



import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class       _lowercase				(  __SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE, unittest.TestCase      ):





	"""simple docstring"""
	__A		      =				IFSuperResolutionPipeline
	__A		      =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
	__A		      =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	__A		      =				PipelineTesterMixin.required_optional_params - {"latents"}




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return self._get_superresolution_dummy_components()




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_=0						):
						"""simple docstring"""

						if str(__UpperCAmelCase						).startswith("mps"						):
											a   	=  torch.manual_seed(__UpperCAmelCase						)
						else:
											a   	=  torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)

						a   	=  floats_tensor((1, 3, 32, 32)						,			rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)

						a   	=  {
						    "prompt": "A painting of a squirrel eating a burger",
						    "image": image,
						    "generator": generator,
						    "num_inference_steps": 2,
						    "output_type": "numpy",
						}

						return inputs




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

						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						self._test_save_load_optional_components()




	@unittest.skipIf(torch_device != "cuda"						,			reason="float16 requires CUDA"						)
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						self._test_save_load_local()




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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


"""simple docstring"""

import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


UpperCamelCase      :							List[str]          =					"\\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      :							int          =					"\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering 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\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n"

UpperCamelCase      :							Optional[Any]          =					"\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer 'id' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no 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 list of Dict {'text': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    'exact': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'total': Number of score considered\n    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'HasAns_f1': The F-score of predicted tokens versus the gold answer\n    'HasAns_total': Number of score considered\n    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'NoAns_f1': The F-score of predicted tokens versus the gold answer\n    'NoAns_total': Number of score considered\n    'best_exact': Best exact match (with varying threshold)\n    'best_exact_thresh': No-answer probability threshold associated to the best exact match\n    'best_f1': Best F1 (with varying threshold)\n    'best_f1_thresh': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}\n"

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





     def 		UpperCAmelCase   (      self ):

      '''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' ),
                      'no_answer_probability': datasets.Value('float32' ),
                  },
                  '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 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase=1.0 ):

      '''simple docstring'''




      __UpperCamelCase							    =			{p['id']: p['no_answer_probability'] for p in predictions}
      __UpperCamelCase							    =			[{'paragraphs': [{'qas': references}]}]
      __UpperCamelCase							    =			{p['id']: p['prediction_text'] for p in predictions}

      __UpperCamelCase							    =			make_qid_to_has_ans(__UpperCAmelCase )  # maps qid to True/False
      __UpperCamelCase							    =			[k for k, v in qid_to_has_ans.items() if v]
      __UpperCamelCase							    =			[k for k, v in qid_to_has_ans.items() if not v]

      __UpperCamelCase    ,      __UpperCamelCase							    =			get_raw_scores(__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			apply_no_ans_threshold(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			apply_no_ans_threshold(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			make_eval_dict(__UpperCAmelCase      ,	__UpperCAmelCase )

      if has_ans_qids:
       __UpperCamelCase							    =			make_eval_dict(__UpperCAmelCase      ,	__UpperCAmelCase      ,	qid_list=__UpperCAmelCase )
       merge_eval(__UpperCAmelCase      ,	__UpperCAmelCase      ,	'HasAns' )
      if no_ans_qids:
       __UpperCamelCase							    =			make_eval_dict(__UpperCAmelCase      ,	__UpperCAmelCase      ,	qid_list=__UpperCAmelCase )
       merge_eval(__UpperCAmelCase      ,	__UpperCAmelCase      ,	'NoAns' )
      find_all_best_thresh(__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase )
      return dict(__UpperCAmelCase )



