
  snake_case__  :    Any							       =1876
  snake_case__  :    str							       =600
  snake_case__  :    Union[str, Any]							       =config.max_speech_positions
  snake_case__  :    Optional[Any]							       =SpeechTaForTextToSpeech(_a	)
 elif task == "s2s":
  snake_case__  :    Union[str, Any]							       =1876
  snake_case__  :    Optional[int]							       =config.max_speech_positions
  snake_case__  :    int							       =SpeechTaForSpeechToSpeech(_a	)
 else:
  raise ValueError(f"Unknown task name: {task}"	)

 if vocab_path:
  snake_case__  :    Optional[Any]							       =SpeechTaTokenizer(_a		,  model_max_length=config.max_text_positions	)

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

 snake_case__  :    Dict							       =SpeechTaFeatureExtractor()
 snake_case__  :    Dict							       =SpeechTaProcessor(tokenizer=_a		,  feature_extractor=_a	)
 processor.save_pretrained(_a	)

 snake_case__  :    Any							       =torch.load(_a	)
 recursively_load_weights(fairseq_checkpoint["""model"""]		,  _a		,  _a	)

 model.save_pretrained(_a	)

 if repo_id:
  print("""Pushing to the hub..."""	)
  processor.push_to_hub(_a	)
  model.push_to_hub(_a	)


if __name__ == "__main__":
       __lowerCamelCase	:		List[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."""
       )

       __lowerCamelCase	:		List[str]										=   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,
       )













from collections import namedtuple

import requests
from lxml import html  # type: ignore

__lowerCamelCase	:		Any										=   namedtuple("""covid_data""", """cases deaths recovered""")







def 			A__							(      _a  :					str = "https://www.worldometers.info/coronavirus/"	):





 '''simple docstring'''







 snake_case__  :    Any							       ="""//div[@class = \"maincounter-number\"]/span/text()"""
 return covid_data(*html.fromstring(requests.get(_a	).content	).xpath(_a	)	)


__lowerCamelCase	:		str										=   """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""

print(fmt.format(*covid_stats()))





