



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, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import LevitImageProcessor






class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						Union[str, Any]=7						,  __UpperCAmelCase     :						Any=3						,  __UpperCAmelCase     :						str=18						,  __UpperCAmelCase     :						Tuple=30						,  __UpperCAmelCase     :						Any=400						,  __UpperCAmelCase     :						List[str]=True						,  __UpperCAmelCase     :						Dict=None						,  __UpperCAmelCase     :						int=True						,  __UpperCAmelCase     :						str=None						,  __UpperCAmelCase     :						List[str]=True						,  __UpperCAmelCase     :						str=[0.5, 0.5, 0.5]						,  __UpperCAmelCase     :						int=[0.5, 0.5, 0.5]						,  )      ->			Any:
							UpperCAmelCase_=       size if size is not None else {"""shortest_edge""": 18}
							UpperCAmelCase_=       crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
							UpperCAmelCase_=       parent
							UpperCAmelCase_=       batch_size
							UpperCAmelCase_=       num_channels
							UpperCAmelCase_=       image_size
							UpperCAmelCase_=       min_resolution
							UpperCAmelCase_=       max_resolution
							UpperCAmelCase_=       do_resize
							UpperCAmelCase_=       size
							UpperCAmelCase_=       do_center_crop
							UpperCAmelCase_=       crop_size
							UpperCAmelCase_=       do_normalize
							UpperCAmelCase_=       image_mean
							UpperCAmelCase_=       image_std




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			int:
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "do_center_crop": self.do_center_crop,
							    "size": self.size,
							    "crop_size": self.crop_size,
							}






@require_torch
@require_vision
class 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: str		       =							LevitImageProcessor if is_vision_available() else None




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			str:
							UpperCAmelCase_=       LevitImageProcessingTester(self					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Optional[int]:
							return self.image_processor_tester.prepare_image_processor_dict()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """image_mean"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """image_std"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """do_normalize"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """do_resize"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """do_center_crop"""					)					)
							self.assertTrue(hasattr(__UpperCAmelCase						,  """size"""					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size						,  {"""shortest_edge""": 18}					)
							self.assertEqual(image_processor.crop_size						,  {"""height""": 18, """width""": 18}					)

							UpperCAmelCase_=       self.image_processing_class.from_dict(self.image_processor_dict						,  size=42						,  crop_size=84					)
							self.assertEqual(image_processor.size						,  {"""shortest_edge""": 42}					)
							self.assertEqual(image_processor.crop_size						,  {"""height""": 84, """width""": 84}					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Optional[Any]:
							pass




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Union[str, Any]:
							# Initialize image_processing
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							UpperCAmelCase_=       prepare_image_inputs(self.image_processor_tester						,  equal_resolution=__UpperCAmelCase					)
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase						,  Image.Image					)

							# Test not batched input
							UpperCAmelCase_=       image_processing(image_inputs[0]						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )						,  )

							# Test batched
							UpperCAmelCase_=       image_processing(__UpperCAmelCase						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Dict:
							# Initialize image_processing
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							UpperCAmelCase_=       prepare_image_inputs(self.image_processor_tester						,  equal_resolution=__UpperCAmelCase						,  numpify=__UpperCAmelCase					)
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase						,  np.ndarray					)

							# Test not batched input
							UpperCAmelCase_=       image_processing(image_inputs[0]						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )						,  )

							# Test batched
							UpperCAmelCase_=       image_processing(__UpperCAmelCase						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[Any]:
							# Initialize image_processing
							UpperCAmelCase_=       self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCAmelCase_=       prepare_image_inputs(self.image_processor_tester						,  equal_resolution=__UpperCAmelCase						,  torchify=__UpperCAmelCase					)
							for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase						,  torch.Tensor					)

							# Test not batched input
							UpperCAmelCase_=       image_processing(image_inputs[0]						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )						,  )

							# Test batched
							UpperCAmelCase_=       image_processing(__UpperCAmelCase						,  return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape						,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )						,  )









from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__A       =						'''\
@misc{wu2016googles,
      title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
'''

__A       =						'''\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the \'GLEU score\'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score\'s range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
'''

__A       =						'''\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    \'google_bleu\': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.44

    Example 2:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results["google_bleu"], 2))
        0.61

    Example 3:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results["google_bleu"], 2))
        0.53

    Example 4:
        >>> hyp1 = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'which\',
        ...         \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'always\',
        ...         \'disobeys\', \'the\', \'commands\', \'of\', \'the\', \'cat\']
        >>> ref1a = [\'It\', \'is\', \'the\', \'guiding\', \'principle\', \'which\',
        ...          \'guarantees\', \'the\', \'rubber\', \'duck\', \'forces\', \'never\',
        ...          \'being\', \'under\', \'the\', \'command\', \'of\', \'the\', \'cat\']
        >>> ref1b = [\'It\', \'is\', \'a\', \'guide\', \'to\', \'action\', \'that\',
        ...          \'ensures\', \'that\', \'the\', \'rubber\', \'duck\', \'will\', \'never\',
        ...          \'heed\', \'the\', \'cat\', \'commands\']
        >>> ref1c = [\'It\', \'is\', \'the\', \'practical\', \'guide\', \'for\', \'the\',
        ...          \'rubber\', \'duck\', \'army\', \'never\', \'to\', \'heed\', \'the\', \'directions\',
        ...          \'of\', \'the\', \'cat\']

        >>> hyp2 = [\'he\', \'read\', \'the\', \'book\', \'because\', \'he\', \'was\',
        ...         \'interested\', \'in\', \'world\', \'history\']
        >>> ref2a = [\'he\', \'was\', \'interested\', \'in\', \'world\', \'history\',
        ...          \'because\', \'he\', \'read\', \'the\', \'book\']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric("google_bleu")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results["google_bleu"], 2))
        0.4
'''






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






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			MetricInfo:
							return datasets.MetricInfo(
							    description=_DESCRIPTION						,  citation=_CITATION						,  inputs_description=_KWARGS_DESCRIPTION						,  features=datasets.Features(
							        {
							            """predictions""": datasets.Sequence(datasets.Value("""string"""						,  id="""token"""					)						,  id="""sequence"""					),
							            """references""": datasets.Sequence(
							                datasets.Sequence(datasets.Value("""string"""						,  id="""token"""					)						,  id="""sequence"""					)						,  id="""references"""					),
							        }					)						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[List[List[str]]]						,  __UpperCAmelCase     :						List[List[str]]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						int = 4						,  )      ->			Dict[str, float]:
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=__UpperCAmelCase						,  hypotheses=__UpperCAmelCase						,  min_len=__UpperCAmelCase						,  max_len=__UpperCAmelCase					)
							}




