



import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()
class 			_snake_case     (   unittest.TestCase):




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




				@property
				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			1
							lowercase__   					=			3
							lowercase__   					=			(32, 32)

							lowercase__   					=			floats_tensor((batch_size, num_channels) + sizes,   rng=random.Random(0  )  ).to(snake_case__  )
							return image




				@property
				def 							A__ (  self   :  str  ):
							torch.manual_seed(0  )
							lowercase__   					=			UNetaDConditionModel(
							    block_out_channels=(32, 32, 64),   layers_per_block=2,   sample_size=32,   in_channels=7,   out_channels=4,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),   up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),   cross_attention_dim=32,   attention_head_dim=8,   use_linear_projection=snake_case__,   only_cross_attention=(True, True, False),   num_class_embeds=100,   )
							return model




				@property
				def 							A__ (  self   :  int  ):
							torch.manual_seed(0  )
							lowercase__   					=			AutoencoderKL(
							    block_out_channels=[32, 32, 64],   in_channels=3,   out_channels=3,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],   latent_channels=4,   )
							return model




				@property
				def 							A__ (  self   :  str  ):
							torch.manual_seed(0  )
							lowercase__   					=			CLIPTextConfig(
							    bos_token_id=0,   eos_token_id=2,   hidden_size=32,   intermediate_size=37,   layer_norm_eps=1e-0_5,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=1000,   hidden_act="gelu",   projection_dim=512,   )
							return CLIPTextModel(snake_case__  )




				def 							A__ (  self   :  Any  ):
							lowercase__   					=			"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowercase__   					=			self.dummy_cond_unet_upscale
							lowercase__   					=			DDPMScheduler()
							lowercase__   					=			DDIMScheduler(prediction_type="v_prediction"  )
							lowercase__   					=			self.dummy_vae
							lowercase__   					=			self.dummy_text_encoder
							lowercase__   					=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							lowercase__   					=			self.dummy_image.cpu().permute(0,   2,   3,   1  )[0]
							lowercase__   					=			Image.fromarray(np.uinta(snake_case__  )  ).convert("RGB"  ).resize((64, 64)  )

							# make sure here that pndm scheduler skips prk
							lowercase__   					=			StableDiffusionUpscalePipeline(
							    unet=snake_case__,   low_res_scheduler=snake_case__,   scheduler=snake_case__,   vae=snake_case__,   text_encoder=snake_case__,   tokenizer=snake_case__,   max_noise_level=350,   )
							lowercase__   					=			sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowercase__   					=			"A painting of a squirrel eating a burger"
							lowercase__   					=			torch.Generator(device=snake_case__  ).manual_seed(0  )
							lowercase__   					=			sd_pipe(
							    [prompt],   image=snake_case__,   generator=snake_case__,   guidance_scale=6.0,   noise_level=20,   num_inference_steps=2,   output_type="np",   )

							lowercase__   					=			output.images

							lowercase__   					=			torch.Generator(device=snake_case__  ).manual_seed(0  )
							lowercase__   					=			sd_pipe(
							    [prompt],   image=snake_case__,   generator=snake_case__,   guidance_scale=6.0,   noise_level=20,   num_inference_steps=2,   output_type="np",   return_dict=snake_case__,   )[0]

							lowercase__   					=			image[0, -3:, -3:, -1]
							lowercase__   					=			image_from_tuple[0, -3:, -3:, -1]

							lowercase__   					=			low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)
							lowercase__   					=			np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]  )

							assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2




				def 							A__ (  self   :  List[Any]  ):
							lowercase__   					=			"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowercase__   					=			self.dummy_cond_unet_upscale
							lowercase__   					=			DDPMScheduler()
							lowercase__   					=			DDIMScheduler(prediction_type="v_prediction"  )
							lowercase__   					=			self.dummy_vae
							lowercase__   					=			self.dummy_text_encoder
							lowercase__   					=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							lowercase__   					=			self.dummy_image.cpu().permute(0,   2,   3,   1  )[0]
							lowercase__   					=			Image.fromarray(np.uinta(snake_case__  )  ).convert("RGB"  ).resize((64, 64)  )

							# make sure here that pndm scheduler skips prk
							lowercase__   					=			StableDiffusionUpscalePipeline(
							    unet=snake_case__,   low_res_scheduler=snake_case__,   scheduler=snake_case__,   vae=snake_case__,   text_encoder=snake_case__,   tokenizer=snake_case__,   max_noise_level=350,   )
							lowercase__   					=			sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowercase__   					=			"A painting of a squirrel eating a burger"
							lowercase__   					=			sd_pipe(
							    2 * [prompt],   image=2 * [low_res_image],   guidance_scale=6.0,   noise_level=20,   num_inference_steps=2,   output_type="np",   )
							lowercase__   					=			output.images
							assert image.shape[0] == 2

							lowercase__   					=			torch.Generator(device=snake_case__  ).manual_seed(0  )
							lowercase__   					=			sd_pipe(
							    [prompt],   image=snake_case__,   generator=snake_case__,   num_images_per_prompt=2,   guidance_scale=6.0,   noise_level=20,   num_inference_steps=2,   output_type="np",   )
							lowercase__   					=			output.images
							assert image.shape[0] == 2




