





"""simple docstring"""



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	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




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

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	1
     __A       :		List[Any]							=	3
     __A       :		Optional[int]							=	(32, 32)

     __A       :		Optional[int]							=	floats_tensor((batch_size, num_channels) + sizes   ,		rng=random.Random(0    )    ).to(__lowerCamelCase    )
     return image

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )
     __A       :		List[str]							=	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=__lowerCamelCase   ,		only_cross_attention=(True, True, False)   ,		num_class_embeds=100   ,		)
     return model

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )
     __A       :		Dict							=	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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.manual_seed(0    )
     __A       :		Optional[Any]							=	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(__lowerCamelCase    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




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

     __A       :		List[Any]							=	self.dummy_image.cpu().permute(0   ,		2   ,		3   ,		1    )[0]
     __A       :		Tuple							=	Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert('''RGB'''    ).resize((64, 64)    )

     # make sure here that pndm scheduler skips prk
     __A       :		Dict							=	StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase   ,		low_res_scheduler=__lowerCamelCase   ,		scheduler=__lowerCamelCase   ,		vae=__lowerCamelCase   ,		text_encoder=__lowerCamelCase   ,		tokenizer=__lowerCamelCase   ,		max_noise_level=350   ,		)
     __A       :		Any							=	sd_pipe.to(__lowerCamelCase    )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase    )

     __A       :		Tuple							=	'''A painting of a squirrel eating a burger'''
     __A       :		List[str]							=	torch.Generator(device=__lowerCamelCase    ).manual_seed(0    )
     __A       :		int							=	sd_pipe(
         [prompt]   ,		image=__lowerCamelCase   ,		generator=__lowerCamelCase   ,		guidance_scale=6.0   ,		noise_level=20   ,		num_inference_steps=2   ,		output_type='''np'''   ,		)

     __A       :		List[str]							=	output.images

     __A       :		Optional[int]							=	torch.Generator(device=__lowerCamelCase    ).manual_seed(0    )
     __A       :		List[str]							=	sd_pipe(
         [prompt]   ,		image=__lowerCamelCase   ,		generator=__lowerCamelCase   ,		guidance_scale=6.0   ,		noise_level=20   ,		num_inference_steps=2   ,		output_type='''np'''   ,		return_dict=__lowerCamelCase   ,		)[0]

     __A       :		int							=	image[0, -3:, -3:, -1]
     __A       :		Union[str, Any]							=	image_from_tuple[0, -3:, -3:, -1]

     __A       :		Union[str, Any]							=	low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     __A       :		Union[str, Any]							=	np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]    )

     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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
     __A       :		Union[str, Any]							=	self.dummy_cond_unet_upscale
     __A       :		str							=	DDPMScheduler()
     __A       :		Optional[Any]							=	DDIMScheduler(prediction_type='''v_prediction'''    )
     __A       :		List[str]							=	self.dummy_vae
     __A       :		Union[str, Any]							=	self.dummy_text_encoder
     __A       :		Tuple							=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

     __A       :		str							=	self.dummy_image.cpu().permute(0   ,		2   ,		3   ,		1    )[0]
     __A       :		List[Any]							=	Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert('''RGB'''    ).resize((64, 64)    )

     # make sure here that pndm scheduler skips prk
     __A       :		Any							=	StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase   ,		low_res_scheduler=__lowerCamelCase   ,		scheduler=__lowerCamelCase   ,		vae=__lowerCamelCase   ,		text_encoder=__lowerCamelCase   ,		tokenizer=__lowerCamelCase   ,		max_noise_level=350   ,		)
     __A       :		Optional[int]							=	sd_pipe.to(__lowerCamelCase    )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase    )

     __A       :		int							=	'''A painting of a squirrel eating a burger'''
     __A       :		Dict							=	sd_pipe(
         2 * [prompt]   ,		image=2 * [low_res_image]   ,		guidance_scale=6.0   ,		noise_level=20   ,		num_inference_steps=2   ,		output_type='''np'''   ,		)
     __A       :		List[str]							=	output.images
     assert image.shape[0] == 2

     __A       :		Tuple							=	torch.Generator(device=__lowerCamelCase    ).manual_seed(0    )
     __A       :		Dict							=	sd_pipe(
         [prompt]   ,		image=__lowerCamelCase   ,		generator=__lowerCamelCase   ,		num_images_per_prompt=2   ,		guidance_scale=6.0   ,		noise_level=20   ,		num_inference_steps=2   ,		output_type='''np'''   ,		)
     __A       :		str							=	output.images
     assert image.shape[0] == 2







 @unittest.skipIf(torch_device != '''cuda'''   ,		'''This test requires a GPU'''    )
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		int							=	self.dummy_cond_unet_upscale
     __A       :		int							=	DDPMScheduler()
     __A       :		Optional[int]							=	DDIMScheduler(prediction_type='''v_prediction'''    )
     __A       :		Optional[Any]							=	self.dummy_vae
     __A       :		Dict							=	self.dummy_text_encoder
     __A       :		Optional[Any]							=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

     __A       :		Dict							=	self.dummy_image.cpu().permute(0   ,		2   ,		3   ,		1    )[0]
     __A       :		List[Any]							=	Image.fromarray(np.uinta(__lowerCamelCase    )    ).convert('''RGB'''    ).resize((64, 64)    )

