





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

if __name__ == "__main__":
							_UpperCamelCase					      =   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)
							_UpperCamelCase					      =   parser.parse_args()

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

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











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 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       42




class 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__     ):


							@register_to_config
							def __init__(				self     :Union[str, Any]				,					__A     :int = 3				,					__A     :int = 3				,					__A     :Tuple[str] = ("DownEncoderBlock2D",)				,					__A     :Tuple[str] = ("UpDecoderBlock2D",)				,					__A     :Tuple[int] = (64,)				,					__A     :int = 1				,					__A     :str = "silu"				,					__A     :int = 3				,					__A     :int = 32				,					__A     :int = 256				,					__A     :int = 32				,					__A     :Optional[int] = None				,					__A     :float = 0.1_8_2_1_5				,					__A     :str = "group"				,					) -> Any:



									"""simple docstring"""



									super().__init__()

									# pass init params to Encoder
									SCREAMING_SNAKE_CASE__								=   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				,					)

									SCREAMING_SNAKE_CASE__								=   vq_embed_dim if vq_embed_dim is not None else latent_channels

									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)
									SCREAMING_SNAKE_CASE__								=   VectorQuantizer(__A				,					__A				,					beta=0.2_5				,					remap=__A				,					sane_index_shape=__A		)
									SCREAMING_SNAKE_CASE__								=   nn.Convad(__A				,					__A				,					1		)

									# pass init params to Decoder
									SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :Union[str, Any]				,					__A     :torch.FloatTensor				,					__A     :bool = True		) -> VQEncoderOutput:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.encoder(__A		)
									SCREAMING_SNAKE_CASE__								=   self.quant_conv(__A		)

									if not return_dict:
											return (h,)

									return VQEncoderOutput(latents=__A		)


							@apply_forward_hook
							def   _snake_case	(				self     :Tuple				,					__A     :torch.FloatTensor				,					__A     :bool = False				,					__A     :bool = True		) -> Union[DecoderOutput, torch.FloatTensor]:



									"""simple docstring"""



									if not force_not_quantize:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.quantize(__A		)
									else:
											SCREAMING_SNAKE_CASE__								=   h
									SCREAMING_SNAKE_CASE__								=   self.post_quant_conv(__A		)
									SCREAMING_SNAKE_CASE__								=   self.decoder(__A				,					quant if self.config.norm_type == """spatial""" else None		)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=__A		)


							def   _snake_case	(				self     :int				,					__A     :torch.FloatTensor				,					__A     :bool = True		) -> Union[DecoderOutput, torch.FloatTensor]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   sample
									SCREAMING_SNAKE_CASE__								=   self.encode(__A		).latents
									SCREAMING_SNAKE_CASE__								=   self.decode(__A		).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=__A		)