




'''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     snake_case_  (       a__		:     List[Any]   ,a__		:     List[str]   ,a__		:     Tuple      ):



    """simple docstring"""




    __lowercase          =						{"""small""": (5_12, 8, 24), """medium""": (10_24, 8, 36), """large""": (15_36, 16, 48)}
    __lowercase    ,__lowercase    ,__lowercase          =						MODELS[model_size]  # set model hyperparameters
    __lowercase          =						ImageGPTConfig(n_embd=a__   ,n_layer=a__   ,n_head=a__      )
    __lowercase          =						ImageGPTForCausalLM(a__      )

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

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


if __name__ == "__main__":
  A							:				List[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."""
  )
  A							:				List[str]			     =	parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )










'''simple docstring'''
import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir






@dataclass
class 	SCREAMING_SNAKE_CASE:
       snake_case_			:					str






class 	SCREAMING_SNAKE_CASE(    unittest.TestCase					):

       def    snake_case__      (   self					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece.model"""					)
           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model"""					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		0					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		1					)

           self.assertIn(
               """The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"""
               """ which is not implemented in the fast tokenizers.""" ,		str(w[0].message					) ,		)




