

'''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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     for attribute in key.split("."				):
          _lowerCAmelCase   :					Tuple        =    getattr(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

     if weight_type is not None:
          _lowerCAmelCase   :					Optional[int]        =    getattr(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				).shape
     else:
          _lowerCAmelCase   :					Dict        =    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   :					Any        =    value
     elif weight_type == "weight_g":
          _lowerCAmelCase   :					Optional[int]        =    value
     elif weight_type == "weight_v":
          _lowerCAmelCase   :					Tuple        =    value
     elif weight_type == "bias":
          _lowerCAmelCase   :					List[Any]        =    value
     elif weight_type == "running_mean":
          _lowerCAmelCase   :					Dict        =    value
     elif weight_type == "running_var":
          _lowerCAmelCase   :					Optional[int]        =    value
     elif weight_type == "num_batches_tracked":
          _lowerCAmelCase   :					int        =    value
     else:
          _lowerCAmelCase   :					List[Any]        =    value

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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    []

     if task == "s2t":
          _lowerCAmelCase   :					Optional[Any]        =    hf_model.speechta.encoder.prenet.feature_encoder
          _lowerCAmelCase   :					List[Any]        =    MAPPING_S2T
          _lowerCAmelCase   :					List[Any]        =    IGNORE_KEYS_S2T
     elif task == "t2s":
          _lowerCAmelCase   :					Tuple        =    None
          _lowerCAmelCase   :					List[str]        =    MAPPING_T2S
          _lowerCAmelCase   :					int        =    IGNORE_KEYS_T2S
     elif task == "s2s":
          _lowerCAmelCase   :					str        =    hf_model.speechta.encoder.prenet.feature_encoder
          _lowerCAmelCase   :					Tuple        =    MAPPING_S2S
          _lowerCAmelCase   :					Optional[int]        =    IGNORE_KEYS_S2S
     else:
          raise ValueError(f'''Unsupported task: {task}'''				)

     for name, value in fairseq_dict.items():
          if should_ignore(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
               logger.info(f'''{name} was ignored'''				)
               continue

          _lowerCAmelCase   :					Tuple        =    False
          if "conv_layers" in name:
               load_conv_layer(
                   SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							hf_model.config.feat_extract_norm == "group"		,							)
               _lowerCAmelCase   :					List[str]        =    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   :					List[Any]        =    key.split(".*."				)
                         if prefix in name and suffix in name:
                              _lowerCAmelCase   :					str        =    suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                    if key in name:
                         _lowerCAmelCase   :					Union[str, Any]        =    True
                         if "*" in mapped_key:
                              _lowerCAmelCase   :					List[Any]        =    name.split(SCREAMING_SNAKE_CASE				)[0].split("."				)[-2]
                              _lowerCAmelCase   :					Union[str, Any]        =    mapped_key.replace("*"		,							SCREAMING_SNAKE_CASE				)
                         if "weight_g" in name:
                              _lowerCAmelCase   :					Optional[int]        =    "weight_g"
                         elif "weight_v" in name:
                              _lowerCAmelCase   :					Any        =    "weight_v"
                         elif "bias" in name:
                              _lowerCAmelCase   :					int        =    "bias"
                         elif "weight" in name:
                              _lowerCAmelCase   :					str        =    "weight"
                         elif "running_mean" in name:
                              _lowerCAmelCase   :					Optional[Any]        =    "running_mean"
                         elif "running_var" in name:
                              _lowerCAmelCase   :					Optional[Any]        =    "running_var"
                         elif "num_batches_tracked" in name:
                              _lowerCAmelCase   :					int        =    "num_batches_tracked"
                         else:
                              _lowerCAmelCase   :					Tuple        =    None
                         set_recursively(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
                    continue
          if not is_used:
               unused_weights.append(SCREAMING_SNAKE_CASE				)

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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Any:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    full_name.split("conv_layers."				)[-1]
     _lowerCAmelCase   :					Tuple        =    name.split("."				)
     _lowerCAmelCase   :					Union[str, Any]        =    int(items[0]				)
     _lowerCAmelCase   :					Tuple        =    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   :					str        =    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   :					Union[str, Any]        =    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   :					List[Any]        =    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   :					Any        =    value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
     else:
          unused_weights.append(SCREAMING_SNAKE_CASE				)







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							)       ->      str:

