'''simple docstring'''


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()






class 						A   (   unittest.TestCase   ):
							a_        =      StableDiffusionLDMaDPipeline
							a_        =      TEXT_TO_IMAGE_PARAMS
							a_        =      TEXT_TO_IMAGE_BATCH_PARAMS
							a_        =      TEXT_TO_IMAGE_IMAGE_PARAMS





							def       snake_case__						(      self				:							Dict	)							->	Dict:
								torch.manual_seed(0	)
								__UpperCAmelCase							=			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     ,		)
								__UpperCAmelCase							=			DDIMScheduler(
								    beta_start=0.0_0_0_8_5     ,		beta_end=0.0_1_2     ,		beta_schedule='''scaled_linear'''     ,		clip_sample=__a     ,		set_alpha_to_one=__a     ,		)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			AutoencoderKL(
								    block_out_channels=[3_2, 6_4]     ,		in_channels=6     ,		out_channels=6     ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,		latent_channels=4     ,		)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			CLIPTextConfig(
								    bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=3_2     ,		intermediate_size=3_7     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_0_0_0     ,		)
								__UpperCAmelCase							=			CLIPTextModel(__a	)
								__UpperCAmelCase							=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

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





							def       snake_case__						(      self				:							str     ,		__a				:							str     ,		__a				:							Optional[Any]=0	)							->	str:
								if str(__a	).startswith('''mps'''	):
									__UpperCAmelCase							=			torch.manual_seed(__a	)
								else:
									__UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(__a	)
								__UpperCAmelCase							=			{
								    '''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       snake_case__						(      self				:							Optional[int]	)							->	Union[str, Any]:
								__UpperCAmelCase							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator

								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			StableDiffusionLDMaDPipeline(**__a	)
								__UpperCAmelCase							=			ldmad_pipe.to(__a	)
								ldmad_pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			self.get_dummy_inputs(__a	)
								__UpperCAmelCase							=			ldmad_pipe(**__a	)
								__UpperCAmelCase				,			__UpperCAmelCase							=			output.rgb, output.depth

								__UpperCAmelCase							=			rgb[0, -3:, -3:, -1]
								__UpperCAmelCase							=			depth[0, -3:, -1]

								assert rgb.shape == (1, 6_4, 6_4, 3)
								assert depth.shape == (1, 6_4, 6_4)

								__UpperCAmelCase							=			np.array(
								    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2]	)
								__UpperCAmelCase							=			np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6]	)

								assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb	).max() < 1e-2
								assert np.abs(image_slice_depth.flatten() - expected_slice_depth	).max() < 1e-2





							def       snake_case__						(      self				:							List[str]	)							->	Any:
								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			StableDiffusionLDMaDPipeline(**__a	)
								__UpperCAmelCase							=			ldmad_pipe.to(__a	)
								ldmad_pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			self.get_dummy_inputs(__a	)
								__UpperCAmelCase							=			3 * [inputs['''prompt''']]

								# forward
								__UpperCAmelCase							=			ldmad_pipe(**__a	)
								__UpperCAmelCase				,			__UpperCAmelCase							=			output.rgb, output.depth
								__UpperCAmelCase							=			rgb_slice_a[0, -3:, -3:, -1]
								__UpperCAmelCase							=			depth_slice_a[0, -3:, -1]

								__UpperCAmelCase							=			self.get_dummy_inputs(__a	)
								__UpperCAmelCase							=			3 * [inputs.pop('''prompt'''	)]

								__UpperCAmelCase							=			ldmad_pipe.tokenizer(
								    __a     ,		padding='''max_length'''     ,		max_length=ldmad_pipe.tokenizer.model_max_length     ,		truncation=__a     ,		return_tensors='''pt'''     ,		)
								__UpperCAmelCase							=			text_inputs['''input_ids'''].to(__a	)

