






import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							,			snake_case__							):
      # Initialise PyTorch model
      __A		   =      json.load(open(snake_case__							,			"r"							)							)
      __A		   =      GPTNeoConfig(
          hidden_size=config_json["n_embd"]							,			num_layers=config_json["n_layer"]							,			num_heads=config_json["n_head"]							,			attention_types=config_json["attention_types"]							,			max_position_embeddings=config_json["n_positions"]							,			resid_dropout=config_json["res_dropout"]							,			embed_dropout=config_json["embed_dropout"]							,			attention_dropout=config_json["attn_dropout"]							,			)
      print(f"Building PyTorch model from configuration: {config}"							)
      __A		   =      GPTNeoForCausalLM(snake_case__							)

      # Load weights from tf checkpoint
      load_tf_weights_in_gpt_neo(snake_case__							,			snake_case__							,			snake_case__							)

      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}"							)
      model.save_pretrained(snake_case__							)


if __name__ == "__main__":
       __A      :       int      =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained mesh-tf model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       __A      :       Any      =	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''







from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__A      :       Tuple      =	datasets.utils.logging.get_logger(__name__)






class 					a__    (			folder_based_builder.FolderBasedBuilderConfig						):
     '''simple docstring'''







     _snake_case             =  None
     _snake_case             =  None







class 					a__    (			folder_based_builder.FolderBasedBuilder						):
     '''simple docstring'''







     _snake_case             =  datasets.Image()
     _snake_case             =  'image'
     _snake_case             =  ImageFolderConfig
     _snake_case             =  42  # definition at the bottom of the script
     _snake_case             =  ImageClassification(image_column='image' ,						label_column='label'						)







__A      :       Any      =	[
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
__A      :       List[Any]      =	IMAGE_EXTENSIONS


