
import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class        A_   (			__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       DiTPipeline
	_UpperCamelCase     :     Optional[Any]							 =       CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	_UpperCamelCase     :     int							 =       PipelineTesterMixin.required_optional_params - {
	    """latents""",
	    """num_images_per_prompt""",
	    """callback""",
	    """callback_steps""",
	}
	_UpperCamelCase     :     int							 =       CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	_UpperCamelCase     :     Optional[Any]							 =       False





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		torch.manual_seed(0       )
		lowercase       =    TransformeraDModel(
		    sample_size=16		,    num_layers=2		,    patch_size=4		,    attention_head_dim=8		,    num_attention_heads=2		,    in_channels=4		,    out_channels=8		,    attention_bias=snake_case		,    activation_fn='gelu-approximate'		,    num_embeds_ada_norm=1000		,    norm_type='ada_norm_zero'		,    norm_elementwise_affine=snake_case		,    )
		lowercase       =    AutoencoderKL()
		lowercase       =    DDIMScheduler()
		lowercase       =    {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
		return components





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=0       ):
		if str(snake_case       ).startswith('mps'       ):
			lowercase       =    torch.manual_seed(snake_case       )
		else:
			lowercase       =    torch.Generator(device=snake_case       ).manual_seed(snake_case       )
		lowercase       =    {
		    'class_labels': [1],
		    'generator': generator,
		    'num_inference_steps': 2,
		    'output_type': 'numpy',
		}
		return inputs





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    'cpu'

		lowercase       =    self.get_dummy_components()
		lowercase       =    self.pipeline_class(**snake_case       )
		pipe.to(snake_case       )
		pipe.set_progress_bar_config(disable=snake_case       )

		lowercase       =    self.get_dummy_inputs(snake_case       )
		lowercase       =    pipe(**snake_case       ).images
		lowercase       =    image[0, -3:, -3:, -1]

		self.assertEqual(image.shape		,    (1, 16, 16, 3)       )
		lowercase       =    np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]       )
		lowercase       =    np.abs(image_slice.flatten() - expected_slice       ).max()
		self.assertLessEqual(snake_case		,    1E-3       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self._test_inference_batch_single_identical(relax_max_difference=snake_case		,    expected_max_diff=1E-3       )





	@unittest.skipIf(
	    torch_device != 'cuda' or not is_xformers_available()		,    reason='XFormers attention is only available with CUDA and `xformers` installed'		,    )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )







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




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    torch.manual_seed(0       )

		lowercase       =    DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'       )
		pipe.to('cuda'       )

		lowercase       =    ['vase', 'umbrella', 'white shark', 'white wolf']
		lowercase       =    pipe.get_label_ids(snake_case       )

		lowercase       =    pipe(snake_case		,    generator=snake_case		,    num_inference_steps=40		,    output_type='np'       ).images

		for word, image in zip(snake_case		,    snake_case       ):
			lowercase       =    load_numpy(
			    F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''       )
			assert np.abs((expected_image - image).max()       ) < 1E-2





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'       )
		lowercase       =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
		pipe.to('cuda'       )

		lowercase       =    ['vase', 'umbrella']
		lowercase       =    pipe.get_label_ids(snake_case       )

		lowercase       =    torch.manual_seed(0       )
		lowercase       =    pipe(snake_case		,    generator=snake_case		,    num_inference_steps=25		,    output_type='np'       ).images

		for word, image in zip(snake_case		,    snake_case       ):
			lowercase       =    load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    F'''/dit/{word}_512.npy'''       )

			assert np.abs((expected_image - image).max()       ) < 1E-1



def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			lowercase       =    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			lowercase       =    max(
			    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) ,							mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							j - wt[i - 1]							) + val[i - 1] ,							)
		lowercase       =    val
	return f[i][j]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [[0] * (w + 1) for _ in range(n + 1							)]

	for i in range(1 ,							n + 1							):
		for w_ in range(1 ,							w + 1							):
			if wt[i - 1] <= w_:
				lowercase       =    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,							dp[i - 1][w_]							)
			else:
				lowercase       =    dp[i - 1][w_]

	return dp[n][w_], dp






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	if not (isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							) and isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							)):
		raise ValueError(
		    'Both the weights and values vectors must be either lists or tuples'							)

	lowercase       =    len(__SCREAMING_SNAKE_CASE							)
	if num_items != len(__SCREAMING_SNAKE_CASE							):
		lowercase       =    (
		    'The number of weights must be the same as the number of values.\n'
		    F'''But got {num_items} weights and {len(__SCREAMING_SNAKE_CASE							)} values'''
		)
		raise ValueError(__SCREAMING_SNAKE_CASE							)
	for i in range(__SCREAMING_SNAKE_CASE							):
		if not isinstance(wt[i] ,							__SCREAMING_SNAKE_CASE							):
			lowercase       =    (
			    'All weights must be integers but got weight of '
			    F'''type {type(wt[i]							)} at index {i}'''
			)
			raise TypeError(__SCREAMING_SNAKE_CASE							)

	lowercase  ,					lowercase       =    knapsack(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	lowercase       =    set()
	_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	return optimal_val, example_optional_set






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			optimal_set.add(__SCREAMING_SNAKE_CASE							)
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							j - wt[i - 1] ,							__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	UpperCAmelCase	      = [3, 2, 4, 4]
	UpperCAmelCase	      = [4, 3, 2, 3]
	UpperCAmelCase	      = 4
	UpperCAmelCase	      = 6
	UpperCAmelCase	      = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
	UpperCAmelCase      , UpperCAmelCase	      = knapsack(w, wt, val, n)
	print(optimal_solution)
	print(mf_knapsack(n, wt, val, w))  # switched the n and w

	# testing the dynamic programming problem with example
	# the optimal subset for the above example are items 3 and 4
	UpperCAmelCase      , UpperCAmelCase	      = knapsack_with_example_solution(w, wt, val)
	assert optimal_solution == 8
	assert optimal_subset == {3, 4}
	print('''optimal_value = ''', optimal_solution)
	print('''An optimal subset corresponding to the optimal value''', optimal_subset)

