

'''simple docstring'''




import datasets

from .evaluate import evaluate


_lowerCAmelCase         =						"\\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"

_lowerCAmelCase         =						"\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"

_lowerCAmelCase         =						"\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   _SCREAMING_SNAKE_CASE   (   datasets.Metric     ):



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      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 			snake_case__  (   self		:   str			,				a__		:   Optional[Any]			,				a__		:   str      ):
      __magic_name__						       =    {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
      __magic_name__						       =    [
          {
              '''paragraphs''': [
                  {
                      '''qas''': [
                          {
                              '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                              '''id''': ref['''id'''],
                          }
                          for ref in references
                      ]
                  }
              ]
          }
      ]
      __magic_name__						       =    evaluate(dataset=a__			,				predictions=a__      )
      return score









'''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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class   _SCREAMING_SNAKE_CASE   (   __a		,__a		,__a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					StableDiffusionInpaintPipeline
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __SCREAMING_SNAKE_CASE					:Tuple      	=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __SCREAMING_SNAKE_CASE					:str      	=					frozenset(
     []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					frozenset([]     )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    UNetaDConditionModel(
          block_out_channels=(32, 64)			,				layers_per_block=2			,				sample_size=32			,				in_channels=9			,				out_channels=4			,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,				cross_attention_dim=32			,				attention_head_dim=(2, 4)			,				use_linear_projection=a__			,				)
      __magic_name__						       =    PNDMScheduler(skip_prk_steps=a__      )
      torch.manual_seed(0      )
      __magic_name__						       =    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      )
      __magic_name__						       =    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			,				)
      __magic_name__						       =    CLIPTextModel(a__      )
      __magic_name__						       =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

      __magic_name__						       =    {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components



 def 			snake_case__  (   self		:   Any			,				a__		:   Optional[int]			,				a__		:   List[Any]=0      ):
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      __magic_name__						       =    floats_tensor((1, 3, 32, 32)			,				rng=random.Random(a__      )      ).to(a__      )
      __magic_name__						       =    image.cpu().permute(0			,				2			,				3			,				1      )[0]
      __magic_name__						       =    Image.fromarray(np.uinta(a__      )      ).convert('''RGB'''      ).resize((64, 64)      )
      __magic_name__						       =    Image.fromarray(np.uinta(image + 4      )      ).convert('''RGB'''      ).resize((64, 64)      )
      if str(a__      ).startswith('''mps'''      ):
           __magic_name__						       =    torch.manual_seed(a__      )
      else:
           __magic_name__						       =    torch.Generator(device=a__      ).manual_seed(a__      )
      __magic_name__						       =    {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': init_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
      }
      return inputs



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
      __magic_name__						       =    self.get_dummy_components()
      __magic_name__						       =    StableDiffusionInpaintPipeline(**a__      )
      __magic_name__						       =    sd_pipe.to(a__      )
      sd_pipe.set_progress_bar_config(disable=a__      )

      __magic_name__						       =    self.get_dummy_inputs(a__      )
      __magic_name__						       =    sd_pipe(**a__      ).images
      __magic_name__						       =    image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __magic_name__						       =    np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]      )

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



 def 			snake_case__  (   self		:   List[Any]      ):
      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )






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



 def 			snake_case__  (   self		:   int      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench.npy'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(a__			,				safety_checker=a__      )
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				output_type='''np'''			,				)
      __magic_name__						       =    output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image      ).max() < 9E-3



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
          '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(
          a__			,				torch_dtype=torch.floataa			,				safety_checker=a__			,				)
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				output_type='''np'''			,				)
      __magic_name__						       =    output.images[0]

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



 def 			snake_case__  (   self		:   List[Any]      ):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''      )
      __magic_name__						       =    load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''      )

      __magic_name__						       =    '''stabilityai/stable-diffusion-2-inpainting'''
      __magic_name__						       =    PNDMScheduler.from_pretrained(a__			,				subfolder='''scheduler'''      )
      __magic_name__						       =    StableDiffusionInpaintPipeline.from_pretrained(
          a__			,				safety_checker=a__			,				scheduler=a__			,				torch_dtype=torch.floataa			,				)
      pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )
      pipe.enable_attention_slicing(1      )
      pipe.enable_sequential_cpu_offload()

      __magic_name__						       =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    pipe(
          prompt=a__			,				image=a__			,				mask_image=a__			,				generator=a__			,				num_inference_steps=2			,				output_type='''np'''			,				)

      __magic_name__						       =    torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






