






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  snake_case__      (							unittest.TestCase   ):
 def __init__(       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__=7    ,     lowerCAmelCase__=3    ,     lowerCAmelCase__=18    ,     lowerCAmelCase__=30    ,     lowerCAmelCase__=4_00    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=1 / 2_55    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=8    ,     )							->   Any:
       __magic_name__    :     str     		=      parent
       __magic_name__    :     int     		=      batch_size
       __magic_name__    :     int     		=      num_channels
       __magic_name__    :     int     		=      image_size
       __magic_name__    :     Optional[Any]     		=      min_resolution
       __magic_name__    :     Optional[int]     		=      max_resolution
       __magic_name__    :     Dict     		=      do_rescale
       __magic_name__    :     List[str]     		=      rescale_factor
       __magic_name__    :     List[str]     		=      do_pad
       __magic_name__    :     Optional[Any]     		=      pad_size
 def     __magic_name__       (       self				)							->   Dict:
       return {
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
           "pad_size": self.pad_size,
       }







 def     __magic_name__       (       self    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=False				)							->   Tuple:
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
             __magic_name__    :     Dict     		=      []
             for i in range(self.batch_size				):
                   image_inputs.append(
                       np.random.randint(
                           2_55    ,     size=(self.num_channels, self.max_resolution, self.max_resolution)    ,     dtype=np.uinta				)				)
       else:
             __magic_name__    :     str     		=      []
             for i in range(self.batch_size				):
                   __magic_name__		,__magic_name__    :     Any     		=      np.random.choice(np.arange(self.min_resolution    ,     self.max_resolution				)    ,     2				)
                   image_inputs.append(np.random.randint(2_55    ,     size=(self.num_channels, width, height)    ,     dtype=np.uinta				)				)

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

       if torchify:
             __magic_name__    :     int     		=      [torch.from_numpy(lowerCAmelCase__				) for x in image_inputs]

       return image_inputs





@require_torch
@require_vision
class  snake_case__      (							lowercase_		,						unittest.TestCase   ):
 lowercase__		:  Optional[Any]							   =					SwinaSRImageProcessor if is_vision_available() else None
 def     __magic_name__       (       self				)							->   List[Any]:
       __magic_name__    :     Optional[Any]     		=      SwinaSRImageProcessingTester(self				)
 @property
 def     __magic_name__       (       self				)							->   Any:
       return self.image_processor_tester.prepare_image_processor_dict()
 def     __magic_name__       (       self				)							->   List[str]:
       __magic_name__    :     Optional[Any]     		=      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     __magic_name__       (       self				)							->   Dict:
       pass
 def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   Tuple:
       __magic_name__		,__magic_name__    :     Optional[Any]     		=      get_image_size(lowerCAmelCase__				)
       __magic_name__    :     Optional[int]     		=      self.image_processor_tester.pad_size

       __magic_name__    :     Optional[int]     		=      (old_height // size + 1) * size - old_height
       __magic_name__    :     Tuple     		=      (old_width // size + 1) * size - old_width
       return old_height + pad_height, old_width + pad_width
 def     __magic_name__       (       self				)							->   List[str]:
       __magic_name__    :     str     		=      self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       __magic_name__    :     Union[str, Any]     		=      self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase__				)
       for image in image_inputs:
             self.assertIsInstance(lowerCAmelCase__    ,     Image.Image				)

       # Test not batched input
       __magic_name__    :     str     		=      image_processor(image_inputs[0]    ,     return_tensors="""pt"""				).pixel_values
       __magic_name__		,__magic_name__    :     Optional[int]     		=      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     __magic_name__       (       self				)							->   List[str]:
       __magic_name__    :     List[str]     		=      self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       __magic_name__    :     Union[str, Any]     		=      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
       __magic_name__    :     Any     		=      image_processor(image_inputs[0]    ,     return_tensors="""pt"""				).pixel_values
       __magic_name__		,__magic_name__    :     Union[str, Any]     		=      self.calculate_expected_size(image_inputs[0]				)
       self.assertEqual(
           encoded_images.shape    ,     (
               1,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )    ,     )







 def     __magic_name__       (       self				)							->   Optional[Any]:
       __magic_name__    :     str     		=      self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       __magic_name__    :     str     		=      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
       __magic_name__    :     Optional[int]     		=      image_processor(image_inputs[0]    ,     return_tensors="""pt"""				).pixel_values
       __magic_name__		,__magic_name__    :     List[str]     		=      self.calculate_expected_size(image_inputs[0]				)
       self.assertEqual(
           encoded_images.shape    ,     (
               1,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )    ,     )








import sacrebleu as scb
from packaging import version

import datasets


__magic_name__:  Optional[int]  	=			"\\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"

__magic_name__:  int  	=			"\\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"

__magic_name__:  List[Any]  	=			"\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  snake_case__      (							datasets.Metric   ):
	def     __magic_name__       (       self				)							->   Optional[int]:
							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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__="exp"    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     )							->   Optional[Any]:
							__magic_name__    :     Optional[int]     		=      len(references[0]				)
							if any(len(lowerCAmelCase__				) != references_per_prediction for refs in references				):
													raise ValueError("""Sacrebleu requires the same number of references for each prediction"""				)
							__magic_name__    :     Dict     		=      [[refs[i] for refs in references] for i in range(lowerCAmelCase__				)]
							__magic_name__    :     List[str]     		=      scb.corpus_bleu(
							    lowerCAmelCase__    ,     lowerCAmelCase__    ,     smooth_method=lowerCAmelCase__    ,     smooth_value=lowerCAmelCase__    ,     force=lowerCAmelCase__    ,     lowercase=lowerCAmelCase__    ,     use_effective_order=lowerCAmelCase__    ,     **({"""tokenize""": tokenize} if tokenize else {})    ,     )
							__magic_name__    :     List[str]     		=      {
							    """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
