


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     UpperCamelCase(  _a     ,							unittest.TestCase   ):
					snake_case_		:				int										=	DiTPipeline
					snake_case_		:				Optional[int]										=	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
					snake_case_		:				Tuple										=	PipelineTesterMixin.required_optional_params - {
					    "latents",
					    "num_images_per_prompt",
					    "callback",
					    "callback_steps",
					}
					snake_case_		:				Tuple										=	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
					snake_case_		:				str										=	False


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	str:




											'''simple docstring'''

											torch.manual_seed(0						)
											__snake_case				=		TransformeraDModel(
											    sample_size=1_6     ,    num_layers=2     ,    patch_size=4     ,    attention_head_dim=8     ,    num_attention_heads=2     ,    in_channels=4     ,    out_channels=8     ,    attention_bias=UpperCamelCase__     ,    activation_fn="gelu-approximate"     ,    num_embeds_ada_norm=1_0_0_0     ,    norm_type="ada_norm_zero"     ,    norm_elementwise_affine=UpperCamelCase__     ,    )
											__snake_case				=		AutoencoderKL()
											__snake_case				=		DDIMScheduler()
											__snake_case				=		{"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
											return components


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0						)			->	Optional[Any]:




											'''simple docstring'''

											if str(UpperCamelCase__						).startswith("mps"						):
																	__snake_case				=		torch.manual_seed(UpperCamelCase__						)
											else:
																	__snake_case				=		torch.Generator(device=UpperCamelCase__						).manual_seed(UpperCamelCase__						)
											__snake_case				=		{
											    "class_labels": [1],
											    "generator": generator,
											    "num_inference_steps": 2,
											    "output_type": "numpy",
											}
											return inputs


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		"cpu"

											__snake_case				=		self.get_dummy_components()
											__snake_case				=		self.pipeline_class(**UpperCamelCase__						)
											pipe.to(UpperCamelCase__						)
											pipe.set_progress_bar_config(disable=UpperCamelCase__						)

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

											self.assertEqual(image.shape     ,    (1, 1_6, 1_6, 3)						)
											__snake_case				=		np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]						)
											__snake_case				=		np.abs(image_slice.flatten() - expected_slice						).max()
											self.assertLessEqual(UpperCamelCase__     ,    1e-3						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	List[Any]:




											'''simple docstring'''

											self._test_inference_batch_single_identical(relax_max_difference=UpperCamelCase__     ,    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	:		Any						)			->	int:




											'''simple docstring'''

											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)






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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	str:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		torch.manual_seed(0						)

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

											__snake_case				=		["vase", "umbrella", "white shark", "white wolf"]
											__snake_case				=		pipe.get_label_ids(UpperCamelCase__						)

											__snake_case				=		pipe(UpperCamelCase__     ,    generator=UpperCamelCase__     ,    num_inference_steps=4_0     ,    output_type="np"						).images

											for word, image in zip(UpperCamelCase__     ,    UpperCamelCase__						):
																	__snake_case				=		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	:		Union[str, Any]						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"						)
											__snake_case				=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
											pipe.to("cuda"						)

											__snake_case				=		["vase", "umbrella"]
											__snake_case				=		pipe.get_label_ids(UpperCamelCase__						)

											__snake_case				=		torch.manual_seed(0						)
											__snake_case				=		pipe(UpperCamelCase__     ,    generator=UpperCamelCase__     ,    num_inference_steps=2_5     ,    output_type="np"						).images

											for word, image in zip(UpperCamelCase__     ,    UpperCamelCase__						):
																	__snake_case				=		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 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		set()

						# Replace all the whitespace in our sentence
						__snake_case				=		input_str.replace(" "      ,	"" )
						for alpha in input_str:
												if "a" <= alpha.lower() <= "z":
																		frequency.add(alpha.lower() )
						return len(_lowerCAmelCase ) == 26





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		[False] * 26
						for char in input_str:
												if char.islower():
																		__snake_case				=		True
												elif char.isupper():
																		__snake_case				=		True
						return all(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						return len({char for char in input_str.lower() if char.isalpha()} ) == 26





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from timeit import timeit

						__snake_case				=		"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
						print(timeit("is_pangram()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_faster()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_fastest()"      ,	setup=_lowerCAmelCase ) )
						# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
						# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





