





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class        _lowerCamelCase      (       __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			StableUnCLIPPipeline
						UpperCAmelCase_ =			TEXT_TO_IMAGE_PARAMS
						UpperCAmelCase_ =			TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCAmelCase_ =			TEXT_TO_IMAGE_IMAGE_PARAMS
						UpperCAmelCase_ =			TEXT_TO_IMAGE_IMAGE_PARAMS

						# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
						UpperCAmelCase_ =			False


						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	32
								UpperCamelCase   =	embedder_hidden_size

								# prior components

								torch.manual_seed(0		)
								UpperCamelCase   =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

								torch.manual_seed(0		)
								UpperCamelCase   =	CLIPTextModelWithProjection(
								    CLIPTextConfig(
								        bos_token_id=0   ,							eos_token_id=2   ,							hidden_size=UpperCamelCase_   ,							projection_dim=UpperCamelCase_   ,							intermediate_size=37   ,							layer_norm_eps=1e-0_5   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=10_00   ,							)		)

								torch.manual_seed(0		)
								UpperCamelCase   =	PriorTransformer(
								    num_attention_heads=2   ,							attention_head_dim=12   ,							embedding_dim=UpperCamelCase_   ,							num_layers=1   ,							)

								torch.manual_seed(0		)
								UpperCamelCase   =	DDPMScheduler(
								    variance_type="fixed_small_log"   ,							prediction_type="sample"   ,							num_train_timesteps=10_00   ,							clip_sample=UpperCamelCase_   ,							clip_sample_range=5.0   ,							beta_schedule="squaredcos_cap_v2"   ,							)

								# regular denoising components

								torch.manual_seed(0		)
								UpperCamelCase   =	StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase_		)
								UpperCamelCase   =	DDPMScheduler(beta_schedule="squaredcos_cap_v2"		)

								torch.manual_seed(0		)
								UpperCamelCase   =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

								torch.manual_seed(0		)
								UpperCamelCase   =	CLIPTextModel(
								    CLIPTextConfig(
								        bos_token_id=0   ,							eos_token_id=2   ,							hidden_size=UpperCamelCase_   ,							projection_dim=32   ,							intermediate_size=37   ,							layer_norm_eps=1e-0_5   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=10_00   ,							)		)

								torch.manual_seed(0		)
								UpperCamelCase   =	UNetaDConditionModel(
								    sample_size=32   ,							in_channels=4   ,							out_channels=4   ,							down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")   ,							up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")   ,							block_out_channels=(32, 64)   ,							attention_head_dim=(2, 4)   ,							class_embed_type="projection"   ,							projection_class_embeddings_input_dim=embedder_projection_dim * 2   ,							cross_attention_dim=UpperCamelCase_   ,							layers_per_block=1   ,							upcast_attention=UpperCamelCase_   ,							use_linear_projection=UpperCamelCase_   ,							)

								torch.manual_seed(0		)
								UpperCamelCase   =	DDIMScheduler(
								    beta_schedule="scaled_linear"   ,							beta_start=0.00085   ,							beta_end=0.012   ,							prediction_type="v_prediction"   ,							set_alpha_to_one=UpperCamelCase_   ,							steps_offset=1   ,							)

								torch.manual_seed(0		)
								UpperCamelCase   =	AutoencoderKL()

								UpperCamelCase   =	{
								    # prior components
								    "prior_tokenizer": prior_tokenizer,
								    "prior_text_encoder": prior_text_encoder,
								    "prior": prior,
								    "prior_scheduler": prior_scheduler,
								    # image noising components
								    "image_normalizer": image_normalizer,
								    "image_noising_scheduler": image_noising_scheduler,
								    # regular denoising components
								    "tokenizer": tokenizer,
								    "text_encoder": text_encoder,
								    "unet": unet,
								    "scheduler": scheduler,
								    "vae": vae,
								}

								return components


						def     snake_case_   (self   ,							__a   ,							__a=0		) ->   int:
								if str(UpperCamelCase_		).startswith("mps"		):
										UpperCamelCase   =	torch.manual_seed(UpperCamelCase_		)
								else:
										UpperCamelCase   =	torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
								UpperCamelCase   =	{
								    "prompt": "A painting of a squirrel eating a burger",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "prior_num_inference_steps": 2,
								    "output_type": "numpy",
								}
								return inputs


