



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_						=       logging.get_logger("""transformers.models.speecht5""")

lowercase_						=       {
    """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_						=       {
    """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_						=       {
    """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_						=       {
    """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_						=       {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
lowercase_						=       {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
lowercase_						=       {
    """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_						=       {
    """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_						=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowercase_						=       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase_						=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase_						=       []
lowercase_						=       [
    """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_						=       IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
lowercase_						=       IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
lowercase_						=       IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
   for attribute in key.split("."					):
      lowercase__   					=			getattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

   if weight_type is not None:
      lowercase__   					=			getattr(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					).shape
   else:
      lowercase__   					=			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":
      lowercase__   					=			value
   elif weight_type == "weight_g":
      lowercase__   					=			value
   elif weight_type == "weight_v":
      lowercase__   					=			value
   elif weight_type == "bias":
      lowercase__   					=			value
   elif weight_type == "running_mean":
      lowercase__   					=			value
   elif weight_type == "running_var":
      lowercase__   					=			value
   elif weight_type == "num_batches_tracked":
      lowercase__   					=			value
   else:
      lowercase__   					=			value

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
   for key in ignore_keys:
      if key.endswith(".*"					):
         if name.startswith(key[:-1]					):
            return True
      elif ".*." in key:
         lowercase__					,    lowercase__   					=			key.split(".*."					)
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
   lowercase__   					=			[]

   if task == "s2t":
      lowercase__   					=			hf_model.speechta.encoder.prenet.feature_encoder
      lowercase__   					=			MAPPING_S2T
      lowercase__   					=			IGNORE_KEYS_S2T
   elif task == "t2s":
      lowercase__   					=			None
      lowercase__   					=			MAPPING_T2S
      lowercase__   					=			IGNORE_KEYS_T2S
   elif task == "s2s":
      lowercase__   					=			hf_model.speechta.encoder.prenet.feature_encoder
      lowercase__   					=			MAPPING_S2S
      lowercase__   					=			IGNORE_KEYS_S2S
   else:
      raise ValueError(f'''Unsupported task: {task}'''					)

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

      lowercase__   					=			False
      if "conv_layers" in name:
         load_conv_layer(
             SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					hf_model.config.feat_extract_norm == "group"  ,					)
         lowercase__   					=			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:
               lowercase__					,    lowercase__   					=			key.split(".*."					)
               if prefix in name and suffix in name:
                  lowercase__   					=			suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
            if key in name:
               lowercase__   					=			True
               if "*" in mapped_key:
                  lowercase__   					=			name.split(SCREAMING_SNAKE_CASE_					)[0].split("."					)[-2]
                  lowercase__   					=			mapped_key.replace("*"  ,					SCREAMING_SNAKE_CASE_					)
               if "weight_g" in name:
                  lowercase__   					=			"weight_g"
               elif "weight_v" in name:
                  lowercase__   					=			"weight_v"
               elif "bias" in name:
                  lowercase__   					=			"bias"
               elif "weight" in name:
                  lowercase__   					=			"weight"
               elif "running_mean" in name:
                  lowercase__   					=			"running_mean"
               elif "running_var" in name:
                  lowercase__   					=			"running_var"
               elif "num_batches_tracked" in name:
                  lowercase__   					=			"num_batches_tracked"
               else:
                  lowercase__   					=			None
               set_recursively(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
            continue
      if not is_used:
         unused_weights.append(SCREAMING_SNAKE_CASE_					)

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
   lowercase__   					=			full_name.split("conv_layers."					)[-1]
   lowercase__   					=			name.split("."					)
   lowercase__   					=			int(items[0]					)
   lowercase__   					=			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.'''					)
         lowercase__   					=			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.'''					)
         lowercase__   					=			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.'''					)
         lowercase__   					=			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.'''					)
         lowercase__   					=			value
         logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
   else:
      unused_weights.append(SCREAMING_SNAKE_CASE_					)



@torch.no_grad()
def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					SCREAMING_SNAKE_CASE_=None  ,					SCREAMING_SNAKE_CASE_=None  ,					):
   if config_path is not None:
      lowercase__   					=			SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
   else:
      lowercase__   					=			SpeechTaConfig()

   if task == "s2t":
      lowercase__   					=			config.max_text_positions
      lowercase__   					=			SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE_					)
   elif task == "t2s":
      lowercase__   					=			1876
      lowercase__   					=			600
      lowercase__   					=			config.max_speech_positions
      lowercase__   					=			SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE_					)
   elif task == "s2s":
      lowercase__   					=			1876
      lowercase__   					=			config.max_speech_positions
      lowercase__   					=			SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE_					)
   else:
      raise ValueError(f'''Unknown task name: {task}'''					)

   if vocab_path:
      lowercase__   					=			SpeechTaTokenizer(SCREAMING_SNAKE_CASE_  ,					model_max_length=config.max_text_positions					)

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

   lowercase__   					=			SpeechTaFeatureExtractor()
   lowercase__   					=			SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_  ,					feature_extractor=SCREAMING_SNAKE_CASE_					)
   processor.save_pretrained(SCREAMING_SNAKE_CASE_					)

   lowercase__   					=			torch.load(SCREAMING_SNAKE_CASE_					)
   recursively_load_weights(fairseq_checkpoint["model"]  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

   model.save_pretrained(SCREAMING_SNAKE_CASE_					)

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


if __name__ == "__main__":
       lowercase_						=       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_						=       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

lowercase_						=       namedtuple("""covid_data""", """cases deaths recovered""")



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_ = "https://www.worldometers.info/coronavirus/"					):
			lowercase__   					=			"//div[@class = \"maincounter-number\"]/span/text()"
			return covid_data(*html.fromstring(requests.get(SCREAMING_SNAKE_CASE_					).content					).xpath(SCREAMING_SNAKE_CASE_					)					)


lowercase_						=       """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()))




