
import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class    lowerCAmelCase__     (	unittest.TestCase			):






		def __init__(		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							Dict[str, int] = None							,			SCREAMING_SNAKE_CASE__		:							int = 32							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							Union[int, float] = 1 / 2_55							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]							,			SCREAMING_SNAKE_CASE__		:							Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]							,			SCREAMING_SNAKE_CASE__		:							bool = True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=7							,			SCREAMING_SNAKE_CASE__		:							List[Any]=30							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=4_00							,			SCREAMING_SNAKE_CASE__		:							int=3							,			)     ->							List[str]:
						__lowerCamelCase    					=	parent
						__lowerCamelCase    					=	do_resize
						__lowerCamelCase    					=	size if size is not None else {'''shortest_edge''': 2_88}
						__lowerCamelCase    					=	size_divisor
						__lowerCamelCase    					=	do_rescale
						__lowerCamelCase    					=	rescale_factor
						__lowerCamelCase    					=	do_normalize
						__lowerCamelCase    					=	do_center_crop
						__lowerCamelCase    					=	image_mean
						__lowerCamelCase    					=	image_std
						__lowerCamelCase    					=	do_pad
						__lowerCamelCase    					=	batch_size
						__lowerCamelCase    					=	num_channels
						__lowerCamelCase    					=	min_resolution
						__lowerCamelCase    					=	max_resolution






		def  __A       (		self		:							List[str]  )     ->							List[Any]:
						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  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							List[str]=False  )     ->							int:
						if not batched:
										__lowerCamelCase    					=	self.size['''shortest_edge''']
										__lowerCamelCase    					=	image_inputs[0]
										if isinstance(SCREAMING_SNAKE_CASE__							,			Image.Image  ):
														__lowerCamelCase					,    __lowerCamelCase    					=	image.size
										else:
														__lowerCamelCase					,    __lowerCamelCase    					=	image.shape[1], image.shape[2]
										__lowerCamelCase    					=	size / min(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										if h < w:
														__lowerCamelCase					,    __lowerCamelCase    					=	size, scale * w
										else:
														__lowerCamelCase					,    __lowerCamelCase    					=	scale * h, size

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

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

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

						return expected_height, expected_width





@require_torch
@require_vision
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Union[str, Any]        =		BridgeTowerImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Dict  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	BridgeTowerImageProcessingTester(self  )






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






		def  __A       (		self		:							Union[str, Any]  )     ->							int:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''image_mean'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''image_std'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_resize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''size'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''size_divisor'''  )  )






		def  __A       (		self		:							int  )     ->							Optional[int]:
						pass






		def  __A       (		self		:							str  )     ->							str:
						# Initialize image processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			Image.Image  )

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

						__lowerCamelCase					,    __lowerCamelCase    					=	self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )
						self.assertEqual(
						    encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

						# Test batched
						__lowerCamelCase    					=	image_processing(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  ).pixel_values

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






		def  __A       (		self		:							Dict  )     ->							List[str]:
						# Initialize image processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random numpy tensors
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__							,			numpify=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			np.ndarray  )

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

						__lowerCamelCase					,    __lowerCamelCase    					=	self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )
						self.assertEqual(
						    encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

						# Test batched
						__lowerCamelCase    					=	image_processing(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  ).pixel_values

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







		def  __A       (		self		:							Dict  )     ->							str:
						# Initialize image processor
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__							,			torchify=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			torch.Tensor  )

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

						__lowerCamelCase					,    __lowerCamelCase    					=	self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE__  )
						self.assertEqual(
						    encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

						# Test batched
						__lowerCamelCase    					=	image_processing(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  ).pixel_values

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





import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\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"

SCREAMING_SNAKE_CASE__   :			int												=    "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"

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






		def  __A       (		self		:							Dict  )     ->							Tuple:
						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#chrf--chrf'''							,			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#chrf--chrf''']							,			reference_urls=[
						        '''https://github.com/m-popovic/chrF''',
						    ]							,			)







		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.CHAR_ORDER							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.WORD_ORDER							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.BETA							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			)     ->							Optional[Any]:
						__lowerCamelCase    					=	len(references[0]  )
						if any(len(SCREAMING_SNAKE_CASE__  ) != 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(SCREAMING_SNAKE_CASE__  )]

						__lowerCamelCase    					=	CHRF(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	sb_chrf.corpus_score(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

						return {
						    "score": output.score,
						    "char_order": output.char_order,
						    "word_order": output.word_order,
						    "beta": output.beta,
						}



