


import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


A			:						Union[str, Any]        =  logging.get_logger(__name__)

A			:						str        =  {
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''detr'''
					A__     =					['''past_key_values''']
					A__     =					{
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					}

					def __init__(self       :		Any ,   _UpperCAmelCase       :		Tuple=True ,   _UpperCAmelCase       :		Optional[int]=None ,   _UpperCAmelCase       :		str=3 ,   _UpperCAmelCase       :		str=100 ,   _UpperCAmelCase       :		Tuple=6 ,   _UpperCAmelCase       :		Optional[Any]=2048 ,   _UpperCAmelCase       :		Optional[int]=8 ,   _UpperCAmelCase       :		int=6 ,   _UpperCAmelCase       :		Optional[int]=2048 ,   _UpperCAmelCase       :		str=8 ,   _UpperCAmelCase       :		Tuple=0.0 ,   _UpperCAmelCase       :		List[str]=0.0 ,   _UpperCAmelCase       :		Optional[Any]=True ,   _UpperCAmelCase       :		str="relu" ,   _UpperCAmelCase       :		int=256 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		List[Any]=0.0 ,   _UpperCAmelCase       :		List[Any]=0.0 ,   _UpperCAmelCase       :		List[str]=0.02 ,   _UpperCAmelCase       :		Union[str, Any]=1.0 ,   _UpperCAmelCase       :		Optional[Any]=False ,   _UpperCAmelCase       :		Optional[Any]="sine" ,   _UpperCAmelCase       :		Union[str, Any]="resnet50" ,   _UpperCAmelCase       :		str=True ,   _UpperCAmelCase       :		Optional[int]=False ,   _UpperCAmelCase       :		int=1 ,   _UpperCAmelCase       :		Dict=5 ,   _UpperCAmelCase       :		Optional[int]=2 ,   _UpperCAmelCase       :		Tuple=1 ,   _UpperCAmelCase       :		Optional[Any]=1 ,   _UpperCAmelCase       :		int=5 ,   _UpperCAmelCase       :		Dict=2 ,   _UpperCAmelCase       :		List[str]=0.1 ,   **_UpperCAmelCase       :		str ,   )		->  str:




							"""simple docstring"""


							if backbone_config is not None and use_timm_backbone:
									raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""						)

							if not use_timm_backbone:
									if backbone_config is None:
											logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""						)
											lowercase__	     =						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]						)
									elif isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
											lowercase__	     =						backbone_config.get("""model_type"""						)
											lowercase__	     =						CONFIG_MAPPING[backbone_model_type]
											lowercase__	     =						config_class.from_dict(_UpperCAmelCase						)
									# set timm attributes to None
									lowercase__     ,      lowercase__     ,      lowercase__	     =						None, None, None

							lowercase__	     =						use_timm_backbone
							lowercase__	     =						backbone_config
							lowercase__	     =						num_channels
							lowercase__	     =						num_queries
							lowercase__	     =						d_model
							lowercase__	     =						encoder_ffn_dim
							lowercase__	     =						encoder_layers
							lowercase__	     =						encoder_attention_heads
							lowercase__	     =						decoder_ffn_dim
							lowercase__	     =						decoder_layers
							lowercase__	     =						decoder_attention_heads
							lowercase__	     =						dropout
							lowercase__	     =						attention_dropout
							lowercase__	     =						activation_dropout
							lowercase__	     =						activation_function
							lowercase__	     =						init_std
							lowercase__	     =						init_xavier_std
							lowercase__	     =						encoder_layerdrop
							lowercase__	     =						decoder_layerdrop
							lowercase__	     =						encoder_layers
							lowercase__	     =						auxiliary_loss
							lowercase__	     =						position_embedding_type
							lowercase__	     =						backbone
							lowercase__	     =						use_pretrained_backbone
							lowercase__	     =						dilation
							# Hungarian matcher
							lowercase__	     =						class_cost
							lowercase__	     =						bbox_cost
							lowercase__	     =						giou_cost
							# Loss coefficients
							lowercase__	     =						mask_loss_coefficient
							lowercase__	     =						dice_loss_coefficient
							lowercase__	     =						bbox_loss_coefficient
							lowercase__	     =						giou_loss_coefficient
							lowercase__	     =						eos_coefficient
							super().__init__(is_encoder_decoder=_UpperCAmelCase ,   **_UpperCAmelCase						)

					@property
					def     lowerCamelCase__	(self       :		List[Any]						)		->  int:




							"""simple docstring"""


							return self.encoder_attention_heads

					@property
					def     lowerCamelCase__	(self       :		int						)		->  int:




							"""simple docstring"""


							return self.d_model

					@classmethod
					def     lowerCamelCase__	(cls       :		Tuple ,   _UpperCAmelCase       :		PretrainedConfig ,   **_UpperCAmelCase       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							return cls(backbone_config=_UpperCAmelCase ,   **_UpperCAmelCase						)


					def     lowerCamelCase__	(self       :		Any						)		->  Dict[str, any]:




							"""simple docstring"""


							lowercase__	     =						copy.deepcopy(self.__dict__						)
							if output["backbone_config"] is not None:
									lowercase__	     =						self.backbone_config.to_dict()
							lowercase__	     =						self.__class__.model_type
							return output






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					version.parse('''1.11'''				)

					@property
					def     lowerCamelCase__	(self       :		Tuple						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


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

					@property
					def     lowerCamelCase__	(self       :		Optional[int]						)		->  float:




							"""simple docstring"""


							return 1E-5


					@property
					def     lowerCamelCase__	(self       :		Any						)		->  int:




							"""simple docstring"""


							return 12





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

import datasets


A			:						Dict        =  '\\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'

A			:						List[str]        =  '\\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'

A			:						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 				A       (			datasets.Metric				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Dict						)		->  Union[str, Any]:




							"""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#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     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		int = CHRF.CHAR_ORDER ,   _UpperCAmelCase       :		int = CHRF.WORD_ORDER ,   _UpperCAmelCase       :		int = CHRF.BETA ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   )		->  int:




							"""simple docstring"""


							lowercase__	     =						len(references[0]						)
							if any(len(_UpperCAmelCase						) != references_per_prediction for refs in references						):
									raise ValueError("""Sacrebleu requires the same number of references for each prediction"""						)
							lowercase__	     =						[[refs[i] for refs in references] for i in range(_UpperCAmelCase						)]

							lowercase__	     =						CHRF(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
							lowercase__	     =						sb_chrf.corpus_score(_UpperCAmelCase ,   _UpperCAmelCase						)

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

