




import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


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


__UpperCAmelCase : List[str] 			=				"\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

__UpperCAmelCase : Any 			=				"\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

__UpperCAmelCase : List[Any] 			=				"\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

__UpperCAmelCase : Dict 			=				{
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}



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






       def  _lowercase   (     self						):




              """simple docstring"""




              return datasets.MetricInfo(
                  description=_DESCRIPTION ,					citation=_CITATION ,					homepage='''https://github.com/google-research/bleurt''' ,					inputs_description=_KWARGS_DESCRIPTION ,					features=datasets.Features(
                      {
                          '''predictions''': datasets.Value('''string''' ,					id='''sequence'''						),
                          '''references''': datasets.Value('''string''' ,					id='''sequence'''						),
                      }						) ,					codebase_urls=['''https://github.com/google-research/bleurt'''] ,					reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696'''] ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if self.config_name == "default":
                     logger.warning(
                         '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
                         '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''						)
                     UpperCamelCase    :	int   	=		'''bleurt-base-128'''

              if self.config_name.lower() in CHECKPOINT_URLS:
                     UpperCamelCase    :	Optional[Any]   	=		self.config_name.lower()

              elif self.config_name.upper() in CHECKPOINT_URLS:
                     UpperCamelCase    :	Optional[int]   	=		self.config_name.upper()

              else:
                     raise KeyError(
                         f"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""						)

              # download the model checkpoint specified by self.config_name and set up the scorer
              UpperCamelCase    :	Union[str, Any]   	=		dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]						)
              UpperCamelCase    :	Optional[Any]   	=		score.BleurtScorer(os.path.join(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		self.scorer.score(references=__SCREAMING_SNAKE_CASE ,					candidates=__SCREAMING_SNAKE_CASE						)
              return {"scores": scores}





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


__UpperCAmelCase : Optional[Any] 			=				logging.get_logger(__name__)

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



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[int]							=					"conditional_detr"
       __UpperCamelCase      :     Optional[Any]							=					["past_key_values"]
       __UpperCamelCase      :     Union[str, Any]							=					{
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=300 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="relu" ,					__SCREAMING_SNAKE_CASE=256 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1.0 ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE="sine" ,					__SCREAMING_SNAKE_CASE="resnet50" ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=0.25 ,					**__SCREAMING_SNAKE_CASE ,					):




              """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.'''						)
                            UpperCamelCase    :	str   	=		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']						)
                     elif isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                            UpperCamelCase    :	Tuple   	=		backbone_config.get('''model_type'''						)
                            UpperCamelCase    :	Optional[Any]   	=		CONFIG_MAPPING[backbone_model_type]
                            UpperCamelCase    :	Any   	=		config_class.from_dict(__SCREAMING_SNAKE_CASE						)

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






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.encoder_attention_heads






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.d_model






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		copy.deepcopy(self.__dict__						)
              if self.backbone_config is not None:
                     UpperCamelCase    :	List[Any]   	=		self.backbone_config.to_dict()
              UpperCamelCase    :	List[Any]   	=		self.__class__.model_type
              return output




class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Dict							=					version.parse("1.11")






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




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






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 1e-5






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 12

