


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

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



def   snake_case						(				_a:     List[Any]		,   _a:     str		,   _a:     Any		,   _a:     str		,   _a:     Tuple							)->		Dict:
      '''simple docstring'''


      for attribute in key.split('.'							):
            lowerCamelCase__           =		getattr(_a		,   _a							)

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

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



def   snake_case						(				_a:     Optional[Any]		,   _a:     List[str]							)->		Any:
      '''simple docstring'''


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



def   snake_case						(				_a:     int		,   _a:     Union[str, Any]		,   _a:     Tuple							)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[]

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

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

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


      lowerCamelCase__           =		full_name.split('conv_layers.'							)[-1]
      lowerCamelCase__           =		name.split('.'							)
      lowerCamelCase__           =		int(items[0]							)
      lowerCamelCase__           =		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.'							)
                  lowerCamelCase__           =		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.'							)
                  lowerCamelCase__           =		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.'							)
                  lowerCamelCase__           =		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.'							)
                  lowerCamelCase__           =		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   snake_case						(				_a:     Optional[int]		,   _a:     int		,   _a:     int		,   _a:     Dict=None		,   _a:     Optional[Any]=None		,   _a:     Any=None		,   )->		Tuple:
      '''simple docstring'''


      if config_path is not None:
            lowerCamelCase__           =		SpeechTaConfig.from_pretrained(_a							)
      else:
            lowerCamelCase__           =		SpeechTaConfig()

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

      if vocab_path:
            lowerCamelCase__           =		SpeechTaTokenizer(_a		,   model_max_length=config.max_text_positions							)

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

      lowerCamelCase__           =		SpeechTaFeatureExtractor()
      lowerCamelCase__           =		SpeechTaProcessor(tokenizer=_a		,   feature_extractor=_a							)
      processor.save_pretrained(_a							)

      lowerCamelCase__           =		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__":
   _snake_case          =							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."
   )

   _snake_case          =							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,
   )







"""simple docstring"""


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
   from .camera import create_pan_cameras
   from .pipeline_shap_e import ShapEPipeline
   from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
   from .renderer import (
       BoundingBoxVolume,
       ImportanceRaySampler,
       MLPNeRFModelOutput,
       MLPNeRSTFModel,
       ShapEParamsProjModel,
       ShapERenderer,
       StratifiedRaySampler,
       VoidNeRFModel,
   )