						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	torch_device == "cpu"

								self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase_		)




						def     snake_case_   (self		) ->   Dict:
								UpperCamelCase   =	torch_device in ["cpu", "mps"]

								self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase_		)





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


						def     snake_case_   (self		) ->   str:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


						def     snake_case_   (self		) ->   Optional[Any]:
								UpperCamelCase   =	load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"		)

								UpperCamelCase   =	StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"   ,							torch_dtype=torch.floataa		)
								pipe.to(UpperCamelCase_		)
								pipe.set_progress_bar_config(disable=UpperCamelCase_		)
								# stable unclip will oom when integration tests are run on a V100,
								# so turn on memory savings
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								UpperCamelCase   =	torch.Generator(device="cpu"		).manual_seed(0		)
								UpperCamelCase   =	pipe("anime turle"   ,							generator=UpperCamelCase_   ,							output_type="np"		)

								UpperCamelCase   =	output.images[0]

								assert image.shape == (7_68, 7_68, 3)

								assert_mean_pixel_difference(UpperCamelCase_   ,							UpperCamelCase_		)




						def     snake_case_   (self		) ->   Tuple:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								UpperCamelCase   =	StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"   ,							torch_dtype=torch.floataa		)
								UpperCamelCase   =	pipe.to(UpperCamelCase_		)
								pipe.set_progress_bar_config(disable=UpperCamelCase_		)
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								UpperCamelCase   =	pipe(
								    "anime turtle"   ,							prior_num_inference_steps=2   ,							num_inference_steps=2   ,							output_type="np"   ,							)

								UpperCamelCase   =	torch.cuda.max_memory_allocated()
								# make sure that less than 7 GB is allocated
								assert mem_bytes < 7 * 10**9













"""simple docstring"""





import argparse
from collections import defaultdict


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	F"{file}_{class_name}_{test_name}"
		done_test[_id] += 1

		with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
				UpperCamelCase   =	f.readlines()

		UpperCamelCase   =	F"class {class_name}("
		UpperCamelCase   =	F"{4 * ' '}def {test_name}("
		UpperCamelCase   =	F"{8 * ' '}{correct_line.split()[0]}"
		UpperCamelCase   =	F"{16 * ' '}{correct_line.split()[0]}"
		UpperCamelCase   =	False
		UpperCamelCase   =	False
		UpperCamelCase   =	False
		UpperCamelCase   =	False
		UpperCamelCase   =	0
		UpperCamelCase   =	0

		UpperCamelCase   =	[]
		for line in lines:
				if line.startswith(_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	True
				elif in_class and line.startswith(_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	True
				elif in_class and in_func and (line.startswith(_SCREAMING_SNAKE_CASE    ) or line.startswith(_SCREAMING_SNAKE_CASE    )):
						UpperCamelCase   =	len(line.split(correct_line.split()[0]    )[0]    )
						count += 1

						if count == done_test[_id]:
								UpperCamelCase   =	True

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

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F"{spaces * ' '}{correct_line}"    )
						UpperCamelCase   =	UpperCamelCase   =	UpperCamelCase   =	UpperCamelCase   =	False
				else:
						new_lines.append(_SCREAMING_SNAKE_CASE    )

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


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None    ):




		"""simple docstring"""


		if fail is not None:
				with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
						UpperCamelCase   =	{l.strip() for l in f.readlines()}
		else:
				UpperCamelCase   =	None

		with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
				UpperCamelCase   =	f.readlines()

		UpperCamelCase   =	defaultdict(_SCREAMING_SNAKE_CASE    )
		for line in correct_lines:
				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	line.split(";"    )
				if test_failures is None or "::".join([file, class_name, test_name]    ) in test_failures:
						overwrite_file(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						lowerCAmelCase__     			=			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)
						lowerCAmelCase__     			=			parser.parse_args()

						main(args.correct_filename, args.fail_filename)






