





'''simple docstring'''

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 ViltImageProcessor






class  UpperCAmelCase			( unittest.TestCase       ):
 def __init__(       self   :Tuple		, lowercase_   :int		, lowercase_   :str=7		, lowercase_   :List[str]=3		, lowercase_   :Union[str, Any]=18		, lowercase_   :Tuple=30		, lowercase_   :Dict=4_00		, lowercase_   :Union[str, Any]=True		, lowercase_   :Optional[int]=None		, lowercase_   :Dict=2		, lowercase_   :Dict=True		, lowercase_   :List[Any]=[0.5, 0.5, 0.5]		, lowercase_   :Optional[int]=[0.5, 0.5, 0.5]		, )->							List[str]:
     A__       =    size if size is not None else {"shortest_edge": 30}
     A__       =    parent
     A__       =    batch_size
     A__       =    num_channels
     A__       =    image_size
     A__       =    min_resolution
     A__       =    max_resolution
     A__       =    do_resize
     A__       =    size
     A__       =    size_divisor
     A__       =    do_normalize
     A__       =    image_mean
     A__       =    image_std
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							List[str]:
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "size_divisor": self.size_divisor,
     }



 def   UpperCAmelCase_							(       self   :str		, lowercase_   :Optional[int]		, lowercase_   :List[str]=False							)->							Union[str, Any]:
     if not batched:
         A__       =    self.size["shortest_edge"]
         A__       =    image_inputs[0]
         if isinstance(lowercase_		, Image.Image							):
             A__,			A__       =    image.size
         else:
             A__,			A__       =    image.shape[1], image.shape[2]
         A__       =    size / min(lowercase_		, lowercase_							)
         if h < w:
             A__,			A__       =    size, scale * w
         else:
             A__,			A__       =    scale * h, size

         A__       =    int((13_33 / 8_00) * size							)
         if max(lowercase_		, lowercase_							) > max_size:
             A__       =    max_size / max(lowercase_		, lowercase_							)
             A__       =    newh * scale
             A__       =    neww * scale

         A__,			A__       =    int(newh + 0.5							), int(neww + 0.5							)
         A__,			A__       =    (
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

     else:
         A__       =    []
         for image in image_inputs:
             A__,			A__       =    self.get_expected_values([image]							)
             expected_values.append((expected_height, expected_width)							)
         A__       =    max(lowercase_		, key=lambda lowercase_							: item[0]							)[0]
         A__       =    max(lowercase_		, key=lambda lowercase_							: item[1]							)[1]

     return expected_height, expected_width






@require_torch
@require_vision
class  UpperCAmelCase			( UpperCAmelCase_    , unittest.TestCase       ):
 __lowercase     	=       ViltImageProcessor if is_vision_available() else None
 def   UpperCAmelCase_							(       self   :str							)->							Any:
     A__       =    ViltImageProcessingTester(self							)
 @property
 def   UpperCAmelCase_							(       self   :Dict							)->							int:
     return self.image_processor_tester.prepare_image_processor_dict()
 def   UpperCAmelCase_							(       self   :Tuple							)->							Optional[Any]:
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(lowercase_		, "image_mean"							)							)
     self.assertTrue(hasattr(lowercase_		, "image_std"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_normalize"							)							)
     self.assertTrue(hasattr(lowercase_		, "do_resize"							)							)
     self.assertTrue(hasattr(lowercase_		, "size"							)							)
     self.assertTrue(hasattr(lowercase_		, "size_divisor"							)							)
 def   UpperCAmelCase_							(       self   :int							)->							Optional[Any]:
     A__       =    self.image_processing_class.from_dict(self.image_processor_dict							)
     self.assertEqual(image_processor.size		, {"shortest_edge": 30}							)

     A__       =    self.image_processing_class.from_dict(self.image_processor_dict		, size=42							)
     self.assertEqual(image_processor.size		, {"shortest_edge": 42}							)
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[int]:
     pass
 def   UpperCAmelCase_							(       self   :Optional[int]							)->							Union[str, Any]:
     # Initialize image_processing
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random PIL images
     A__       =    prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase_							)
     for image in image_inputs:
         self.assertIsInstance(lowercase_		, Image.Image							)

     # Test not batched input
     A__       =    image_processing(image_inputs[0]		, return_tensors="pt"							).pixel_values

