



'''simple docstring'''



import argparse
import json
import re
import string
import sys
from collections import Counter


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[str]			)		->						Union[str, Any]:

				def remove_articles(UpperCamelCase__				:Optional[Any]			):
								return re.sub(R'''\b(a|an|the)\b'''   ,				''' '''   ,				UpperCamelCase__			)

				def white_space_fix(UpperCamelCase__				:Optional[Any]			):
								return " ".join(text.split()			)

				def remove_punc(UpperCamelCase__				:Any			):
								snake_case__      :   str      =    set(string.punctuation			)
								return "".join(ch for ch in text if ch not in exclude			)

				def lower(UpperCamelCase__				:List[str]			):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__			)			)			)			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:Any			)		->						List[Any]:
				snake_case__      :   str      =    normalize_answer(UpperCamelCase__			).split()
				snake_case__      :   List[str]      =    normalize_answer(UpperCamelCase__			).split()
				snake_case__      :   Tuple      =    Counter(UpperCamelCase__			) & Counter(UpperCamelCase__			)
				snake_case__      :   Union[str, Any]      =    sum(common.values()			)
				if num_same == 0:
								return 0
				snake_case__      :   Tuple      =    1.0 * num_same / len(UpperCamelCase__			)
				snake_case__      :   str      =    1.0 * num_same / len(UpperCamelCase__			)
				snake_case__      :   str      =    (2 * precision * recall) / (precision + recall)
				return fa


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[Any]   ,				UpperCamelCase__				:str			)		->						int:
				return normalize_answer(UpperCamelCase__			) == normalize_answer(UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:Any   ,				UpperCamelCase__				:List[str]   ,				UpperCamelCase__				:List[str]			)		->						Tuple:
				snake_case__      :   Tuple      =    []
				for ground_truth in ground_truths:
								snake_case__      :   Tuple      =    metric_fn(UpperCamelCase__   ,				UpperCamelCase__			)
								scores_for_ground_truths.append(UpperCamelCase__			)
				return max(UpperCamelCase__			)


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[str]   ,				UpperCamelCase__				:Union[str, Any]			)		->						List[Any]:
				snake_case__      :   Optional[Any]      =    0
				snake_case__      :   Union[str, Any]      =    []
				for passage in dataset:
								for qa in passage["qas"]:
												total += 1
												if qa["id"] not in predictions:
																snake_case__      :   str      =    F'''Unanswered question {qa["id"]} will receive score 0.'''
																print(UpperCamelCase__   ,				file=sys.stderr			)
																continue

												snake_case__      :   str      =    [x['''text'''] for x in qa['''answers''']]
												snake_case__      :   Union[str, Any]      =    predictions[qa['''id''']]

												snake_case__      :   List[Any]      =    metric_max_over_ground_truths(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)
												if int(_exact_match			) == 1:
																correct_ids.append(qa['''id''']			)
												exact_match += _exact_match

												fa += metric_max_over_ground_truths(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)

				snake_case__      :   List[str]      =    exact_match / total
				snake_case__      :   int      =    fa / total

				return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
				_lowercase       :						List[Any]						="1.0"
				_lowercase       :						List[Any]						=argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
				parser.add_argument("data_file", help="The dataset file in JSON format.")
				parser.add_argument("pred_file", help="The model prediction file in JSON format.")
				parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
				_lowercase       :						int						=parser.parse_args()

				with open(args.data_file) as data_file:
								_lowercase       :						Tuple						=json.load(data_file)
								if dataset_json["version"] != expected_version:
												print(
												    F"Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}",
												    file=sys.stderr,
												)
								_lowercase       :						Optional[int]						=dataset_json["data"]

				with open(args.pred_file) as pred_file:
								_lowercase       :						List[str]						=json.load(pred_file)

				_lowercase		,	_lowercase       :						List[str]						=evaluate(dataset, predictions)

				if args.output_correct_ids:
								print(F"Output {len(correct_ids)} correctly answered question IDs.")
								with open("correct_ids.json", "w") as f:
												json.dump(correct_ids, f)









'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 			_SCREAMING_SNAKE_CASE				(lowercase__,	lowercase__,	unittest.TestCase      ):
			A__								=  StableDiffusionXLPipeline
			A__								=  TEXT_TO_IMAGE_PARAMS
			A__								=  TEXT_TO_IMAGE_BATCH_PARAMS
			A__								=  TEXT_TO_IMAGE_IMAGE_PARAMS
			A__								=  TEXT_TO_IMAGE_IMAGE_PARAMS



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      Tuple:
							"""simple docstring"""





							torch.manual_seed(0			)
							snake_case__      :   Any      =    UNetaDConditionModel(
							    block_out_channels=(32, 64)    ,			layers_per_block=2    ,			sample_size=32    ,			in_channels=4    ,			out_channels=4    ,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,			attention_head_dim=(2, 4)    ,			use_linear_projection=__UpperCamelCase    ,			addition_embed_type='''text_time'''    ,			addition_time_embed_dim=8    ,			transformer_layers_per_block=(1, 2)    ,			projection_class_embeddings_input_dim=80    ,			cross_attention_dim=64    ,			)
							snake_case__      :   Tuple      =    EulerDiscreteScheduler(
							    beta_start=0.00085    ,			beta_end=0.012    ,			steps_offset=1    ,			beta_schedule='''scaled_linear'''    ,			timestep_spacing='''leading'''    ,			)
							torch.manual_seed(0			)
							snake_case__      :   Union[str, Any]      =    AutoencoderKL(
							    block_out_channels=[32, 64]    ,			in_channels=3    ,			out_channels=3    ,			down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,			up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,			latent_channels=4    ,			sample_size=128    ,			)
							torch.manual_seed(0			)
							snake_case__      :   List[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=32    ,			)
							snake_case__      :   List[str]      =    CLIPTextModel(__UpperCamelCase			)
							snake_case__      :   Dict      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    ,			local_files_only=__UpperCamelCase			)

