

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()
lowerCAmelCase    : List[str]              =     logging.get_logger('''transformers.models.speecht5''')

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





def       _lowercase     (   __UpperCamelCase  :		Dict ,							__UpperCamelCase  :		Union[str, Any] ,							__UpperCamelCase  :		Union[str, Any] ,							__UpperCamelCase  :		int ,							__UpperCamelCase  :		List[Any]			):
      for attribute in key.split("""."""			):
            snake_case__											= getattr(__UpperCamelCase ,							__UpperCamelCase			)

      if weight_type is not None:
            snake_case__											= getattr(__UpperCamelCase ,							__UpperCamelCase			).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       _lowercase     (   __UpperCamelCase  :		Optional[Any] ,							__UpperCamelCase  :		List[Any]			):
      for key in ignore_keys:
            if key.endswith(""".*"""			):
                  if name.startswith(key[:-1]			):
                        return True
            elif ".*." in key:
                  snake_case__											= key.split(""".*."""			)
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False





def       _lowercase     (   __UpperCamelCase  :		str ,							__UpperCamelCase  :		int ,							__UpperCamelCase  :		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(__UpperCamelCase ,							__UpperCamelCase			):
                  logger.info(F'''{name} was ignored'''			)
                  continue

            snake_case__											= False
            if "conv_layers" in name:
                  load_conv_layer(
                      __UpperCamelCase ,							__UpperCamelCase ,							__UpperCamelCase ,							__UpperCamelCase ,							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__											= 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(__UpperCamelCase			)[0].split("""."""			)[-2]
                                    snake_case__											= mapped_key.replace("""*""" ,							__UpperCamelCase			)
                              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(__UpperCamelCase ,							__UpperCamelCase ,							__UpperCamelCase ,							__UpperCamelCase ,							__UpperCamelCase			)
                        continue
            if not is_used:
                  unused_weights.append(__UpperCamelCase			)

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





def       _lowercase     (   __UpperCamelCase  :		Optional[int] ,							__UpperCamelCase  :		Optional[int] ,							__UpperCamelCase  :		Tuple ,							__UpperCamelCase  :		str ,							__UpperCamelCase  :		List[str]			):
      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(__UpperCamelCase			)





@torch.no_grad()
def       _lowercase     (   __UpperCamelCase  :		List[Any] ,							__UpperCamelCase  :		int ,							__UpperCamelCase  :		Optional[Any] ,							__UpperCamelCase  :		List[Any]=None ,							__UpperCamelCase  :		Tuple=None ,							__UpperCamelCase  :		int=None ,							):
      if config_path is not None:
            snake_case__											= SpeechTaConfig.from_pretrained(__UpperCamelCase			)
      else:
            snake_case__											= SpeechTaConfig()

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

      if vocab_path:
            snake_case__											= SpeechTaTokenizer(__UpperCamelCase ,							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=__UpperCamelCase ,							rstrip=__UpperCamelCase			)
            snake_case__											= mask_token
            tokenizer.add_special_tokens({"""mask_token""": mask_token}			)
            tokenizer.add_tokens(["""<ctc_blank>"""]			)

      snake_case__											= SpeechTaFeatureExtractor()
      snake_case__											= SpeechTaProcessor(tokenizer=__UpperCamelCase ,							feature_extractor=__UpperCamelCase			)
      processor.save_pretrained(__UpperCamelCase			)

      snake_case__											= torch.load(__UpperCamelCase			)
      recursively_load_weights(fairseq_checkpoint["""model"""] ,							__UpperCamelCase ,							__UpperCamelCase			)

      model.save_pretrained(__UpperCamelCase			)

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


if __name__ == "__main__":
       lowerCAmelCase    : List[str]              =     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.'''
       )

       lowerCAmelCase    : Optional[int]              =     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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase  :      Tuple			 ={
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Tuple			 =["""MobileNetV2FeatureExtractor"""]
					_lowerCAmelCase  :      Optional[int]			 =["""MobileNetV2ImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[str]			 =[
					    """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileNetV2ForImageClassification""",
					    """MobileNetV2ForSemanticSegmentation""",
					    """MobileNetV2Model""",
					    """MobileNetV2PreTrainedModel""",
					    """load_tf_weights_in_mobilenet_v2""",
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileNetVaConfig,
					    MobileNetVaOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
										from .image_processing_mobilenet_va import MobileNetVaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilenet_va import (
										    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaForSemanticSegmentation,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

					_lowerCAmelCase  :      Any			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)