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


lowerCAmelCase__	:  Optional[int]						=  (30, 4_87)
lowerCAmelCase__	:  int						=  (36, 10_24)
lowerCAmelCase__	:  str						=  (1_00, 1_00)
lowerCAmelCase__	:  Optional[Any]						=  1_00

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

lowerCAmelCase__,	lowerCAmelCase__	:  str						=  os.path.split(__file__)
lowerCAmelCase__	:  Any						=  os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__       )  ->   List[Any]:
				with ArrowWriter(features=A__     ,					path=os.path.join(A__     ,					'beta.arrow'       )       ) as writer:
								for key, record in dummy_data:
												snake_case__   :      str   				= my_features.encode_example(A__       )
												writer.write(A__       )
								snake_case__				,    snake_case__   :      Optional[int]   				= writer.finalize()
@get_duration
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   List[str]:
				snake_case__   :      List[str]   				= datasets.Dataset.from_file(
				    filename=os.path.join(A__     ,					'beta.arrow'       )     ,					info=datasets.DatasetInfo(features=A__       )       )
				for _ in dataset:
								pass
@get_duration
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   Any:
				snake_case__   :      Optional[Any]   				= datasets.Dataset.from_file(
				    filename=os.path.join(A__     ,					'beta.arrow'       )     ,					info=datasets.DatasetInfo(features=A__       )       )
				dataset.set_format('numpy'       )
				for _ in dataset:
								pass
@get_duration
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   List[str]:
				snake_case__   :      Union[str, Any]   				= 10
				snake_case__   :      Tuple   				= datasets.Dataset.from_file(
				    filename=os.path.join(A__     ,					'beta.arrow'       )     ,					info=datasets.DatasetInfo(features=A__       )       )
				for i in range(0     ,					len(A__       )     ,					A__       ):
								snake_case__   :      Optional[Any]   				= dataset[i : i + batch_size]
@get_duration
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   Any:
				snake_case__   :      Tuple   				= 10
				snake_case__   :      List[str]   				= datasets.Dataset.from_file(
				    filename=os.path.join(A__     ,					'beta.arrow'       )     ,					info=datasets.DatasetInfo(features=A__       )       )
				dataset.set_format('numpy'       )
				for i in range(0     ,					len(A__       )     ,					A__       ):
								snake_case__   :      List[Any]   				= dataset[i : i + batch_size]
@get_duration
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   Optional[Any]:
				snake_case__   :      Optional[Any]   				= datasets.Dataset.from_file(
				    filename=os.path.join(A__     ,					'beta.arrow'       )     ,					info=datasets.DatasetInfo(features=A__       )       )
				for col in feats:
								snake_case__   :      List[str]   				= dataset[col]
@get_duration
def 					UpperCamelCase__		(							A__     ,					A__       )  ->   List[str]:
				snake_case__   :      List[str]   				= datasets.Dataset.from_file(
				    filename=os.path.join(A__     ,					'beta.arrow'       )     ,					info=datasets.DatasetInfo(features=A__       )       )
				dataset.set_format('numpy'       )
				for col in feats:
								snake_case__   :      Any   				= dataset[col]
def 					UpperCamelCase__		(							)  ->   List[str]:
				snake_case__   :      Optional[int]   				= {}
				snake_case__   :      Optional[Any]   				= (
				    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:
								snake_case__   :      Tuple   				= datasets.Features({'image': ArrayaD(A__     ,					dtype='float32'       )}       )
								snake_case__   :      List[Any]   				= generate_examples(features=A__     ,					num_examples=A__       )
								snake_case__   :      Union[str, Any]   				= write(A__     ,					A__     ,					A__       )
								for read_func in read_functions:
												snake_case__   :      Dict   				= read_func(A__     ,					A__       )

				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])}
								# )
								snake_case__   :      List[Any]   				= datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'       )       )       )}       )
								snake_case__   :      Tuple   				= generate_examples(
								    features=A__     ,					num_examples=A__     ,					seq_shapes={'image': SPEED_TEST_SHAPE}       )
								snake_case__   :      str   				= write(A__     ,					A__     ,					A__       )
								for read_func in read_functions:
												snake_case__   :      int   				= read_func(A__     ,					A__       )

				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])}
								# )
								snake_case__   :      Dict   				= datasets.Features({'image': datasets.Sequence(datasets.Value('float32'       )       )}       )
								snake_case__   :      Optional[int]   				= generate_examples(
								    features=A__     ,					num_examples=A__     ,					seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}     ,					)
								snake_case__   :      Tuple   				= write(A__     ,					A__     ,					A__       )
								for read_func in read_functions:
												snake_case__   :      int   				= read_func(A__     ,					A__       )

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


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



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


lowerCAmelCase__	:  Union[str, Any]						=  False





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




					def      __a     (  self						)	->    str:



									'''simple docstring'''







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




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







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

									snake_case__   :      Union[str, Any]   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

									snake_case__   :      Dict   				= torch.manual_seed(0						)
									snake_case__   :      Tuple   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(__UpperCamelCase						)
													snake_case__   :      List[Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase						)

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

									snake_case__   :      str   				= generator.manual_seed(0						)
									snake_case__   :      List[str]   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			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      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= 'cyberpunk 2077'
									snake_case__   :      Dict   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
									snake_case__   :      Union[str, Any]   				= torch.manual_seed(0						)
									snake_case__   :      Union[str, Any]   				= pipe(
									    prompt=__UpperCamelCase       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=50       ,			output_type='numpy'       ,			).images

									snake_case__   :      int   				= image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									snake_case__   :      List[str]   				= np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

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


