import datasets

from .evaluate import evaluate


__UpperCamelCase	: List[Any]    		=  '\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n'

__UpperCamelCase	: Optional[Any]    		=  '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'

__UpperCamelCase	: int    		=  '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\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\': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\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 CUAD 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\n    \'aupr\': Area Under the Precision-Recall curve\n    \'prec_at_80_recall\': Precision at 80% recall\n    \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n    >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n    >>> cuad_metric = datasets.load_metric("cuad")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'





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


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





												'''simple docstring'''





												return datasets.MetricInfo(
												    description=_DESCRIPTION					,	citation=_CITATION					,	inputs_description=_KWARGS_DESCRIPTION					,	features=datasets.Features(
												        {
												            '''predictions''': {
												                '''id''': datasets.Value('''string'''		),
												                '''prediction_text''': datasets.features.Sequence(datasets.Value('''string'''		)		),
												            },
												            '''references''': {
												                '''id''': datasets.Value('''string'''		),
												                '''answers''': datasets.features.Sequence(
												                    {
												                        '''text''': datasets.Value('''string'''		),
												                        '''answer_start''': datasets.Value('''int32'''		),
												                    }		),
												            },
												        }		)					,	codebase_urls=['''https://www.atticusprojectai.org/cuad''']					,	reference_urls=['''https://www.atticusprojectai.org/cuad''']					,	)



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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
												SCREAMING_SNAKE_CASE							:					str           =							[
												    {
												        '''paragraphs''': [
												            {
												                '''qas''': [
												                    {
												                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
												                        '''id''': ref['''id'''],
												                    }
												                    for ref in references
												                ]
												            }
												        ]
												    }
												]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							evaluate(dataset=lowerCamelCase_					,	predictions=lowerCamelCase_		)
												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()





