




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()
__a          =			logging.get_logger('transformers.models.speecht5')

__a          =			{
    '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',
}
__a          =			{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
__a          =			{
    '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',
}
__a          =			{
    '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',
}
__a          =			{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
__a          =			{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
__a          =			{
    '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',
}
__a          =			{
    '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',
}
__a          =			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__a          =			{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__a          =			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__a          =			[]
__a          =			[
    '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',
]
__a          =			IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
__a          =			IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
__a          =			IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     for attribute in key.split('''.'''				):
          UpperCAmelCase_      :							List[str]       =      getattr(_lowercase			,						_lowercase				)

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

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







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in ignore_keys:
          if key.endswith('''.*'''				):
               if name.startswith(key[:-1]				):
                    return True
          elif ".*." in key:
               UpperCAmelCase_,   UpperCAmelCase_      :							Optional[int]       =      key.split('''.*.'''				)
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							List[Any]       =      []

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

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

          UpperCAmelCase_      :							Optional[int]       =      False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						hf_model.config.feat_extract_norm == '''group'''			,						)
               UpperCAmelCase_      :							Union[str, 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:
                         UpperCAmelCase_,   UpperCAmelCase_      :							Optional[Any]       =      key.split('''.*.'''				)
                         if prefix in name and suffix in name:
                              UpperCAmelCase_      :							List[Any]       =      suffix

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

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      full_name.split('''conv_layers.'''				)[-1]
     UpperCAmelCase_      :							Tuple       =      name.split('''.'''				)
     UpperCAmelCase_      :							Union[str, Any]       =      int(items[0]				)
     UpperCAmelCase_      :							Union[str, Any]       =      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_      :							Any       =      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_      :							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.'''				)
               UpperCAmelCase_      :							Optional[int]       =      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_      :							str       =      value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
     else:
          unused_weights.append(_lowercase				)







@torch.no_grad()
def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase=None			,						_lowercase=None			,						_lowercase=None			,						):

     '''simple docstring'''


     if config_path is not None:
          UpperCAmelCase_      :							Tuple       =      SpeechTaConfig.from_pretrained(_lowercase				)
     else:
          UpperCAmelCase_      :							Optional[int]       =      SpeechTaConfig()

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

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

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

     UpperCAmelCase_      :							int       =      SpeechTaFeatureExtractor()
     UpperCAmelCase_      :							Union[str, Any]       =      SpeechTaProcessor(tokenizer=_lowercase			,						feature_extractor=_lowercase				)
     processor.save_pretrained(_lowercase				)

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

     model.save_pretrained(_lowercase				)

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


if __name__ == "__main__":
     __a          =			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.'
     )

     __a          =			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 argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
__a          =			logging.getLogger(__name__)

if __name__ == "__main__":
     __a          =			argparse.ArgumentParser(
         description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
     )
     parser.add_argument(
         '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
     )
     parser.add_argument(
         '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
     )
     parser.add_argument('--vocab_size', default=30_522, type=int)
     __a          =			parser.parse_args()

     logger.info(F"""Loading data from {args.data_file}""")
     with open(args.data_file, 'rb') as fp:
          __a          =			pickle.load(fp)

     logger.info('Counting occurrences for MLM.')
     __a          =			Counter()
     for tk_ids in data:
          counter.update(tk_ids)
     __a          =			[0] * args.vocab_size
     for k, v in counter.items():
          __a          =			v

     logger.info(F"""Dump to {args.token_counts_dump}""")
     with open(args.token_counts_dump, 'wb') as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)