




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = StableDiffusionXLPipeline
					_lowerCamelCase		  = TEXT_TO_IMAGE_PARAMS
					_lowerCamelCase		  = TEXT_TO_IMAGE_BATCH_PARAMS
					_lowerCamelCase		  = TEXT_TO_IMAGE_IMAGE_PARAMS
					_lowerCamelCase		  = TEXT_TO_IMAGE_IMAGE_PARAMS

					def 	lowercase_							(				self					):







						'''simple docstring'''




						torch.manual_seed(0					)
						lowerCamelCase_							:		str							   =							UNetaDConditionModel(
						    block_out_channels=(32, 64) ,       layers_per_block=2 ,       sample_size=32 ,       in_channels=4 ,       out_channels=4 ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,       attention_head_dim=(2, 4) ,       use_linear_projection=__lowercase ,       addition_embed_type='text_time' ,       addition_time_embed_dim=8 ,       transformer_layers_per_block=(1, 2) ,       projection_class_embeddings_input_dim=80 ,       cross_attention_dim=64 ,       )
						lowerCamelCase_							:		int							   =							EulerDiscreteScheduler(
						    beta_start=0.0_00_85 ,       beta_end=0.0_12 ,       steps_offset=1 ,       beta_schedule='scaled_linear' ,       timestep_spacing='leading' ,       )
						torch.manual_seed(0					)
						lowerCamelCase_							:		List[Any]							   =							AutoencoderKL(
						    block_out_channels=[32, 64] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,       latent_channels=4 ,       sample_size=128 ,       )
						torch.manual_seed(0					)
						lowerCamelCase_							:		Dict							   =							CLIPTextConfig(
						    bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=32 ,       intermediate_size=37 ,       layer_norm_eps=1E-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=1_000 ,       hidden_act='gelu' ,       projection_dim=32 ,       )
						lowerCamelCase_							:		Any							   =							CLIPTextModel(__lowercase					)
						lowerCamelCase_							:		Any							   =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' ,       local_files_only=__lowercase					)

						lowerCamelCase_							:		int							   =							CLIPTextModelWithProjection(__lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' ,       local_files_only=__lowercase					)

						lowerCamelCase_							:		Any							   =							{
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'text_encoder_2': text_encoder_a,
						    'tokenizer_2': tokenizer_a,
						    # "safety_checker": None,
						    # "feature_extractor": None,
						}
						return components

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase=0					):







						'''simple docstring'''




						if str(__lowercase					).startswith('mps'					):
							lowerCamelCase_							:		Optional[Any]							   =							torch.manual_seed(__lowercase					)
						else:
							lowerCamelCase_							:		Optional[int]							   =							torch.Generator(device=__lowercase					).manual_seed(__lowercase					)
						lowerCamelCase_							:		List[str]							   =							{
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 5.0,
						    'output_type': 'numpy',
						}
						return inputs

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							'cpu'  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase_							:		List[str]							   =							self.get_dummy_components()
						lowerCamelCase_							:		Tuple							   =							StableDiffusionXLPipeline(**__lowercase					)
						lowerCamelCase_							:		int							   =							sd_pipe.to(__lowercase					)
						sd_pipe.set_progress_bar_config(disable=__lowercase					)

						lowerCamelCase_							:		Optional[int]							   =							self.get_dummy_inputs(__lowercase					)
						lowerCamelCase_							:		Any							   =							sd_pipe(**__lowercase					).images
						lowerCamelCase_							:		str							   =							image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						lowerCamelCase_							:		Any							   =							np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]					)

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

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							self.get_dummy_components()
						lowerCamelCase_							:		List[str]							   =							StableDiffusionXLPipeline(**__lowercase					)
						lowerCamelCase_							:		Tuple							   =							sd_pipe.to(__lowercase					)
						lowerCamelCase_							:		Optional[Any]							   =							sd_pipe.to(__lowercase					)
						sd_pipe.set_progress_bar_config(disable=__lowercase					)

						# forward without prompt embeds
						lowerCamelCase_							:		Dict							   =							self.get_dummy_inputs(__lowercase					)
						lowerCamelCase_							:		int							   =							3 * ['this is a negative prompt']
						lowerCamelCase_							:		Union[str, Any]							   =							negative_prompt
						lowerCamelCase_							:		Union[str, Any]							   =							3 * [inputs['prompt']]

						lowerCamelCase_							:		Dict							   =							sd_pipe(**__lowercase					)
						lowerCamelCase_							:		int							   =							output.images[0, -3:, -3:, -1]

