





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


snake_case_				=      logging.get_logger(__name__)

snake_case_				=      {
    '''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_		(      _A):
		lowerCamelCase     :Optional[Any]							=  "layoutlmv2"







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







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

				return detectrona_config








import sys

from transformers import AutoTokenizer


snake_case_				=      sys.argv[1]
snake_case_				=      sys.argv[2]
snake_case_				=      int(sys.argv[3])

snake_case_				=      0

snake_case_				=      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
				for line in f_p:
								snake_case_				=      line.rstrip()

								if not line:
												print(line)
												snake_case_				=      0
												continue

								snake_case_				=      line.split()[0]

								snake_case_				=      len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('''''')
												print(line)
												snake_case_				=      current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

