
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    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     (      snake_case_			,       snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = StableDiffusionSAGPipeline
	lowerCAmelCase__         = TEXT_TO_IMAGE_PARAMS
	lowerCAmelCase__         = TEXT_TO_IMAGE_BATCH_PARAMS
	lowerCAmelCase__         = TEXT_TO_IMAGE_IMAGE_PARAMS
	lowerCAmelCase__         = TEXT_TO_IMAGE_IMAGE_PARAMS
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					torch.manual_seed(0 )
					lowerCamelCase_						=			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						,  )
					lowerCamelCase_						=			DDIMScheduler(
					    beta_start=0.0_0_0_8_5						,  beta_end=0.0_1_2						,  beta_schedule="scaled_linear"						,  clip_sample=lowercase						,  set_alpha_to_one=lowercase						,  )
					torch.manual_seed(0 )
					lowerCamelCase_						=			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 )
					lowerCamelCase_						=			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=1000						,  )
					lowerCamelCase_						=			CLIPTextModel(lowercase )
					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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=0 )				->       List[str]:
					if str(lowercase ).startswith("mps" ):
									lowerCamelCase_						=			torch.manual_seed(lowercase )
					else:
									lowerCamelCase_						=			torch.Generator(device=lowercase ).manual_seed(lowercase )
					lowerCamelCase_						=			{
					    "prompt": ".",
					    "generator": generator,
					    "num_inference_steps": 2,
					    "guidance_scale": 1.0,
					    "sag_scale": 1.0,
					    "output_type": "numpy",
					}
					return inputs



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					super().test_inference_batch_single_identical(expected_max_diff=3e-3 )




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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
					lowerCamelCase_						=			sag_pipe.to(lowercase )
					sag_pipe.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			"."
					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			sag_pipe(
					    [prompt]						,  generator=lowercase						,  guidance_scale=7.5						,  sag_scale=1.0						,  num_inference_steps=20						,  output_type="np" )

					lowerCamelCase_						=			output.images

					lowerCamelCase_						=			image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					lowerCamelCase_						=			np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9] )

					assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
					lowerCamelCase_						=			sag_pipe.to(lowercase )
					sag_pipe.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			"."
					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			sag_pipe(
					    [prompt]						,  generator=lowercase						,  guidance_scale=7.5						,  sag_scale=1.0						,  num_inference_steps=20						,  output_type="np" )

					lowerCamelCase_						=			output.images

					lowerCamelCase_						=			image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					lowerCamelCase_						=			np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1] )

					assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
					lowerCamelCase_						=			sag_pipe.to(lowercase )
					sag_pipe.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			"."
					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			sag_pipe(
					    [prompt]						,  width=768						,  height=512						,  generator=lowercase						,  guidance_scale=7.5						,  sag_scale=1.0						,  num_inference_steps=20						,  output_type="np"						,  )

					lowerCamelCase_						=			output.images

					assert image.shape == (1, 512, 768, 3)










from collections.abc import Generator
from math import sin

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			bytes:






	"""simple docstring"""



	if len(__magic_name__					) != 32:
		raise ValueError("""Input must be of length 32"""					)

	UpperCamelCase     :int          =	B""""""
	for i in [3, 2, 1, 0]:
		little_endian += string_aa[8 * i : 8 * i + 8]
	return little_endian

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int					)		->			bytes:






	"""simple docstring"""



	if i < 0:
		raise ValueError("""Input must be non-negative"""					)

	UpperCamelCase     :Any          =	format(__magic_name__	,			"""08x"""					)[-8:]
	UpperCamelCase     :Union[str, Any]          =	B""""""
	for i in [3, 2, 1, 0]:
		little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8"""					)
	return little_endian_hex

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			bytes:






	"""simple docstring"""



	UpperCamelCase     :str          =	B""""""
	for char in message:
		bit_string += format(__magic_name__	,			"""08b"""					).encode("""utf-8"""					)
	UpperCamelCase     :Any          =	format(len(__magic_name__					)	,			"""064b"""					).encode("""utf-8"""					)

	# Pad bit_string to a multiple of 512 chars
	bit_string += b"1"
	while len(__magic_name__					) % 512 != 448:
		bit_string += b"0"
	bit_string += to_little_endian(start_len[32:]					) + to_little_endian(start_len[:32]					)

	return bit_string

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			Generator[list[int], None, None]:






