





"""simple docstring"""




import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 					UpperCamelCase_    (       UpperCamelCase__			,					unittest.TestCase      ):
							lowerCamelCase_          =   HerbertTokenizer
							lowerCamelCase_          =   HerbertTokenizerFast
							lowerCamelCase_          =   True


							def        _snake_case					(							self      :str						)				->				str:



									"""simple docstring"""



									super().setUp()

									# Use a simpler test file without japanese/chinese characters
									with open(f'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''							,			encoding="""utf-8"""						) as f_data:
											SCREAMING_SNAKE_CASE__								= f_data.read().replace("""\n\n"""							,			"""\n"""						).strip()

									SCREAMING_SNAKE_CASE__								= [
									    """<s>""",
									    """</s>""",
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """,</w>""",
									    """<unk>""",
									]
									SCREAMING_SNAKE_CASE__								= dict(zip(__A							,			range(len(__A						)						)						)						)
									SCREAMING_SNAKE_CASE__								= ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

									SCREAMING_SNAKE_CASE__								= os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES["""vocab_file"""]						)
									SCREAMING_SNAKE_CASE__								= os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES["""merges_file"""]						)
									with open(self.vocab_file							,			"""w"""						) as fp:
											fp.write(json.dumps(__A						)						)
									with open(self.merges_file							,			"""w"""						) as fp:
											fp.write("""\n""".join(__A						)						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= """lower newer"""
									SCREAMING_SNAKE_CASE__								= """lower newer"""
									return input_text, output_text


							def        _snake_case					(							self      :Any						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.tokenizer_class(vocab_file=self.vocab_file							,			merges_file=self.merges_file						)

									SCREAMING_SNAKE_CASE__								= """lower"""
									SCREAMING_SNAKE_CASE__								= ["""low""", """er</w>"""]
									SCREAMING_SNAKE_CASE__								= tokenizer.tokenize(__A						)
									self.assertListEqual(__A							,			__A						)

									SCREAMING_SNAKE_CASE__								= tokens + ["""<unk>"""]
									SCREAMING_SNAKE_CASE__								= [16, 17, 23]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						)							,			__A						)


							def        _snake_case					(							self      :Tuple						)				->				Optional[Any]:



									"""simple docstring"""



									if not self.test_rust_tokenizer:
											return

									SCREAMING_SNAKE_CASE__								= self.get_tokenizer()
									SCREAMING_SNAKE_CASE__								= self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE__								= """lower,newer"""

									SCREAMING_SNAKE_CASE__								= tokenizer.tokenize(__A						)
									SCREAMING_SNAKE_CASE__								= rust_tokenizer.tokenize(__A						)
									self.assertListEqual(__A							,			__A						)

									SCREAMING_SNAKE_CASE__								= tokenizer.encode(__A							,			add_special_tokens=__A						)
									SCREAMING_SNAKE_CASE__								= rust_tokenizer.encode(__A							,			add_special_tokens=__A						)
									self.assertListEqual(__A							,			__A						)

									SCREAMING_SNAKE_CASE__								= self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE__								= tokenizer.encode(__A						)
									SCREAMING_SNAKE_CASE__								= rust_tokenizer.encode(__A						)
									self.assertListEqual(__A							,			__A						)


							@slow
							def        _snake_case					(							self      :Optional[int]						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.tokenizer_class.from_pretrained("""allegro/herbert-base-cased"""						)

									SCREAMING_SNAKE_CASE__								= tokenizer.encode("""konstruowanie sekwencji"""							,			add_special_tokens=__A						)
									SCREAMING_SNAKE_CASE__								= tokenizer.encode("""konstruowanie wielu sekwencji"""							,			add_special_tokens=__A						)

									SCREAMING_SNAKE_CASE__								= tokenizer.build_inputs_with_special_tokens(__A						)
									SCREAMING_SNAKE_CASE__								= tokenizer.build_inputs_with_special_tokens(__A							,			__A						)

									assert encoded_sentence == [0] + text + [2]
									assert encoded_pair == [0] + text + [2] + text_a + [2]






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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Union[str, Any]      ):
		# Construct configuration depending on size
		SCREAMING_SNAKE_CASE__								= {"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE__								= ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= ImageGPTForCausalLM(UpperCamelCase__      )

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

		# Save pytorch-model
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''      )
		torch.save(model.state_dict()   ,				UpperCamelCase__      )
		print(f'''Save configuration file to {pytorch_config_dump_path}'''      )
		with open(UpperCamelCase__   ,				"""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
	)