
"""simple docstring"""



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class       lowerCAmelCase__				(						A_	):
     __a						=					42
     __a						=					42


if is_transformers_available() and is_torch_available():
 from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





"""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,
 )



