code: 
  
  
  
  
  
  '''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       (							_lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Optional[int]		, _lowerCamelCase      :      Any ):
      # Construct configuration depending on size
      A__       =    {"small": (5_12, 8, 24), "medium": (10_24, 8, 36), "large": (15_36, 16, 48)}
      A__,			A__,			A__       =    MODELS[model_size]  # set model hyperparameters
      A__       =    ImageGPTConfig(n_embd=_lowerCamelCase		, n_layer=_lowerCamelCase		, n_head=_lowerCamelCase )
      A__       =    ImageGPTForCausalLM(_lowerCamelCase )
  
      # Load weights from numpy
      load_tf_weights_in_imagegpt(_lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase )
  
      # Save pytorch-model
      A__       =    pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      A__       =    pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
      torch.save(model.state_dict()		, _lowerCamelCase )
      print(F"Save configuration file to {pytorch_config_dump_path}" )
      with open(_lowerCamelCase		, "w"		, encoding="utf-8" ) as f:
          f.write(config.to_json_string() )
  
  
  if __name__ == "__main__":
    __lowerCAmelCase							:			Tuple				=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."
    )
    __lowerCAmelCase							:			Tuple				=parser.parse_args()
    convert_imagegpt_checkpoint_to_pytorch(
        args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
    )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import os
  import tempfile
  import unittest
  from typing import List
  
  from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
  from transformers.testing_utils import require_tokenizers
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  
  
  
  
  
  
  @require_tokenizers
  class      UpperCAmelCase			( unittest.TestCase       ):
   __lowercase     	=       EsmTokenizer
   def   UpperCAmelCase_							(       self   :Any							)->							int:
       super().setUp()
       A__       =    tempfile.mkdtemp()
       # fmt: off
       A__       =    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
       # fmt: on
       A__       =    os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES["vocab_file"]							)
       with open(self.vocab_file		, "w"		, encoding="utf-8"							) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)
   def   UpperCAmelCase_							(       self   :Any		, **lowercase_   :int							)->							List[PreTrainedTokenizerBase]:
       return [self.get_tokenizer(**lowercase_							)]
   def   UpperCAmelCase_							(       self   :Any		, **lowercase_   :Dict							)->							PreTrainedTokenizer:
       return self.tokenizer_class.from_pretrained(self.tmpdirname		, **lowercase_							)
   def   UpperCAmelCase_							(       self   :int							)->							Dict:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    tokenizer.tokenize("LAGVS"							)
       self.assertListEqual(lowercase_		, ["L", "A", "G", "V", "S"]							)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_							)		, [4, 5, 6, 7, 8]							)
   def   UpperCAmelCase_							(       self   :Tuple							)->							Tuple:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    "LAGVS"
       self.assertListEqual(tokenizer.encode(lowercase_							)		, [0, 4, 5, 6, 7, 8, 2]							)
   def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    ["LAGVS", "WCB"]
       A__       =    tokenizer(lowercase_		, padding=lowercase_							)["input_ids"]
  
       self.assertListEqual(lowercase_		, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]							)
   def   UpperCAmelCase_							(       self   :str							)->							Any:
       A__       =    self.tokenizer_class(self.vocab_file							)
  
       A__       =    ["LAGVS", "WCB"]
       A__       =    tokenizer(lowercase_		, padding=lowercase_							)["input_ids"]
  
       self.assertListEqual(lowercase_		, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]							)
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							List[str]:
       A__       =    self.get_tokenizers(fast=lowercase_							)
       for tokenizer in tokenizers:
           with self.subTest(F"{tokenizer.__class__.__name__}"							):
               A__       =    "<unk>"
               A__       =    "<mask>"
  
               A__       =    tokenizer.tokenize(lowercase_							)
               A__       =    tokenizer.tokenize(lowercase_							)
  
               self.assertEqual(len(lowercase_							)		, 1							)
               self.assertEqual(len(lowercase_							)		, 1							)
               self.assertEqual(token_a[0]		, lowercase_							)
               self.assertEqual(token_a[0]		, lowercase_							)
  
  
  
  