



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


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

__snake_case		:Tuple   	=  {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}

class 					_A (			_UpperCamelCase					):
							UpperCamelCase__						:					str		      =		'''mobilenet_v2'''



							def __init__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=3  ,	__SCREAMING_SNAKE_CASE							:  str=224  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=1.0  ,	__SCREAMING_SNAKE_CASE							:  Any=8  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=8  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=6  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=32  ,	__SCREAMING_SNAKE_CASE							:  Tuple=True  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=True  ,	__SCREAMING_SNAKE_CASE							:  Any="relu6"  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Tuple=0.8  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=0.02  ,	__SCREAMING_SNAKE_CASE							:  int=0.0_01  ,	__SCREAMING_SNAKE_CASE							:  Tuple=255  ,	**__SCREAMING_SNAKE_CASE							:  Tuple  ,	):


											'''simple docstring'''

											super().__init__(**_UpperCAmelCase)

											if depth_multiplier <= 0:
															raise ValueError('''depth_multiplier must be greater than zero.''')

											__a		 =       num_channels
											__a		 =       image_size
											__a		 =       depth_multiplier
											__a		 =       depth_divisible_by
											__a		 =       min_depth
											__a		 =       expand_ratio
											__a		 =       output_stride
											__a		 =       first_layer_is_expansion
											__a		 =       finegrained_output
											__a		 =       hidden_act
											__a		 =       tf_padding
											__a		 =       classifier_dropout_prob
											__a		 =       initializer_range
											__a		 =       layer_norm_eps
											__a		 =       semantic_loss_ignore_index


class 					_A (			_UpperCamelCase					):
							UpperCamelCase__						:					List[Any]		      =		version.parse('''1.11'''					)



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


											'''simple docstring'''

											return OrderedDict([('''pixel_values''', {0: '''batch'''})])



							@property
							def 	_lowerCamelCase    (	self							:  Tuple):


											'''simple docstring'''

											if self.task == "image-classification":
															return OrderedDict([('''logits''', {0: '''batch'''})])
											else:
															return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])





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


											'''simple docstring'''

											return 1E-4










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)





