
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_ (__SCREAMING_SNAKE_CASE			,  unittest.TestCase							):
	"""simple docstring"""

	lowerCamelCase     :       Dict            =			DiTPipeline
	lowerCamelCase     :       Tuple            =			CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
	lowerCamelCase     :       Dict            =			PipelineTesterMixin.required_optional_params - {
	    'latents',
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	lowerCamelCase     :       Tuple            =			CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
	lowerCamelCase     :       Optional[int]            =			False







	def       SCREAMING_SNAKE_CASE__     (     self:     Union[str, Any]			):
								torch.manual_seed(0			)
								_lowerCAmelCase :Optional[Any]      	=						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=_SCREAMING_SNAKE_CASE							,   activation_fn='gelu-approximate'							,   num_embeds_ada_norm=1000							,   norm_type='ada_norm_zero'							,   norm_elementwise_affine=_SCREAMING_SNAKE_CASE							,   )
								_lowerCAmelCase :Optional[Any]      	=						AutoencoderKL()
								_lowerCAmelCase :List[Any]      	=						DDIMScheduler()
								_lowerCAmelCase :Dict      	=						{"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
								return components







	def       SCREAMING_SNAKE_CASE__     (     self:     int							,   _UpperCAmelCase:     List[str]							,   _UpperCAmelCase:     Optional[int]=0			):

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







	def       SCREAMING_SNAKE_CASE__     (     self:     Optional[int]			):
								_lowerCAmelCase :Tuple      	=						"cpu"

								_lowerCAmelCase :Any      	=						self.get_dummy_components()
								_lowerCAmelCase :Any      	=						self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
								pipe.to(_SCREAMING_SNAKE_CASE			)
								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

								_lowerCAmelCase :List[Any]      	=						self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)
								_lowerCAmelCase :Tuple      	=						pipe(**_SCREAMING_SNAKE_CASE			).images
								_lowerCAmelCase :Tuple      	=						image[0, -3:, -3:, -1]

								self.assertEqual(image.shape							,   (1, 16, 16, 3)			)
								_lowerCAmelCase :str      	=						np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]			)
								_lowerCAmelCase :Any      	=						np.abs(image_slice.flatten() - expected_slice			).max()
								self.assertLessEqual(_SCREAMING_SNAKE_CASE							,   1e-3			)







	def       SCREAMING_SNAKE_CASE__     (     self:     Optional[int]			):
								self._test_inference_batch_single_identical(relax_max_difference=_SCREAMING_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:     str			):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)





@require_torch_gpu
@slow
class 	UpperCAmelCase_ (unittest.TestCase							):
	"""simple docstring"""

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







	def       SCREAMING_SNAKE_CASE__     (     self:     Any			):
								_lowerCAmelCase :List[Any]      	=						torch.manual_seed(0			)

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

								_lowerCAmelCase :Any      	=						["vase", "umbrella", "white shark", "white wolf"]
								_lowerCAmelCase :Optional[Any]      	=						pipe.get_label_ids(_SCREAMING_SNAKE_CASE			)

								_lowerCAmelCase :Optional[Any]      	=						pipe(_SCREAMING_SNAKE_CASE							,   generator=_SCREAMING_SNAKE_CASE							,   num_inference_steps=40							,   output_type='np'			).images

								for word, image in zip(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE			):
															_lowerCAmelCase :int      	=						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:     Dict			):
								_lowerCAmelCase :Optional[Any]      	=						DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'			)
								_lowerCAmelCase :Optional[int]      	=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
								pipe.to('cuda'			)

								_lowerCAmelCase :Tuple      	=						["vase", "umbrella"]
								_lowerCAmelCase :Dict      	=						pipe.get_label_ids(_SCREAMING_SNAKE_CASE			)

								_lowerCAmelCase :List[str]      	=						torch.manual_seed(0			)
								_lowerCAmelCase :Tuple      	=						pipe(_SCREAMING_SNAKE_CASE							,   generator=_SCREAMING_SNAKE_CASE							,   num_inference_steps=25							,   output_type='np'			).images

								for word, image in zip(_SCREAMING_SNAKE_CASE							,   _SCREAMING_SNAKE_CASE			):
															_lowerCAmelCase :str      	=						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




"""simple docstring"""






import argparse
from collections import defaultdict


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Optional[int]:
			__lowerCAmelCase :    str				=			F"""{file}_{class_name}_{test_name}"""
			done_test[_id] += 1

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    List[str]				=			f.readlines()

			__lowerCAmelCase :    Tuple				=			F"""class {class_name}("""
			__lowerCAmelCase :    Tuple				=			F"""{4 * ' '}def {test_name}("""
			__lowerCAmelCase :    List[str]				=			F"""{8 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    List[Any]				=			F"""{16 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    Any				=			False
			__lowerCAmelCase :    List[Any]				=			False
			__lowerCAmelCase :    Optional[Any]				=			False
			__lowerCAmelCase :    str				=			False
			__lowerCAmelCase :    Optional[int]				=			0
			__lowerCAmelCase :    Dict				=			0

			__lowerCAmelCase :    int				=			[]
			for line in lines:
						if line.startswith(__snake_case      ):
									__lowerCAmelCase :    Any				=			True
						elif in_class and line.startswith(__snake_case      ):
									__lowerCAmelCase :    str				=			True
						elif in_class and in_func and (line.startswith(__snake_case      ) or line.startswith(__snake_case      )):
									__lowerCAmelCase :    Optional[Any]				=			len(line.split(correct_line.split()[0]      )[0]      )
									count += 1

									if count == done_test[_id]:
												__lowerCAmelCase :    Optional[int]				=			True

						if in_class and in_func and in_line:
									if ")" not in line:
												continue
									else:
												__lowerCAmelCase :    Dict				=			True

						if in_class and in_func and in_line and insert_line:
									new_lines.append(F"""{spaces * ' '}{correct_line}"""      )
									__lowerCAmelCase :    Union[str, Any]				=			False
						else:
									new_lines.append(__snake_case      )

			with open(__snake_case   ,"w"      ) as f:
						for line in new_lines:
									f.write(__snake_case      )


def      _lowercase							(    __snake_case   ,__snake_case=None      )		->  int:
			if fail is not None:
						with open(__snake_case   ,"r"      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			{l.strip() for l in f.readlines()}
			else:
						__lowerCAmelCase :    Optional[Any]				=			None

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			f.readlines()

			__lowerCAmelCase :    Optional[Any]				=			defaultdict(__snake_case      )
			for line in correct_lines:
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			line.split(";"      )
						if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
									overwrite_file(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )


if __name__ == "__main__":
	__snake_case				:  Union[str, Any] =       argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	__snake_case				:  int =       parser.parse_args()

	main(args.correct_filename, args.fail_filename)