





'''simple docstring'''




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase         =   500000

__UpperCAmelCase   ,     __UpperCAmelCase         =   os.path.split(__file__)
__UpperCAmelCase         =   os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))



@get_duration
def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Any						=	dataset.select(range(0	,	len(snake_case__					)	,	2					)					)



@get_duration
def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Dict						=	dataset.sort("""numbers"""					)



@get_duration
def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[str]						=	dataset.shuffle()



@get_duration
def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	dataset.train_test_split(0.1					)



@get_duration
def 						__A						(       lowerCamelCase_	,	lowerCamelCase_=10					):


		"""simple docstring"""



		for shard_id in range(snake_case__					):
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	dataset.shard(snake_case__	,	snake_case__					)



def 						__A						(       ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[Any]						=	{"""num examples""": SPEED_TEST_N_EXAMPLES}
		SCREAMING_SNAKE_CASE				:							Dict						=	(select, sort, shuffle, train_test_split, shard)
		with tempfile.TemporaryDirectory() as tmp_dir:
				print("""generating dataset"""					)
				SCREAMING_SNAKE_CASE				:							int						=	datasets.Features({"""text""": datasets.Value("""string"""					), """numbers""": datasets.Value("""float32"""					)}					)
				SCREAMING_SNAKE_CASE				:							int						=	generate_example_dataset(
				    os.path.join(snake_case__	,	"""dataset.arrow"""					)	,	snake_case__	,	num_examples=snake_case__					)
				print("""Functions"""					)
				for func in functions:
						print(func.__name__					)
						SCREAMING_SNAKE_CASE				:							Tuple						=	func(snake_case__					)

		with open(snake_case__	,	"""wb"""					) as f:
				f.write(json.dumps(snake_case__					).encode("""utf-8"""					)					)


if __name__ == "__main__":  # useful to run the profiler
							benchmark_indices_mapping()










'''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 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	{"""small""": (5_12, 8, 24), """medium""": (10_24, 8, 36), """large""": (15_36, 16, 48)}
		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE				:							Any						=	ImageGPTConfig(n_embd=lowerCamelCase_	,	n_layer=lowerCamelCase_	,	n_head=lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							str						=	ImageGPTForCausalLM(lowerCamelCase_					)

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

		# Save pytorch-model
		SCREAMING_SNAKE_CASE				:							Any						=	pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	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__":
							__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
							)



