

'''simple docstring'''


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_snake_case				:				Dict       							=		50000
_snake_case				:				Optional[Any]       							=		5000

_snake_case				:				Union[str, Any]       							=		os.path.split(__file__)
_snake_case				:				str       							=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 		snake_case_   (UpperCamelCase	:  datasets.Dataset			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							for i in range(UpperCAmelCase_				):
														_a     					=    dataset[i]
@get_duration
def 		snake_case_   (UpperCamelCase	:  datasets.Dataset			,						UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Any				):





							'''simple docstring'''


							for i in range(0			,						len(UpperCAmelCase_				)			,						UpperCAmelCase_				):
														_a     					=    dataset[i : i + batch_size]
@get_duration
def 		snake_case_   (UpperCamelCase	:  datasets.Dataset			,						UpperCamelCase	:  Dict			,						UpperCamelCase	:  Dict				):





							'''simple docstring'''


							with dataset.formatted_as(type=UpperCAmelCase_				):
														for i in range(UpperCAmelCase_				):
																					_a     					=    dataset[i]
@get_duration
def 		snake_case_   (UpperCamelCase	:  datasets.Dataset			,						UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  List[str]			,						UpperCamelCase	:  Union[str, Any]				):





							'''simple docstring'''


							with dataset.formatted_as(type=UpperCAmelCase_				):
														for i in range(0			,						UpperCAmelCase_			,						UpperCAmelCase_				):
																					_a     					=    dataset[i : i + batch_size]
def 		snake_case_   ():





							'''simple docstring'''


							_a     					=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
							_a     					=    [
							    (read, {'''length''': SMALL_TEST}),
							    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
							    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
							    (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
							    (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
							    (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
							    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
							    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
							]

							_a     					=    [
							    (read, {'''length''': SMALL_TEST}),
							    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
							    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
							    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
							    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
							    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
							]
							with tempfile.TemporaryDirectory() as tmp_dir:
														print('''generating dataset'''				)
														_a     					=    datasets.Features(
														    {'''list''': datasets.Sequence(datasets.Value('''float32'''				)				), '''numbers''': datasets.Value('''float32'''				)}				)
														_a     					=    generate_example_dataset(
														    os.path.join(UpperCAmelCase_			,						'''dataset.arrow'''				)			,						UpperCAmelCase_			,						num_examples=UpperCAmelCase_			,						seq_shapes={'''list''': (100,)}			,						)
														print('''first set of iterations'''				)
														for func, kwargs in functions:
																					print(func.__name__			,						str(UpperCAmelCase_				)				)
																					_a     					=    func(UpperCAmelCase_			,						**UpperCAmelCase_				)

														print('''shuffling dataset'''				)
														_a     					=    dataset.shuffle()
														print('''Second set of iterations (after shuffling'''				)
														for func, kwargs in functions_shuffled:
																					print('''shuffled '''			,						func.__name__			,						str(UpperCAmelCase_				)				)
																					_a     					=    func(
																					    UpperCAmelCase_			,						**UpperCAmelCase_				)

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


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







'''simple docstring'''


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class       A   (		_a   ):
							lowercase_          =    42
							lowercase_          =    42


if is_transformers_available() and is_torch_available():
				from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