								__UpperCAmelCase							=			ldmad_pipe.text_encoder(__a	)[0]

								__UpperCAmelCase							=			prompt_embeds

								# forward
								__UpperCAmelCase							=			ldmad_pipe(**__a	)
								__UpperCAmelCase				,			__UpperCAmelCase							=			output.rgb, output.depth
								__UpperCAmelCase							=			rgb_slice_a[0, -3:, -3:, -1]
								__UpperCAmelCase							=			depth_slice_a[0, -3:, -1]

								assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()	).max() < 1e-4
								assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()	).max() < 1e-4






							def       snake_case__						(      self				:							List[str]	)							->	List[Any]:
								__UpperCAmelCase							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			PNDMScheduler(skip_prk_steps=__a	)
								__UpperCAmelCase							=			StableDiffusionLDMaDPipeline(**__a	)
								__UpperCAmelCase							=			ldmad_pipe.to(__a	)
								ldmad_pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			self.get_dummy_inputs(__a	)
								__UpperCAmelCase							=			'''french fries'''
								__UpperCAmelCase							=			ldmad_pipe(**__a     ,		negative_prompt=__a	)

								__UpperCAmelCase				,			__UpperCAmelCase							=			output.rgb, output.depth
								__UpperCAmelCase							=			rgb[0, -3:, -3:, -1]
								__UpperCAmelCase							=			depth[0, -3:, -1]

								assert rgb.shape == (1, 6_4, 6_4, 3)
								assert depth.shape == (1, 6_4, 6_4)

								__UpperCAmelCase							=			np.array(
								    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7]	)
								__UpperCAmelCase							=			np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5]	)
								assert np.abs(rgb_slice.flatten() - expected_slice_rgb	).max() < 1e-2
								assert np.abs(depth_slice.flatten() - expected_slice_depth	).max() < 1e-2






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





							def       snake_case__						(      self				:							Any	)							->	Any:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							Dict     ,		__a				:							List[str]="cpu"     ,		__a				:							Union[str, Any]=torch.floataa     ,		__a				:							Optional[Any]=0	)							->	List[str]:
								__UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(__a	)
								__UpperCAmelCase							=			np.random.RandomState(__a	).standard_normal((1, 4, 6_4, 6_4)	)
								__UpperCAmelCase							=			torch.from_numpy(__a	).to(device=__a     ,		dtype=__a	)
								__UpperCAmelCase							=			{
								    '''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       snake_case__						(      self				:							int	)							->	Any:
								__UpperCAmelCase							=			StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''	)
								__UpperCAmelCase							=			ldmad_pipe.to(__a	)
								ldmad_pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			self.get_inputs(__a	)
								__UpperCAmelCase							=			ldmad_pipe(**__a	)
								__UpperCAmelCase				,			__UpperCAmelCase							=			output.rgb, output.depth
								__UpperCAmelCase							=			rgb[0, -3:, -3:, -1].flatten()
								__UpperCAmelCase							=			rgb[0, -3:, -1].flatten()

								assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
								assert depth.shape == (1, 5_1_2, 5_1_2)

								__UpperCAmelCase							=			np.array(
								    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6]	)
								__UpperCAmelCase							=			np.array(
								    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6]	)
								assert np.abs(rgb_slice - expected_slice_rgb	).max() < 3e-3
								assert np.abs(depth_slice - expected_slice_depth	).max() < 3e-3






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





							def       snake_case__						(      self				:							List[str]	)							->	Dict:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





							def       snake_case__						(      self				:							Optional[Any]     ,		__a				:							List[str]     ,		__a				:							Any="cpu"     ,		__a				:							List[str]=torch.floataa     ,		__a				:							Dict=0	)							->	List[Any]:
								__UpperCAmelCase							=			torch.Generator(device=__a	).manual_seed(__a	)
								__UpperCAmelCase							=			np.random.RandomState(__a	).standard_normal((1, 4, 6_4, 6_4)	)
								__UpperCAmelCase							=			torch.from_numpy(__a	).to(device=__a     ,		dtype=__a	)
								__UpperCAmelCase							=			{
								    '''prompt''': '''a photograph of an astronaut riding a horse''',
								    '''latents''': latents,
								    '''generator''': generator,
								    '''num_inference_steps''': 5_0,
								    '''guidance_scale''': 7.5,
								    '''output_type''': '''numpy''',
								}
								return inputs





							def       snake_case__						(      self				:							Tuple	)							->	int:
								__UpperCAmelCase							=			StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''	).to(__a	)
								ldmad_pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			self.get_inputs(__a	)
								__UpperCAmelCase							=			ldmad_pipe(**__a	)
								__UpperCAmelCase				,			__UpperCAmelCase							=			output.rgb, output.depth

