



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size


class 	a__       (       unittest.TestCase       ):


					"""simple docstring"""



					def __init__(		self     :Tuple      ,  lowercase__     :str      ,  lowercase__     :str=7      ,  lowercase__     :Tuple=3      ,  lowercase__     :Any=18      ,  lowercase__     :Optional[int]=30      ,  lowercase__     :List[Any]=400      ,  lowercase__     :int=True      ,  lowercase__     :Dict=1 / 255      ,  lowercase__     :List[str]=True      ,  lowercase__     :Optional[int]=8      ,  ):
										lowercase						   =       parent
										lowercase						   =       batch_size
										lowercase						   =       num_channels
										lowercase						   =       image_size
										lowercase						   =       min_resolution
										lowercase						   =       max_resolution
										lowercase						   =       do_rescale
										lowercase						   =       rescale_factor
										lowercase						   =       do_pad
										lowercase						   =       pad_size

					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										return {
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										    "pad_size": self.pad_size,
										}



					def 				__UpperCAmelCase		(		self     :Tuple      ,  lowercase__     :List[str]=False      ,  lowercase__     :Tuple=False      ,  lowercase__     :str=False    ):
										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
															lowercase						   =       []
															for i in range(self.batch_size    ):
																				image_inputs.append(
																				    np.random.randint(
																				        255      ,  size=(self.num_channels, self.max_resolution, self.max_resolution)      ,  dtype=np.uinta    )    )
										else:
															lowercase						   =       []
															for i in range(self.batch_size    ):
																				lowercase						   =       np.random.choice(np.arange(self.min_resolution      ,  self.max_resolution    )      ,  2    )
																				image_inputs.append(np.random.randint(255      ,  size=(self.num_channels, width, height)      ,  dtype=np.uinta    )    )

										if not numpify and not torchify:
															# PIL expects the channel dimension as last dimension
															lowercase						   =       [Image.fromarray(np.moveaxis(_lowerCamelCase      ,  0      ,  -1    )    ) for x in image_inputs]

										if torchify:
															lowercase						   =       [torch.from_numpy(_lowerCamelCase    ) for x in image_inputs]

										return image_inputs




@require_torch
@require_vision
class 	a__       (       a__  ,			unittest.TestCase       ):


					"""simple docstring"""



					A__  :				Tuple					 =				SwinaSRImageProcessor if is_vision_available() else None

					def 				__UpperCAmelCase		(		self     :List[str]    ):
										lowercase						   =       SwinaSRImageProcessingTester(self    )

					@property
					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										return self.image_processor_tester.prepare_image_processor_dict()

					def 				__UpperCAmelCase		(		self     :Any    ):
										lowercase						   =       self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(_lowerCamelCase      ,  'do_rescale'    )    )
										self.assertTrue(hasattr(_lowerCamelCase      ,  'rescale_factor'    )    )
										self.assertTrue(hasattr(_lowerCamelCase      ,  'do_pad'    )    )
										self.assertTrue(hasattr(_lowerCamelCase      ,  'pad_size'    )    )

					def 				__UpperCAmelCase		(		self     :Tuple    ):
										pass

					def 				__UpperCAmelCase		(		self     :Optional[int]      ,  lowercase__     :Optional[int]    ):
										lowercase						   =       get_image_size(_lowerCamelCase    )
										lowercase						   =       self.image_processor_tester.pad_size

										lowercase						   =       (old_height // size + 1) * size - old_height
										lowercase						   =       (old_width // size + 1) * size - old_width
										return old_height + pad_height, old_width + pad_width

					def 				__UpperCAmelCase		(		self     :int    ):
										lowercase						   =       self.image_processing_class(**self.image_processor_dict    )
										# create random PIL images
										lowercase						   =       self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase    )
										for image in image_inputs:
															self.assertIsInstance(_lowerCamelCase      ,  Image.Image    )

										# Test not batched input
										lowercase						   =       image_processor(image_inputs[0]      ,  return_tensors='pt'    ).pixel_values
										lowercase						   =       self.calculate_expected_size(np.array(image_inputs[0]    )    )
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,  )

					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										lowercase						   =       self.image_processing_class(**self.image_processor_dict    )
										# create random numpy tensors
										lowercase						   =       self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase      ,  numpify=_lowerCamelCase    )
										for image in image_inputs:
															self.assertIsInstance(_lowerCamelCase      ,  np.ndarray    )