     # put models in fp16, except vae as it overflows in fp16
     __A       :		List[Any]							=	unet.half()
     __A       :		int							=	text_encoder.half()

     # make sure here that pndm scheduler skips prk
     __A       :		Tuple							=	StableDiffusionUpscalePipeline(
         unet=__lowerCamelCase   ,		low_res_scheduler=__lowerCamelCase   ,		scheduler=__lowerCamelCase   ,		vae=__lowerCamelCase   ,		text_encoder=__lowerCamelCase   ,		tokenizer=__lowerCamelCase   ,		max_noise_level=350   ,		)
     __A       :		Union[str, Any]							=	sd_pipe.to(__lowerCamelCase    )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase    )

     __A       :		int							=	'''A painting of a squirrel eating a burger'''
     __A       :		Tuple							=	torch.manual_seed(0    )
     __A       :		str							=	sd_pipe(
         [prompt]   ,		image=__lowerCamelCase   ,		generator=__lowerCamelCase   ,		num_inference_steps=2   ,		output_type='''np'''   ,		).images

     __A       :		Any							=	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	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




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

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''    )
     __A       :		Any							=	load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat.npy'''    )

     __A       :		List[Any]							=	'''stabilityai/stable-diffusion-x4-upscaler'''
     __A       :		List[Any]							=	StableDiffusionUpscalePipeline.from_pretrained(__lowerCamelCase    )
     pipe.to(__lowerCamelCase    )
     pipe.set_progress_bar_config(disable=__lowerCamelCase    )
     pipe.enable_attention_slicing()

     __A       :		Optional[int]							=	'''a cat sitting on a park bench'''

     __A       :		Union[str, Any]							=	torch.manual_seed(0    )
     __A       :		Optional[Any]							=	pipe(
         prompt=__lowerCamelCase   ,		image=__lowerCamelCase   ,		generator=__lowerCamelCase   ,		output_type='''np'''   ,		)
     __A       :		List[Any]							=	output.images[0]

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

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''    )
     __A       :		Optional[int]							=	load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
         '''/upsampled_cat_fp16.npy'''    )

     __A       :		Optional[int]							=	'''stabilityai/stable-diffusion-x4-upscaler'''
     __A       :		str							=	StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase   ,		torch_dtype=torch.floataa   ,		)
     pipe.to(__lowerCamelCase    )
     pipe.set_progress_bar_config(disable=__lowerCamelCase    )
     pipe.enable_attention_slicing()

     __A       :		Optional[int]							=	'''a cat sitting on a park bench'''

     __A       :		Any							=	torch.manual_seed(0    )
     __A       :		Optional[int]							=	pipe(
         prompt=__lowerCamelCase   ,		image=__lowerCamelCase   ,		generator=__lowerCamelCase   ,		output_type='''np'''   ,		)
     __A       :		List[str]							=	output.images[0]

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







 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __A       :		List[str]							=	load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/sd2-upscale/low_res_cat.png'''    )

     __A       :		Optional[Any]							=	'''stabilityai/stable-diffusion-x4-upscaler'''
     __A       :		Optional[int]							=	StableDiffusionUpscalePipeline.from_pretrained(
         __lowerCamelCase   ,		torch_dtype=torch.floataa   ,		)
     pipe.to(__lowerCamelCase    )
     pipe.set_progress_bar_config(disable=__lowerCamelCase    )
     pipe.enable_attention_slicing(1    )
     pipe.enable_sequential_cpu_offload()

     __A       :		Tuple							=	'''a cat sitting on a park bench'''

     __A       :		List[Any]							=	torch.manual_seed(0    )
     __A       :		Any							=	pipe(
         prompt=__lowerCamelCase   ,		image=__lowerCamelCase   ,		generator=__lowerCamelCase   ,		num_inference_steps=5   ,		output_type='''np'''   ,		)

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











"""simple docstring"""



import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_					=     """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_					=     """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_					=     """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,				_KWARGS_DESCRIPTION	)
class 						__snake_case   (						datasets.Metric	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return datasets.MetricInfo(
         description=_DESCRIPTION   ,		citation=_CITATION   ,		inputs_description=_KWARGS_DESCRIPTION   ,		features=datasets.Features(
             {
                 '''predictions''': datasets.Value('''string'''   ,		id='''sequence'''    ),
                 '''references''': datasets.Value('''string'''   ,		id='''sequence'''    ),
             }    )   ,		codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge''']   ,		reference_urls=[
             '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
             '''https://github.com/google-research/google-research/tree/master/rouge''',
         ]   ,		)







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




     if rouge_types is None:
         __A       :		Dict							=	['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

     __A       :		Dict							=	rouge_scorer.RougeScorer(rouge_types=__lowerCamelCase   ,		use_stemmer=__lowerCamelCase    )
     if use_aggregator:
         __A       :		Dict							=	scoring.BootstrapAggregator()
     else:
         __A       :		Optional[Any]							=	[]

     for ref, pred in zip(__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		List[Any]							=	scorer.score(__lowerCamelCase   ,		__lowerCamelCase    )
         if use_aggregator:
             aggregator.add_scores(__lowerCamelCase    )
         else:
             scores.append(__lowerCamelCase    )

     if use_aggregator:
         __A       :		Optional[Any]							=	aggregator.aggregate()
     else:
         __A       :		List[Any]							=	{}
         for key in scores[0]:
             __A       :		Tuple							=	[score[key] for score in scores]

     return result




