

from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime





class      snake_case_      (					_a				):


							"""simple docstring"""





							def __init__(			self       ,    *_A       ,    **_A				):
												super().__init__(*_A       ,    **_A				)




							def 							A__	(			self       ,    _A       ,    _A				):
												__lowerCAmelCase       =				(
												    next_prime(value % self.size_table				)
												    if not is_prime(value % self.size_table				)
												    else value % self.size_table
												)  # gt = bigger than
												return next_prime_gt - (data % next_prime_gt)




							def 							A__	(			self       ,    _A       ,    _A       ,    _A				):
												return (increment * self.__hash_function_a(_A       ,    _A				)) % self.size_table




							def 							A__	(			self       ,    _A       ,    _A=None				):
												__lowerCAmelCase       =				1
												__lowerCAmelCase       =				self.hash_function(_A				)

												while self.values[new_key] is not None and self.values[new_key] != key:
																	__lowerCAmelCase       =				(
																	    self.__hash_double_function(_A       ,    _A       ,    _A				)
																	    if self.balanced_factor() >= self.lim_charge
																	    else None
																	)
																	if new_key is None:
																						break
																	else:
																						i += 1

												return new_key









import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    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      snake_case_      (					_a      ,			_a      ,			unittest.TestCase				):


							"""simple docstring"""





							__UpperCAmelCase      =StableDiffusionParadigmsPipeline
							__UpperCAmelCase      =TEXT_TO_IMAGE_PARAMS
							__UpperCAmelCase      =TEXT_TO_IMAGE_BATCH_PARAMS
							__UpperCAmelCase      =TEXT_TO_IMAGE_IMAGE_PARAMS
							__UpperCAmelCase      =TEXT_TO_IMAGE_IMAGE_PARAMS




							def 							A__	(			self				):
												torch.manual_seed(0				)
												__lowerCAmelCase       =				UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4)       ,    layers_per_block=2       ,    sample_size=3_2       ,    in_channels=4       ,    out_channels=4       ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,    cross_attention_dim=3_2       ,    attention_head_dim=(2, 4)       ,    use_linear_projection=_A       ,    )
												__lowerCAmelCase       =				DDIMParallelScheduler(
												    beta_start=0.0_0085       ,    beta_end=0.012       ,    beta_schedule='scaled_linear'       ,    clip_sample=_A       ,    set_alpha_to_one=_A       ,    )
												torch.manual_seed(0				)
												__lowerCAmelCase       =				AutoencoderKL(
												    block_out_channels=[3_2, 6_4]       ,    in_channels=3       ,    out_channels=3       ,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,    latent_channels=4       ,    sample_size=1_2_8       ,    )
												torch.manual_seed(0				)
												__lowerCAmelCase       =				CLIPTextConfig(
												    bos_token_id=0       ,    eos_token_id=2       ,    hidden_size=3_2       ,    intermediate_size=3_7       ,    layer_norm_eps=1e-0_5       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    pad_token_id=1       ,    vocab_size=1_0_0_0       ,    hidden_act='gelu'       ,    projection_dim=5_1_2       ,    )
												__lowerCAmelCase       =				CLIPTextModel(_A				)
												__lowerCAmelCase       =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

												__lowerCAmelCase       =				{
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'safety_checker': None,
												    'feature_extractor': None,
												}
												return components




							def 							A__	(			self       ,    _A       ,    _A=0				):
												if str(_A				).startswith('mps'				):
																	__lowerCAmelCase       =				torch.manual_seed(_A				)
												else:
																	__lowerCAmelCase       =				torch.Generator(device=_A				).manual_seed(_A				)
												__lowerCAmelCase       =				{
												    'prompt': 'a photograph of an astronaut riding a horse',
												    'generator': generator,
												    'num_inference_steps': 1_0,
												    'guidance_scale': 6.0,
												    'output_type': 'numpy',
												    'parallel': 3,
												    'debug': True,
												}
												return inputs




							def 							A__	(			self				):
												__lowerCAmelCase       =				'cpu'  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase       =				self.get_dummy_components()
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline(**_A				)
												__lowerCAmelCase       =				sd_pipe.to(_A				)
												sd_pipe.set_progress_bar_config(disable=_A				)

												__lowerCAmelCase       =				self.get_dummy_inputs(_A				)
												__lowerCAmelCase       =				sd_pipe(**_A				).images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1]
												assert image.shape == (1, 6_4, 6_4, 3)

												__lowerCAmelCase       =				np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]				)

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




							def 							A__	(			self				):
												__lowerCAmelCase       =				'cpu'  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase       =				self.get_dummy_components()
												torch.manual_seed(0				)
												__lowerCAmelCase       =				DDPMParallelScheduler()
												torch.manual_seed(0				)
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline(**_A				)
												__lowerCAmelCase       =				sd_pipe.to(_A				)
												sd_pipe.set_progress_bar_config(disable=_A				)

												__lowerCAmelCase       =				self.get_dummy_inputs(_A				)
												__lowerCAmelCase       =				sd_pipe(**_A				).images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1]
												assert image.shape == (1, 6_4, 6_4, 3)

												__lowerCAmelCase       =				np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]				)

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




							def 							A__	(			self				):
												super().test_inference_batch_consistent(batch_sizes=[1, 2]				)




							def 							A__	(			self				):
												super().test_inference_batch_single_identical(batch_size=2       ,    expected_max_diff=3e-3				)




							def 							A__	(			self				):
												__lowerCAmelCase       =				'cpu'  # ensure determinism for the device-dependent torch.Generator
												__lowerCAmelCase       =				self.get_dummy_components()
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline(**_A				)
												__lowerCAmelCase       =				sd_pipe.to(_A				)
												sd_pipe.set_progress_bar_config(disable=_A				)

												__lowerCAmelCase       =				self.get_dummy_inputs(_A				)
												__lowerCAmelCase       =				'french fries'
												__lowerCAmelCase       =				sd_pipe(**_A       ,    negative_prompt=_A				)
												__lowerCAmelCase       =				output.images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1]

												assert image.shape == (1, 6_4, 6_4, 3)

												__lowerCAmelCase       =				np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]				)

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





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


							"""simple docstring"""





							def 							A__	(			self				):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 							A__	(			self       ,    _A=0				):
												__lowerCAmelCase       =				torch.Generator(device=_A				).manual_seed(_A				)
												__lowerCAmelCase       =				{
												    'prompt': 'a photograph of an astronaut riding a horse',
												    'generator': generator,
												    'num_inference_steps': 1_0,
												    'guidance_scale': 7.5,
												    'output_type': 'numpy',
												    'parallel': 3,
												    'debug': True,
												}
												return inputs




							def 							A__	(			self				):
												__lowerCAmelCase       =				'stabilityai/stable-diffusion-2-base'
												__lowerCAmelCase       =				DDIMParallelScheduler.from_pretrained(_A       ,    subfolder='scheduler'				)
												__lowerCAmelCase       =				StableDiffusionParadigmsPipeline.from_pretrained(_A       ,    scheduler=_A       ,    safety_checker=_A				)
												pipe.to(_A				)
												pipe.set_progress_bar_config(disable=_A				)
												pipe.enable_attention_slicing()

												__lowerCAmelCase       =				self.get_inputs()
												__lowerCAmelCase       =				pipe(**_A				).images
												__lowerCAmelCase       =				image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 5_1_2, 5_1_2, 3)

												__lowerCAmelCase       =				np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]				)

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






