


'''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    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= 1
							UpperCamelCase__   :Tuple												= 3
							UpperCamelCase__   :Union[str, Any]												= (32, 32)

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




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :Dict												= 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=UpperCamelCase_    ,   only_cross_attention=(True, True, False)    ,   num_class_embeds=100    ,   )
							return model




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__   :int												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							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='''gelu'''    ,   projection_dim=512    ,   )
							return CLIPTextModel(UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							UpperCamelCase__   :Optional[Any]												= self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1							)[0]
							UpperCamelCase__   :Optional[int]												= Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((64, 64)							)

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

							UpperCamelCase__   :Optional[Any]												= '''A painting of a squirrel eating a burger'''
							UpperCamelCase__   :Union[str, Any]												= torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__   :Optional[int]												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   )

							UpperCamelCase__   :int												= output.images

							UpperCamelCase__   :Union[str, Any]												= torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__   :str												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   return_dict=UpperCamelCase_    ,   )[0]

							UpperCamelCase__   :List[Any]												= image[0, -3:, -3:, -1]
							UpperCamelCase__   :Dict												= image_from_tuple[0, -3:, -3:, -1]

							UpperCamelCase__   :Tuple												= low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)
							UpperCamelCase__   :Tuple												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

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

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

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

							UpperCamelCase__   :List[Any]												= torch.Generator(device=UpperCamelCase_							).manual_seed(0							)
							UpperCamelCase__   :Union[str, Any]												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   num_images_per_prompt=2    ,   guidance_scale=6.0    ,   noise_level=20    ,   num_inference_steps=2    ,   output_type='''np'''    ,   )
							UpperCamelCase__   :List[Any]												= output.images
							assert image.shape[0] == 2




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

							'''simple docstring'''


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

							UpperCamelCase__   :Union[str, Any]												= self.dummy_image.cpu().permute(0    ,   2    ,   3    ,   1							)[0]
							UpperCamelCase__   :Optional[Any]												= Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							).resize((64, 64)							)

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

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

							UpperCamelCase__   :Any												= '''A painting of a squirrel eating a burger'''
							UpperCamelCase__   :Any												= torch.manual_seed(0							)
							UpperCamelCase__   :Dict												= sd_pipe(
							    [prompt]    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   num_inference_steps=2    ,   output_type='''np'''    ,   ).images

							UpperCamelCase__   :Tuple												= low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)



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

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

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

							UpperCamelCase__   :Optional[Any]												= '''a cat sitting on a park bench'''

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

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							UpperCamelCase__   :Tuple												= '''stabilityai/stable-diffusion-x4-upscaler'''
							UpperCamelCase__   :int												= StableDiffusionUpscalePipeline.from_pretrained(
							    UpperCamelCase_    ,   torch_dtype=torch.floataa    ,   )
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__   :int												= '''a cat sitting on a park bench'''

							UpperCamelCase__   :int												= torch.manual_seed(0							)
							UpperCamelCase__   :int												= pipe(
							    prompt=UpperCamelCase_    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   output_type='''np'''    ,   )
							UpperCamelCase__   :Dict												= output.images[0]

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

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

							UpperCamelCase__   :Union[str, Any]												= '''stabilityai/stable-diffusion-x4-upscaler'''
							UpperCamelCase__   :List[Any]												= StableDiffusionUpscalePipeline.from_pretrained(
							    UpperCamelCase_    ,   torch_dtype=torch.floataa    ,   )
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing(1							)
							pipe.enable_sequential_cpu_offload()

							UpperCamelCase__   :List[Any]												= '''a cat sitting on a park bench'''

							UpperCamelCase__   :Any												= torch.manual_seed(0							)
							UpperCamelCase__   :Union[str, Any]												= pipe(
							    prompt=UpperCamelCase_    ,   image=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   num_inference_steps=5    ,   output_type='''np'''    ,   )

							UpperCamelCase__   :Optional[Any]												= torch.cuda.max_memory_allocated()
							# make sure that less than 2.9 GB is allocated
							assert mem_bytes < 2.9 * 10**9







import re
import string

import numpy as np

import datasets


UpperCAmelCase_ :						Dict      =      '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''

UpperCAmelCase_ :						Any      =      '''
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    25.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    50.0


    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    75.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results["exact_match"], 1))
    100.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    33.3

'''

UpperCAmelCase_ :						Tuple      =      '''
'''

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





	def 						_A (		self				:					Optional[int]	):
		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"""	),
		        }	) ,  reference_urls=[] ,  )





	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					List[str]=None ,  __lowerCamelCase				:					Optional[int]=False ,  __lowerCamelCase				:					int=False ,  __lowerCamelCase				:					Optional[int]=False ,  ):
		if regexes_to_ignore is not None:
			for s in regexes_to_ignore:
				UpperCamelCase     :str          =	np.array([re.sub(__lowerCamelCase ,  """""" ,  __lowerCamelCase	) for x in predictions]	)
				UpperCamelCase     :Tuple          =	np.array([re.sub(__lowerCamelCase ,  """""" ,  __lowerCamelCase	) for x in references]	)
		else:
			UpperCamelCase     :Any          =	np.asarray(__lowerCamelCase	)
			UpperCamelCase     :str          =	np.asarray(__lowerCamelCase	)

		if ignore_case:
			UpperCamelCase     :Tuple          =	np.char.lower(__lowerCamelCase	)
			UpperCamelCase     :Any          =	np.char.lower(__lowerCamelCase	)

		if ignore_punctuation:
			UpperCamelCase     :Optional[int]          =	string.punctuation.maketrans("""""" ,  """""" ,  string.punctuation	)
			UpperCamelCase     :Optional[Any]          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)
			UpperCamelCase     :List[str]          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)

		if ignore_numbers:
			UpperCamelCase     :Tuple          =	string.digits.maketrans("""""" ,  """""" ,  string.digits	)
			UpperCamelCase     :Dict          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)
			UpperCamelCase     :Tuple          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)

		UpperCamelCase     :int          =	predictions == references

		return {"exact_match": np.mean(__lowerCamelCase	) * 100}
