'''simple docstring'''





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:		Optional[int]			=     logging.get_logger(__name__)

lowerCAmelCase_	:		Union[str, Any]			=     {
    """asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''sew'''





				def __init__(						self					:						Optional[Any]       ,		lowercase__					:						str=32       ,		lowercase__					:						Dict=768       ,		lowercase__					:						Optional[int]=12       ,		lowercase__					:						str=12       ,		lowercase__					:						List[Any]=3_072       ,		lowercase__					:						Tuple=2       ,		lowercase__					:						Dict="gelu"       ,		lowercase__					:						int=0.1       ,		lowercase__					:						str=0.1       ,		lowercase__					:						List[str]=0.1       ,		lowercase__					:						Dict=0.0       ,		lowercase__					:						Any=0.1       ,		lowercase__					:						Optional[int]=0.1       ,		lowercase__					:						str=0.0_2       ,		lowercase__					:						Optional[Any]=1e-5       ,		lowercase__					:						Union[str, Any]="group"       ,		lowercase__					:						Optional[int]="gelu"       ,		lowercase__					:						Optional[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,		lowercase__					:						Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,		lowercase__					:						Tuple=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,		lowercase__					:						Dict=False       ,		lowercase__					:						Any=128       ,		lowercase__					:						str=16       ,		lowercase__					:						Union[str, Any]=True       ,		lowercase__					:						Dict=0.0_5       ,		lowercase__					:						Any=10       ,		lowercase__					:						List[Any]=2       ,		lowercase__					:						Tuple=0.0       ,		lowercase__					:						Dict=10       ,		lowercase__					:						Optional[Any]=0       ,		lowercase__					:						Union[str, Any]="mean"       ,		lowercase__					:						int=False       ,		lowercase__					:						List[Any]=False       ,		lowercase__					:						List[str]=256       ,		lowercase__					:						Optional[int]=0       ,		lowercase__					:						str=1       ,		lowercase__					:						Optional[Any]=2       ,		**lowercase__					:						Optional[int]       ,		)							->int:


										'''simple docstring'''




										super().__init__(**lowercase__       ,		pad_token_id=lowercase__       ,		bos_token_id=lowercase__       ,		eos_token_id=lowercase__					)
										_UpperCamelCase			:			List[str]   			=						hidden_size
										_UpperCamelCase			:			Optional[int]   			=						feat_extract_norm
										_UpperCamelCase			:			Any   			=						feat_extract_activation
										_UpperCamelCase			:			Dict   			=						list(lowercase__					)
										_UpperCamelCase			:			str   			=						list(lowercase__					)
										_UpperCamelCase			:			Tuple   			=						list(lowercase__					)
										_UpperCamelCase			:			Tuple   			=						conv_bias
										_UpperCamelCase			:			Dict   			=						num_conv_pos_embeddings
										_UpperCamelCase			:			List[str]   			=						num_conv_pos_embedding_groups
										_UpperCamelCase			:			Any   			=						len(self.conv_dim					)
										_UpperCamelCase			:			Optional[int]   			=						num_hidden_layers
										_UpperCamelCase			:			Optional[int]   			=						intermediate_size
										_UpperCamelCase			:			str   			=						squeeze_factor
										_UpperCamelCase			:			Optional[Any]   			=						hidden_act
										_UpperCamelCase			:			List[str]   			=						num_attention_heads
										_UpperCamelCase			:			Optional[Any]   			=						hidden_dropout
										_UpperCamelCase			:			Union[str, Any]   			=						attention_dropout
										_UpperCamelCase			:			List[Any]   			=						activation_dropout
										_UpperCamelCase			:			str   			=						feat_proj_dropout
										_UpperCamelCase			:			Optional[int]   			=						final_dropout
										_UpperCamelCase			:			List[Any]   			=						layerdrop
										_UpperCamelCase			:			Dict   			=						layer_norm_eps
										_UpperCamelCase			:			Any   			=						initializer_range
										_UpperCamelCase			:			Optional[Any]   			=						vocab_size

										if (
										    (len(self.conv_stride					) != self.num_feat_extract_layers)
										    or (len(self.conv_kernel					) != self.num_feat_extract_layers)
										    or (len(self.conv_dim					) != self.num_feat_extract_layers)
										):
																raise ValueError(
																    "Configuration for convolutional layers is incorrect."
																    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
																    f'''but is `len(config.conv_dim) = {len(self.conv_dim					)}`, `len(config.conv_stride)'''
																    f'''= {len(self.conv_stride					)}`, `len(config.conv_kernel) = {len(self.conv_kernel					)}`.'''					)

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										_UpperCamelCase			:			Optional[Any]   			=						apply_spec_augment
										_UpperCamelCase			:			str   			=						mask_time_prob
										_UpperCamelCase			:			Optional[Any]   			=						mask_time_length
										_UpperCamelCase			:			Dict   			=						mask_time_min_masks
										_UpperCamelCase			:			Optional[Any]   			=						mask_feature_prob
										_UpperCamelCase			:			Any   			=						mask_feature_length
										_UpperCamelCase			:			Tuple   			=						mask_feature_min_masks

										# ctc loss
										_UpperCamelCase			:			Union[str, Any]   			=						ctc_loss_reduction
										_UpperCamelCase			:			Any   			=						ctc_zero_infinity

										# sequence classification
										_UpperCamelCase			:			Any   			=						use_weighted_layer_sum
										_UpperCamelCase			:			Tuple   			=						classifier_proj_size







				@property
				def  snake_case__				(						self					:						List[str]					)							->Dict:


										'''simple docstring'''




										return functools.reduce(operator.mul       ,		self.conv_stride       ,		1					)



'''simple docstring'''





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_	:		Optional[int]			=     logging.getLogger(__name__)

if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[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)
			lowerCAmelCase_	:		str			=     parser.parse_args()

			logger.info(f"""Loading data from {args.data_file}""")
			with open(args.data_file, """rb""") as fp:
						lowerCAmelCase_	:		Tuple			=     pickle.load(fp)

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


