

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int] )   ->       Tuple:
				_UpperCAmelCase       :		Union[str, Any] 		=    [tensor.shape for tensor in tensor_list]
				return all(shape == shapes[0] for shape in shapes[1:] )

class 				a  (	UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			StableDiffusionLatentUpscalePipeline
						_lowercase    						=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    "height",
						    "width",
						    "cross_attention_kwargs",
						    "negative_prompt_embeds",
						    "prompt_embeds",
						}
						_lowercase    						=			PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
						_lowercase    						=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						_lowercase    						=			frozenset(
						    []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						_lowercase    						=			frozenset([]				)

						_lowercase    						=			True


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    1
										_UpperCAmelCase       :		List[str] 		=    4
										_UpperCAmelCase       :		Dict 		=    (16, 16)

										_UpperCAmelCase       :		List[Any] 		=    floats_tensor((batch_size, num_channels) + sizes						,					rng=random.Random(0	)	).to(A_	)
										return image


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		Optional[Any] 		=    UNetaDConditionModel(
										    act_fn="gelu"						,					attention_head_dim=8						,					norm_num_groups=A_						,					block_out_channels=[32, 32, 64, 64]						,					time_cond_proj_dim=160						,					conv_in_kernel=1						,					conv_out_kernel=1						,					cross_attention_dim=32						,					down_block_types=(
										        "KDownBlock2D",
										        "KCrossAttnDownBlock2D",
										        "KCrossAttnDownBlock2D",
										        "KCrossAttnDownBlock2D",
										    )						,					in_channels=8						,					mid_block_type=A_						,					only_cross_attention=A_						,					out_channels=5						,					resnet_time_scale_shift="scale_shift"						,					time_embedding_type="fourier"						,					timestep_post_act="gelu"						,					up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")						,					)
										_UpperCAmelCase       :		Optional[int] 		=    AutoencoderKL(
										    block_out_channels=[32, 32, 64, 64]						,					in_channels=3						,					out_channels=3						,					down_block_types=[
										        "DownEncoderBlock2D",
										        "DownEncoderBlock2D",
										        "DownEncoderBlock2D",
										        "DownEncoderBlock2D",
										    ]						,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]						,					latent_channels=4						,					)
										_UpperCAmelCase       :		Union[str, Any] 		=    EulerDiscreteScheduler(prediction_type="sample"	)
										_UpperCAmelCase       :		Any 		=    CLIPTextConfig(
										    bos_token_id=0						,					eos_token_id=2						,					hidden_size=32						,					intermediate_size=37						,					layer_norm_eps=1e-05						,					num_attention_heads=4						,					num_hidden_layers=5						,					pad_token_id=1						,					vocab_size=1000						,					hidden_act="quick_gelu"						,					projection_dim=512						,					)
										_UpperCAmelCase       :		Optional[Any] 		=    CLIPTextModel(A_	)
										_UpperCAmelCase       :		List[Any] 		=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										_UpperCAmelCase       :		List[Any] 		=    {
										    "unet": model.eval(),
										    "vae": vae.eval(),
										    "scheduler": scheduler,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										}

										return components


						def        _UpperCAmelCase				(       self						,					A_						,					A_=0	):







										'''simple docstring'''


										if str(A_	).startswith("mps"	):
														_UpperCAmelCase       :		Union[str, Any] 		=    torch.manual_seed(A_	)
										else:
														_UpperCAmelCase       :		Tuple 		=    torch.Generator(device=A_	).manual_seed(A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    {
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": self.dummy_image.cpu(),
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}
										return inputs


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    "cpu"

										_UpperCAmelCase       :		List[Any] 		=    self.get_dummy_components()
										_UpperCAmelCase       :		Any 		=    self.pipeline_class(**A_	)
										pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		List[str] 		=    self.get_dummy_inputs(A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    pipe(**A_	).images
										_UpperCAmelCase       :		List[Any] 		=    image[0, -3:, -3:, -1]