     """simple docstring"""







     if config_path is not None:
          _lowerCAmelCase   :					List[str]        =    SpeechTaConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
     else:
          _lowerCAmelCase   :					List[Any]        =    SpeechTaConfig()

     if task == "s2t":
          _lowerCAmelCase   :					Dict        =    config.max_text_positions
          _lowerCAmelCase   :					Union[str, Any]        =    SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE				)
     elif task == "t2s":
          _lowerCAmelCase   :					int        =    1876
          _lowerCAmelCase   :					Dict        =    600
          _lowerCAmelCase   :					Tuple        =    config.max_speech_positions
          _lowerCAmelCase   :					Dict        =    SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE				)
     elif task == "s2s":
          _lowerCAmelCase   :					int        =    1876
          _lowerCAmelCase   :					Tuple        =    config.max_speech_positions
          _lowerCAmelCase   :					Any        =    SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE				)
     else:
          raise ValueError(f'''Unknown task name: {task}'''				)

     if vocab_path:
          _lowerCAmelCase   :					Union[str, Any]        =    SpeechTaTokenizer(SCREAMING_SNAKE_CASE		,							model_max_length=config.max_text_positions				)

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

     _lowerCAmelCase   :					List[str]        =    SpeechTaFeatureExtractor()
     _lowerCAmelCase   :					Optional[Any]        =    SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE		,							feature_extractor=SCREAMING_SNAKE_CASE				)
     processor.save_pretrained(SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					Dict        =    torch.load(SCREAMING_SNAKE_CASE				)
     recursively_load_weights(fairseq_checkpoint["model"]		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

     model.save_pretrained(SCREAMING_SNAKE_CASE				)

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


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,
  )









import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Optional[int]:

     """simple docstring"""







     return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    {}

     for key, value in state_dict.items():
          if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
               continue

          _lowerCAmelCase   :					Union[str, Any]        =    key.replace("heads.cmd.mim_head.cls.predictions"		,							"mmm_image_head"				)
          _lowerCAmelCase   :					int        =    key.replace("heads.cmd.mlm_head.cls.predictions"		,							"mmm_text_head"				)
          _lowerCAmelCase   :					Any        =    key.replace("heads.cmd.itm_head.cls"		,							"itm_head"				)
          _lowerCAmelCase   :					Optional[Any]        =    key.replace("heads.cmd.itm_head.pooler"		,							"itm_head.pooler"				)
          _lowerCAmelCase   :					Any        =    key.replace("heads.cmd.clip_head.logit_scale"		,							"flava.logit_scale"				)
          _lowerCAmelCase   :					List[Any]        =    key.replace("heads.fairseq_mlm.cls.predictions"		,							"mlm_head"				)
          _lowerCAmelCase   :					Union[str, Any]        =    key.replace("heads.imagenet.mim_head.cls.predictions"		,							"mim_head"				)
          _lowerCAmelCase   :					Optional[int]        =    key.replace("mm_text_projection"		,							"flava.text_to_mm_projection"				)
          _lowerCAmelCase   :					int        =    key.replace("mm_image_projection"		,							"flava.image_to_mm_projection"				)
          _lowerCAmelCase   :					List[str]        =    key.replace("image_encoder.module"		,							"flava.image_model"				)
          _lowerCAmelCase   :					Optional[int]        =    key.replace("text_encoder.module"		,							"flava.text_model"				)
          _lowerCAmelCase   :					List[str]        =    key.replace("mm_encoder.module.encoder.cls_token"		,							"flava.multimodal_model.cls_token"				)
          _lowerCAmelCase   :					Dict        =    key.replace("mm_encoder.module"		,							"flava.multimodal_model"				)
          _lowerCAmelCase   :					Dict        =    key.replace("text_projection"		,							"flava.text_projection"				)
          _lowerCAmelCase   :					Optional[Any]        =    key.replace("image_projection"		,							"flava.image_projection"				)

          _lowerCAmelCase   :					List[Any]        =    value.float()

     for key, value in codebook_state_dict.items():
          _lowerCAmelCase   :					int        =    value

     return upgrade







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None				)       ->      Tuple:

     """simple docstring"""







     if config_path is not None:
          _lowerCAmelCase   :					Dict        =    FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
     else:
          _lowerCAmelCase   :					Dict        =    FlavaConfig()

     _lowerCAmelCase   :					List[Any]        =    FlavaForPreTraining(SCREAMING_SNAKE_CASE				).eval()

     _lowerCAmelCase   :					Optional[int]        =    convert_dalle_checkpoint(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							save_checkpoint=SCREAMING_SNAKE_CASE				)

     if os.path.exists(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Dict        =    torch.load(SCREAMING_SNAKE_CASE		,							map_location="cpu"				)
     else:
          _lowerCAmelCase   :					Tuple        =    torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE		,							map_location="cpu"				)

     _lowerCAmelCase   :					Optional[int]        =    upgrade_state_dict(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     hf_model.load_state_dict(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    hf_model.state_dict()
     _lowerCAmelCase   :					Optional[Any]        =    count_parameters(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    count_parameters(SCREAMING_SNAKE_CASE				) + count_parameters(SCREAMING_SNAKE_CASE				)

     assert torch.allclose(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							atol=1e-3				)

     hf_model.save_pretrained(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  __UpperCAmelCase    				=     argparse.ArgumentParser()
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
  parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
  parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
  __UpperCAmelCase    				=     parser.parse_args()

  convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






