




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       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Tuple ,  __UpperCamelCase    :		Tuple ,  __UpperCamelCase    :		Tuple   )						->							List[Any]:
    # Initialise PyTorch model
    UpperCAmelCase_											=  json.load(open(__UpperCamelCase ,  '''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(__UpperCamelCase   )

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

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


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











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

import diffusers.utils.hub_utils



class     a  (     unittest.TestCase    ):

     '''simple docstring'''





     @patch('''diffusers.utils.hub_utils.get_full_repo_name'''       )
     def 						lowerCamelCase_     (			self     :    List[Any]      ,					__snake_case     :    Mock       ):
         UpperCAmelCase_											=  '''full_repo_name'''
         with TemporaryDirectory() as tmpdir:
             # Dummy args values
             UpperCAmelCase_											=  Mock()
             UpperCAmelCase_											=  tmpdir
             UpperCAmelCase_											=  0
             UpperCAmelCase_											=  '''hub_token'''
             UpperCAmelCase_											=  '''dataset_name'''
             UpperCAmelCase_											=  0.01
             UpperCAmelCase_											=  10_00_00
             UpperCAmelCase_											=  1_00_00
             UpperCAmelCase_											=  0.01
             UpperCAmelCase_											=  0.02
             UpperCAmelCase_											=  0.03
             UpperCAmelCase_											=  0.0_005
             UpperCAmelCase_											=  0.000_001
             UpperCAmelCase_											=  1
             UpperCAmelCase_											=  10
             UpperCAmelCase_											=  0.001
             UpperCAmelCase_											=  0.1
             UpperCAmelCase_											=  0.2
             UpperCAmelCase_											=  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()       )





