"""simple docstring"""

import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__lowercase    =				(30, 487)
__lowercase    =				(36, 1_024)
__lowercase    =				(100, 100)
__lowercase    =				100

__lowercase    =				datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

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


@get_duration
def    lowerCAmelCase			(__UpperCamelCase							:    str							,					__UpperCamelCase							:    Optional[int]							,					__UpperCamelCase							:    Dict			):
							"""simple docstring"""


							with ArrowWriter(features=__UpperCamelCase							,					path=os.path.join(__UpperCamelCase							,					'''beta.arrow'''			)			) as writer:
														for key, record in dummy_data:
																					__UpperCamelCase							=my_features.encode_example(__UpperCamelCase			)
																					writer.write(__UpperCamelCase			)
														__UpperCamelCase	,      __UpperCamelCase							=writer.finalize()


@get_duration
def    lowerCAmelCase			(__UpperCamelCase							:    List[Any]							,					__UpperCamelCase							:    str			):
							"""simple docstring"""


							__UpperCamelCase							=datasets.Dataset.from_file(
							    filename=os.path.join(__UpperCamelCase							,					'''beta.arrow'''			)							,					info=datasets.DatasetInfo(features=__UpperCamelCase			)			)
							for _ in dataset:
														pass


@get_duration
def    lowerCAmelCase			(__UpperCamelCase							:    Optional[int]							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							__UpperCamelCase							=datasets.Dataset.from_file(
							    filename=os.path.join(__UpperCamelCase							,					'''beta.arrow'''			)							,					info=datasets.DatasetInfo(features=__UpperCamelCase			)			)
							dataset.set_format('''numpy'''			)
							for _ in dataset:
														pass


@get_duration
def    lowerCAmelCase			(__UpperCamelCase							:    List[str]							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							__UpperCamelCase							=1_0
							__UpperCamelCase							=datasets.Dataset.from_file(
							    filename=os.path.join(__UpperCamelCase							,					'''beta.arrow'''			)							,					info=datasets.DatasetInfo(features=__UpperCamelCase			)			)
							for i in range(0							,					len(__UpperCamelCase			)							,					__UpperCamelCase			):
														__UpperCamelCase							=dataset[i : i + batch_size]


@get_duration
def    lowerCAmelCase			(__UpperCamelCase							:    Tuple							,					__UpperCamelCase							:    Dict			):
							"""simple docstring"""


							__UpperCamelCase							=1_0
							__UpperCamelCase							=datasets.Dataset.from_file(
							    filename=os.path.join(__UpperCamelCase							,					'''beta.arrow'''			)							,					info=datasets.DatasetInfo(features=__UpperCamelCase			)			)
							dataset.set_format('''numpy'''			)
							for i in range(0							,					len(__UpperCamelCase			)							,					__UpperCamelCase			):
														__UpperCamelCase							=dataset[i : i + batch_size]


@get_duration
def    lowerCAmelCase			(__UpperCamelCase							:    Optional[int]							,					__UpperCamelCase							:    Any			):
							"""simple docstring"""


							__UpperCamelCase							=datasets.Dataset.from_file(
							    filename=os.path.join(__UpperCamelCase							,					'''beta.arrow'''			)							,					info=datasets.DatasetInfo(features=__UpperCamelCase			)			)
							for col in feats:
														__UpperCamelCase							=dataset[col]


@get_duration
def    lowerCAmelCase			(__UpperCamelCase							:    Tuple							,					__UpperCamelCase							:    Optional[int]			):
							"""simple docstring"""


							__UpperCamelCase							=datasets.Dataset.from_file(
							    filename=os.path.join(__UpperCamelCase							,					'''beta.arrow'''			)							,					info=datasets.DatasetInfo(features=__UpperCamelCase			)			)
							dataset.set_format('''numpy'''			)
							for col in feats:
														__UpperCamelCase							=dataset[col]


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							={}
							__UpperCamelCase							=(
							    read_unformated,
							    read_formatted_as_numpy,
							    read_batch_unformated,
							    read_batch_formatted_as_numpy,
							    read_col_unformated,
							    read_col_formatted_as_numpy,
							)
							with tempfile.TemporaryDirectory() as tmp_dir:
														__UpperCamelCase							=datasets.Features({'''image''': ArrayaD(__UpperCamelCase							,					dtype='''float32'''			)}			)
														__UpperCamelCase							=generate_examples(features=__UpperCamelCase							,					num_examples=__UpperCamelCase			)
														__UpperCamelCase							=write(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase			)
														for read_func in read_functions:
																					__UpperCamelCase							=read_func(__UpperCamelCase							,					__UpperCamelCase			)

							with tempfile.TemporaryDirectory() as tmp_dir:
														# don't use fixed length for fair comparison
														# feats = datasets.Features(
														#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
														# )
														__UpperCamelCase							=datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''			)			)			)}			)
														__UpperCamelCase							=generate_examples(
														    features=__UpperCamelCase							,					num_examples=__UpperCamelCase							,					seq_shapes={'''image''': SPEED_TEST_SHAPE}			)
														__UpperCamelCase							=write(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase			)
														for read_func in read_functions:
																					__UpperCamelCase							=read_func(__UpperCamelCase							,					__UpperCamelCase			)

							with tempfile.TemporaryDirectory() as tmp_dir:
														# don't use fixed length for fair comparison
														# feats = datasets.Features(
														#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
														# )
														__UpperCamelCase							=datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''			)			)}			)
														__UpperCamelCase							=generate_examples(
														    features=__UpperCamelCase							,					num_examples=__UpperCamelCase							,					seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}							,					)
														__UpperCamelCase							=write(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase			)
														for read_func in read_functions:
																					__UpperCamelCase							=read_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_array_xd()




"""simple docstring"""

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowercase    =				False






@nightly
@require_torch_gpu
class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[Any]:
													'''simple docstring'''



													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																				pipe.save_pretrained(UpperCamelCase__				)
																				__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCamelCase__				)

													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							=generator.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt='''first prompt'''     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=2     , output_type='''numpy'''     , ).images

													assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"





						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							='''cyberpunk 2077'''
													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=pipe(
													    prompt=UpperCamelCase__     , image=UpperCamelCase__     , text_to_image_strength=0.75     , generator=UpperCamelCase__     , guidance_scale=7.5     , num_inference_steps=50     , output_type='''numpy'''     , ).images

													__UpperCamelCase							=image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase							=np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2