										self.assertEqual(image.shape						,					(1, 256, 256, 3)	)
										_UpperCAmelCase       :		List[str] 		=    np.array(
										    [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]	)
										_UpperCAmelCase       :		List[str] 		=    np.abs(image_slice.flatten() - expected_slice	).max()
										self.assertLessEqual(A_						,					1e-3	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().test_attention_slicing_forward_pass(expected_max_diff=7e-3	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().test_cpu_offload_forward_pass(expected_max_diff=3e-3	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().test_inference_batch_single_identical(expected_max_diff=7e-3	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().test_save_load_local(expected_max_difference=3e-3	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().test_save_load_optional_components(expected_max_difference=3e-3	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    [
										    "DDIMScheduler",
										    "DDPMScheduler",
										    "PNDMScheduler",
										    "HeunDiscreteScheduler",
										    "EulerAncestralDiscreteScheduler",
										    "KDPM2DiscreteScheduler",
										    "KDPM2AncestralDiscreteScheduler",
										    "DPMSolverSDEScheduler",
										]
										_UpperCAmelCase       :		Dict 		=    self.get_dummy_components()
										_UpperCAmelCase       :		Any 		=    self.pipeline_class(**A_	)

										# make sure that PNDM does not need warm-up
										pipe.scheduler.register_to_config(skip_prk_steps=A_	)

										pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)
										_UpperCAmelCase       :		Tuple 		=    self.get_dummy_inputs(A_	)
										_UpperCAmelCase       :		List[Any] 		=    2

										_UpperCAmelCase       :		Union[str, Any] 		=    []
										for scheduler_enum in KarrasDiffusionSchedulers:
														if scheduler_enum.name in skip_schedulers:
																		# no sigma schedulers are not supported
																		# no schedulers
																		continue

														_UpperCAmelCase       :		List[Any] 		=    getattr(A_						,					scheduler_enum.name	)
														_UpperCAmelCase       :		int 		=    scheduler_cls.from_config(pipe.scheduler.config	)
														_UpperCAmelCase       :		Tuple 		=    pipe(**A_	)[0]
														outputs.append(A_	)

										assert check_same_shape(A_	)







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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    torch.manual_seed(33	)

										_UpperCAmelCase       :		Dict 		=    StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"						,					torch_dtype=torch.floataa	)
										pipe.to("cuda"	)

										_UpperCAmelCase       :		Dict 		=    StableDiffusionLatentUpscalePipeline.from_pretrained(
										    "stabilityai/sd-x2-latent-upscaler"						,					torch_dtype=torch.floataa	)
										upscaler.to("cuda"	)

										_UpperCAmelCase       :		List[str] 		=    "a photo of an astronaut high resolution, unreal engine, ultra realistic"

										_UpperCAmelCase       :		int 		=    pipe(A_						,					generator=A_						,					output_type="latent"	).images

										_UpperCAmelCase       :		List[Any] 		=    upscaler(
										    prompt=A_						,					image=A_						,					num_inference_steps=20						,					guidance_scale=0						,					generator=A_						,					output_type="np"						,					).images[0]

										_UpperCAmelCase       :		Tuple 		=    load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"	)
										assert np.abs((expected_image - image).mean()	) < 5e-2





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    torch.manual_seed(33	)

										_UpperCAmelCase       :		str 		=    StableDiffusionLatentUpscalePipeline.from_pretrained(
										    "stabilityai/sd-x2-latent-upscaler"						,					torch_dtype=torch.floataa	)
										upscaler.to("cuda"	)

										_UpperCAmelCase       :		str 		=    "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

										_UpperCAmelCase       :		Optional[int] 		=    load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"	)

										_UpperCAmelCase       :		int 		=    upscaler(
										    prompt=A_						,					image=A_						,					num_inference_steps=20						,					guidance_scale=0						,					generator=A_						,					output_type="np"						,					).images[0]

										_UpperCAmelCase       :		str 		=    load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"	)
										assert np.abs((expected_image - image).max()	) < 5e-2





import fire

from utils import calculate_rouge, save_json

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  Any )   ->       Dict:
				_UpperCAmelCase       :		Optional[Any] 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()]
				_UpperCAmelCase       :		str 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()][: len(lowerCAmelCase )]
				_UpperCAmelCase       :		str 		=    calculate_rouge(lowerCAmelCase  ,      lowerCAmelCase  ,      **lowerCAmelCase )
				if save_path is not None:
								save_json(lowerCAmelCase  ,      lowerCAmelCase  ,      indent=lowerCAmelCase )
				return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)


