





'''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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block






@dataclass
class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			torch.FloatTensor






class       UpperCamelCase__  (		__lowerCAmelCase	,__lowerCAmelCase     ):







	@register_to_config
	def __init__(				self			:   Union[str, Any] , lowerCamelCase			:   int = 6_5_5_3_6 , lowerCamelCase			:   Optional[int] = None , lowerCamelCase			:   int = 2 , lowerCamelCase			:   int = 2 , lowerCamelCase			:   int = 0 , lowerCamelCase			:   str = "fourier" , lowerCamelCase			:   bool = True , lowerCamelCase			:   bool = False , lowerCamelCase			:   float = 0.0 , lowerCamelCase			:   Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , lowerCamelCase			:   Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , lowerCamelCase			:   Tuple[str] = "UNetMidBlock1D" , lowerCamelCase			:   str = None , lowerCamelCase			:   Tuple[int] = (3_2, 3_2, 6_4) , lowerCamelCase			:   str = None , lowerCamelCase			:   int = 8 , lowerCamelCase			:   int = 1 , lowerCamelCase			:   bool = False , ):




				'''simple docstring'''
				super().__init__()
				a__     = sample_size

				# time
				if time_embedding_type == "fourier":
							a__     = GaussianFourierProjection(
							    embedding_size=8 , set_W_to_weight=lowerCamelCase , log=lowerCamelCase , flip_sin_to_cos=lowerCamelCase		)
							a__     = 2 * block_out_channels[0]
				elif time_embedding_type == "positional":
							a__     = Timesteps(
							    block_out_channels[0] , flip_sin_to_cos=lowerCamelCase , downscale_freq_shift=lowerCamelCase		)
							a__     = block_out_channels[0]

				if use_timestep_embedding:
							a__     = block_out_channels[0] * 4
							a__     = TimestepEmbedding(
							    in_channels=lowerCamelCase , time_embed_dim=lowerCamelCase , act_fn=lowerCamelCase , out_dim=block_out_channels[0] , )

				a__     = nn.ModuleList([]		)
				a__     = None
				a__     = nn.ModuleList([]		)
				a__     = None

				# down
				a__     = in_channels
				for i, down_block_type in enumerate(lowerCamelCase		):
							a__     = output_channel
							a__     = block_out_channels[i]

							if i == 0:
										input_channel += extra_in_channels

							a__     = i == len(lowerCamelCase		) - 1

							a__     = get_down_block(
							    lowerCamelCase , num_layers=lowerCamelCase , in_channels=lowerCamelCase , out_channels=lowerCamelCase , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
							self.down_blocks.append(lowerCamelCase		)

				# mid
				a__     = get_mid_block(
				    lowerCamelCase , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=lowerCamelCase , add_downsample=lowerCamelCase , )

				# up
				a__     = list(reversed(lowerCamelCase		)		)
				a__     = reversed_block_out_channels[0]
				if out_block_type is None:
							a__     = out_channels
				else:
							a__     = block_out_channels[0]

				for i, up_block_type in enumerate(lowerCamelCase		):
							a__     = output_channel
							a__     = (
							    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase		) - 1 else final_upsample_channels
							)

							a__     = i == len(lowerCamelCase		) - 1

							a__     = get_up_block(
							    lowerCamelCase , num_layers=lowerCamelCase , in_channels=lowerCamelCase , out_channels=lowerCamelCase , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
							self.up_blocks.append(lowerCamelCase		)
							a__     = output_channel

				# out
				a__     = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 3_2		)
				a__     = get_out_block(
				    out_block_type=lowerCamelCase , num_groups_out=lowerCamelCase , embed_dim=block_out_channels[0] , out_channels=lowerCamelCase , act_fn=lowerCamelCase , fc_dim=block_out_channels[-1] // 4 , )







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   torch.FloatTensor , lowerCamelCase			:   Union[torch.Tensor, float, int] , lowerCamelCase			:   bool = True , ):




				'''simple docstring'''
				a__     = timestep
				if not torch.is_tensor(lowerCamelCase		):
							a__     = torch.tensor([timesteps] , dtype=torch.long , device=sample.device		)
				elif torch.is_tensor(lowerCamelCase		) and len(timesteps.shape		) == 0:
							a__     = timesteps[None].to(sample.device		)

				a__     = self.time_proj(lowerCamelCase		)
				if self.config.use_timestep_embedding:
							a__     = self.time_mlp(lowerCamelCase		)
				else:
							a__     = timestep_embed[..., None]
							a__     = timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
							a__     = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

				# 2. down
				a__     = ()
				for downsample_block in self.down_blocks:
							a__   ,				a__     = downsample_block(hidden_states=lowerCamelCase , temb=lowerCamelCase		)
							down_block_res_samples += res_samples

				# 3. mid
				if self.mid_block:
							a__     = self.mid_block(lowerCamelCase , lowerCamelCase		)

				# 4. up
				for i, upsample_block in enumerate(self.up_blocks		):
							a__     = down_block_res_samples[-1:]
							a__     = down_block_res_samples[:-1]
							a__     = upsample_block(lowerCamelCase , res_hidden_states_tuple=lowerCamelCase , temb=lowerCamelCase		)

				# 5. post-process
				if self.out_block:
							a__     = self.out_block(lowerCamelCase , lowerCamelCase		)

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=lowerCamelCase		)










'''simple docstring'''


import argparse
import json

from tqdm import tqdm







def    _lowerCamelCase  ()   ->    Any:
			a__     = argparse.ArgumentParser()

			# Required parameters
			parser.add_argument(
			    "--src_path"					,  type=__lowerCamelCase					,  default="biencoder-nq-dev.json"					,  help="Path to raw DPR training data"					,  )
			parser.add_argument(
			    "--evaluation_set"					,  type=__lowerCamelCase					,  help="where to store parsed evaluation_set file"					,  )
			parser.add_argument(
			    "--gold_data_path"					,  type=__lowerCamelCase					,  help="where to store parsed gold_data_path file"					,  )
			a__     = 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:
						a__     = json.load(__lowerCamelCase     )
						for dpr_record in tqdm(__lowerCamelCase     ):
									a__     = dpr_record["question"]
									a__     = [context["title"] for context in dpr_record["positive_ctxs"]]
									eval_file.write(question + "\n"     )
									gold_file.write("\t".join(__lowerCamelCase     ) + "\n"     )


if __name__ == "__main__":
		main()



