



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


a_       :int				  =				logging.get_logger(__name__)

a_       :Union[str, 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  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     """layoutlmv2"""






     def __init__(	self  :  int,			_snake_case  :  Optional[Any]=3_0_5_2_2,			_snake_case  :  Union[str, Any]=7_6_8,			_snake_case  :  str=1_2,			_snake_case  :  Dict=1_2,			_snake_case  :  int=3_0_7_2,			_snake_case  :  List[str]="gelu",			_snake_case  :  Union[str, Any]=0.1,			_snake_case  :  List[str]=0.1,			_snake_case  :  Union[str, Any]=5_1_2,			_snake_case  :  str=2,			_snake_case  :  Any=0.0_2,			_snake_case  :  Optional[Any]=1e-12,			_snake_case  :  Optional[Any]=0,			_snake_case  :  str=1_0_2_4,			_snake_case  :  Union[str, Any]=1_2_8,			_snake_case  :  int=3_2,			_snake_case  :  int=True,			_snake_case  :  Optional[Any]=2_5_6,			_snake_case  :  List[Any]=6_4,			_snake_case  :  int=True,			_snake_case  :  Any=[7, 7, 2_5_6],			_snake_case  :  Union[str, Any]=1_2_8,			_snake_case  :  Dict=1_2_8,			_snake_case  :  Union[str, Any]=True,			_snake_case  :  List[str]=True,			_snake_case  :  Union[str, Any]=False,			_snake_case  :  Optional[int]=None,			**_snake_case  :  Optional[int],			)       ->List[Any]:
            super().__init__(
                vocab_size=_snake_case,			hidden_size=_snake_case,			num_hidden_layers=_snake_case,			num_attention_heads=_snake_case,			intermediate_size=_snake_case,			hidden_act=_snake_case,			hidden_dropout_prob=_snake_case,			attention_probs_dropout_prob=_snake_case,			max_position_embeddings=_snake_case,			type_vocab_size=_snake_case,			initializer_range=_snake_case,			layer_norm_eps=_snake_case,			pad_token_id=_snake_case,			**_snake_case,			)
            snake_case__							:     Any            =     max_ad_position_embeddings
            snake_case__							:     Union[str, Any]            =     max_rel_pos
            snake_case__							:     Optional[int]            =     rel_pos_bins
            snake_case__							:     int            =     fast_qkv
            snake_case__							:     str            =     max_rel_ad_pos
            snake_case__							:     List[Any]            =     rel_ad_pos_bins
            snake_case__							:     Union[str, Any]            =     convert_sync_batchnorm
            snake_case__							:     Any            =     image_feature_pool_shape
            snake_case__							:     Optional[int]            =     coordinate_size
            snake_case__							:     Union[str, Any]            =     shape_size
            snake_case__							:     Tuple            =     has_relative_attention_bias
            snake_case__							:     Optional[Any]            =     has_spatial_attention_bias
            snake_case__							:     Any            =     has_visual_segment_embedding
            snake_case__							:     List[Any]            =     (
                detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
            )






     @classmethod
     def        lowercase_       (	self  :  Any )       ->Any:
            return {
                "MODEL.MASK_ON": True,
                "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
                "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
                "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
                "MODEL.ANCHOR_GENERATOR.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
                "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
                "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_0_0_0,
                "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_0_0_0,
                "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_0_0_0,
                "MODEL.POST_NMS_TOPK_TEST": 1_0_0_0,
                "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": 1_4,
                "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
                "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
                "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
                "MODEL.RESNETS.DEPTH": 1_0_1,
                "MODEL.RESNETS.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
                "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
                "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
                "MODEL.RESNETS.NUM_GROUPS": 3_2,
                "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
                "MODEL.RESNETS.STRIDE_IN_1X1": False,
            }






     def        lowercase_       (	self  :  List[str] )       ->Any:
            snake_case__							:     Any            =     detectrona.config.get_cfg()
            for k, v in self.detectrona_config_args.items():
                   snake_case__							:     Any            =     k.split('.' )
                   snake_case__							:     int            =     detectrona_config
                   for attribute in attributes[:-1]:
                          snake_case__							:     Optional[int]            =     getattr(_snake_case,			_snake_case )
                   setattr(_snake_case,			attributes[-1],			_snake_case )

            return detectrona_config











import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class  snake_case__						(	lowerCAmelCase_    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     BertweetTokenizer
     _SCREAMING_SNAKE_CASE            =     False






     def        lowercase_       (	self  :  List[str] )       ->Union[str, Any]:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            snake_case__							:     List[Any]            =     ['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
            snake_case__							:     Tuple            =     dict(zip(_snake_case,			range(len(_snake_case ) ) ) )
            snake_case__							:     int            =     ['#version: 0.2', 'a m</w>']
            snake_case__							:     Dict            =     {'unk_token': '<unk>'}

            snake_case__							:     Tuple            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['vocab_file'] )
            snake_case__							:     Tuple            =     os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES['merges_file'] )
            with open(self.vocab_file,			'w',			encoding='utf-8' ) as fp:
                   for token in vocab_tokens:
                          fp.write(F'''{token} {vocab_tokens[token]}\n''' )
            with open(self.merges_file,			'w',			encoding='utf-8' ) as fp:
                   fp.write('\n'.join(_snake_case ) )






     def        lowercase_       (	self  :  List[Any],			**_snake_case  :  List[Any] )       ->Optional[Any]:
            kwargs.update(self.special_tokens_map )
            return BertweetTokenizer.from_pretrained(self.tmpdirname,			**_snake_case )






     def        lowercase_       (	self  :  Optional[Any],			_snake_case  :  Tuple )       ->Union[str, Any]:
            snake_case__							:     List[str]            =     'I am VinAI Research'
            snake_case__							:     str            =     'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
            return input_text, output_text






     def        lowercase_       (	self  :  str )       ->int:
            snake_case__							:     Dict            =     BertweetTokenizer(self.vocab_file,			self.merges_file,			**self.special_tokens_map )
            snake_case__							:     List[str]            =     'I am VinAI Research'
            snake_case__							:     str            =     'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
            snake_case__							:     Optional[int]            =     tokenizer.tokenize(_snake_case )
            self.assertListEqual(_snake_case,			_snake_case )

            snake_case__							:     Tuple            =     tokens + [tokenizer.unk_token]

            snake_case__							:     Dict            =     [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ),			_snake_case )






