"""simple docstring"""



import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

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


enable_full_determinism()
class 						__UpperCAmelCase    (__A							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				LDMTextToImagePipeline
			_UpperCamelCase  :						List[Any]					     =				TEXT_TO_IMAGE_PARAMS - {
			    'negative_prompt',
			    'negative_prompt_embeds',
			    'cross_attention_kwargs',
			    'prompt_embeds',
			}
			_UpperCamelCase  :						Union[str, Any]					     =				PipelineTesterMixin.required_optional_params - {
			    'num_images_per_prompt',
			    'callback',
			    'callback_steps',
			}
			_UpperCamelCase  :						Any					     =				TEXT_TO_IMAGE_BATCH_PARAMS
			_UpperCamelCase  :						Optional[Any]					     =				False





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										torch.manual_seed(0							)
										A__       :     List[Any]								=			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""")   ,      cross_attention_dim=32   ,      )
										A__       :     Tuple								=			DDIMScheduler(
										    beta_start=0.0_00_85   ,      beta_end=0.0_12   ,      beta_schedule="""scaled_linear"""   ,      clip_sample=snake_case_   ,      set_alpha_to_one=snake_case_   ,      )
										torch.manual_seed(0							)
										A__       :     List[str]								=			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   ,      )
										torch.manual_seed(0							)
										A__       :     Union[str, Any]								=			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   ,      )
										A__       :     Union[str, Any]								=			CLIPTextModel(snake_case_							)
										A__       :     List[Any]								=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

										A__       :     str								=			{
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """vqvae""": vae,
										    """bert""": text_encoder,
										    """tokenizer""": tokenizer,
										}
										return components





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=0							):







										'''simple docstring'''


										if str(snake_case_							).startswith("""mps"""							):
																	A__       :     Optional[Any]								=			torch.manual_seed(snake_case_							)
										else:
																	A__       :     Any								=			torch.Generator(device=snake_case_							).manual_seed(snake_case_							)
										A__       :     Optional[Any]								=			{
										    """prompt""": """A painting of a squirrel eating a burger""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}
										return inputs





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator

										A__       :     str								=			self.get_dummy_components()
										A__       :     Any								=			LDMTextToImagePipeline(**snake_case_							)
										pipe.to(snake_case_							)
										pipe.set_progress_bar_config(disable=snake_case_							)

										A__       :     List[str]								=			self.get_dummy_inputs(snake_case_							)
										A__       :     str								=			pipe(**snake_case_							).images
										A__       :     str								=			image[0, -3:, -3:, -1]

										assert image.shape == (1, 16, 16, 3)
										A__       :     Any								=			np.array([0.61_01, 0.61_56, 0.56_22, 0.48_95, 0.66_61, 0.38_04, 0.57_48, 0.61_36, 0.50_14]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3





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







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=torch.floataa   ,      snake_case_=0							):







										'''simple docstring'''


										A__       :     int								=			torch.manual_seed(snake_case_							)
										A__       :     Any								=			np.random.RandomState(snake_case_							).standard_normal((1, 4, 32, 32)							)
										A__       :     List[str]								=			torch.from_numpy(snake_case_							).to(device=snake_case_   ,      dtype=snake_case_							)
										A__       :     Union[str, Any]								=			{
										    """prompt""": """A painting of a squirrel eating a burger""",
										    """latents""": latents,
										    """generator""": generator,
										    """num_inference_steps""": 3,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}
										return inputs





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""							).to(snake_case_							)
										pipe.set_progress_bar_config(disable=snake_case_							)

										A__       :     str								=			self.get_inputs(snake_case_							)
										A__       :     Tuple								=			pipe(**snake_case_							).images
										A__       :     List[str]								=			image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 256, 256, 3)
										A__       :     Tuple								=			np.array([0.5_18_25, 0.5_28_50, 0.5_25_43, 0.5_42_58, 0.5_23_04, 0.5_25_69, 0.5_43_63, 0.5_52_76, 0.5_68_78]							)
										A__       :     Tuple								=			np.abs(expected_slice - image_slice							).max()
										assert max_diff < 1E-3





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







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=torch.floataa   ,      snake_case_=0							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			torch.manual_seed(snake_case_							)
										A__       :     Optional[Any]								=			np.random.RandomState(snake_case_							).standard_normal((1, 4, 32, 32)							)
										A__       :     Optional[Any]								=			torch.from_numpy(snake_case_							).to(device=snake_case_   ,      dtype=snake_case_							)
										A__       :     Optional[int]								=			{
										    """prompt""": """A painting of a squirrel eating a burger""",
										    """latents""": latents,
										    """generator""": generator,
										    """num_inference_steps""": 50,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}
										return inputs





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[int]								=			LDMTextToImagePipeline.from_pretrained("""CompVis/ldm-text2im-large-256"""							).to(snake_case_							)
										pipe.set_progress_bar_config(disable=snake_case_							)

										A__       :     Tuple								=			self.get_inputs(snake_case_							)
										A__       :     Optional[int]								=			pipe(**snake_case_							).images[0]

										A__       :     Union[str, Any]								=			load_numpy(
										    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"""							)
										A__       :     int								=			np.abs(expected_image - image							).max()
										assert max_diff < 1E-3






"""simple docstring"""



from __future__ import annotations

def  _A(		lowerCAmelCase							):
							if len(lowerCAmelCase							) == 0:
														return []
							A__				,							A__       :     Dict								=			min(lowerCAmelCase							), max(lowerCAmelCase							)
							A__       :     List[Any]								=			int(max_value - min_value							) + 1
							A__       :     list[list]								=			[[] for _ in range(lowerCAmelCase							)]

							for i in my_list:
														buckets[int(i - min_value							)].append(lowerCAmelCase							)

							return [v for bucket in buckets for v in sorted(lowerCAmelCase							)]


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
				assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





