


'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A   :  int       		=				logging.get_logger(__name__)

A   :  Optional[Any]       		=				{
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
   import detectrona


class 				__lowerCamelCase				( a_ ):
    """simple docstring"""
    a									=					"layoutlmv2"


    def __init__(						self    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							List[Any]=30522   ,							SCREAMING_SNAKE_CASE    :							str=768   ,							SCREAMING_SNAKE_CASE    :							List[Any]=12   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=12   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=3072   ,							SCREAMING_SNAKE_CASE    :							Optional[int]="gelu"   ,							SCREAMING_SNAKE_CASE    :							Any=0.1   ,							SCREAMING_SNAKE_CASE    :							int=0.1   ,							SCREAMING_SNAKE_CASE    :							Dict=512   ,							SCREAMING_SNAKE_CASE    :							Dict=2   ,							SCREAMING_SNAKE_CASE    :							Any=0.02   ,							SCREAMING_SNAKE_CASE    :							List[Any]=1e-12   ,							SCREAMING_SNAKE_CASE    :							str=0   ,							SCREAMING_SNAKE_CASE    :							int=1024   ,							SCREAMING_SNAKE_CASE    :							int=128   ,							SCREAMING_SNAKE_CASE    :							List[str]=32   ,							SCREAMING_SNAKE_CASE    :							List[str]=True   ,							SCREAMING_SNAKE_CASE    :							int=256   ,							SCREAMING_SNAKE_CASE    :							List[str]=64   ,							SCREAMING_SNAKE_CASE    :							Any=True   ,							SCREAMING_SNAKE_CASE    :							Any=[7, 7, 256]   ,							SCREAMING_SNAKE_CASE    :							List[str]=128   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=128   ,							SCREAMING_SNAKE_CASE    :							Tuple=True   ,							SCREAMING_SNAKE_CASE    :							int=True   ,							SCREAMING_SNAKE_CASE    :							List[Any]=False   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=None   ,							**SCREAMING_SNAKE_CASE    :							str   ,							):
           super().__init__(
               vocab_size=SCREAMING_SNAKE_CASE   ,							hidden_size=SCREAMING_SNAKE_CASE   ,							num_hidden_layers=SCREAMING_SNAKE_CASE   ,							num_attention_heads=SCREAMING_SNAKE_CASE   ,							intermediate_size=SCREAMING_SNAKE_CASE   ,							hidden_act=SCREAMING_SNAKE_CASE   ,							hidden_dropout_prob=SCREAMING_SNAKE_CASE   ,							attention_probs_dropout_prob=SCREAMING_SNAKE_CASE   ,							max_position_embeddings=SCREAMING_SNAKE_CASE   ,							type_vocab_size=SCREAMING_SNAKE_CASE   ,							initializer_range=SCREAMING_SNAKE_CASE   ,							layer_norm_eps=SCREAMING_SNAKE_CASE   ,							pad_token_id=SCREAMING_SNAKE_CASE   ,							**SCREAMING_SNAKE_CASE   ,							)
           _A  :			Tuple											=      max_ad_position_embeddings
           _A  :			Optional[Any]											=      max_rel_pos
           _A  :			List[str]											=      rel_pos_bins
           _A  :			Tuple											=      fast_qkv
           _A  :			Tuple											=      max_rel_ad_pos
           _A  :			Optional[Any]											=      rel_ad_pos_bins
           _A  :			Optional[Any]											=      convert_sync_batchnorm
           _A  :			List[Any]											=      image_feature_pool_shape
           _A  :			List[str]											=      coordinate_size
           _A  :			int											=      shape_size
           _A  :			int											=      has_relative_attention_bias
           _A  :			Dict											=      has_spatial_attention_bias
           _A  :			List[str]											=      has_visual_segment_embedding
           _A  :			Union[str, Any]											=      (
               detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
           )


    @classmethod
    def      A					(						self    :							List[Any]):
           return {
               "MODEL.MASK_ON": True,
               "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
               "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
               "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
               "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
               "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
               "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
               "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
               "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
               "MODEL.POST_NMS_TOPK_TEST": 1000,
               "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
               "MODEL.ROI_HEADS.NUM_CLASSES": 5,
               "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
               "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
               "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
               "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
               "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
               "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
               "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
               "MODEL.RESNETS.DEPTH": 101,
               "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
               "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
               "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
               "MODEL.RESNETS.NUM_GROUPS": 32,
               "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
               "MODEL.RESNETS.STRIDE_IN_1X1": False,
           }


    def      A					(						self    :							Tuple):
           _A  :			List[Any]											=      detectrona.config.get_cfg()
           for k, v in self.detectrona_config_args.items():
                  _A  :			str											=      k.split('.')
                  _A  :			Dict											=      detectrona_config
                  for attribute in attributes[:-1]:
                         _A  :			Tuple											=      getattr(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                  setattr(SCREAMING_SNAKE_CASE   ,							attributes[-1]   ,							SCREAMING_SNAKE_CASE)

           return detectrona_config










'''simple docstring'''







from unittest.mock import patch

import datasets
from datasets import Dataset






def    lowerCAmelCase__ (       ):
       _A  :			Optional[int]											=      Dataset.from_dict({'col_1': [3, 2, 0, 1]}     )

       with patch('tqdm.auto.tqdm'     ) as mock_tqdm:
              datasets.disable_progress_bar()
              dset.map(lambda lowerCamelCase     : {"col_2": x["col_1"] + 1}     )
              mock_tqdm.assert_not_called()

              mock_tqdm.reset_mock()

              datasets.enable_progress_bar()
              dset.map(lambda lowerCamelCase     : {"col_2": x["col_1"] + 1}     )
              mock_tqdm.assert_called()






