






'''simple docstring'''

import copy

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


UpperCamelCase							=logging.get_logger(__name__)

UpperCamelCase							={
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Union[str, Any]      =						'''deta'''
 __a			:						int      =						{
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }
 def __init__(							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=9_00			,     __lowerCAmelCase=20_48			,     __lowerCAmelCase=6			,     __lowerCAmelCase=20_48			,     __lowerCAmelCase=8			,     __lowerCAmelCase=6			,     __lowerCAmelCase=10_24			,     __lowerCAmelCase=8			,     __lowerCAmelCase=0.0			,     __lowerCAmelCase=True			,     __lowerCAmelCase="relu"			,     __lowerCAmelCase=2_56			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=0.0			,     __lowerCAmelCase=0.0			,     __lowerCAmelCase=0.02			,     __lowerCAmelCase=1.0			,     __lowerCAmelCase=True			,     __lowerCAmelCase=False			,     __lowerCAmelCase="sine"			,     __lowerCAmelCase=5			,     __lowerCAmelCase=4			,     __lowerCAmelCase=4			,     __lowerCAmelCase=True			,     __lowerCAmelCase=3_00			,     __lowerCAmelCase=True			,     __lowerCAmelCase=True			,     __lowerCAmelCase=1			,     __lowerCAmelCase=5			,     __lowerCAmelCase=2			,     __lowerCAmelCase=1			,     __lowerCAmelCase=1			,     __lowerCAmelCase=5			,     __lowerCAmelCase=2			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=0.25			,     **__lowerCAmelCase			,     ):
       if backbone_config is None:
             logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
             UpperCamelCase_				:   int					    =							CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""] )
       else:
             if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   UpperCamelCase_				:   List[str]					    =							backbone_config.pop("""model_type""" )
                   UpperCamelCase_				:   Optional[int]					    =							CONFIG_MAPPING[backbone_model_type]
                   UpperCamelCase_				:   Dict					    =							config_class.from_dict(__lowerCAmelCase )

       UpperCamelCase_				:   Tuple					    =							backbone_config
       UpperCamelCase_				:   Union[str, Any]					    =							num_queries
       UpperCamelCase_				:   List[str]					    =							max_position_embeddings
       UpperCamelCase_				:   str					    =							d_model
       UpperCamelCase_				:   Optional[Any]					    =							encoder_ffn_dim
       UpperCamelCase_				:   int					    =							encoder_layers
       UpperCamelCase_				:   Tuple					    =							encoder_attention_heads
       UpperCamelCase_				:   int					    =							decoder_ffn_dim
       UpperCamelCase_				:   Union[str, Any]					    =							decoder_layers
       UpperCamelCase_				:   List[Any]					    =							decoder_attention_heads
       UpperCamelCase_				:   Optional[Any]					    =							dropout
       UpperCamelCase_				:   Optional[Any]					    =							attention_dropout
       UpperCamelCase_				:   str					    =							activation_dropout
       UpperCamelCase_				:   Dict					    =							activation_function
       UpperCamelCase_				:   Dict					    =							init_std
       UpperCamelCase_				:   Tuple					    =							init_xavier_std
       UpperCamelCase_				:   List[Any]					    =							encoder_layerdrop
       UpperCamelCase_				:   str					    =							auxiliary_loss
       UpperCamelCase_				:   Union[str, Any]					    =							position_embedding_type
       # deformable attributes
       UpperCamelCase_				:   List[Any]					    =							num_feature_levels
       UpperCamelCase_				:   str					    =							encoder_n_points
       UpperCamelCase_				:   Any					    =							decoder_n_points
       UpperCamelCase_				:   Optional[Any]					    =							two_stage
       UpperCamelCase_				:   Optional[int]					    =							two_stage_num_proposals
       UpperCamelCase_				:   Optional[int]					    =							with_box_refine
       UpperCamelCase_				:   Tuple					    =							assign_first_stage
       if two_stage is True and with_box_refine is False:
             raise ValueError("""If two_stage is True, with_box_refine must be True.""" )
       # Hungarian matcher
       UpperCamelCase_				:   Tuple					    =							class_cost
       UpperCamelCase_				:   Any					    =							bbox_cost
       UpperCamelCase_				:   List[str]					    =							giou_cost
       # Loss coefficients
       UpperCamelCase_				:   List[str]					    =							mask_loss_coefficient
       UpperCamelCase_				:   Union[str, Any]					    =							dice_loss_coefficient
       UpperCamelCase_				:   Union[str, Any]					    =							bbox_loss_coefficient
       UpperCamelCase_				:   Union[str, Any]					    =							giou_loss_coefficient
       UpperCamelCase_				:   int					    =							eos_coefficient
       UpperCamelCase_				:   Optional[int]					    =							focal_alpha
       super().__init__(is_encoder_decoder=__lowerCAmelCase			,     **__lowerCAmelCase )
 @property
 def   _UpperCAmelCase       (							self ):
       return self.encoder_attention_heads
 @property
 def   _UpperCAmelCase       (							self ):
       return self.d_model





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							copy.deepcopy(self.__dict__ )
       UpperCamelCase_				:   Dict					    =							self.backbone_config.to_dict()
       UpperCamelCase_				:   Optional[int]					    =							self.__class__.model_type
       return output














'''simple docstring'''

from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


UpperCamelCase							="\\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"

UpperCamelCase							="\\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"

UpperCamelCase							="\\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   _UpperCAmelCase       (							self ):
       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   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = 1			,     __lowerCAmelCase = 4			,     ):
       return {
           "google_bleu": gleu_score.corpus_gleu(
               list_of_references=__lowerCAmelCase			,     hypotheses=__lowerCAmelCase			,     min_len=__lowerCAmelCase			,     max_len=__lowerCAmelCase )
       }






