



from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case		:str   	=  logging.get_logger(__name__)

__snake_case		:Tuple   	=  {
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					Union[str, Any]		      =		'''efficientnet'''



       def __init__(	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  int = 3  ,	__SCREAMING_SNAKE_CASE							:  int = 600  ,	__SCREAMING_SNAKE_CASE							:  float = 2.0  ,	__SCREAMING_SNAKE_CASE							:  float = 3.1  ,	__SCREAMING_SNAKE_CASE							:  int = 8  ,	__SCREAMING_SNAKE_CASE							:  List[int] = [3, 3, 5, 3, 5, 5, 3]  ,	__SCREAMING_SNAKE_CASE							:  List[int] = [32, 16, 24, 40, 80, 112, 192]  ,	__SCREAMING_SNAKE_CASE							:  List[int] = [16, 24, 40, 80, 112, 192, 320]  ,	__SCREAMING_SNAKE_CASE							:  List[int] = []  ,	__SCREAMING_SNAKE_CASE							:  List[int] = [1, 2, 2, 2, 1, 2, 1]  ,	__SCREAMING_SNAKE_CASE							:  List[int] = [1, 2, 2, 3, 3, 4, 1]  ,	__SCREAMING_SNAKE_CASE							:  List[int] = [1, 6, 6, 6, 6, 6, 6]  ,	__SCREAMING_SNAKE_CASE							:  float = 0.25  ,	__SCREAMING_SNAKE_CASE							:  str = "swish"  ,	__SCREAMING_SNAKE_CASE							:  int = 2_560  ,	__SCREAMING_SNAKE_CASE							:  str = "mean"  ,	__SCREAMING_SNAKE_CASE							:  float = 0.02  ,	__SCREAMING_SNAKE_CASE							:  float = 0.0_01  ,	__SCREAMING_SNAKE_CASE							:  float = 0.99  ,	__SCREAMING_SNAKE_CASE							:  float = 0.5  ,	__SCREAMING_SNAKE_CASE							:  float = 0.2  ,	**__SCREAMING_SNAKE_CASE							:  Any  ,	):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)

           __a		 =       num_channels
           __a		 =       image_size
           __a		 =       width_coefficient
           __a		 =       depth_coefficient
           __a		 =       depth_divisor
           __a		 =       kernel_sizes
           __a		 =       in_channels
           __a		 =       out_channels
           __a		 =       depthwise_padding
           __a		 =       strides
           __a		 =       num_block_repeats
           __a		 =       expand_ratios
           __a		 =       squeeze_expansion_ratio
           __a		 =       hidden_act
           __a		 =       hidden_dim
           __a		 =       pooling_type
           __a		 =       initializer_range
           __a		 =       batch_norm_eps
           __a		 =       batch_norm_momentum
           __a		 =       dropout_rate
           __a		 =       drop_connect_rate
           __a		 =       sum(__SCREAMING_SNAKE_CASE) * 4


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



       @property
       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

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





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


           '''simple docstring'''

           return 1E-5










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)





