



"""simple docstring"""

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class SCREAMING_SNAKE_CASE__       (		UpperCamelCase__   ):
						_lowerCAmelCase    						=       4_2







class SCREAMING_SNAKE_CASE__       (		UpperCamelCase__  ,       UpperCamelCase__   ):



						@register_to_config
						def __init__(self    ,  _lowercase = 3    ,  _lowercase = 3    ,  _lowercase = ("DownEncoderBlock2D",)    ,  _lowercase = ("UpDecoderBlock2D",)    ,  _lowercase = (64,)    ,  _lowercase = 1    ,  _lowercase = "silu"    ,  _lowercase = 3    ,  _lowercase = 32    ,  _lowercase = 256    ,  _lowercase = 32    ,  _lowercase = None    ,  _lowercase = 0.1_8215    ,  _lowercase = "group"    ,  ):





													'''simple docstring'''

													super().__init__()

													# pass init params to Encoder
													__a		:		Optional[int]						=							Encoder(
													    in_channels=__A    ,  out_channels=__A    ,  down_block_types=__A    ,  block_out_channels=__A    ,  layers_per_block=__A    ,  act_fn=__A    ,  norm_num_groups=__A    ,  double_z=__A    ,  )

													__a		:		List[Any]						=							vq_embed_dim if vq_embed_dim is not None else latent_channels

													__a		:		int						=							nn.Convad(__A    ,  __A    ,  1				)
													__a		:		Optional[int]						=							VectorQuantizer(__A    ,  __A    ,  beta=0.25    ,  remap=__A    ,  sane_index_shape=__A				)
													__a		:		Union[str, Any]						=							nn.Convad(__A    ,  __A    ,  1				)

													# pass init params to Decoder
													__a		:		int						=							Decoder(
													    in_channels=__A    ,  out_channels=__A    ,  up_block_types=__A    ,  block_out_channels=__A    ,  layers_per_block=__A    ,  act_fn=__A    ,  norm_num_groups=__A    ,  norm_type=__A    ,  )



						@apply_forward_hook
						def      lowerCAmelCase__(self    ,  _lowercase    ,  _lowercase = True				):





													'''simple docstring'''

													__a		:		Dict						=							self.encoder(__A				)
													__a		:		Any						=							self.quant_conv(__A				)

													if not return_dict:
																				return (h,)

													return VQEncoderOutput(latents=__A				)



						@apply_forward_hook
						def      lowerCAmelCase__(self    ,  _lowercase    ,  _lowercase = False    ,  _lowercase = True				):





													'''simple docstring'''

													if not force_not_quantize:
																				__a				,  __a				,  __a		:		Union[str, Any]						=							self.quantize(__A				)
													else:
																				__a		:		Union[str, Any]						=							h
													__a		:		Tuple						=							self.post_quant_conv(__A				)
													__a		:		List[Any]						=							self.decoder(__A    ,  quant if self.config.norm_type == """spatial""" else None				)

													if not return_dict:
																				return (dec,)

													return DecoderOutput(sample=__A				)





						def      lowerCAmelCase__(self    ,  _lowercase    ,  _lowercase = True				):





													'''simple docstring'''

													__a		:		Dict						=							sample
													__a		:		Any						=							self.encode(__A				).latents
													__a		:		Union[str, Any]						=							self.decode(__A				).sample

													if not return_dict:
																				return (dec,)

													return DecoderOutput(sample=__A				)








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)