import re
import string

import numpy as np

import datasets


__UpperCamelCase	: List[str]    		=  '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

__UpperCamelCase	: Optional[Any]    		=  '\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

__UpperCamelCase	: Union[str, Any]    		=  '\n'





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


						def  __A ( self			:       Optional[int]		):





												'''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'''		),
												        }		)					,	reference_urls=[]					,	)



						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       int=None					,	UpperCamelCase__			:       Optional[int]=False					,	UpperCamelCase__			:       Dict=False					,	UpperCamelCase__			:       Union[str, Any]=False					,	):





												'''simple docstring'''





												if regexes_to_ignore is not None:
																		for s in regexes_to_ignore:
																								SCREAMING_SNAKE_CASE							:					int           =							np.array([re.sub(UpperCamelCase__					,	''''''					,	UpperCamelCase__		) for x in predictions]		)
																								SCREAMING_SNAKE_CASE							:					List[Any]           =							np.array([re.sub(UpperCamelCase__					,	''''''					,	UpperCamelCase__		) for x in references]		)
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							np.asarray(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					str           =							np.asarray(UpperCamelCase__		)

												if ignore_case:
																		SCREAMING_SNAKE_CASE							:					Dict           =							np.char.lower(UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[str]           =							np.char.lower(UpperCamelCase__		)

												if ignore_punctuation:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							string.punctuation.maketrans(''''''					,	''''''					,	string.punctuation		)
																		SCREAMING_SNAKE_CASE							:					List[str]           =							np.char.translate(UpperCamelCase__					,	table=UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							np.char.translate(UpperCamelCase__					,	table=UpperCamelCase__		)

												if ignore_numbers:
																		SCREAMING_SNAKE_CASE							:					Any           =							string.digits.maketrans(''''''					,	''''''					,	string.digits		)
																		SCREAMING_SNAKE_CASE							:					Tuple           =							np.char.translate(UpperCamelCase__					,	table=UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							np.char.translate(UpperCamelCase__					,	table=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							predictions == references

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






import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin





@skip_mps
class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	IFPipeline
						UpperCamelCase_  =	TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
						UpperCamelCase_  =	TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCamelCase_  =	PipelineTesterMixin.required_optional_params - {"""latents"""}


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												return self._get_dummy_components()


						def  __A ( self			:       int					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       int=0		):





												'''simple docstring'''





												if str(UpperCamelCase__		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							torch.manual_seed(UpperCamelCase__		)
												else:
																		SCREAMING_SNAKE_CASE							:					Dict           =							torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					int           =							{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												self._test_save_load_optional_components()


						@unittest.skipIf(torch_device != '''cuda'''					,	reason='''float16 requires CUDA'''		)
						def  __A ( self			:       Any		):





												'''simple docstring'''





												super().test_save_load_floataa(expected_max_diff=1E-1		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												self._test_save_load_local()


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2					,	)



						@unittest.skipIf(
						    torch_device != '''cuda''' or not is_xformers_available()					,	reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,	)
						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)






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


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''					,	variant='''fp16'''					,	torch_dtype=torch.floataa		)

												SCREAMING_SNAKE_CASE							:					str           =							IFSuperResolutionPipeline.from_pretrained(
												    '''DeepFloyd/IF-II-L-v1.0'''					,	variant='''fp16'''					,	torch_dtype=torch.floataa					,	text_encoder=UpperCamelCase__					,	tokenizer=UpperCamelCase__		)

												# pre compute text embeddings and remove T5 to save memory

												pipe_a.text_encoder.to('''cuda'''		)

												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Dict           =							pipe_a.encode_prompt('''anime turtle'''					,	device='''cuda'''		)

												del pipe_a.tokenizer
												del pipe_a.text_encoder
												gc.collect()

												SCREAMING_SNAKE_CASE							:					Tuple           =							None
												SCREAMING_SNAKE_CASE							:					str           =							None

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

												self._test_if(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# img2img

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							IFImgaImgPipeline(**pipe_a.components		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							IFImgaImgSuperResolutionPipeline(**pipe_a.components		)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

												self._test_if_imgaimg(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# inpainting

												SCREAMING_SNAKE_CASE							:					Tuple           =							IFInpaintingPipeline(**pipe_a.components		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							IFInpaintingSuperResolutionPipeline(**pipe_a.components		)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

												self._test_if_inpainting(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					List[str]           =							torch.Generator(device='''cpu'''		).manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	num_inference_steps=2					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							output.images[0]

												assert image.shape == (64, 64, 3)

												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 13 * 10**9

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)

												# pipeline 2

												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					int           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=2					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Tuple           =							output.images[0]

												assert image.shape == (256, 256, 3)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 10**9

												SCREAMING_SNAKE_CASE							:					int           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					Tuple           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	num_inference_steps=2					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					int           =							output.images[0]

												assert image.shape == (64, 64, 3)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 10 * 10**9

												SCREAMING_SNAKE_CASE							:					Tuple           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)

												# pipeline 2

												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							floats_tensor((1, 3, 256, 256)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					str           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	original_image=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=2					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							output.images[0]

												assert image.shape == (256, 256, 3)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 10**9

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)



						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					str           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(1		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.Generator(device='''cpu'''		).manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	mask_image=UpperCamelCase__					,	num_inference_steps=2					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Any           =							output.images[0]

												assert image.shape == (64, 64, 3)

												SCREAMING_SNAKE_CASE							:					Any           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 10 * 10**9

												SCREAMING_SNAKE_CASE							:					List[Any]           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)

												# pipeline 2

												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					str           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					int           =							floats_tensor((1, 3, 256, 256)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							floats_tensor((1, 3, 256, 256)					,	rng=random.Random(1		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Dict           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	mask_image=UpperCamelCase__					,	original_image=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=2					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							output.images[0]

												assert image.shape == (256, 256, 3)

												SCREAMING_SNAKE_CASE							:					Any           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 10**9

												SCREAMING_SNAKE_CASE							:					Tuple           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)






def    A			(					):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()





