import datasets

from .evaluate import evaluate


__UpperCamelCase	: List[Any]    		=  '\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

__UpperCamelCase	: str    		=  '\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'

__UpperCamelCase	: Union[str, Any]    		=  '\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric("squad")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n'





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


						def  __A ( self			:       str		):





												'''simple docstring'''





												return datasets.MetricInfo(
												    description=_DESCRIPTION					,	citation=_CITATION					,	inputs_description=_KWARGS_DESCRIPTION					,	features=datasets.Features(
												        {
												            '''predictions''': {'''id''': datasets.Value('''string'''		), '''prediction_text''': datasets.Value('''string'''		)},
												            '''references''': {
												                '''id''': datasets.Value('''string'''		),
												                '''answers''': datasets.features.Sequence(
												                    {
												                        '''text''': datasets.Value('''string'''		),
												                        '''answer_start''': datasets.Value('''int32'''		),
												                    }		),
												            },
												        }		)					,	codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']					,	reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']					,	)



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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[
												    {
												        '''paragraphs''': [
												            {
												                '''qas''': [
												                    {
												                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
												                        '''id''': ref['''id'''],
												                    }
												                    for ref in references
												                ]
												            }
												        ]
												    }
												]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							evaluate(dataset=UpperCamelCase__					,	predictions=UpperCamelCase__		)
												return score






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()





