
"""simple docstring"""



from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}

class       lowerCAmelCase__				(						A_	):
     __a						=					"""trajectory_transformer"""
     __a						=					["""past_key_values"""]
     __a						=					{
         """hidden_size""": """n_embd""",
         """num_attention_heads""": """n_head""",
         """num_hidden_layers""": """n_layer""",
     }







     def __init__(							self			:			Optional[Any]    ,      _lowerCamelCase			:			Any=100    ,      _lowerCamelCase			:			Optional[Any]=5    ,      _lowerCamelCase			:			Union[str, Any]=1    ,      _lowerCamelCase			:			List[str]=1    ,      _lowerCamelCase			:			Optional[int]=249    ,      _lowerCamelCase			:			List[Any]=6    ,      _lowerCamelCase			:			Any=17    ,      _lowerCamelCase			:			Optional[Any]=25    ,      _lowerCamelCase			:			Optional[Any]=4    ,      _lowerCamelCase			:			List[Any]=4    ,      _lowerCamelCase			:			Tuple=128    ,      _lowerCamelCase			:			int=0.1    ,      _lowerCamelCase			:			Optional[int]=0.1    ,      _lowerCamelCase			:			Dict=0.1    ,      _lowerCamelCase			:			Union[str, Any]=0.0_0_0_6    ,      _lowerCamelCase			:			Optional[Any]=512    ,      _lowerCamelCase			:			Optional[int]=0.0_2    ,      _lowerCamelCase			:			List[str]=1e-12    ,      _lowerCamelCase			:			Dict=1    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			Any=1    ,      _lowerCamelCase			:			str=50256    ,      _lowerCamelCase			:			int=50256    ,      **_lowerCamelCase			:			List[str]    ,      ):
       _snake_case					     =       vocab_size
       _snake_case					     =       action_weight
       _snake_case					     =       reward_weight
       _snake_case					     =       value_weight
       _snake_case					     =       max_position_embeddings
       _snake_case					     =       block_size
       _snake_case					     =       action_dim
       _snake_case					     =       observation_dim
       _snake_case					     =       transition_dim
       _snake_case					     =       learning_rate
       _snake_case					     =       n_layer
       _snake_case					     =       n_head
       _snake_case					     =       n_embd
       _snake_case					     =       embd_pdrop
       _snake_case					     =       attn_pdrop
       _snake_case					     =       resid_pdrop
       _snake_case					     =       initializer_range
       _snake_case					     =       layer_norm_eps
       _snake_case					     =       kaiming_initializer_range
       _snake_case					     =       use_cache
       super().__init__(pad_token_id=_lowerCamelCase    ,      bos_token_id=_lowerCamelCase    ,      eos_token_id=_lowerCamelCase    ,      **_lowerCamelCase			)





"""simple docstring"""



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

UpperCAmelCase__          = {
    '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',
}
UpperCAmelCase__          = {
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
UpperCAmelCase__          = {
    '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',
}
UpperCAmelCase__          = {
    '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',
}
UpperCAmelCase__          = {
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
UpperCAmelCase__          = {
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
UpperCAmelCase__          = {
    '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',
}
UpperCAmelCase__          = {
    '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',
}
UpperCAmelCase__          = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCAmelCase__          = {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase__          = {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCAmelCase__          = []
UpperCAmelCase__          = [
    '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',
]
UpperCAmelCase__          = IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
UpperCAmelCase__          = IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
UpperCAmelCase__          = IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       Dict		)     ->					List[Any]:
  for attribute in key.split('''.'''		):
    _snake_case					     =       getattr(__lowerCamelCase ,					__lowerCamelCase		)

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

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

  if task == "s2t":
    _snake_case					     =       hf_model.speechta.encoder.prenet.feature_encoder
    _snake_case					     =       MAPPING_S2T
    _snake_case					     =       IGNORE_KEYS_S2T
  elif task == "t2s":
    _snake_case					     =       None
    _snake_case					     =       MAPPING_T2S
    _snake_case					     =       IGNORE_KEYS_T2S
  elif task == "s2s":
    _snake_case					     =       hf_model.speechta.encoder.prenet.feature_encoder
    _snake_case					     =       MAPPING_S2S
    _snake_case					     =       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

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          _snake_case					     =       True
          if "*" in mapped_key:
            _snake_case					     =       name.split(__lowerCamelCase		)[0].split('''.'''		)[-2]
            _snake_case					     =       mapped_key.replace('''*''' ,					__lowerCamelCase		)
          if "weight_g" in name:
            _snake_case					     =       '''weight_g'''
          elif "weight_v" in name:
            _snake_case					     =       '''weight_v'''
          elif "bias" in name:
            _snake_case					     =       '''bias'''
          elif "weight" in name:
            _snake_case					     =       '''weight'''
          elif "running_mean" in name:
            _snake_case					     =       '''running_mean'''
          elif "running_var" in name:
            _snake_case					     =       '''running_var'''
          elif "num_batches_tracked" in name:
            _snake_case					     =       '''num_batches_tracked'''
          else:
            _snake_case					     =       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   :       Any ,					__lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[Any] ,					__lowerCamelCase   :       Tuple		)     ->					List[Any]:
  _snake_case					     =       full_name.split('''conv_layers.'''		)[-1]
  _snake_case					     =       name.split('''.'''		)
  _snake_case					     =       int(items[0]		)
  _snake_case					     =       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					     =       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					     =       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					     =       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					     =       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   :       Union[str, Any] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Optional[int]=None ,					__lowerCamelCase   :       int=None ,					__lowerCamelCase   :       Union[str, Any]=None ,					)     ->					Dict:
  if config_path is not None:
    _snake_case					     =       SpeechTaConfig.from_pretrained(__lowerCamelCase		)
  else:
    _snake_case					     =       SpeechTaConfig()

  if task == "s2t":
    _snake_case					     =       config.max_text_positions
    _snake_case					     =       SpeechTaForSpeechToText(__lowerCamelCase		)
  elif task == "t2s":
    _snake_case					     =       18_76
    _snake_case					     =       6_00
    _snake_case					     =       config.max_speech_positions
    _snake_case					     =       SpeechTaForTextToSpeech(__lowerCamelCase		)
  elif task == "s2s":
    _snake_case					     =       18_76
    _snake_case					     =       config.max_speech_positions
    _snake_case					     =       SpeechTaForSpeechToSpeech(__lowerCamelCase		)
  else:
    raise ValueError(f'''Unknown task name: {task}'''		)

  if vocab_path:
    _snake_case					     =       SpeechTaTokenizer(__lowerCamelCase ,					model_max_length=config.max_text_positions		)

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

  _snake_case					     =       SpeechTaFeatureExtractor()
  _snake_case					     =       SpeechTaProcessor(tokenizer=__lowerCamelCase ,					feature_extractor=__lowerCamelCase		)
  processor.save_pretrained(__lowerCamelCase		)

  _snake_case					     =       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__":
 UpperCAmelCase__          = 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.'
 )

 UpperCAmelCase__          = 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,
 )



