





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


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

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

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			List[Any]				=    "conditional_detr"
     __snake_case					:			Optional[int]				=    ["past_key_values"]
     __snake_case					:			Optional[int]				=    {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self		:					Optional[int]		,lowerCamelCase__		:					List[Any]=True		,lowerCamelCase__		:					Optional[int]=None		,lowerCamelCase__		:					List[str]=3		,lowerCamelCase__		:					Dict=300		,lowerCamelCase__		:					Tuple=6		,lowerCamelCase__		:					int=2048		,lowerCamelCase__		:					List[str]=8		,lowerCamelCase__		:					Tuple=6		,lowerCamelCase__		:					Optional[Any]=2048		,lowerCamelCase__		:					List[Any]=8		,lowerCamelCase__		:					List[Any]=0.0		,lowerCamelCase__		:					Union[str, Any]=0.0		,lowerCamelCase__		:					List[Any]=True		,lowerCamelCase__		:					Optional[Any]="relu"		,lowerCamelCase__		:					List[str]=256		,lowerCamelCase__		:					Optional[int]=0.1		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Union[str, Any]=0.0		,lowerCamelCase__		:					Tuple=0.02		,lowerCamelCase__		:					Optional[Any]=1.0		,lowerCamelCase__		:					Any=False		,lowerCamelCase__		:					Dict="sine"		,lowerCamelCase__		:					Any="resnet50"		,lowerCamelCase__		:					Tuple=True		,lowerCamelCase__		:					Dict=False		,lowerCamelCase__		:					List[str]=2		,lowerCamelCase__		:					Optional[int]=5		,lowerCamelCase__		:					Union[str, Any]=2		,lowerCamelCase__		:					Optional[Any]=1		,lowerCamelCase__		:					Any=1		,lowerCamelCase__		:					int=2		,lowerCamelCase__		:					Optional[Any]=5		,lowerCamelCase__		:					List[str]=2		,lowerCamelCase__		:					Dict=0.25		,**lowerCamelCase__		:					str		,)  ->	Tuple:




           '''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.""" )
                       SCREAMING_SNAKE_CASE	=		CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
                 elif isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                       SCREAMING_SNAKE_CASE	=		backbone_config.get("""model_type""" )
                       SCREAMING_SNAKE_CASE	=		CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE	=		config_class.from_dict(lowerCamelCase__ )

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






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




           '''simple docstring'''


           return self.encoder_attention_heads






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




           '''simple docstring'''


           return self.d_model






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		copy.deepcopy(self.__dict__ )
           if self.backbone_config is not None:
                 SCREAMING_SNAKE_CASE	=		self.backbone_config.to_dict()
           SCREAMING_SNAKE_CASE	=		self.__class__.model_type
           return output

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			Dict				=    version.parse("1.11"     )






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	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        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	float:




           '''simple docstring'''


           return 1e-5






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	int:




           '''simple docstring'''


           return 12









from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


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

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

SCREAMING_SNAKE_CASE_		     = """\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""

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



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	MetricInfo:




           '''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""" ),
                   } )		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					List[List[List[str]]]		,lowerCamelCase__		:					List[List[str]]		,lowerCamelCase__		:					int = 1		,lowerCamelCase__		:					int = 4		,)  ->	Dict[str, float]:




           '''simple docstring'''


           return {
               "google_bleu": gleu_score.corpus_gleu(
                   list_of_references=lowerCamelCase__		,hypotheses=lowerCamelCase__		,min_len=lowerCamelCase__		,max_len=lowerCamelCase__ )
           }


