


"""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   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				torch.FloatTensor





class   _a      (      SCREAMING_SNAKE_CASE_		,		SCREAMING_SNAKE_CASE_ ):
    @register_to_config
    def __init__( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					int = 3							,					SCREAMING_SNAKE_CASE__							:					int = 3							,					SCREAMING_SNAKE_CASE__							:					Tuple[str] = ("DownEncoderBlock2D",)							,					SCREAMING_SNAKE_CASE__							:					Tuple[str] = ("UpDecoderBlock2D",)							,					SCREAMING_SNAKE_CASE__							:					Tuple[int] = (64,)							,					SCREAMING_SNAKE_CASE__							:					int = 1							,					SCREAMING_SNAKE_CASE__							:					str = "silu"							,					SCREAMING_SNAKE_CASE__							:					int = 3							,					SCREAMING_SNAKE_CASE__							:					int = 32							,					SCREAMING_SNAKE_CASE__							:					int = 2_56							,					SCREAMING_SNAKE_CASE__							:					int = 32							,					SCREAMING_SNAKE_CASE__							:					Optional[int] = None							,					SCREAMING_SNAKE_CASE__							:					float = 0.1_82_15							,					SCREAMING_SNAKE_CASE__							:					str = "group"							,					):
          super().__init__()

          # pass init params to Encoder
          lowerCamelCase__           =		Encoder(
              in_channels=SCREAMING_SNAKE_CASE__							,					out_channels=SCREAMING_SNAKE_CASE__							,					down_block_types=SCREAMING_SNAKE_CASE__							,					block_out_channels=SCREAMING_SNAKE_CASE__							,					layers_per_block=SCREAMING_SNAKE_CASE__							,					act_fn=SCREAMING_SNAKE_CASE__							,					norm_num_groups=SCREAMING_SNAKE_CASE__							,					double_z=SCREAMING_SNAKE_CASE__							,					)

          lowerCamelCase__           =		vq_embed_dim if vq_embed_dim is not None else latent_channels

          lowerCamelCase__           =		nn.Convad(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					1				)
          lowerCamelCase__           =		VectorQuantizer(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					beta=0.25							,					remap=SCREAMING_SNAKE_CASE__							,					sane_index_shape=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		nn.Convad(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					1				)

          # pass init params to Decoder
          lowerCamelCase__           =		Decoder(
              in_channels=SCREAMING_SNAKE_CASE__							,					out_channels=SCREAMING_SNAKE_CASE__							,					up_block_types=SCREAMING_SNAKE_CASE__							,					block_out_channels=SCREAMING_SNAKE_CASE__							,					layers_per_block=SCREAMING_SNAKE_CASE__							,					act_fn=SCREAMING_SNAKE_CASE__							,					norm_num_groups=SCREAMING_SNAKE_CASE__							,					norm_type=SCREAMING_SNAKE_CASE__							,					)
    @apply_forward_hook
    def      _UpperCamelCase       ( self							:					Dict							,					SCREAMING_SNAKE_CASE__							:					torch.FloatTensor							,					SCREAMING_SNAKE_CASE__							:					bool = True				):
          lowerCamelCase__           =		self.encoder(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.quant_conv(SCREAMING_SNAKE_CASE__				)

          if not return_dict:
                return (h,)

          return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE__				)
    @apply_forward_hook
    def      _UpperCamelCase       ( self							:					Any							,					SCREAMING_SNAKE_CASE__							:					torch.FloatTensor							,					SCREAMING_SNAKE_CASE__							:					bool = False							,					SCREAMING_SNAKE_CASE__							:					bool = True				):
          # also go through quantization layer
          if not force_not_quantize:
                lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		self.quantize(SCREAMING_SNAKE_CASE__				)
          else:
                lowerCamelCase__           =		h
          lowerCamelCase__           =		self.post_quant_conv(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.decoder(SCREAMING_SNAKE_CASE__							,					quant if self.config.norm_type == 'spatial' else None				)

          if not return_dict:
                return (dec,)

          return DecoderOutput(sample=SCREAMING_SNAKE_CASE__				)


    def      _UpperCamelCase       ( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					torch.FloatTensor							,					SCREAMING_SNAKE_CASE__							:					bool = True				):
          lowerCamelCase__           =		sample
          lowerCamelCase__           =		self.encode(SCREAMING_SNAKE_CASE__				).latents
          lowerCamelCase__           =		self.decode(SCREAMING_SNAKE_CASE__				).sample

          if not return_dict:
                return (dec,)

          return DecoderOutput(sample=SCREAMING_SNAKE_CASE__				)







"""simple docstring"""


import argparse
import json

from tqdm import tqdm



def   snake_case						(				)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '--src_path'		,   type=_a		,   default='biencoder-nq-dev.json'		,   help='Path to raw DPR training data'		,   )
      parser.add_argument(
          '--evaluation_set'		,   type=_a		,   help='where to store parsed evaluation_set file'		,   )
      parser.add_argument(
          '--gold_data_path'		,   type=_a		,   help='where to store parsed gold_data_path file'		,   )
      lowerCamelCase__           =		parser.parse_args()

      with open(args.src_path		,   'r'							) as src_file, open(args.evaluation_set		,   'w'							) as eval_file, open(
          args.gold_data_path		,   'w'							) as gold_file:
            lowerCamelCase__           =		json.load(_a							)
            for dpr_record in tqdm(_a							):
                  lowerCamelCase__           =		dpr_record['question']
                  lowerCamelCase__           =		[context['title'] for context in dpr_record['positive_ctxs']]
                  eval_file.write(question + '\n'							)
                  gold_file.write('\t'.join(_a							) + '\n'							)


if __name__ == "__main__":
   main()



