





import sacrebleu as scb
from packaging import version

import datasets


_SCREAMING_SNAKE_CASE   						=		"""\
@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\",
}
"""

_SCREAMING_SNAKE_CASE   						=		"""\
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.
"""

_SCREAMING_SNAKE_CASE   						=		"""
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 SCREAMING_SNAKE_CASE_    (       datasets.Metric       ):






						def UpperCAmelCase_					(   self   :   str						)				->     List[str]:







							"""simple docstring"""


							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   :   List[Any]		, _A   :   List[str]		, _A   :   Optional[Any]		, _A   :   Tuple="exp"		, _A   :   Tuple=None		, _A   :   Optional[Any]=False		, _A   :   Dict=False		, _A   :   List[str]=None		, _A   :   Dict=False		, )				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      List[str]           =						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'						)
							snake_case_		:      Optional[Any]           =						[[refs[i] for refs in references] for i in range(_A						)]
							snake_case_		:      Any           =						scb.corpus_bleu(
							    _A		, _A		, smooth_method=_A		, smooth_value=_A		, force=_A		, lowercase=_A		, use_effective_order=_A		, **({'tokenize': tokenize} if tokenize else {})		, )
							snake_case_		:      int           =						{
							    '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









"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase							:       Optional[Any]       					=		logging.get_logger(__name__)

_UpperCamelCase							:       int       					=		{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	'''mobilevitv2'''





    def __init__(				self      :   Any							,						A      :   Any=3							,						A      :   Any=2_5_6							,						A      :   Optional[int]=2							,						A      :   str=2.0							,						A      :   Union[str, Any]="swish"							,						A      :   str=3							,						A      :   Tuple=3_2							,						A      :   Optional[int]=0.1							,						A      :   str=0.02							,						A      :   int=1E-5							,						A      :   List[Any]=5_1_2							,						A      :   str=[6, 1_2, 1_8]							,						A      :   Any=0.1							,						A      :   Union[str, Any]=2_5_5							,						A      :   Optional[Any]=[2, 4, 3]							,						A      :   List[Any]=[1_2_8, 1_9_2, 2_5_6]							,						A      :   Tuple=1.0							,						A      :   Union[str, Any]=2							,						A      :   List[Any]=0.0							,						A      :   int=0.0							,						**A      :   Optional[Any]							,						):


      '''simple docstring'''




      super().__init__(**A     )

      a	:     Optional[Any]							=  num_channels
      a	:     List[Any]							=  image_size
      a	:     Tuple							=  patch_size
      a	:     Optional[Any]							=  expand_ratio
      a	:     Any							=  hidden_act
      a	:     Dict							=  conv_kernel_size
      a	:     Optional[int]							=  output_stride
      a	:     Optional[Any]							=  initializer_range
      a	:     Tuple							=  layer_norm_eps
      a	:     Optional[int]							=  n_attn_blocks
      a	:     Tuple							=  base_attn_unit_dims
      a	:     List[str]							=  width_multiplier
      a	:     Dict							=  ffn_multiplier
      a	:     Dict							=  ffn_dropout
      a	:     Optional[int]							=  attn_dropout
      a	:     Tuple							=  classifier_dropout_prob

      # decode head attributes for semantic segmentation
      a	:     str							=  aspp_out_channels
      a	:     List[Any]							=  atrous_rates
      a	:     Optional[int]							=  aspp_dropout_prob
      a	:     Optional[Any]							=  semantic_loss_ignore_index






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	version.parse('''1.11'''			)





    @property
    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]     )





    @property
    def 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      if self.task == "image-classification":
        return OrderedDict([('logits', {0: 'batch'})]     )
      else:
        return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]     )





    @property
    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




      return 1E-4




