



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


__snake_case		:List[str]   	=  logging.get_logger(__name__)

__snake_case		:Dict   	=  {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}

class 					_A (			__UpperCAmelCase					):
							UpperCamelCase__						:					Optional[Any]		      =		'''conditional_detr'''
							UpperCamelCase__						:					str		      =		['''past_key_values''']
							UpperCamelCase__						:					Union[str, Any]		      =		{
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}



							def __init__(	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Any=None  ,	__SCREAMING_SNAKE_CASE							:  int=3  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=300  ,	__SCREAMING_SNAKE_CASE							:  Tuple=6  ,	__SCREAMING_SNAKE_CASE							:  Tuple=2_048  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=8  ,	__SCREAMING_SNAKE_CASE							:  int=6  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=2_048  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=8  ,	__SCREAMING_SNAKE_CASE							:  Tuple=0.0  ,	__SCREAMING_SNAKE_CASE							:  str=0.0  ,	__SCREAMING_SNAKE_CASE							:  str=True  ,	__SCREAMING_SNAKE_CASE							:  Any="relu"  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=256  ,	__SCREAMING_SNAKE_CASE							:  int=0.1  ,	__SCREAMING_SNAKE_CASE							:  str=0.0  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=0.0  ,	__SCREAMING_SNAKE_CASE							:  List[str]=0.02  ,	__SCREAMING_SNAKE_CASE							:  int=1.0  ,	__SCREAMING_SNAKE_CASE							:  List[str]=False  ,	__SCREAMING_SNAKE_CASE							:  Tuple="sine"  ,	__SCREAMING_SNAKE_CASE							:  Dict="resnet50"  ,	__SCREAMING_SNAKE_CASE							:  List[str]=True  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=False  ,	__SCREAMING_SNAKE_CASE							:  Any=2  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=5  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=2  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=1  ,	__SCREAMING_SNAKE_CASE							:  List[str]=1  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=2  ,	__SCREAMING_SNAKE_CASE							:  List[str]=5  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=2  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=0.25  ,	**__SCREAMING_SNAKE_CASE							:  int  ,	):


											'''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.''')
																			__a		 =       CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
															elif isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
																			__a		 =       backbone_config.get('''model_type''')
																			__a		 =       CONFIG_MAPPING[backbone_model_type]
																			__a		 =       config_class.from_dict(__SCREAMING_SNAKE_CASE)

											__a		 =       use_timm_backbone
											__a		 =       backbone_config
											__a		 =       num_channels
											__a		 =       num_queries
											__a		 =       d_model
											__a		 =       encoder_ffn_dim
											__a		 =       encoder_layers
											__a		 =       encoder_attention_heads
											__a		 =       decoder_ffn_dim
											__a		 =       decoder_layers
											__a		 =       decoder_attention_heads
											__a		 =       dropout
											__a		 =       attention_dropout
											__a		 =       activation_dropout
											__a		 =       activation_function
											__a		 =       init_std
											__a		 =       init_xavier_std
											__a		 =       encoder_layerdrop
											__a		 =       decoder_layerdrop
											__a		 =       encoder_layers
											__a		 =       auxiliary_loss
											__a		 =       position_embedding_type
											__a		 =       backbone
											__a		 =       use_pretrained_backbone
											__a		 =       dilation
											# Hungarian matcher
											__a		 =       class_cost
											__a		 =       bbox_cost
											__a		 =       giou_cost
											# Loss coefficients
											__a		 =       mask_loss_coefficient
											__a		 =       dice_loss_coefficient
											__a		 =       cls_loss_coefficient
											__a		 =       bbox_loss_coefficient
											__a		 =       giou_loss_coefficient
											__a		 =       focal_alpha
											super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE)



							@property
							def 	_lowerCamelCase    (	self							:  Any):


											'''simple docstring'''

											return self.encoder_attention_heads



							@property
							def 	_lowerCamelCase    (	self							:  Tuple):


											'''simple docstring'''

											return self.d_model





							def 	_lowerCamelCase    (	self							:  Optional[int]):


											'''simple docstring'''

											__a		 =       copy.deepcopy(self.__dict__)
											if self.backbone_config is not None:
															__a		 =       self.backbone_config.to_dict()
											__a		 =       self.__class__.model_type
											return output


class 					_A (			__UpperCAmelCase					):
							UpperCamelCase__						:					Any		      =		version.parse('''1.11'''					)



							@property
							def 	_lowerCamelCase    (	self							:  Optional[Any]):


											'''simple docstring'''

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



							@property
							def 	_lowerCamelCase    (	self							:  List[str]):


											'''simple docstring'''

											return 1E-5





							@property
							def 	_lowerCamelCase    (	self							:  Tuple):


											'''simple docstring'''

											return 12










import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__snake_case		:Tuple   	=  logging.getLogger(__name__)

if __name__ == "__main__":
 __snake_case		:Union[str, Any]   	=  argparse.ArgumentParser(
     description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
 )
 parser.add_argument(
     '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
 )
 parser.add_argument(
     '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
 )
 parser.add_argument('''--vocab_size''', default=3_0522, type=int)
 __snake_case		:List[str]   	=  parser.parse_args()

 logger.info(f'Loading data from {args.data_file}')
 with open(args.data_file, '''rb''') as fp:
  __snake_case		:Optional[Any]   	=  pickle.load(fp)

 logger.info('''Counting occurrences for MLM.''')
 __snake_case		:Dict   	=  Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 __snake_case		:Optional[Any]   	=  [0] * args.vocab_size
 for k, v in counter.items():
  __snake_case		:Any   	=  v

 logger.info(f'Dump to {args.token_counts_dump}')
 with open(args.token_counts_dump, '''wb''') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