				@unittest.skipIf(torch_device != "cuda",   "This test requires a GPU"  )
				def 							A__ (  self   :  Tuple  ):
							lowercase__   					=			self.dummy_cond_unet_upscale
							lowercase__   					=			DDPMScheduler()
							lowercase__   					=			DDIMScheduler(prediction_type="v_prediction"  )
							lowercase__   					=			self.dummy_vae
							lowercase__   					=			self.dummy_text_encoder
							lowercase__   					=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							lowercase__   					=			self.dummy_image.cpu().permute(0,   2,   3,   1  )[0]
							lowercase__   					=			Image.fromarray(np.uinta(snake_case__  )  ).convert("RGB"  ).resize((64, 64)  )

							# put models in fp16, except vae as it overflows in fp16
							lowercase__   					=			unet.half()
							lowercase__   					=			text_encoder.half()

							# make sure here that pndm scheduler skips prk
							lowercase__   					=			StableDiffusionUpscalePipeline(
							    unet=snake_case__,   low_res_scheduler=snake_case__,   scheduler=snake_case__,   vae=snake_case__,   text_encoder=snake_case__,   tokenizer=snake_case__,   max_noise_level=350,   )
							lowercase__   					=			sd_pipe.to(snake_case__  )
							sd_pipe.set_progress_bar_config(disable=snake_case__  )

							lowercase__   					=			"A painting of a squirrel eating a burger"
							lowercase__   					=			torch.manual_seed(0  )
							lowercase__   					=			sd_pipe(
							    [prompt],   image=snake_case__,   generator=snake_case__,   num_inference_steps=2,   output_type="np",   ).images

							lowercase__   					=			low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)
@slow
@require_torch_gpu
class 			_snake_case     (   unittest.TestCase):




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




				def 							A__ (  self   :  Dict  ):
							lowercase__   					=			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-upscale/low_res_cat.png"  )
							lowercase__   					=			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
							    "/upsampled_cat.npy"  )

							lowercase__   					=			"stabilityai/stable-diffusion-x4-upscaler"
							lowercase__   					=			StableDiffusionUpscalePipeline.from_pretrained(snake_case__  )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )
							pipe.enable_attention_slicing()

							lowercase__   					=			"a cat sitting on a park bench"

							lowercase__   					=			torch.manual_seed(0  )
							lowercase__   					=			pipe(
							    prompt=snake_case__,   image=snake_case__,   generator=snake_case__,   output_type="np",   )
							lowercase__   					=			output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image  ).max() < 1e-3




				def 							A__ (  self   :  Any  ):
							lowercase__   					=			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-upscale/low_res_cat.png"  )
							lowercase__   					=			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
							    "/upsampled_cat_fp16.npy"  )

							lowercase__   					=			"stabilityai/stable-diffusion-x4-upscaler"
							lowercase__   					=			StableDiffusionUpscalePipeline.from_pretrained(
							    snake_case__,   torch_dtype=torch.floataa,   )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )
							pipe.enable_attention_slicing()

							lowercase__   					=			"a cat sitting on a park bench"

							lowercase__   					=			torch.manual_seed(0  )
							lowercase__   					=			pipe(
							    prompt=snake_case__,   image=snake_case__,   generator=snake_case__,   output_type="np",   )
							lowercase__   					=			output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image  ).max() < 5e-1




				def 							A__ (  self   :  Union[str, Any]  ):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowercase__   					=			load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-upscale/low_res_cat.png"  )

							lowercase__   					=			"stabilityai/stable-diffusion-x4-upscaler"
							lowercase__   					=			StableDiffusionUpscalePipeline.from_pretrained(
							    snake_case__,   torch_dtype=torch.floataa,   )
							pipe.to(snake_case__  )
							pipe.set_progress_bar_config(disable=snake_case__  )
							pipe.enable_attention_slicing(1  )
							pipe.enable_sequential_cpu_offload()

							lowercase__   					=			"a cat sitting on a park bench"

							lowercase__   					=			torch.manual_seed(0  )
							lowercase__   					=			pipe(
							    prompt=snake_case__,   image=snake_case__,   generator=snake_case__,   num_inference_steps=5,   output_type="np",   )

							lowercase__   					=			torch.cuda.max_memory_allocated()
							# make sure that less than 2.9 GB is allocated
							assert mem_bytes < 2.9 * 10**9




import fire

from utils import calculate_rouge, save_json



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					**SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()]
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()][: len(SCREAMING_SNAKE_CASE_					)]
			lowercase__   					=			calculate_rouge(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					**SCREAMING_SNAKE_CASE_					)
			if save_path is not None:
						save_json(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					indent=SCREAMING_SNAKE_CASE_					)
			return metrics  # these print nicely


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




