





import copy
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
from ..auto import CONFIG_MAPPING


__lowercase       :Optional[int]       =  logging.get_logger(__name__)

__lowercase       :str       =  {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"conditional_detr"
							snake_case_							 =						["past_key_values"]
							snake_case_							 =						{
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}

							def __init__(      self				:     Optional[int]							,	a				:     List[str]=True							,	a				:     Optional[Any]=None							,	a				:     Optional[int]=3							,	a				:     Tuple=3_00							,	a				:     int=6							,	a				:     str=20_48							,	a				:     str=8							,	a				:     Optional[Any]=6							,	a				:     str=20_48							,	a				:     Dict=8							,	a				:     Optional[int]=0.0							,	a				:     Union[str, Any]=0.0							,	a				:     Any=True							,	a				:     List[Any]="relu"							,	a				:     int=2_56							,	a				:     Optional[int]=0.1							,	a				:     Union[str, Any]=0.0							,	a				:     List[str]=0.0							,	a				:     Optional[int]=0.02							,	a				:     str=1.0							,	a				:     Optional[int]=False							,	a				:     str="sine"							,	a				:     List[str]="resnet50"							,	a				:     Tuple=True							,	a				:     List[Any]=False							,	a				:     Any=2							,	a				:     Union[str, Any]=5							,	a				:     int=2							,	a				:     Dict=1							,	a				:     Optional[Any]=1							,	a				:     List[Any]=2							,	a				:     Optional[int]=5							,	a				:     List[str]=2							,	a				:     List[Any]=0.25							,	**a				:     Any							,	)	->List[Any]:
													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."    )
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					CONFIG_MAPPING["resnet"](out_features=["stage4"]    )
																			elif isinstance(a							,	a    ):
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					backbone_config.get("model_type"    )
																									SCREAMING_SNAKE_CASE__     :				Tuple   =					CONFIG_MAPPING[backbone_model_type]
																									SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					config_class.from_dict(a    )

													SCREAMING_SNAKE_CASE__     :				Any   =					use_timm_backbone
													SCREAMING_SNAKE_CASE__     :				List[str]   =					backbone_config
													SCREAMING_SNAKE_CASE__     :				Dict   =					num_channels
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					num_queries
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					d_model
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					encoder_ffn_dim
													SCREAMING_SNAKE_CASE__     :				Dict   =					encoder_layers
													SCREAMING_SNAKE_CASE__     :				Any   =					encoder_attention_heads
													SCREAMING_SNAKE_CASE__     :				Dict   =					decoder_ffn_dim
													SCREAMING_SNAKE_CASE__     :				int   =					decoder_layers
													SCREAMING_SNAKE_CASE__     :				List[str]   =					decoder_attention_heads
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					dropout
													SCREAMING_SNAKE_CASE__     :				Any   =					attention_dropout
													SCREAMING_SNAKE_CASE__     :				Any   =					activation_dropout
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					activation_function
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					init_std
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					init_xavier_std
													SCREAMING_SNAKE_CASE__     :				Tuple   =					encoder_layerdrop
													SCREAMING_SNAKE_CASE__     :				List[str]   =					decoder_layerdrop
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					encoder_layers
													SCREAMING_SNAKE_CASE__     :				int   =					auxiliary_loss
													SCREAMING_SNAKE_CASE__     :				List[str]   =					position_embedding_type
													SCREAMING_SNAKE_CASE__     :				int   =					backbone
													SCREAMING_SNAKE_CASE__     :				Tuple   =					use_pretrained_backbone
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					dilation
													# Hungarian matcher
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					class_cost
													SCREAMING_SNAKE_CASE__     :				int   =					bbox_cost
													SCREAMING_SNAKE_CASE__     :				int   =					giou_cost
													# Loss coefficients
													SCREAMING_SNAKE_CASE__     :				Dict   =					mask_loss_coefficient
													SCREAMING_SNAKE_CASE__     :				int   =					dice_loss_coefficient
													SCREAMING_SNAKE_CASE__     :				Any   =					cls_loss_coefficient
													SCREAMING_SNAKE_CASE__     :				int   =					bbox_loss_coefficient
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					giou_loss_coefficient
													SCREAMING_SNAKE_CASE__     :				Dict   =					focal_alpha
													super().__init__(is_encoder_decoder=a							,	**a    )

							@property
							def      A_  (      self				:     List[Any]    )	->int:
													return self.encoder_attention_heads

							@property
							def      A_  (      self				:     int    )	->int:
													return self.d_model






							def      A_  (      self				:     Dict    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				int   =					copy.deepcopy(self.__dict__    )
													if self.backbone_config is not None:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					self.backbone_config.to_dict()
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.__class__.model_type
													return output







class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						version.parse("1.11"							)

							@property
							def      A_  (      self				:     List[str]    )	->Mapping[str, Mapping[int, str]]:
													return OrderedDict(
													    [
													        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
													        ("pixel_mask", {0: "batch"}),
													    ]    )

							@property
							def      A_  (      self				:     Optional[int]    )	->float:
													return 1E-5






							@property
							def      A_  (      self				:     Any    )	->int:
													return 12






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__lowercase       :Tuple       =  "\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

__lowercase       :str       =  "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

__lowercase       :List[Any]       =  "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"



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






							"""simple docstring"""

							def      A_  (      self				:     List[Any]    )	->MetricInfo:
													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"    ),
													        }    )							,	)






							def      A_  (      self				:     str							,	a				:     List[List[List[str]]]							,	a				:     List[List[str]]							,	a				:     int = 1							,	a				:     int = 4							,	)	->Dict[str, float]:
													return {
													    "google_bleu": gleu_score.corpus_gleu(
													        list_of_references=a							,	hypotheses=a							,	min_len=a							,	max_len=a    )
													}