





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


try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (
         VersatileDiffusionDualGuidedPipeline,
         VersatileDiffusionImageVariationPipeline,
         VersatileDiffusionPipeline,
         VersatileDiffusionTextToImagePipeline,
     )
else:
     from .modeling_text_unet import UNetFlatConditionModel
     from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
     from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
     from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
     from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline








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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Any:
       for attribute in key.split("."  ):
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  )

       if weight_type is not None:
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  ).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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[Any]:
       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(UpperCamelCase_					,		UpperCamelCase_  ):
                     logger.info(F'''{name} was ignored'''  )
                     continue

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

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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		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(UpperCamelCase_  )




@torch.no_grad()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		)     ->		str:
       if config_path is not None:
              UpperCamelCase_          =  SpeechTaConfig.from_pretrained(UpperCamelCase_  )
       else:
              UpperCamelCase_          =  SpeechTaConfig()

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

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

       UpperCamelCase_          =  SpeechTaFeatureExtractor()
       UpperCamelCase_          =  SpeechTaProcessor(tokenizer=UpperCamelCase_					,		feature_extractor=UpperCamelCase_  )
       processor.save_pretrained(UpperCamelCase_  )

       UpperCamelCase_          =  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__":
     _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,
     )