						# forward with prompt embeds
						lowerCamelCase_							:		List[Any]							   =							self.get_dummy_inputs(__lowercase					)
						lowerCamelCase_							:		str							   =							3 * ['this is a negative prompt']
						lowerCamelCase_							:		Dict							   =							3 * [inputs.pop('prompt'					)]

						(
						    (
						    lowerCamelCase_
						)   ,			(
						    lowerCamelCase_
						)   ,			(
						    lowerCamelCase_
						)   ,			(
						    lowerCamelCase_
						)   ,			
						)							:		Dict							   =							sd_pipe.encode_prompt(__lowercase ,       negative_prompt=__lowercase					)

						lowerCamelCase_							:		Any							   =							sd_pipe(
						    **__lowercase ,       prompt_embeds=__lowercase ,       negative_prompt_embeds=__lowercase ,       pooled_prompt_embeds=__lowercase ,       negative_pooled_prompt_embeds=__lowercase ,       )
						lowerCamelCase_							:		str							   =							output.images[0, -3:, -3:, -1]

						# make sure that it's equal
						assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()					).max() < 1E-4

					def 	lowercase_							(				self					):







						'''simple docstring'''




						super().test_attention_slicing_forward_pass(expected_max_diff=3E-3					)





					def 	lowercase_							(				self					):







						'''simple docstring'''




						super().test_inference_batch_single_identical(expected_max_diff=3E-3					)


@slow
@require_torch_gpu
class 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def 	lowercase_							(				self					):







						'''simple docstring'''




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

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase="cpu" ,       __lowercase=torch.floataa ,       __lowercase=0					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							torch.Generator(device=__lowercase					).manual_seed(__lowercase					)
						lowerCamelCase_							:		str							   =							np.random.RandomState(__lowercase					).standard_normal((1, 4, 64, 64)					)
						lowerCamelCase_							:		Optional[Any]							   =							torch.from_numpy(__lowercase					).to(device=__lowercase ,       dtype=__lowercase					)
						lowerCamelCase_							:		Any							   =							{
						    'prompt': 'a photograph of an astronaut riding a horse',
						    'latents': latents,
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 7.5,
						    'output_type': 'numpy',
						}
						return inputs





					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							   =							DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'					)
						pipe.to(__lowercase					)
						pipe.set_progress_bar_config(disable=__lowercase					)

						lowerCamelCase_							:		int							   =							self.get_inputs(__lowercase					)
						lowerCamelCase_							:		Union[str, Any]							   =							pipe(**__lowercase					).images
						lowerCamelCase_							:		int							   =							image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						lowerCamelCase_							:		Any							   =							np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]					)
						assert np.abs(image_slice - expected_slice					).max() < 7E-3






import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__              =						1_0_0_0_0_0_0_0_0_0_0_0
UpperCamelCase__              =						1_0_0_0_0

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






def 					snake_case					(							A						,   A			)							->							datasets.Dataset:




	'''simple docstring'''


	lowerCamelCase_							:		int							   =							pa.Table.from_pydict({'col': [0] * chunk_size}			)
	lowerCamelCase_							:		List[str]							   =							pa.concat_tables([table] * (num_examples // chunk_size)			)
	return datasets.Dataset(A						,   fingerprint='table_100B'			)


@dataclass
class 				__SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							np.random.default_rng(self.seed					)
						lowerCamelCase_							:		Any							   =							rng.integers(low=self.low ,       high=self.high ,       size=self.size					).tolist()

					def __iter__(				self					):







						'''simple docstring'''




						return iter(self._sampled_values					)





					def __len__(				self					):







						'''simple docstring'''




						return self.size






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							dataset[0]






@get_duration
def 					snake_case					(							A			)							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							dataset[-1]






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							dataset[range(len(A			) // 2						,   len(A			) // 2 + 1024			)]






@get_duration
def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							dataset[RandIter(0						,   len(A			)						,   1024						,   seed=42			)]






def 					snake_case					(							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							{'num examples': SPEED_TEST_N_EXAMPLES}
	lowerCamelCase_							:		Optional[int]							   =							(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
	print('generating dataset'			)
	lowerCamelCase_							:		List[Any]							   =							generate_100B_dataset(num_examples=A						,   chunk_size=A			)
	print('Functions'			)
	for func in functions:
		print(func.__name__			)
		lowerCamelCase_							:		str							   =							func(A			)

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


if __name__ == "__main__":  # useful to run the profiler
							benchmark_table_100B()