	"""simple docstring"""



	if len(__magic_name__					) % 512 != 0:
		raise ValueError("""Input must have length that's a multiple of 512"""					)

	for pos in range(0	,			len(__magic_name__					)	,			512					):
		UpperCamelCase     :Tuple          =	bit_string[pos : pos + 512]
		UpperCamelCase     :Optional[int]          =	[]
		for i in range(0	,			512	,			32					):
			block_words.append(int(to_little_endian(block[i : i + 32]					)	,			2					)					)
		yield block_words

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int					)		->			int:






	"""simple docstring"""



	if i < 0:
		raise ValueError("""Input must be non-negative"""					)

	UpperCamelCase     :List[str]          =	format(__magic_name__	,			"""032b"""					)
	UpperCamelCase     :Any          =	""""""
	for c in i_str:
		new_str += "1" if c == "0" else "0"
	return int(__magic_name__	,			2					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  int					)		->			int:






	"""simple docstring"""



	return (a + b) % 2**32

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  int					)		->			int:






	"""simple docstring"""



	if i < 0:
		raise ValueError("""Input must be non-negative"""					)
	if shift < 0:
		raise ValueError("""Shift must be non-negative"""					)
	return ((i << shift) ^ (i >> (32 - shift))) % 2**32

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  bytes					)		->			bytes:






	"""simple docstring"""



	UpperCamelCase     :Tuple          =	preprocess(__magic_name__					)

	UpperCamelCase     :List[str]          =	[int(2**32 * abs(sin(i + 1					)					)					) for i in range(64					)]

	# Starting states
	UpperCamelCase     :Union[str, Any]          =	0X67_45_23_01
	UpperCamelCase     :Union[str, Any]          =	0XEF_CD_AB_89
	UpperCamelCase     :List[str]          =	0X98_BA_DC_FE
	UpperCamelCase     :int          =	0X10_32_54_76

	UpperCamelCase     :int          =	[
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    7,
	    12,
	    17,
	    22,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    5,
	    9,
	    14,
	    20,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    4,
	    11,
	    16,
	    23,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	    6,
	    10,
	    15,
	    21,
	]

	# Process bit string in chunks, each with 16 32-char words
	for block_words in get_block_words(__magic_name__					):
		UpperCamelCase     :Optional[Any]          =	aa
		UpperCamelCase     :Any          =	ba
		UpperCamelCase     :Tuple          =	ca
		UpperCamelCase     :List[str]          =	da

		# Hash current chunk
		for i in range(64					):
			if i <= 15:
				# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
				UpperCamelCase     :int          =	d ^ (b & (c ^ d))
				UpperCamelCase     :Optional[int]          =	i
			elif i <= 31:
				# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
				UpperCamelCase     :str          =	c ^ (d & (b ^ c))
				UpperCamelCase     :Union[str, Any]          =	(5 * i + 1) % 16
			elif i <= 47:
				UpperCamelCase     :str          =	b ^ c ^ d
				UpperCamelCase     :Optional[int]          =	(3 * i + 5) % 16
			else:
				UpperCamelCase     :List[str]          =	c ^ (b | not_aa(__magic_name__					))
				UpperCamelCase     :int          =	(7 * i) % 16
			UpperCamelCase     :Dict          =	(f + a + added_consts[i] + block_words[g]) % 2**32
			UpperCamelCase     :Tuple          =	d
			UpperCamelCase     :str          =	c
			UpperCamelCase     :Tuple          =	b
			UpperCamelCase     :Optional[Any]          =	sum_aa(__magic_name__	,			left_rotate_aa(__magic_name__	,			shift_amounts[i]					)					)

		# Add hashed chunk to running total
		UpperCamelCase     :List[str]          =	sum_aa(__magic_name__	,			__magic_name__					)
		UpperCamelCase     :str          =	sum_aa(__magic_name__	,			__magic_name__					)
		UpperCamelCase     :int          =	sum_aa(__magic_name__	,			__magic_name__					)
		UpperCamelCase     :Optional[Any]          =	sum_aa(__magic_name__	,			__magic_name__					)

	UpperCamelCase     :Optional[Any]          =	reformat_hex(__magic_name__					) + reformat_hex(__magic_name__					) + reformat_hex(__magic_name__					) + reformat_hex(__magic_name__					)
	return digest


if __name__ == "__main__":
	import doctest

	doctest.testmod()
