



'''simple docstring'''



import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 							__UpperCAmelCase				(				UpperCamelCase__				:Tuple   ,				UpperCamelCase__				:Optional[int]   ,				UpperCamelCase__				:Union[str, Any]			)		->						Any:
				# Construct configuration depending on size
				snake_case__      :   Tuple      =    {'''small''': (512, 8, 24), '''medium''': (1024, 8, 36), '''large''': (1536, 16, 48)}
				snake_case__  ,      snake_case__  ,      snake_case__      :   int      =    MODELS[model_size]  # set model hyperparameters
				snake_case__      :   Any      =    ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__			)
				snake_case__      :   str      =    ImageGPTForCausalLM(UpperCamelCase__			)

				# Load weights from numpy
				load_tf_weights_in_imagegpt(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)

				# Save pytorch-model
				snake_case__      :   Tuple      =    pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				snake_case__      :   str      =    pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''			)
				torch.save(model.state_dict()   ,				UpperCamelCase__			)
				print(F'''Save configuration file to {pytorch_config_dump_path}'''			)
				with open(UpperCamelCase__   ,				'''w'''   ,				encoding='''utf-8'''			) as f:
								f.write(config.to_json_string()			)


if __name__ == "__main__":
				_lowercase       :						Optional[Any]						=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--imagegpt_checkpoint_path",
				    default=None,
				    type=str,
				    required=True,
				    help="Path to the TensorFlow checkpoint path.",
				)
				parser.add_argument(
				    "--model_size",
				    default=None,
				    type=str,
				    required=True,
				    help="Size of the model (can be either 'small', 'medium' or 'large').",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				_lowercase       :						Dict						=parser.parse_args()
				convert_imagegpt_checkpoint_to_pytorch(
				    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
				)







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase							=logging.get_logger(__name__)

_lowerCamelCase							={
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "mask_emb": "masked_spec_embed",
}







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     for attribute in key.split('.'     ):
          SCREAMING_SNAKE_CASE 		=getattr(lowerCAmelCase_,       lowerCAmelCase_     )

     if weight_type is not None:
          SCREAMING_SNAKE_CASE 		=getattr(lowerCAmelCase_,       lowerCAmelCase_     ).shape
     else:
          SCREAMING_SNAKE_CASE 		=hf_pointer.shape

     assert hf_shape == value.shape, (
         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":
          SCREAMING_SNAKE_CASE 		=value
     elif weight_type == "weight_g":
          SCREAMING_SNAKE_CASE 		=value
     elif weight_type == "weight_v":
          SCREAMING_SNAKE_CASE 		=value
     elif weight_type == "bias":
          SCREAMING_SNAKE_CASE 		=value
     else:
          SCREAMING_SNAKE_CASE 		=value

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







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[]
     SCREAMING_SNAKE_CASE 		=fairseq_model.state_dict()

     SCREAMING_SNAKE_CASE 		=hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          SCREAMING_SNAKE_CASE 		=False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       hf_model.config.feat_extract_norm == 'group',       )
               SCREAMING_SNAKE_CASE 		=True
          else:
               for key, mapped_key in MAPPING.items():
                    SCREAMING_SNAKE_CASE 		=mapped_key

                    if key in name:
                         SCREAMING_SNAKE_CASE 		=True
                         if "*" in mapped_key:
                              SCREAMING_SNAKE_CASE 		=name.split(lowerCAmelCase_     )[0].split('.'     )[-2]
                              SCREAMING_SNAKE_CASE 		=mapped_key.replace('*',       lowerCAmelCase_     )
                         if "weight_g" in name:
                              SCREAMING_SNAKE_CASE 		='weight_g'
                         elif "weight_v" in name:
                              SCREAMING_SNAKE_CASE 		='weight_v'
                         elif "weight" in name:
                              SCREAMING_SNAKE_CASE 		='weight'
                         elif "bias" in name:
                              SCREAMING_SNAKE_CASE 		='bias'
                         else:
                              SCREAMING_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      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=full_name.split('conv_layers.'     )[-1]
     SCREAMING_SNAKE_CASE 		=name.split('.'     )
     SCREAMING_SNAKE_CASE 		=int(items[0]     )
     SCREAMING_SNAKE_CASE 		=int(items[1]     )

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   F'{full_name} has size {value.shape}, but'
                   F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
               )
               SCREAMING_SNAKE_CASE 		=value
               logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'     )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   F'{full_name} has size {value.shape}, but'
                   F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
               )
               SCREAMING_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:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                   " found."
               )
               SCREAMING_SNAKE_CASE 		=value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   F'{full_name} has size {value.shape}, but'
                   F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
               )
               SCREAMING_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_     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=HubertConfig()
     SCREAMING_SNAKE_CASE 		=model.config

     SCREAMING_SNAKE_CASE 		=fs_config.activation_dropout
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=fs_config.attention_dropout
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=eval(fs_config.extractor_conv_feature_layers     )
     SCREAMING_SNAKE_CASE 		=[x[0] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		=[x[1] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		=[x[2] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		='gelu'
     SCREAMING_SNAKE_CASE 		='layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=fs_config.activation_fn
     SCREAMING_SNAKE_CASE 		=fs_config.dropout
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_embed_dim
     SCREAMING_SNAKE_CASE 		=0.02
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_ffn_embed_dim
     SCREAMING_SNAKE_CASE 		=1e-5
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_attention_heads
     SCREAMING_SNAKE_CASE 		=fs_config.conv_pos_groups
     SCREAMING_SNAKE_CASE 		=fs_config.conv_pos
     SCREAMING_SNAKE_CASE 		=len(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_layers

     return config







@torch.no_grad()
def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_=None     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=distilhubert().model.model

     if config_path is not None:
          SCREAMING_SNAKE_CASE 		=HubertConfig.from_pretrained(lowerCAmelCase_     )
     else:
          SCREAMING_SNAKE_CASE 		=convert_config(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=model.eval()

     SCREAMING_SNAKE_CASE 		=WavaVecaFeatureExtractor(
         feature_size=1,       sampling_rate=16000,       padding_value=0,       do_normalize=lowerCAmelCase_,       return_attention_mask=lowerCAmelCase_,       )
     SCREAMING_SNAKE_CASE 		=HubertModel(lowerCAmelCase_     )

     recursively_load_weights(lowerCAmelCase_,       lowerCAmelCase_     )

     feature_extractor.save_pretrained(lowerCAmelCase_     )
     hf_model.save_pretrained(lowerCAmelCase_     )


if __name__ == "__main__":
 _lowerCamelCase							=argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 _lowerCamelCase							=parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)