										# Test not batched input
										lowercase						   =       image_processor(image_inputs[0]      ,  return_tensors='pt'    ).pixel_values
										lowercase						   =       self.calculate_expected_size(image_inputs[0]    )
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,  )



					def 				__UpperCAmelCase		(		self     :int    ):
										lowercase						   =       self.image_processing_class(**self.image_processor_dict    )
										# create random PyTorch tensors
										lowercase						   =       self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase      ,  torchify=_lowerCamelCase    )
										for image in image_inputs:
															self.assertIsInstance(_lowerCamelCase      ,  torch.Tensor    )

										# Test not batched input
										lowercase						   =       image_processor(image_inputs[0]      ,  return_tensors='pt'    ).pixel_values
										lowercase						   =       self.calculate_expected_size(image_inputs[0]    )
										self.assertEqual(
										    encoded_images.shape      ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )      ,  )












'''simple docstring'''




import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


snake_case__ 							=    {0: """null""", 1: """short""", 2: """long""", 3: """yes""", 4: """no"""}
snake_case__ 							=    set("""""".join(["""‘""", """’""", """´""", """`""", """.""", """,""", """-""", """\""""]))




def     snake_case__     (			lowerCamelCase__					: List[str]      ,		lowerCamelCase__					: Tuple=0      ,		lowerCamelCase__					: Tuple=None      ) -> Optional[Any]:
					return [" ".join(x.split(''' '''      )[begin:end]      ) for x in answers if len(x.split(''' '''      )      ) > 1]




def     snake_case__     (			lowerCamelCase__					: Any      ,		lowerCamelCase__					: Any=False      ) -> List[Any]:
					if make_sub_answers:
										# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
										# *e.g.* if the correct answer contains a prefix such as "the", or "a"
										A_			:  Union[str, Any]			=				(
										    given_answers + get_sub_answers(lowerCamelCase__      ,		begin=1      ) + get_sub_answers(lowerCamelCase__      ,		end=-1      )
										)
					A_			:  List[Any]			=				[]
					for answer in given_answers:
										A_			:  Dict			=				answer.replace('''_'''      ,		''' '''      ).lower()
										A_			:  str			=				''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias      )
										answers.append(''' '''.join(alias.split()      ).strip()      )
					return set(lowerCamelCase__      )




def     snake_case__     (			lowerCamelCase__					: Optional[Any]      ,		lowerCamelCase__					: Dict      ,		lowerCamelCase__					: Union[str, Any]=1      ,		lowerCamelCase__					: Any=1_0_0      ) -> Any:
					A_			,A_			:  str			=				jax.lax.top_k(lowerCamelCase__      ,		lowerCamelCase__      )
					A_			,A_			:  str			=				jax.lax.top_k(lowerCamelCase__      ,		lowerCamelCase__      )

					A_			:  List[Any]			=				best_end_idx[:, None] - best_start_idx[None, :]
					A_			:  Optional[Any]			=				jnp.logical_or(widths < 0      ,		widths > max_size      )
					A_			:  Optional[int]			=				(best_end_scores[:, None] + best_start_scores[None, :]) - (1e8 * mask)
					A_			:  str			=				jnp.argmax(lowerCamelCase__      ).item()

					return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]




def     snake_case__     (			lowerCamelCase__					: List[str]      ) -> Union[str, Any]:
					A_			:  str			=				sample['''question''']['''text''']
					A_			:  Tuple			=				sample['''document''']['''tokens''']['''token''']
					A_			:  List[Any]			=				sample['''document''']['''tokens''']['''is_html''']
					A_			:  Tuple			=				sample['''annotations''']['''long_answer''']
					A_			:  List[Any]			=				sample['''annotations''']['''short_answers''']

					A_			:  Optional[Any]			=				''' '''.join([context[i] for i in range(len(lowerCamelCase__      )      ) if not is_html[i]]      )

					# 0 - No ; 1 - Yes
					for answer in sample["annotations"]["yes_no_answer"]:
										if answer == 0 or answer == 1:
															return {
															    "question": question,
															    "context": context_string,
															    "short": [],
															    "long": [],
															    "category": "no" if answer == 0 else "yes",
															}

					A_			:  List[Any]			=				[]
					for s in short_answers:
										short_targets.extend(s['''text''']      )
					A_			:  Tuple			=				list(set(lowerCamelCase__      )      )

					A_			:  Tuple			=				[]
					for s in long_answers:
										if s["start_token"] == -1:
															continue
										A_			:  Dict			=				context[s['''start_token'''] : s['''end_token''']]
										A_			:  Tuple			=				is_html[s['''start_token'''] : s['''end_token''']]
										A_			:  Optional[Any]			=				''' '''.join([answer[i] for i in range(len(lowerCamelCase__      )      ) if not html[i]]      )
										if new_answer not in long_targets:
															long_targets.append(lowerCamelCase__      )

