

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 						__UpperCamelCase    (					lowercase     ,     lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     StableDiffusionSAGPipeline
	SCREAMING_SNAKE_CASE__    				=     TEXT_TO_IMAGE_PARAMS
	SCREAMING_SNAKE_CASE__    				=     TEXT_TO_IMAGE_BATCH_PARAMS
	SCREAMING_SNAKE_CASE__    				=     TEXT_TO_IMAGE_IMAGE_PARAMS
	SCREAMING_SNAKE_CASE__    				=     TEXT_TO_IMAGE_IMAGE_PARAMS
	SCREAMING_SNAKE_CASE__    				=     False




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		torch.manual_seed(0							)
		UpperCAmelCase_       				=			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		,	)
		UpperCAmelCase_       				=			DDIMScheduler(
		    beta_start=0.00_085		,	beta_end=0.012		,	beta_schedule="scaled_linear"		,	clip_sample=lowerCAmelCase		,	set_alpha_to_one=lowerCAmelCase		,	)
		torch.manual_seed(0							)
		UpperCAmelCase_       				=			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							)
		UpperCAmelCase_       				=			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		,	)
		UpperCAmelCase_       				=			CLIPTextModel(lowerCAmelCase							)
		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       __A					(		self	:						Any		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						List[Any]=0							):


		'''simple docstring'''







		if str(lowerCAmelCase							).startswith("mps"							):
			UpperCAmelCase_       				=			torch.manual_seed(lowerCAmelCase							)
		else:
			UpperCAmelCase_       				=			torch.Generator(device=lowerCAmelCase							).manual_seed(lowerCAmelCase							)
		UpperCAmelCase_       				=			{
		    "prompt": ".",
		    "generator": generator,
		    "num_inference_steps": 2,
		    "guidance_scale": 1.0,
		    "sag_scale": 1.0,
		    "output_type": "numpy",
		}
		return inputs




	def       __A					(		self	:						Any							):


		'''simple docstring'''







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


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




	def       __A					(		self	:						int							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"							)
		UpperCAmelCase_       				=			sag_pipe.to(lowerCAmelCase							)
		sag_pipe.set_progress_bar_config(disable=lowerCAmelCase							)

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

		UpperCAmelCase_       				=			output.images

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

		assert image.shape == (1, 512, 512, 3)
		UpperCAmelCase_       				=			np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]							)

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




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"							)
		UpperCAmelCase_       				=			sag_pipe.to(lowerCAmelCase							)
		sag_pipe.set_progress_bar_config(disable=lowerCAmelCase							)

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

		UpperCAmelCase_       				=			output.images

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

		assert image.shape == (1, 512, 512, 3)
		UpperCAmelCase_       				=			np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]							)

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




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"							)
		UpperCAmelCase_       				=			sag_pipe.to(lowerCAmelCase							)
		sag_pipe.set_progress_bar_config(disable=lowerCAmelCase							)

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

		UpperCAmelCase_       				=			output.images

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


def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			generate_pascal_triangle(A    )
	for row_idx in range(A    ):
		# Print left spaces
		for _ in range(num_rows - row_idx - 1    ):
			print(end=" "    )
		# Print row values
		for col_idx in range(row_idx + 1    ):
			if col_idx != row_idx:
				print(triangle[row_idx][col_idx]       ,     end=" "    )
			else:
				print(triangle[row_idx][col_idx]       ,     end=""    )
		print()



def       __lowerCAmelCase      (						A    ):

	if not isinstance(A       ,     A    ):
		raise TypeError("The input value of 'num_rows' should be 'int'"    )

	if num_rows == 0:
		return []
	elif num_rows < 0:
		raise ValueError(
		    "The input value of 'num_rows' should be greater than or equal to 0"    )

	UpperCAmelCase_       				=			[]
	for current_row_idx in range(A    ):
		UpperCAmelCase_       				=			populate_current_row(A       ,     A    )
		triangle.append(A    )
	return triangle



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			[-1] * (current_row_idx + 1)
	# first and last elements of current row are equal to 1
	UpperCAmelCase_				,   UpperCAmelCase_       				=			1, 1
	for current_col_idx in range(1       ,     A    ):
		calculate_current_element(
		    A       ,     A       ,     A       ,     A    )
	return current_row



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A       ,     ):
	UpperCAmelCase_       				=			triangle[current_row_idx - 1][current_col_idx - 1]
	UpperCAmelCase_       				=			triangle[current_row_idx - 1][current_col_idx]
	UpperCAmelCase_       				=			above_to_left_elt + above_to_right_elt



def       __lowerCAmelCase      (						A    ):

	if not isinstance(A       ,     A    ):
		raise TypeError("The input value of 'num_rows' should be 'int'"    )

	if num_rows == 0:
		return []
	elif num_rows < 0:
		raise ValueError(
		    "The input value of 'num_rows' should be greater than or equal to 0"    )

	UpperCAmelCase_       				=			[[1]]

	for row_index in range(1       ,     A    ):
		UpperCAmelCase_       				=			[0] + result[-1] + [0]
		UpperCAmelCase_       				=			row_index + 1
		# Calculate the number of distinct elements in a row
		UpperCAmelCase_       				=			sum(divmod(A       ,     2    )    )
		UpperCAmelCase_       				=			[
		    temp_row[i - 1] + temp_row[i] for i in range(1       ,     distinct_elements + 1    )
		]
		UpperCAmelCase_       				=			row_first_half[: (row_index + 1) // 2]
		row_second_half.reverse()
		UpperCAmelCase_       				=			row_first_half + row_second_half
		result.append(A    )

	return result



def       __lowerCAmelCase      (						):
	from collections.abc import Callable
	from timeit import timeit

	def benchmark_a_function(A       ,     A    ) -> None:
		UpperCAmelCase_       				=			F"{func.__name__}({value})"
		UpperCAmelCase_       				=			timeit(F"__main__.{call}"       ,     setup="import __main__"    )
		# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
		print(F"{call:38} -- {timing:.4f} seconds"    )

	for value in range(15    ):  # (1, 7, 14):
		for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
			benchmark_a_function(A       ,     A    )
		print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()