



import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class      __lowerCAmelCase       (     A							,		unittest.TestCase		):
			UpperCamelCase     =	MPNetTokenizer
			UpperCamelCase     =	MPNetTokenizerFast
			UpperCamelCase     =	True
			UpperCamelCase     =	True



			def        _lowerCamelCase			(		self  :    List[Any])  ->       List[Any]:



						"""simple docstring"""






						super().setUp()

						_UpperCAmelCase									=  [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						_UpperCAmelCase									=  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        _lowerCamelCase			(		self  :    Union[str, Any]					,						A  :    Tuple)  ->       Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase									=  'UNwant\u00E9d,running'
						_UpperCAmelCase									=  'unwanted, running'
						return input_text, output_text



			def        _lowerCamelCase			(		self  :    Optional[Any])  ->       str:



						"""simple docstring"""






						_UpperCAmelCase									=  self.tokenizer_class(self.vocab_file)

						_UpperCAmelCase									=  tokenizer.tokenize('UNwant\u00E9d,running')
						self.assertListEqual(A					,						['un', '##want', '##ed', ',', 'runn', '##ing'])
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A)					,						[9, 6, 7, 12, 10, 11])



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



						"""simple docstring"""






						_UpperCAmelCase									=  self.tokenizer_class.from_pretrained('microsoft/mpnet-base')

						_UpperCAmelCase									=  tokenizer.encode('sequence builders'					,						add_special_tokens=A)
						_UpperCAmelCase									=  tokenizer.encode('multi-sequence build'					,						add_special_tokens=A)

						_UpperCAmelCase									=  tokenizer.build_inputs_with_special_tokens(A)
						_UpperCAmelCase									=  tokenizer.build_inputs_with_special_tokens(A					,						A)

						assert encoded_sentence == [0] + text + [2]
						assert encoded_pair == [0] + text + [2] + [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 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

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

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  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__":
							UpperCAmelCase__  =			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."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
