






'''simple docstring'''

import copy

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


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

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






class     a_						(      lowerCamelCase    ):
  lowercase							=				"""deta"""
  lowercase							=				{
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=900      ,		_SCREAMING_SNAKE_CASE=2048      ,		_SCREAMING_SNAKE_CASE=6      ,		_SCREAMING_SNAKE_CASE=2048      ,		_SCREAMING_SNAKE_CASE=8      ,		_SCREAMING_SNAKE_CASE=6      ,		_SCREAMING_SNAKE_CASE=1024      ,		_SCREAMING_SNAKE_CASE=8      ,		_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=0.0_2      ,		_SCREAMING_SNAKE_CASE=1.0      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE="sine"      ,		_SCREAMING_SNAKE_CASE=5      ,		_SCREAMING_SNAKE_CASE=4      ,		_SCREAMING_SNAKE_CASE=4      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=300      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=5      ,		_SCREAMING_SNAKE_CASE=2      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=1      ,		_SCREAMING_SNAKE_CASE=5      ,		_SCREAMING_SNAKE_CASE=2      ,		_SCREAMING_SNAKE_CASE=0.1      ,		_SCREAMING_SNAKE_CASE=0.2_5      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Optional[int]:



        """simple docstring"""






        if backbone_config is None:
              logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""				)
              UpperCamelCase      = CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]				)
        else:
              if isinstance(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
                    UpperCamelCase      = backbone_config.pop("""model_type"""				)
                    UpperCamelCase      = CONFIG_MAPPING[backbone_model_type]
                    UpperCamelCase      = config_class.from_dict(_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = backbone_config
        UpperCamelCase      = num_queries
        UpperCamelCase      = max_position_embeddings
        UpperCamelCase      = d_model
        UpperCamelCase      = encoder_ffn_dim
        UpperCamelCase      = encoder_layers
        UpperCamelCase      = encoder_attention_heads
        UpperCamelCase      = decoder_ffn_dim
        UpperCamelCase      = decoder_layers
        UpperCamelCase      = decoder_attention_heads
        UpperCamelCase      = dropout
        UpperCamelCase      = attention_dropout
        UpperCamelCase      = activation_dropout
        UpperCamelCase      = activation_function
        UpperCamelCase      = init_std
        UpperCamelCase      = init_xavier_std
        UpperCamelCase      = encoder_layerdrop
        UpperCamelCase      = auxiliary_loss
        UpperCamelCase      = position_embedding_type
        # deformable attributes
        UpperCamelCase      = num_feature_levels
        UpperCamelCase      = encoder_n_points
        UpperCamelCase      = decoder_n_points
        UpperCamelCase      = two_stage
        UpperCamelCase      = two_stage_num_proposals
        UpperCamelCase      = with_box_refine
        UpperCamelCase      = 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      = class_cost
        UpperCamelCase      = bbox_cost
        UpperCamelCase      = giou_cost
        # Loss coefficients
        UpperCamelCase      = mask_loss_coefficient
        UpperCamelCase      = dice_loss_coefficient
        UpperCamelCase      = bbox_loss_coefficient
        UpperCamelCase      = giou_loss_coefficient
        UpperCamelCase      = eos_coefficient
        UpperCamelCase      = focal_alpha
        super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  @property
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        return self.encoder_attention_heads

  @property
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        return self.d_model


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



        """simple docstring"""






        UpperCamelCase      = copy.deepcopy(self.__dict__				)
        UpperCamelCase      = self.backbone_config.to_dict()
        UpperCamelCase      = self.__class__.model_type
        return output








'''simple docstring'''

import re
import string

import numpy as np

import datasets


SCREAMING_SNAKE_CASE__     =       '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

SCREAMING_SNAKE_CASE__     =       '\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

SCREAMING_SNAKE_CASE__     =       '\n'






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

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return datasets.MetricInfo(
            description=_DESCRIPTION      ,		citation=_CITATION      ,		inputs_description=_KWARGS_DESCRIPTION      ,		features=datasets.Features(
                {
                    """predictions""": datasets.Value("""string"""      ,		id="""sequence"""				),
                    """references""": datasets.Value("""string"""      ,		id="""sequence"""				),
                }				)      ,		reference_urls=[]      ,		)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		)  ->				List[Any]:



        """simple docstring"""






        if regexes_to_ignore is not None:
              for s in regexes_to_ignore:
                    UpperCamelCase      = np.array([re.sub(_SCREAMING_SNAKE_CASE      ,		""""""      ,		_SCREAMING_SNAKE_CASE				) for x in predictions]				)
                    UpperCamelCase      = np.array([re.sub(_SCREAMING_SNAKE_CASE      ,		""""""      ,		_SCREAMING_SNAKE_CASE				) for x in references]				)
        else:
              UpperCamelCase      = np.asarray(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = np.asarray(_SCREAMING_SNAKE_CASE				)

        if ignore_case:
              UpperCamelCase      = np.char.lower(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = np.char.lower(_SCREAMING_SNAKE_CASE				)

        if ignore_punctuation:
              UpperCamelCase      = string.punctuation.maketrans(""""""      ,		""""""      ,		string.punctuation				)
              UpperCamelCase      = np.char.translate(_SCREAMING_SNAKE_CASE      ,		table=_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = np.char.translate(_SCREAMING_SNAKE_CASE      ,		table=_SCREAMING_SNAKE_CASE				)

        if ignore_numbers:
              UpperCamelCase      = string.digits.maketrans(""""""      ,		""""""      ,		string.digits				)
              UpperCamelCase      = np.char.translate(_SCREAMING_SNAKE_CASE      ,		table=_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = np.char.translate(_SCREAMING_SNAKE_CASE      ,		table=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = predictions == references

        return {"exact_match": np.mean(_SCREAMING_SNAKE_CASE				) * 100}
