
"""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


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 			_lowercase  (							unittest.TestCase						):




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_=7						,					UpperCAmelCase_=3						,					UpperCAmelCase_=18						,					UpperCAmelCase_=30						,					UpperCAmelCase_=400						,					UpperCAmelCase_=True						,					UpperCAmelCase_=1 / 255						,					UpperCAmelCase_=True						,					UpperCAmelCase_=8						,					)						->				List[Any]:
			lowerCamelCase	:     Optional[Any]            =       parent
			lowerCamelCase	:     Union[str, Any]            =       batch_size
			lowerCamelCase	:     str            =       num_channels
			lowerCamelCase	:     Optional[int]            =       image_size
			lowerCamelCase	:     List[str]            =       min_resolution
			lowerCamelCase	:     Dict            =       max_resolution
			lowerCamelCase	:     Union[str, Any]            =       do_rescale
			lowerCamelCase	:     Union[str, Any]            =       rescale_factor
			lowerCamelCase	:     Union[str, Any]            =       do_pad
			lowerCamelCase	:     Tuple            =       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						,					UpperCAmelCase_=False						,					UpperCAmelCase_=False						,					UpperCAmelCase_=False   )						->				Optional[int]:
			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

			if equal_resolution:
					lowerCamelCase	:     Any            =       []
					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:
					lowerCamelCase	:     Optional[int]            =       []
					for i in range(self.batch_size   ):
							lowerCamelCase      ,       lowerCamelCase	:     str            =       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
					lowerCamelCase	:     str            =       [Image.fromarray(np.moveaxis(UpperCAmelCase_						,					0						,					-1   )   ) for x in image_inputs]

			if torchify:
					lowerCamelCase	:     str            =       [torch.from_numpy(UpperCAmelCase_   ) for x in image_inputs]

			return image_inputs






@require_torch
@require_vision
class 			_lowercase  (							__UpperCAmelCase							,			unittest.TestCase						):
	lowercase_								=      SwinaSRImageProcessor if is_vision_available() else None




	def      _UpperCamelCase       (					self   )						->				str:
			lowerCamelCase	:     List[str]            =       SwinaSRImageProcessingTester(self   )




	@property
	def      _UpperCamelCase       (					self   )						->				List[Any]:
			return self.image_processor_tester.prepare_image_processor_dict()




	def      _UpperCamelCase       (					self   )						->				Any:
			lowerCamelCase	:     Any            =       self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(UpperCAmelCase_						,					'do_rescale'   )   )
			self.assertTrue(hasattr(UpperCAmelCase_						,					'rescale_factor'   )   )
			self.assertTrue(hasattr(UpperCAmelCase_						,					'do_pad'   )   )
			self.assertTrue(hasattr(UpperCAmelCase_						,					'pad_size'   )   )




	def      _UpperCamelCase       (					self   )						->				Tuple:
			pass




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Tuple:
			lowerCamelCase      ,       lowerCamelCase	:     Tuple            =       get_image_size(UpperCAmelCase_   )
			lowerCamelCase	:     str            =       self.image_processor_tester.pad_size

			lowerCamelCase	:     Tuple            =       (old_height // size + 1) * size - old_height
			lowerCamelCase	:     str            =       (old_width // size + 1) * size - old_width
			return old_height + pad_height, old_width + pad_width




	def      _UpperCamelCase       (					self   )						->				int:
			# Initialize image_processor
			lowerCamelCase	:     Union[str, Any]            =       self.image_processing_class(**self.image_processor_dict   )
			# create random PIL images
			lowerCamelCase	:     Dict            =       self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_   )
			for image in image_inputs:
					self.assertIsInstance(UpperCAmelCase_						,					Image.Image   )

			# Test not batched input
			lowerCamelCase	:     List[Any]            =       image_processor(image_inputs[0]						,					return_tensors='pt'   ).pixel_values
			lowerCamelCase      ,       lowerCamelCase	:     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      _UpperCamelCase       (					self   )						->				List[Any]:
			# Initialize image_processor
			lowerCamelCase	:     List[Any]            =       self.image_processing_class(**self.image_processor_dict   )
			# create random numpy tensors
			lowerCamelCase	:     List[Any]            =       self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_						,					numpify=UpperCAmelCase_   )
			for image in image_inputs:
					self.assertIsInstance(UpperCAmelCase_						,					np.ndarray   )

			# Test not batched input
			lowerCamelCase	:     List[Any]            =       image_processor(image_inputs[0]						,					return_tensors='pt'   ).pixel_values
			lowerCamelCase      ,       lowerCamelCase	:     int            =       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   )						->				Union[str, Any]:
			# Initialize image_processor
			lowerCamelCase	:     Union[str, Any]            =       self.image_processing_class(**self.image_processor_dict   )
			# create random PyTorch tensors
			lowerCamelCase	:     Tuple            =       self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_						,					torchify=UpperCAmelCase_   )
			for image in image_inputs:
					self.assertIsInstance(UpperCAmelCase_						,					torch.Tensor   )

			# Test not batched input
			lowerCamelCase	:     int            =       image_processor(image_inputs[0]						,					return_tensors='pt'   ).pixel_values
			lowerCamelCase      ,       lowerCamelCase	:     List[Any]            =       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


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

UpperCAmelCase_    ="""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

UpperCAmelCase_    ="""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

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`): 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).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    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`).
    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:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    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`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


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





				'''simple docstring'''



				def    __snake_case		(			self    ):
											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    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_="exp"							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			UpperCAmelCase_=False							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			):
											lowerCAmelCase =		len(references[0]    )
											if any(len(UpperCAmelCase_    ) != references_per_prediction for refs in references    ):
																		raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
											lowerCAmelCase =		[[refs[i] for refs in references] for i in range(UpperCAmelCase_    )]
											lowerCAmelCase =		scb.corpus_bleu(
											    UpperCAmelCase_							,			UpperCAmelCase_							,			smooth_method=UpperCAmelCase_							,			smooth_value=UpperCAmelCase_							,			force=UpperCAmelCase_							,			lowercase=UpperCAmelCase_							,			use_effective_order=UpperCAmelCase_							,			**({'''tokenize''': tokenize} if tokenize else {})							,			)
											lowerCAmelCase =		{
											    '''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



