




'''simple docstring'''





import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD


def 					UpperCamelCase_(     snake_case							:	List[str]					):

							'''simple docstring'''






							def wrapper(*snake_case							:	Optional[Any]							,    **snake_case							:	Union[str, Any]					):
														snake_case_      =				timeit.default_timer()
														snake_case_      =				func(*snake_case							,    **snake_case					)
														snake_case_      =				timeit.default_timer() - starttime
														return delta

							snake_case_      =				func.__name__

							return wrapper


def 					UpperCamelCase_(     snake_case							:	dict							,    snake_case							:	Tuple=1_0_0							,    snake_case							:	Optional[Any]=None					):

							'''simple docstring'''






							snake_case_      =				[]
							snake_case_      =				seq_shapes or {}
							for i in range(snake_case					):
														snake_case_      =				{}
														for col_id, (k, v) in enumerate(features.items()					):
																					if isinstance(snake_case							,    _ArrayXD					):
																												snake_case_      =				np.random.rand(*v.shape					).astype(v.dtype					)
																					elif isinstance(snake_case							,    datasets.Value					):
																												if v.dtype == "string":
																																			snake_case_      =				"The small grey turtle was surprisingly fast when challenged."
																												else:
																																			snake_case_      =				np.random.randint(1_0							,    size=1					).astype(v.dtype					).item()
																					elif isinstance(snake_case							,    datasets.Sequence					):
																												while isinstance(snake_case							,    datasets.Sequence					):
																																			snake_case_      =				v.feature
																												snake_case_      =				seq_shapes[k]
																												snake_case_      =				np.random.rand(*snake_case					).astype(v.dtype					)
																					snake_case_      =				data

														dummy_data.append((i, example)					)

							return dummy_data




def 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	Tuple							,    snake_case							:	str=1_0_0							,    snake_case							:	str=None					):

							'''simple docstring'''






							snake_case_      =				generate_examples(snake_case							,    num_examples=snake_case							,    seq_shapes=snake_case					)

							with ArrowWriter(features=snake_case							,    path=snake_case					) as writer:
														for key, record in dummy_data:
																					snake_case_      =				features.encode_example(snake_case					)
																					writer.write(snake_case					)

														snake_case_ ,				snake_case_      =				writer.finalize()

							if not num_final_examples == num_examples:
														raise ValueError(
														    f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'					)

							snake_case_      =				datasets.Dataset.from_file(filename=snake_case							,    info=datasets.DatasetInfo(features=snake_case					)					)

							return dataset










'''simple docstring'''





import logging
import os

from .state import PartialState




class 			_snake_case		(     logging.LoggerAdapter							):




			@staticmethod
			def        lowerCAmelCase__    ( a__							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				PartialState()
										return not main_process_only or (main_process_only and state.is_main_process)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							*a__    ,							**a__							)    ->    List[Any]:

										'''simple docstring'''




										if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    "You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."							)
										snake_case_      =				kwargs.pop("main_process_only"    ,							a__							)
										snake_case_      =				kwargs.pop("in_order"    ,							a__							)

										if self.isEnabledFor(a__							):
																	if self._should_log(a__							):
																								snake_case_ ,				snake_case_      =				self.process(a__    ,							a__							)
																								self.logger.log(a__    ,							a__    ,							*a__    ,							**a__							)

																	elif in_order:
																								snake_case_      =				PartialState()
																								for i in range(state.num_processes							):
																															if i == state.process_index:
																																						snake_case_ ,				snake_case_      =				self.process(a__    ,							a__							)
																																						self.logger.log(a__    ,							a__    ,							*a__    ,							**a__							)
																															state.wait_for_everyone()


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	str = None					):

							'''simple docstring'''






							if log_level is None:
														snake_case_      =				os.environ.get("ACCELERATE_LOG_LEVEL"							,    snake_case					)
							snake_case_      =				logging.getLogger(snake_case					)
							if log_level is not None:
														logger.setLevel(log_level.upper()					)
														logger.root.setLevel(log_level.upper()					)
							return MultiProcessAdapter(snake_case							,    {}					)




