


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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:
   for attribute in key.split("""."""   ):
      UpperCAmelCase							=     getattr(lowerCAmelCase_  ,						lowerCAmelCase_   )

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

   logger.info(F"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."""   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Union[str, Any]:
   for key in ignore_keys:
      if key.endswith(""".*"""   ):
         if name.startswith(key[:-1]   ):
            return True
      elif ".*." in key:
         UpperCAmelCase				,  UpperCAmelCase							=     key.split(""".*."""   )
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Optional[int]:
   UpperCAmelCase							=     []

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

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

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

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

   logger.warning(F"""Unused weights: {unused_weights}"""   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[str]:
   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:
         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							=     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							=     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							=     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							=     value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(lowerCAmelCase_   )
@torch.no_grad()
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						)						->str:
   if config_path is not None:
      UpperCAmelCase							=     SpeechTaConfig.from_pretrained(lowerCAmelCase_   )
   else:
      UpperCAmelCase							=     SpeechTaConfig()

   if task == "s2t":
      UpperCAmelCase							=     config.max_text_positions
      UpperCAmelCase							=     SpeechTaForSpeechToText(lowerCAmelCase_   )
   elif task == "t2s":
      UpperCAmelCase							=     1_8_7_6
      UpperCAmelCase							=     6_0_0
      UpperCAmelCase							=     config.max_speech_positions
      UpperCAmelCase							=     SpeechTaForTextToSpeech(lowerCAmelCase_   )
   elif task == "s2s":
      UpperCAmelCase							=     1_8_7_6
      UpperCAmelCase							=     config.max_speech_positions
      UpperCAmelCase							=     SpeechTaForSpeechToSpeech(lowerCAmelCase_   )
   else:
      raise ValueError(F"""Unknown task name: {task}"""   )

   if vocab_path:
      UpperCAmelCase							=     SpeechTaTokenizer(lowerCAmelCase_  ,						model_max_length=config.max_text_positions   )

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

   UpperCAmelCase							=     SpeechTaFeatureExtractor()
   UpperCAmelCase							=     SpeechTaProcessor(tokenizer=lowerCAmelCase_  ,						feature_extractor=lowerCAmelCase_   )
   processor.save_pretrained(lowerCAmelCase_   )

   UpperCAmelCase							=     torch.load(lowerCAmelCase_   )
   recursively_load_weights(fairseq_checkpoint["""model"""]  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )

   model.save_pretrained(lowerCAmelCase_   )

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


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 pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu






@require_cpu
class   __lowercase				(  unittest.TestCase  ):





      def        _lowercase				(		self						:					Any       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     torch.nn.Linear(1_0					,       1_0       )
         UpperCAmelCase							=     torch.optim.SGD(model.parameters()					,       0.1       )
         UpperCAmelCase							=     Accelerator()
         UpperCAmelCase							=     accelerator.prepare(__lowerCamelCase       )
         try:
            pickle.loads(pickle.dumps(__lowerCamelCase       )       )
         except Exception as e:
            self.fail(F"""Accelerated optimizer pickling failed with {e}"""       )
         AcceleratorState._reset_state()