					A_			:  str			=				'''long_short''' if len(short_targets + long_targets      ) > 0 else '''null'''

					return {
					    "question": question,
					    "context": context_string,
					    "short": short_targets,
					    "long": long_targets,
					    "category": category,
					}




def     snake_case__     (			) -> List[Any]:
					A_			:  int			=				load_from_disk('''natural-questions-validation'''      )
					A_			:  Optional[int]			=				dataset.map(lowerCamelCase__      ).remove_columns(['''annotations''', '''document''', '''id''']      )
					print(lowerCamelCase__      )

					A_			:  Tuple			=				dataset.filter(lambda lowerCamelCase__      : (len(x['''question''']      ) + len(x['''context''']      )) < 4 * 4_0_9_6      )
					A_			:  Optional[Any]			=				short_validation_dataset.filter(lambda lowerCamelCase__      : x["category"] != "null"      )
					short_validation_dataset

					A_			:  Union[str, Any]			=				'''vasudevgupta/flax-bigbird-natural-questions'''
					A_			:  List[str]			=				FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase__      )
					A_			:  int			=				BigBirdTokenizerFast.from_pretrained(lowerCamelCase__      )

					@jax.jit
					def forward(*lowerCamelCase__					: Any      ,		**lowerCamelCase__					: Optional[Any]      ):
										A_			,A_			,A_			:  Tuple			=				model(*lowerCamelCase__      ,		**lowerCamelCase__      )
										return start_logits, end_logits, jnp.argmax(lowerCamelCase__      ,		axis=-1      )

					def evaluate(lowerCamelCase__					: List[str]      ):
										# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
										A_			:  List[Any]			=				tokenizer(
										    example['''question''']      ,		example['''context''']      ,		return_tensors='''np'''      ,		max_length=4_0_9_6      ,		padding='''max_length'''      ,		truncation=lowerCamelCase__      ,		)

										A_			,A_			,A_			:  Optional[Any]			=				forward(**lowerCamelCase__      )

										A_			:  Optional[Any]			=				CATEGORY_MAPPING[category.item()]

										A_			:  str			=				example['''long'''] + example['''short''']
										if example["category"] in ["yes", "no", "null"]:
															A_			:  str			=				[example['''category''']]
										A_			:  Optional[Any]			=				example['''category'''] != '''null'''
										# Now target can be: "yes", "no", "null", "list of long & short answers"

										if predicted_category in ["yes", "no", "null"]:
															A_			:  int			=				[predicted_category]
															A_			:  Optional[int]			=				example['''output'''] == example['''targets''']
															A_			:  Dict			=				predicted_category != '''null'''
															return example

										A_			:  List[Any]			=				3_8 if predicted_category == '''short''' else 1_0_2_4
										A_			,A_			:  str			=				get_best_valid_start_end_idx(
										    start_scores[0]      ,		end_scores[0]      ,		top_k=8      ,		max_size=lowerCamelCase__      )

										A_			:  List[Any]			=				inputs['''input_ids'''][0].tolist()
										A_			:  Dict			=				[tokenizer.decode(input_ids[start_score : end_score + 1]      )]

										A_			:  Optional[int]			=				expand_to_aliases(example['''targets''']      ,		make_sub_answers=lowerCamelCase__      )
										A_			:  str			=				expand_to_aliases(example['''output''']      )

										# some preprocessing to both prediction and answer
										A_			:  Tuple			=				{''''''.join(a.split()      ) for a in answers}
										A_			:  Optional[int]			=				{''''''.join(p.split()      ) for p in predictions}
										A_			:  List[str]			=				{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

										# if there is a common element, it's a exact match
										A_			:  List[Any]			=				len(list(answers & predictions      )      ) > 0
										A_			:  Dict			=				predicted_category != '''null''' and len(lowerCamelCase__      ) > 0

										return example

					A_			:  Dict			=				short_validation_dataset.map(lowerCamelCase__      )

					A_			:  List[str]			=				len(lowerCamelCase__      )
					A_			:  Any			=				len(short_validation_dataset.filter(lambda lowerCamelCase__      : x["match"] == 1      )      )
					print('''EM score:'''      ,		(matched / total) * 1_0_0      ,		'''%'''      )


if __name__ == "__main__":
							main()



