

import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						__UpperCamelCase    (					unittest.TestCase						):




	@property
	def       __A					(		self	:						int							):


		'''simple docstring'''







		torch.manual_seed(0							)
		UpperCAmelCase_       				=			UNetaDModel(
		    block_out_channels=(32, 64)		,	layers_per_block=2		,	sample_size=32		,	in_channels=3		,	out_channels=3		,	down_block_types=("DownBlock2D", "AttnDownBlock2D")		,	up_block_types=("AttnUpBlock2D", "UpBlock2D")		,	)
		return model




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.dummy_uncond_unet
		UpperCAmelCase_       				=			ScoreSdeVeScheduler()

		UpperCAmelCase_       				=			ScoreSdeVePipeline(unet=lowerCAmelCase		,	scheduler=lowerCAmelCase							)
		sde_ve.to(lowerCAmelCase							)
		sde_ve.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			torch.manual_seed(0							)
		UpperCAmelCase_       				=			sde_ve(num_inference_steps=2		,	output_type="numpy"		,	generator=lowerCAmelCase							).images

		UpperCAmelCase_       				=			torch.manual_seed(0							)
		UpperCAmelCase_       				=			sde_ve(num_inference_steps=2		,	output_type="numpy"		,	generator=lowerCAmelCase		,	return_dict=lowerCAmelCase							)[
		    0
		]

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

		assert image.shape == (1, 32, 32, 3)
		UpperCAmelCase_       				=			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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


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




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			"google/ncsnpp-church-256"
		UpperCAmelCase_       				=			UNetaDModel.from_pretrained(lowerCAmelCase							)

		UpperCAmelCase_       				=			ScoreSdeVeScheduler.from_pretrained(lowerCAmelCase							)

		UpperCAmelCase_       				=			ScoreSdeVePipeline(unet=lowerCAmelCase		,	scheduler=lowerCAmelCase							)
		sde_ve.to(lowerCAmelCase							)
		sde_ve.set_progress_bar_config(disable=lowerCAmelCase							)

		UpperCAmelCase_       				=			torch.manual_seed(0							)
		UpperCAmelCase_       				=			sde_ve(num_inference_steps=10		,	output_type="numpy"		,	generator=lowerCAmelCase							).images

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

		assert image.shape == (1, 256, 256, 3)

		UpperCAmelCase_       				=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

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







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()