								__UpperCAmelCase							=			0.4_9_5_5_8_6
								__UpperCAmelCase							=			0.3_3_7_9_5_5_1_5
								__UpperCAmelCase							=			1_1_2.4_8_5_1_8
								__UpperCAmelCase							=			9_8.4_8_9_7_4_6
								assert np.abs(expected_rgb_mean - rgb.mean()	) < 1e-3
								assert np.abs(expected_rgb_std - rgb.std()	) < 1e-3
								assert np.abs(expected_depth_mean - depth.mean()	) < 1e-3
								assert np.abs(expected_depth_std - depth.std()	) < 1e-3






							def       snake_case__						(      self				:							List[Any]	)							->	str:
								__UpperCAmelCase							=			StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''	).to(__a	)
								ldmad_pipe.set_progress_bar_config(disable=__a	)

								__UpperCAmelCase							=			self.get_inputs(__a	)
								__UpperCAmelCase							=			ldmad_pipe(**__a	)
								__UpperCAmelCase				,			__UpperCAmelCase							=			output.rgb, output.depth

								__UpperCAmelCase							=			0.4_1_9_4_1_2_7
								__UpperCAmelCase							=			0.3_5_3_7_5_5_8_6
								__UpperCAmelCase							=			0.5_6_3_8_5_0_2
								__UpperCAmelCase							=			0.3_4_6_8_6_1_0_3

								assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
								assert depth.shape == (1, 5_1_2, 5_1_2, 1)
								assert np.abs(expected_rgb_mean - rgb.mean()	) < 1e-3
								assert np.abs(expected_rgb_std - rgb.std()	) < 1e-3
								assert np.abs(expected_depth_mean - depth.mean()	) < 1e-3
								assert np.abs(expected_depth_std - depth.std()	) < 1e-3

'''simple docstring'''


def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple       ):


	"""simple docstring"""
	# if the collection is empty, returns empty
	if collection == []:
		return []

	# get some information about the collection
	__UpperCAmelCase							=			len(UpperCamelCase__       )
	__UpperCAmelCase							=			max(UpperCamelCase__       )
	__UpperCAmelCase							=			min(UpperCamelCase__       )

	# create the counting array
	__UpperCAmelCase							=			coll_max + 1 - coll_min
	__UpperCAmelCase							=			[0] * counting_arr_length

	# count how much a number appears in the collection
	for number in collection:
		counting_arr[number - coll_min] += 1

	# sum each position with it's predecessors. now, counting_arr[i] tells
	# us how many elements <= i has in the collection
	for i in range(1    ,					UpperCamelCase__       ):
		__UpperCAmelCase							=			counting_arr[i] + counting_arr[i - 1]

	# create the output collection
	__UpperCAmelCase							=			[0] * coll_len

	# place the elements in the output, respecting the original order (stable
	# sort) from end to begin, updating counting_arr
	for i in reversed(range(0    ,					UpperCamelCase__       )       ):
		__UpperCAmelCase							=			collection[i]
		counting_arr[collection[i] - coll_min] -= 1

	return ordered





def       lowerCAmelCase					(		UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	return "".join([chr(UpperCamelCase__       ) for i in counting_sort([ord(UpperCamelCase__       ) for c in string]       )]       )


if __name__ == "__main__":
					# Test string sort
					assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"

					__lowerCAmelCase	:       str						       =	input("Enter numbers separated by a comma:\n").strip()
					__lowerCAmelCase	:       List[Any]						       =	[int(item) for item in user_input.split(",")]
					print(counting_sort(unsorted))