     A__,			A__       =    self.image_processor_tester.get_expected_values(lowercase_							)
     self.assertEqual(
         encoded_images.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )

     # Test batched
     A__       =    image_processing(lowercase_		, return_tensors="pt"							).pixel_values

     A__,			A__       =    self.image_processor_tester.get_expected_values(lowercase_		, batched=lowercase_							)
     self.assertEqual(
         encoded_images.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )		, )
 def   UpperCAmelCase_							(       self   :Dict							)->							Union[str, Any]:
     # Initialize image_processing
     A__       =    self.image_processing_class(**self.image_processor_dict							)
     # create random numpy tensors
     A__       =    prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase_		, numpify=lowercase_							)
     for image in image_inputs:
         self.assertIsInstance(lowercase_		, np.ndarray							)

     # Test not batched input
     A__       =    image_processing(image_inputs[0]		, return_tensors="pt"							).pixel_values

     A__,			A__       =    self.image_processor_tester.get_expected_values(lowercase_							)
     self.assertEqual(
         encoded_images.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )

     # Test batched
     A__       =    image_processing(lowercase_		, return_tensors="pt"							).pixel_values

     A__,			A__       =    self.image_processor_tester.get_expected_values(lowercase_		, batched=lowercase_							)
     self.assertEqual(
         encoded_images.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )		, )



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

     # Test not batched input
     A__       =    image_processing(image_inputs[0]		, return_tensors="pt"							).pixel_values

     A__,			A__       =    self.image_processor_tester.get_expected_values(lowercase_							)
     self.assertEqual(
         encoded_images.shape		, (1, self.image_processor_tester.num_channels, expected_height, expected_width)		, )

     # Test batched
     A__       =    image_processing(lowercase_		, return_tensors="pt"							).pixel_values

     A__,			A__       =    self.image_processor_tester.get_expected_values(lowercase_		, batched=lowercase_							)
     self.assertEqual(
         encoded_images.shape		, (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         )		, )










'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


__lowerCAmelCase							:			Tuple				="\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__lowerCAmelCase							:			Dict				="\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

__lowerCAmelCase							:			Dict				="\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    , _KWARGS_DESCRIPTION       )
class      UpperCAmelCase			( datasets.Metric       ):
 def   UpperCAmelCase_							(       self   :int							)->							Union[str, Any]:
     if version.parse(scb.__version__							) < version.parse("1.4.12"							):
         raise ImportWarning(
             "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
             "You can install it with `pip install \"sacrebleu>=1.4.12\"`."							)
     return datasets.MetricInfo(
         description=_DESCRIPTION		, citation=_CITATION		, homepage="https://github.com/mjpost/sacreBLEU"		, inputs_description=_KWARGS_DESCRIPTION		, features=datasets.Features(
             {
                 "predictions": datasets.Value("string"		, id="sequence"							),
                 "references": datasets.Sequence(datasets.Value("string"		, id="sequence"							)		, id="references"							),
             }							)		, codebase_urls=["https://github.com/mjpost/sacreBLEU"]		, reference_urls=[
             "https://github.com/mjpost/sacreBLEU",
             "https://en.wikipedia.org/wiki/BLEU",
             "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
         ]		, )



 def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]		, lowercase_   :Optional[Any]		, lowercase_   :List[str]="exp"		, lowercase_   :str=None		, lowercase_   :Tuple=False		, lowercase_   :Any=False		, lowercase_   :Optional[Any]=None		, lowercase_   :Any=False		, )->							Tuple:
     A__       =    len(references[0]							)
     if any(len(lowercase_							) != references_per_prediction for refs in references							):
         raise ValueError("Sacrebleu requires the same number of references for each prediction"							)
     A__       =    [[refs[i] for refs in references] for i in range(lowercase_							)]
     A__       =    scb.corpus_bleu(
         lowercase_		, lowercase_		, smooth_method=lowercase_		, smooth_value=lowercase_		, force=lowercase_		, lowercase=lowercase_		, use_effective_order=lowercase_		, **({"tokenize": tokenize} if tokenize else {})		, )
     A__       =    {
         "score": output.score,
         "counts": output.counts,
         "totals": output.totals,
         "precisions": output.precisions,
         "bp": output.bp,
         "sys_len": output.sys_len,
         "ref_len": output.ref_len,
     }
     return output_dict