							snake_case__      :   Optional[int]      =    CLIPTextModelWithProjection(__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    ,			local_files_only=__UpperCamelCase			)

							snake_case__      :   List[str]      =    {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''text_encoder_2''': text_encoder_a,
							    '''tokenizer_2''': tokenizer_a,
							    # "safety_checker": None,
							    # "feature_extractor": None,
							}
							return components



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Union[str, Any]=0			)      ->      Optional[Any]:
							"""simple docstring"""





							if str(__UpperCamelCase			).startswith('''mps'''			):
											snake_case__      :   List[Any]      =    torch.manual_seed(__UpperCamelCase			)
							else:
											snake_case__      :   List[Any]      =    torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)
							snake_case__      :   Dict      =    {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 5.0,
							    '''output_type''': '''numpy''',
							}
							return inputs



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      Tuple:
							"""simple docstring"""





							snake_case__      :   Any      =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							snake_case__      :   Union[str, Any]      =    self.get_dummy_components()
							snake_case__      :   List[Any]      =    StableDiffusionXLPipeline(**__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    sd_pipe.to(__UpperCamelCase			)
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase			)

							snake_case__      :   Dict      =    self.get_dummy_inputs(__UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    sd_pipe(**__UpperCamelCase			).images
							snake_case__      :   List[Any]      =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							snake_case__      :   Union[str, Any]      =    np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]			)

							assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2



			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      Optional[int]:
							"""simple docstring"""





							snake_case__      :   List[str]      =    self.get_dummy_components()
							snake_case__      :   Optional[Any]      =    StableDiffusionXLPipeline(**__UpperCamelCase			)
							snake_case__      :   str      =    sd_pipe.to(__UpperCamelCase			)
							snake_case__      :   Tuple      =    sd_pipe.to(__UpperCamelCase			)
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase			)

							# forward without prompt embeds
							snake_case__      :   str      =    self.get_dummy_inputs(__UpperCamelCase			)
							snake_case__      :   Any      =    3 * ['''this is a negative prompt''']
							snake_case__      :   Optional[int]      =    negative_prompt
							snake_case__      :   Tuple      =    3 * [inputs['''prompt''']]

							snake_case__      :   Tuple      =    sd_pipe(**__UpperCamelCase			)
							snake_case__      :   str      =    output.images[0, -3:, -3:, -1]

							# forward with prompt embeds
							snake_case__      :   Tuple      =    self.get_dummy_inputs(__UpperCamelCase			)
							snake_case__      :   List[Any]      =    3 * ['''this is a negative prompt''']
							snake_case__      :   List[Any]      =    3 * [inputs.pop('''prompt'''			)]

							(
							    (
							    snake_case__
							)  ,      (
							    snake_case__
							)  ,      (
							    snake_case__
							)  ,      (
							    snake_case__
							)  ,      
							)      :   List[Any]      =    sd_pipe.encode_prompt(__UpperCamelCase    ,			negative_prompt=__UpperCamelCase			)

							snake_case__      :   Dict      =    sd_pipe(
							    **__UpperCamelCase    ,			prompt_embeds=__UpperCamelCase    ,			negative_prompt_embeds=__UpperCamelCase    ,			pooled_prompt_embeds=__UpperCamelCase    ,			negative_pooled_prompt_embeds=__UpperCamelCase    ,			)
							snake_case__      :   Optional[Any]      =    output.images[0, -3:, -3:, -1]

							# make sure that it's equal
							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1e-4



			def 				lowerCAmelCase (     self		:						Dict			)      ->      Dict:
							"""simple docstring"""





							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3			)







			def 				lowerCAmelCase (     self		:						int			)      ->      Dict:
							"""simple docstring"""





							super().test_inference_batch_single_identical(expected_max_diff=3e-3			)






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



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						List[str]="cpu"    ,			__UpperCamelCase		:						Tuple=torch.floataa    ,			__UpperCamelCase		:						List[str]=0			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Dict      =    torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)
							snake_case__      :   int      =    np.random.RandomState(__UpperCamelCase			).standard_normal((1, 4, 64, 64)			)
							snake_case__      :   List[Any]      =    torch.from_numpy(__UpperCamelCase			).to(device=__UpperCamelCase    ,			dtype=__UpperCamelCase			)
							snake_case__      :   int      =    {
							    '''prompt''': '''a photograph of an astronaut riding a horse''',
							    '''latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs







			def 				lowerCAmelCase (     self		:						int			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Dict      =    DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''			)
							pipe.to(__UpperCamelCase			)
							pipe.set_progress_bar_config(disable=__UpperCamelCase			)

							snake_case__      :   Optional[Any]      =    self.get_inputs(__UpperCamelCase			)
							snake_case__      :   Any      =    pipe(**__UpperCamelCase			).images
							snake_case__      :   Optional[int]      =    image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							snake_case__      :   Tuple      =    np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]			)
							assert np.abs(image_slice - expected_slice			).max() < 7e-3




