



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils





class    __lowerCAmelCase					(	unittest.TestCase			):




				@patch("""diffusers.utils.hub_utils.get_full_repo_name"""						)
				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       """full_repo_name"""
									with TemporaryDirectory() as tmpdir:
														# Dummy args values
														_lowerCAmelCase         =       Mock()
														_lowerCAmelCase         =       tmpdir
														_lowerCAmelCase         =       0
														_lowerCAmelCase         =       """hub_token"""
														_lowerCAmelCase         =       """dataset_name"""
														_lowerCAmelCase         =       0.01
														_lowerCAmelCase         =       100000
														_lowerCAmelCase         =       10000
														_lowerCAmelCase         =       0.01
														_lowerCAmelCase         =       0.02
														_lowerCAmelCase         =       0.03
														_lowerCAmelCase         =       0.0005
														_lowerCAmelCase         =       0.00_0001
														_lowerCAmelCase         =       1
														_lowerCAmelCase         =       10
														_lowerCAmelCase         =       0.001
														_lowerCAmelCase         =       0.1
														_lowerCAmelCase         =       0.2
														_lowerCAmelCase         =       True

														# Model card mush be rendered and saved
														diffusers.utils.hub_utils.create_model_card(_snake_case		,							model_name="""model_name"""						)
														self.assertTrue((Path(_snake_case						) / """README.md""").is_file()						)







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							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=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}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

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

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		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									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





