



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


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'data2vec-vision'







							def __init__(     self,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.0,    __a=0.0,    __a=0.02,    __a=1E-12,    __a=224,    __a=16,    __a=3,    __a=False,    __a=False,    __a=False,    __a=False,    __a=0.1,    __a=0.1,    __a=True,    __a=[3, 5, 7, 11],    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase  :							Dict				     =					hidden_size
														_lowerCAmelCase  :							List[Any]				     =					num_hidden_layers
														_lowerCAmelCase  :							Any				     =					num_attention_heads
														_lowerCAmelCase  :							str				     =					intermediate_size
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_act
														_lowerCAmelCase  :							int				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Dict				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Dict				     =					initializer_range
														_lowerCAmelCase  :							List[str]				     =					layer_norm_eps

														_lowerCAmelCase  :							Optional[int]				     =					image_size
														_lowerCAmelCase  :							List[Any]				     =					patch_size
														_lowerCAmelCase  :							Optional[Any]				     =					num_channels
														_lowerCAmelCase  :							str				     =					use_mask_token
														_lowerCAmelCase  :							List[str]				     =					use_absolute_position_embeddings
														_lowerCAmelCase  :							str				     =					use_relative_position_bias
														_lowerCAmelCase  :							List[str]				     =					use_shared_relative_position_bias
														_lowerCAmelCase  :							List[str]				     =					layer_scale_init_value
														_lowerCAmelCase  :							List[Any]				     =					drop_path_rate
														_lowerCAmelCase  :							Union[str, Any]				     =					use_mean_pooling
														# decode head attributes (semantic segmentation)
														_lowerCAmelCase  :							Tuple				     =					out_indices
														_lowerCAmelCase  :							Tuple				     =					pool_scales
														# auxiliary head attributes (semantic segmentation)
														_lowerCAmelCase  :							Optional[int]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							int				     =					auxiliary_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_num_convs
														_lowerCAmelCase  :							int				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					semantic_loss_ignore_index



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	version.parse('1.11')







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

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







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 1E-4





import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_snake_case	=						"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

_snake_case	=						"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

_snake_case	=						"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"



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







							def      snake_case__   (     self):

														'''simple docstring'''

														return datasets.MetricInfo(
														    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
														        {
														            "predictions": datasets.Sequence(datasets.Value("string",    id="token"),    id="sequence"),
														            "references": datasets.Sequence(
														                datasets.Sequence(datasets.Value("string",    id="token"),    id="sequence"),    id="references"),
														        }),    codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"],    reference_urls=[
														        "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=4,    __a=False):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					compute_bleu(
														    reference_corpus=__a,    translation_corpus=__a,    max_order=__a,    smooth=__a)
														((_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase)     ,					(_lowerCAmelCase))  :							Dict				     =					score
														return {
														    "bleu": bleu,
														    "precisions": precisions,
														    "brevity_penalty": bp,
														    "length_ratio": ratio,
														    "translation_length": translation_length,
														    "reference_length": reference_length,
														}
