import sacrebleu as scb
from packaging import version

import datasets


lowercase_  							=			"\\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"

lowercase_  							=			"\\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"

lowercase_  							=			"\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      SCREAMING_SNAKE_CASE__					(  datasets.Metric    ):

       def 			snake_case__    (							self					:     Optional[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					:     Dict			,			_lowerCAmelCase					:     int			,			_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Optional[int]="exp"			,			_lowerCAmelCase					:     Tuple=None			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     Any=False			,			_lowerCAmelCase					:     Optional[Any]=None			,			_lowerCAmelCase					:     Any=False			,			):
           __snake_case       :				Optional[Any] 			=    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"""			)
           __snake_case       :				str 			=    [[refs[i] for refs in references] for i in range(_lowerCAmelCase			)]
           __snake_case       :				Optional[int] 			=    scb.corpus_bleu(
               _lowerCAmelCase			,			_lowerCAmelCase			,			smooth_method=_lowerCAmelCase			,			smooth_value=_lowerCAmelCase			,			force=_lowerCAmelCase			,			lowercase=_lowerCAmelCase			,			use_effective_order=_lowerCAmelCase			,			**({"""tokenize""": tokenize} if tokenize else {})			,			)
           __snake_case       :				Dict 			=    {
               """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




import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "facebook/hubert-base-ls960": "https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[str]          =	"hubert"

       def __init__(							self					:     List[str]			,			_lowerCAmelCase					:     Union[str, Any]=32			,			_lowerCAmelCase					:     Any=7_68			,			_lowerCAmelCase					:     Optional[Any]=12			,			_lowerCAmelCase					:     Optional[int]=12			,			_lowerCAmelCase					:     int=30_72			,			_lowerCAmelCase					:     Optional[Any]="gelu"			,			_lowerCAmelCase					:     List[str]=0.1			,			_lowerCAmelCase					:     Any=0.1			,			_lowerCAmelCase					:     List[str]=0.1			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Dict=0.0			,			_lowerCAmelCase					:     List[str]=0.1			,			_lowerCAmelCase					:     Optional[int]=0.1			,			_lowerCAmelCase					:     Dict=0.02			,			_lowerCAmelCase					:     Any=1e-5			,			_lowerCAmelCase					:     str="group"			,			_lowerCAmelCase					:     Any="gelu"			,			_lowerCAmelCase					:     List[str]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)			,			_lowerCAmelCase					:     Any=(5, 2, 2, 2, 2, 2, 2)			,			_lowerCAmelCase					:     Dict=(10, 3, 3, 3, 3, 2, 2)			,			_lowerCAmelCase					:     List[str]=False			,			_lowerCAmelCase					:     Optional[Any]=1_28			,			_lowerCAmelCase					:     int=16			,			_lowerCAmelCase					:     Tuple=False			,			_lowerCAmelCase					:     int=True			,			_lowerCAmelCase					:     int=0.05			,			_lowerCAmelCase					:     Any=10			,			_lowerCAmelCase					:     Dict=2			,			_lowerCAmelCase					:     Dict=0.0			,			_lowerCAmelCase					:     Optional[int]=10			,			_lowerCAmelCase					:     Union[str, Any]=0			,			_lowerCAmelCase					:     int="sum"			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=False			,			_lowerCAmelCase					:     Optional[Any]=2_56			,			_lowerCAmelCase					:     Any=0			,			_lowerCAmelCase					:     List[Any]=1			,			_lowerCAmelCase					:     List[str]=2			,			**_lowerCAmelCase					:     Optional[int]			,			):
           super().__init__(**_lowerCAmelCase			,			pad_token_id=_lowerCAmelCase			,			bos_token_id=_lowerCAmelCase			,			eos_token_id=_lowerCAmelCase			)
           __snake_case       :				Optional[int] 			=    hidden_size
           __snake_case       :				Optional[Any] 			=    feat_extract_norm
           __snake_case       :				Optional[int] 			=    feat_extract_activation
           __snake_case       :				List[str] 			=    list(_lowerCAmelCase			)
           __snake_case       :				Any 			=    list(_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    list(_lowerCAmelCase			)
           __snake_case       :				Any 			=    conv_bias
           __snake_case       :				Tuple 			=    num_conv_pos_embeddings
           __snake_case       :				List[Any] 			=    num_conv_pos_embedding_groups
           __snake_case       :				Union[str, Any] 			=    len(self.conv_dim			)
           __snake_case       :				List[Any] 			=    num_hidden_layers
           __snake_case       :				Tuple 			=    intermediate_size
           __snake_case       :				Optional[int] 			=    hidden_act
           __snake_case       :				Union[str, Any] 			=    num_attention_heads
           __snake_case       :				str 			=    hidden_dropout
           __snake_case       :				Union[str, Any] 			=    attention_dropout
           __snake_case       :				Dict 			=    activation_dropout
           __snake_case       :				List[Any] 			=    feat_proj_layer_norm
           __snake_case       :				List[str] 			=    feat_proj_dropout
           __snake_case       :				List[Any] 			=    final_dropout
           __snake_case       :				str 			=    layerdrop
           __snake_case       :				List[str] 			=    layer_norm_eps
           __snake_case       :				Optional[Any] 			=    initializer_range
           __snake_case       :				Optional[int] 			=    vocab_size
           __snake_case       :				Dict 			=    do_stable_layer_norm
           __snake_case       :				Tuple 			=    use_weighted_layer_sum
           __snake_case       :				List[Any] 			=    classifier_proj_size

           if (
               (len(self.conv_stride			) != self.num_feat_extract_layers)
               or (len(self.conv_kernel			) != self.num_feat_extract_layers)
               or (len(self.conv_dim			) != self.num_feat_extract_layers)
           ):
               raise ValueError(
                   """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                   """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                   f''' {len(self.conv_dim			)}`, `len(config.conv_stride) = {len(self.conv_stride			)}`,'''
                   f''' `len(config.conv_kernel) = {len(self.conv_kernel			)}`.'''			)

           # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
           __snake_case       :				Union[str, Any] 			=    apply_spec_augment
           __snake_case       :				List[str] 			=    mask_time_prob
           __snake_case       :				Dict 			=    mask_time_length
           __snake_case       :				int 			=    mask_time_min_masks
           __snake_case       :				List[str] 			=    mask_feature_prob
           __snake_case       :				Tuple 			=    mask_feature_length
           __snake_case       :				int 			=    mask_feature_min_masks

           # ctc loss
           __snake_case       :				Dict 			=    ctc_loss_reduction
           __snake_case       :				List[str] 			=    ctc_zero_infinity





       @property
       def 			snake_case__    (							self					:     Any			):
           return functools.reduce(operator.mul			,			self.conv_stride			,			1			)



