





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__: str							   =						logging.get_logger('transformers.models.speecht5')

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


def       UpperCamelCase__(			UpperCamelCase__     :			Tuple					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Union[str, Any]    )->Union[str, Any]:
       for attribute in key.split('''.'''    ):
              A__		   =		getattr(UpperCamelCase__					,	UpperCamelCase__    )

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

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


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       for key in ignore_keys:
              if key.endswith('''.*'''    ):
                     if name.startswith(key[:-1]    ):
                            return True
              elif ".*." in key:
                     A__   ,      A__		   =		key.split('''.*.'''    )
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			Tuple					,	UpperCamelCase__     :			List[str]    )->int:
       A__		   =		[]

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

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

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

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

       if vocab_path:
              A__		   =		SpeechTaTokenizer(UpperCamelCase__					,	model_max_length=config.max_text_positions    )

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

       A__		   =		SpeechTaFeatureExtractor()
       A__		   =		SpeechTaProcessor(tokenizer=UpperCamelCase__					,	feature_extractor=UpperCamelCase__    )
       processor.save_pretrained(UpperCamelCase__    )

       A__		   =		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__":
       a__: Optional[Any]							   =						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__: Any							   =						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 warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)





