




from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
							import torch

UpperCamelCase__              =						logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
							from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






							def 					snake_case					(							cls			)							->							Tuple:




								'''simple docstring'''


								return cls






def 					snake_case					(							A						,   A = None						,   A = None						,   A = None						,   A = None						,   )							->							List[str]:




	'''simple docstring'''


	lowerCamelCase_							:		Dict							   =							device
	lowerCamelCase_							:		List[str]							   =							shape[0]

	lowerCamelCase_							:		Optional[Any]							   =							layout or torch.strided
	lowerCamelCase_							:		List[str]							   =							device or torch.device('cpu'			)

	if generator is not None:
		lowerCamelCase_							:		str							   =							generator.device.type if not isinstance(A						,   A			) else generator[0].device.type
		if gen_device_type != device.type and gen_device_type == "cpu":
			lowerCamelCase_							:		List[Any]							   =							'cpu'
			if device != "mps":
				logger.info(
				    f"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
				    f" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
				    f" slighly speed up this function by passing a generator that was created on the {device} device."			)
		elif gen_device_type != device.type and gen_device_type == "cuda":
			raise ValueError(f"Cannot generate a {device} tensor from a generator of type {gen_device_type}."			)

	if isinstance(A						,   A			):
		lowerCamelCase_							:		int							   =							(1,) + shape[1:]
		lowerCamelCase_							:		List[str]							   =							[
		    torch.randn(A						,   generator=generator[i]						,   device=A						,   dtype=A						,   layout=A			)
		    for i in range(A			)
		]
		lowerCamelCase_							:		List[Any]							   =							torch.cat(A						,   dim=0			).to(A			)
	else:
		lowerCamelCase_							:		List[str]							   =							torch.randn(A						,   generator=A						,   device=A						,   dtype=A						,   layout=A			).to(A			)

	return latents






def 					snake_case					(							A			)							->							Any:




	'''simple docstring'''


	if is_torch_version('<'						,   '2.0.0'			) or not hasattr(A						,   '_dynamo'			):
		return False
	return isinstance(A						,   torch._dynamo.eval_frame.OptimizedModule			)






import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__              =						1_0_0_0_0_0_0_0_0_0_0_0
UpperCamelCase__              =						1_0_0_0_0

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






def 					snake_case					(							A						,   A			)							->							datasets.Dataset:




	'''simple docstring'''


	lowerCamelCase_							:		int							   =							pa.Table.from_pydict({'col': [0] * chunk_size}			)
	lowerCamelCase_							:		List[str]							   =							pa.concat_tables([table] * (num_examples // chunk_size)			)
	return datasets.Dataset(A						,   fingerprint='table_100B'			)


@dataclass
class 				__SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							np.random.default_rng(self.seed					)
						lowerCamelCase_							:		Any							   =							rng.integers(low=self.low ,       high=self.high ,       size=self.size					).tolist()

					def __iter__(				self					):







						'''simple docstring'''




						return iter(self._sampled_values					)





					def __len__(				self					):







						'''simple docstring'''




						return self.size






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							dataset[0]






@get_duration
def 					snake_case					(							A			)							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							dataset[-1]






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							dataset[range(len(A			) // 2						,   len(A			) // 2 + 1024			)]






@get_duration
def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							dataset[RandIter(0						,   len(A			)						,   1024						,   seed=42			)]






def 					snake_case					(							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							{'num examples': SPEED_TEST_N_EXAMPLES}
	lowerCamelCase_							:		Optional[int]							   =							(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
	print('generating dataset'			)
	lowerCamelCase_							:		List[Any]							   =							generate_100B_dataset(num_examples=A						,   chunk_size=A			)
	print('Functions'			)
	for func in functions:
		print(func.__name__			)
		lowerCamelCase_							:		str							   =							func(A			)

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


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