



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

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

							# Save pytorch-model
							a__:       Union[str, Any]											=pytorch_dump_folder_path + "/" + WEIGHTS_NAME
							a__:       List[str]											=pytorch_dump_folder_path + "/" + CONFIG_NAME
							print(F"Save PyTorch model to {pytorch_weights_dump_path}"						)
							torch.save(model.state_dict()     , __magic_name__						)
							print(F"Save configuration file to {pytorch_config_dump_path}"						)
							with open(__magic_name__     , "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
		)










import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__UpperCAmelCase						  =     10_00_00_00_00_00
__UpperCAmelCase						  =     1_00_00

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





def 						__lowerCamelCase       (      __magic_name__	:  int     , __magic_name__	:  int						):
							a__:       Any											=pa.Table.from_pydict({"col": [0] * chunk_size}						)
							a__:       Optional[Any]											=pa.concat_tables([table] * (num_examples // chunk_size)						)
							return datasets.Dataset(__magic_name__     , fingerprint="table_100B"						)


@dataclass
class 	lowerCamelCase__ :
						_lowerCAmelCase								= 42
						_lowerCAmelCase								= 42
						_lowerCAmelCase								= 42
						_lowerCAmelCase								= 42


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													a__:       Tuple											=np.random.default_rng(self.seed			)
													a__:       Tuple											=rng.integers(low=self.low      ,       high=self.high      ,       size=self.size			).tolist()


						def __iter__( self						:  Any			):
													return iter(self._sampled_values			)







						def __len__( self						:  Any			):
													return self.size





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       Union[str, Any]											=dataset[0]





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       Optional[Any]											=dataset[-1]





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       List[str]											=dataset[range(len(__magic_name__						) // 2     , len(__magic_name__						) // 2 + 1_024						)]





@get_duration
def 						__lowerCamelCase       (      __magic_name__	:  datasets.Dataset						):
							a__:       Optional[int]											=dataset[RandIter(0     , len(__magic_name__						)     , 1_024     , seed=42						)]





def 						__lowerCamelCase       (      ):
							a__:       Dict											={"num examples": SPEED_TEST_N_EXAMPLES}
							a__:       Union[str, Any]											=(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
							print("generating dataset"						)
							a__:       Dict											=generate_100B_dataset(num_examples=__magic_name__     , chunk_size=__magic_name__						)
							print("Functions"						)
							for func in functions:
														print(func.__name__						)
														a__:       Dict											=func(__magic_name__						)

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


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()





