





'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class  a_					:

    '''simple docstring'''


    def __init__(		self			,		A			,		A=sys.maxsize	)		->       Any:
        _SCREAMING_SNAKE_CASE     =      '''bilinear'''
        _SCREAMING_SNAKE_CASE     =      max_size
        _SCREAMING_SNAKE_CASE     =      short_edge_length





    def __call__(		self			,		A	)		->       Any:
        _SCREAMING_SNAKE_CASE     =      []
        for img in imgs:
            _SCREAMING_SNAKE_CASE     =      img.shape[:2]
            # later: provide list and randomly choose index for resize
            _SCREAMING_SNAKE_CASE     =      np.random.randint(self.short_edge_length[0]			,		self.short_edge_length[1] + 1	)
            if size == 0:
                return img
            _SCREAMING_SNAKE_CASE     =      size * 1.0 / min(__snake_case			,		__snake_case	)
            if h < w:
                _SCREAMING_SNAKE_CASE     =      size, scale * w
            else:
                _SCREAMING_SNAKE_CASE     =      scale * h, size
            if max(__snake_case			,		__snake_case	) > self.max_size:
                _SCREAMING_SNAKE_CASE     =      self.max_size * 1.0 / max(__snake_case			,		__snake_case	)
                _SCREAMING_SNAKE_CASE     =      newh * scale
                _SCREAMING_SNAKE_CASE     =      neww * scale
            _SCREAMING_SNAKE_CASE     =      int(neww + 0.5	)
            _SCREAMING_SNAKE_CASE     =      int(newh + 0.5	)

            if img.dtype == np.uinta:
                _SCREAMING_SNAKE_CASE     =      Image.fromarray(__snake_case	)
                _SCREAMING_SNAKE_CASE     =      pil_image.resize((neww, newh)			,		PILImageResampling.BILINEAR	)
                _SCREAMING_SNAKE_CASE     =      np.asarray(__snake_case	)
            else:
                _SCREAMING_SNAKE_CASE     =      img.permute(2			,		0			,		1	).unsqueeze(0	)  # 3, 0, 1)  # hw(c) -> nchw
                _SCREAMING_SNAKE_CASE     =      nn.functional.interpolate(
                    __snake_case			,		(newh, neww)			,		mode=self.interp_method			,		align_corners=__snake_case	).squeeze(0	)
            img_augs.append(__snake_case	)

        return img_augs




class  a_					:

    '''simple docstring'''


    def __init__(		self			,		A	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,		cfg.INPUT.MAX_SIZE_TEST	)
        _SCREAMING_SNAKE_CASE     =      cfg.INPUT.FORMAT
        _SCREAMING_SNAKE_CASE     =      cfg.SIZE_DIVISIBILITY
        _SCREAMING_SNAKE_CASE     =      cfg.PAD_VALUE
        _SCREAMING_SNAKE_CASE     =      cfg.INPUT.MAX_SIZE_TEST
        _SCREAMING_SNAKE_CASE     =      cfg.MODEL.DEVICE
        _SCREAMING_SNAKE_CASE     =      torch.tensor(cfg.MODEL.PIXEL_STD	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)			,		1			,		1	)
        _SCREAMING_SNAKE_CASE     =      torch.tensor(cfg.MODEL.PIXEL_MEAN	).to(self.device	).view(len(cfg.MODEL.PIXEL_STD	)			,		1			,		1	)
        _SCREAMING_SNAKE_CASE     =      lambda A	: (x - self.pixel_mean) / self.pixel_std


    def       snake_case_(		self			,		A	)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      tuple(max(__snake_case	) for s in zip(*[img.shape for img in images]	)	)
        _SCREAMING_SNAKE_CASE     =      [im.shape[-2:] for im in images]
        _SCREAMING_SNAKE_CASE     =      [
            nn.functional.pad(
                __snake_case			,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,		value=self.pad_value			,		)
            for size, im in zip(__snake_case			,		__snake_case	)
        ]

        return torch.stack(__snake_case	), torch.tensor(__snake_case	)





    def __call__(		self			,		A			,		A=False	)		->       str:
        with torch.no_grad():
            if not isinstance(__snake_case			,		__snake_case	):
                _SCREAMING_SNAKE_CASE     =      [images]
            if single_image:
                assert len(__snake_case	) == 1
            for i in range(len(__snake_case	)	):
                if isinstance(images[i]			,		torch.Tensor	):
                    images.insert(__snake_case			,		images.pop(__snake_case	).to(self.device	).float()	)
                elif not isinstance(images[i]			,		torch.Tensor	):
                    images.insert(
                        __snake_case			,		torch.as_tensor(img_tensorize(images.pop(__snake_case	)			,		input_format=self.input_format	)	)
                        .to(self.device	)
                        .float()			,		)
            # resize smallest edge
            _SCREAMING_SNAKE_CASE     =      torch.tensor([im.shape[:2] for im in images]	)
            _SCREAMING_SNAKE_CASE     =      self.aug(__snake_case	)
            # transpose images and convert to torch tensors
            # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
            # now normalize before pad to avoid useless arithmetic
            _SCREAMING_SNAKE_CASE     =      [self.normalizer(__snake_case	) for x in images]
            # now pad them to do the following operations
            _SCREAMING_SNAKE_CASE     =      self.pad(__snake_case	)
            # Normalize

            if self.size_divisibility > 0:
                raise NotImplementedError()
            # pad
            _SCREAMING_SNAKE_CASE     =      torch.true_divide(__snake_case			,		__snake_case	)
            if single_image:
                return images[0], sizes[0], scales_yx[0]
            else:
                return images, sizes, scales_yx





def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   Union[str, Any]   )						->Optional[int]:
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes






def lowerCamelCase						(					__lowerCamelCase			:   Dict ,	__lowerCamelCase			:   Tuple[int, int]   )						->Tuple:
    assert torch.isfinite(_lowerCAmelCase   ).all(), "Box tensor contains infinite or NaN!"
    _SCREAMING_SNAKE_CASE     =      box_size
    tensor[:, 0].clamp_(min=0 ,	max=_lowerCAmelCase   )
    tensor[:, 1].clamp_(min=0 ,	max=_lowerCAmelCase   )
    tensor[:, 2].clamp_(min=0 ,	max=_lowerCAmelCase   )
    tensor[:, 3].clamp_(min=0 ,	max=_lowerCAmelCase   )










'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


lowercase_         =			"""\
@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\",
}
"""

lowercase_         =			"""\
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.
"""

lowercase_         =			"""
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  a_					(    datasets.Metric	):

    '''simple docstring'''


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



