





import warnings
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


_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      {
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "segformer"


							def __init__(				self     :Optional[int]				,					__A     :Optional[Any]=3				,					__A     :Optional[Any]=4				,					__A     :int=[2, 2, 2, 2]				,					__A     :int=[8, 4, 2, 1]				,					__A     :str=[32, 64, 160, 256]				,					__A     :Optional[int]=[7, 3, 3, 3]				,					__A     :Optional[int]=[4, 2, 2, 2]				,					__A     :int=[1, 2, 5, 8]				,					__A     :List[str]=[4, 4, 4, 4]				,					__A     :Union[str, Any]="gelu"				,					__A     :Tuple=0.0				,					__A     :int=0.0				,					__A     :Optional[int]=0.1				,					__A     :Any=0.0_2				,					__A     :Union[str, Any]=0.1				,					__A     :Dict=1E-6				,					__A     :Optional[int]=256				,					__A     :Dict=255				,					**__A     :Any				,					) -> Dict:



									"""simple docstring"""



									super().__init__(**__A		)

									if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
											warnings.warn(
											    """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
											    """ removed, as the behaviour will default to that of reshape_last_stage = True."""				,					__A				,					)

									SCREAMING_SNAKE_CASE__								=   num_channels
									SCREAMING_SNAKE_CASE__								=   num_encoder_blocks
									SCREAMING_SNAKE_CASE__								=   depths
									SCREAMING_SNAKE_CASE__								=   sr_ratios
									SCREAMING_SNAKE_CASE__								=   hidden_sizes
									SCREAMING_SNAKE_CASE__								=   patch_sizes
									SCREAMING_SNAKE_CASE__								=   strides
									SCREAMING_SNAKE_CASE__								=   mlp_ratios
									SCREAMING_SNAKE_CASE__								=   num_attention_heads
									SCREAMING_SNAKE_CASE__								=   hidden_act
									SCREAMING_SNAKE_CASE__								=   hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								=   attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								=   classifier_dropout_prob
									SCREAMING_SNAKE_CASE__								=   initializer_range
									SCREAMING_SNAKE_CASE__								=   drop_path_rate
									SCREAMING_SNAKE_CASE__								=   layer_norm_eps
									SCREAMING_SNAKE_CASE__								=   decoder_hidden_size
									SCREAMING_SNAKE_CASE__								=   kwargs.get("""reshape_last_stage"""				,					__A		)
									SCREAMING_SNAKE_CASE__								=   semantic_loss_ignore_index




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       version.parse("1.11"     )


							@property
							def   _snake_case	(				self     :int		) -> Mapping[str, Mapping[int, str]]:



									"""simple docstring"""



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


							@property
							def   _snake_case	(				self     :Union[str, Any]		) -> float:



									"""simple docstring"""



									return 1E-4


							@property
							def   _snake_case	(				self     :List[str]		) -> int:



									"""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
)
_lowerCamelCase 					=      logging.getLogger(__name__)

if __name__ == "__main__":
	_lowerCamelCase 					=      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=30522, type=int)
	_lowerCamelCase 					=      parser.parse_args()

	logger.info(F'''Loading data from {args.data_file}''')
	with open(args.data_file, 'rb') as fp:
		_lowerCamelCase 					=      pickle.load(fp)

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