






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


A__      :     int       =					5_00_00
A__      :     Union[str, Any]       =					50_00

A__      :     Tuple       =					os.path.split(__file__)
A__      :     int       =					os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				for i in range(_lowerCamelCase							):
								lowercase__  					=     dataset[i]






@get_duration
def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				for i in range(0						,  len(_lowerCamelCase							)						,  _lowerCamelCase							):
								lowercase__  					=     dataset[i : i + batch_size]






@get_duration
def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				with dataset.formatted_as(type=_lowerCamelCase							):
								for i in range(_lowerCamelCase							):
												lowercase__  					=     dataset[i]






@get_duration
def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				with dataset.formatted_as(type=_lowerCamelCase							):
								for i in range(0						,  _lowerCamelCase						,  _lowerCamelCase							):
												lowercase__  					=     dataset[i : i + batch_size]






def     a     (		):






				'''simple docstring'''
				lowercase__  					=     {'''num examples''': SPEED_TEST_N_EXAMPLES}
				lowercase__  					=     [
				    (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}),
				]

				lowercase__  					=     [
				    (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'''							)
								lowercase__  					=     datasets.Features(
								    {'''list''': datasets.Sequence(datasets.Value('''float32'''							)							), '''numbers''': datasets.Value('''float32'''							)}							)
								lowercase__  					=     generate_example_dataset(
								    os.path.join(_lowerCamelCase						,  '''dataset.arrow'''							)						,  _lowerCamelCase						,  num_examples=_lowerCamelCase						,  seq_shapes={'''list''': (100,)}						,  )
								print('''first set of iterations'''							)
								for func, kwargs in functions:
												print(func.__name__						,  str(_lowerCamelCase							)							)
												lowercase__  					=     func(_lowerCamelCase						,  **_lowerCamelCase							)

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

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


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











'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
  from .pipeline_kandinsky import KandinskyPipeline
  from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
  from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
  from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
  from .text_encoder import MultilingualCLIP



