



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  :				List[str]       ,      __lowerCamelCase  :				Optional[int]       ,      __lowerCamelCase  :				Union[str, Any]					):
       # Construct configuration depending on size
       snake_case :     Any           =   {"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
       snake_case :     int           =   MODELS[model_size]  # set model hyperparameters
       snake_case :     Tuple           =   ImageGPTConfig(n_embd=__lowerCamelCase       ,      n_layer=__lowerCamelCase       ,      n_head=__lowerCamelCase					)
       snake_case :     List[str]           =   ImageGPTForCausalLM(__lowerCamelCase					)

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

       # Save pytorch-model
       snake_case :     Dict           =   pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       snake_case :     str           =   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							= 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							= parser.parse_args()
   convert_imagegpt_checkpoint_to_pytorch(
       args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
   )







import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						UpperCAmelCase	(		A_ ,unittest.TestCase				):
   A__      :	Dict		  =  PreTrainedTokenizerFast
   A__      :	str		  =  False
   A__      :	List[Any]		  =  True
   A__      :	List[str]		  =  "tokenizer_file"



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   False  # because we don't have pretrained_vocab_files_map
          super().setUp()
          snake_case :     Optional[int]           =   True

          snake_case :     List[str]           =   ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
          snake_case :     Optional[Any]           =   "SaulLu/dummy-tokenizer-bytelevel-bpe"

          # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
          snake_case :     int           =   [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

          snake_case :     List[Any]           =   PreTrainedTokenizerFast.from_pretrained(model_paths[0]				)
          tokenizer.save_pretrained(self.tmpdirname				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			int:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			int:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			Union[str, Any]:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			List[str]:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     Optional[int]           =   self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                        try:
                               snake_case :     Any           =   tempfile.mkdtemp()
                               snake_case :     int           =   self.rust_tokenizer_class.from_pretrained(snake_case__					,		**snake_case__				)

                               tokenizer.save_pretrained(self.tmpdirname				)
                               super().test_training_new_tokenizer()
                        finally:
                               # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                               # is restored
                               shutil.rmtree(self.tmpdirname				)
                               snake_case :     int           =   tmpdirname_orig



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Dict:


          '''simple docstring'''


          snake_case :     Optional[int]           =   self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                        try:
                               snake_case :     Union[str, Any]           =   tempfile.mkdtemp()
                               snake_case :     str           =   self.rust_tokenizer_class.from_pretrained(snake_case__					,		**snake_case__				)

                               tokenizer.save_pretrained(self.tmpdirname				)
                               super().test_training_new_tokenizer_with_special_tokens_change()
                        finally:
                               # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                               # is restored
                               shutil.rmtree(self.tmpdirname				)
                               snake_case :     Dict           =   tmpdirname_orig



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     int           =   self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name				)

          snake_case :     int           =   ("a" for _ in range(10_00				))
          snake_case :     Optional[int]           =   tokenizer.train_new_from_iterator(text_iterator=snake_case__					,		length=10_00					,		vocab_size=50				)

          snake_case :     str           =   new_tokenizer.encode("a🤗"				)
          self.assertEqual(snake_case__					,		[64, 1_72, 2_53, 97, 2_45]				)






@require_tokenizers
class 						UpperCAmelCase	(		unittest.TestCase				):



   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     List[str]           =   AutoTokenizer.from_pretrained("bert-base-cased"				)
          snake_case :     Union[str, Any]           =   json.loads(tokenizer._tokenizer.to_str()				)
          snake_case :     Tuple           =   len(snake_case__				)

          with tempfile.TemporaryDirectory() as tmp_dir:
                 # Hack to save this in the tokenizer_config.json
                 snake_case :     List[str]           =   ["tokenizer.4.0.0.json"]
                 tokenizer.save_pretrained(snake_case__				)
                 json.dump(snake_case__					,		open(os.path.join(snake_case__					,		"tokenizer.4.0.0.json"				)					,		"w"				)				)

                 # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
                 snake_case :     Optional[Any]           =   AutoTokenizer.from_pretrained(snake_case__				)
                 self.assertEqual(len(snake_case__				)					,		len(snake_case__				) + 1				)
                 snake_case :     Union[str, Any]           =   json.loads(new_tokenizer._tokenizer.to_str()				)
                 self.assertIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)

                 # Will need to be adjusted if we reach v42 and this test is still here.
                 # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
                 shutil.move(os.path.join(snake_case__					,		"tokenizer.4.0.0.json"				)					,		os.path.join(snake_case__					,		"tokenizer.42.0.0.json"				)				)
                 snake_case :     Optional[Any]           =   ["tokenizer.42.0.0.json"]
                 tokenizer.save_pretrained(snake_case__				)
                 snake_case :     Dict           =   AutoTokenizer.from_pretrained(snake_case__				)
                 self.assertEqual(len(snake_case__				)					,		len(snake_case__				)				)
                 snake_case :     Optional[int]           =   json.loads(new_tokenizer._tokenizer.to_str()				)
                 self.assertNotIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     int           =   "hf-internal-testing/test-two-tokenizers"

          # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
          snake_case :     int           =   AutoTokenizer.from_pretrained(snake_case__				)
          self.assertEqual(len(snake_case__				)					,		2_89_97				)
          snake_case :     Optional[int]           =   json.loads(tokenizer._tokenizer.to_str()				)
          self.assertIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)

          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers

          snake_case :     Dict           =   "3.0.0"
          snake_case :     Union[str, Any]           =   old_transformers.models.auto.AutoTokenizer.from_pretrained(snake_case__				)
          self.assertEqual(len(snake_case__				)					,		2_89_96				)
          snake_case :     Optional[Any]           =   json.loads(old_tokenizer._tokenizer.to_str()				)
          self.assertNotIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)






@require_tokenizers
class 						UpperCAmelCase	(		unittest.TestCase				):



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Tuple           =   PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"				)
          snake_case :     List[Any]           =   "The Matrix is a 1999 science fiction action film."

          with concurrent.futures.ThreadPoolExecutor() as executor:
                 snake_case :     Union[str, Any]           =   [executor.submit(self.fetch					,		snake_case__					,		snake_case__				) for i in range(10				)]
                 snake_case :     Optional[Any]           =   [future.result() for future in futures]
                 self.assertEqual(snake_case__					,		[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10				)]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      str					,		snake_case__	:      List[str]					,		snake_case__	:      str				)	->			List[str]:


          '''simple docstring'''


          return tokenizer.encode(snake_case__					,		truncation="longest_first"					,		padding="longest"				)





