





'''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
  )










'''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_							)



