


import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


lowerCAmelCase           =		logging.get_logger(__name__)





class       A    (   a_ ):


							def __init__(self      ,				lowerCAmelCase					):
									super().__init__()
									__lowercase=			nn.ModuleList(_lowerCamelCase					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = None      ,				lowerCAmelCase = False      ,				lowerCAmelCase = True      ,				):
									for i, (image, scale, controlnet) in enumerate(zip(_lowerCamelCase      ,				_lowerCamelCase      ,				self.nets					)					):
											__lowercase,							__lowercase=			controlnet(
											    _lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase      ,				_lowerCamelCase      ,				)

											# merge samples
											if i == 0:
													__lowercase,							__lowercase=			down_samples, mid_sample
											else:
													__lowercase=			[
													    samples_prev + samples_curr
													    for samples_prev, samples_curr in zip(_lowerCamelCase      ,				_lowerCamelCase					)
													]
													mid_block_res_sample += mid_sample

									return down_block_res_samples, mid_block_res_sample


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True      ,				lowerCAmelCase = None      ,				lowerCAmelCase = False      ,				lowerCAmelCase = None      ,				):
									__lowercase=			0
									__lowercase=			save_directory
									for controlnet in self.nets:
											controlnet.save_pretrained(
											    _lowerCamelCase      ,				is_main_process=_lowerCamelCase      ,				save_function=_lowerCamelCase      ,				safe_serialization=_lowerCamelCase      ,				variant=_lowerCamelCase      ,				)

											idx += 1
											__lowercase=			model_path_to_save + f'_{idx}'







							@classmethod
							def 			_A       (cls      ,				lowerCAmelCase      ,				**lowerCAmelCase					):
									__lowercase=			0
									__lowercase=			[]

									# load controlnet and append to list until no controlnet directory exists anymore
									# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
									# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
									__lowercase=			pretrained_model_path
									while os.path.isdir(_lowerCamelCase					):
											__lowercase=			ControlNetModel.from_pretrained(_lowerCamelCase      ,				**_lowerCamelCase					)
											controlnets.append(_lowerCamelCase					)

											idx += 1
											__lowercase=			pretrained_model_path + f'_{idx}'

									logger.info(f'{len(_lowerCamelCase					)} controlnets loaded from {pretrained_model_path}.'					)

									if len(_lowerCamelCase					) == 0:
											raise ValueError(
											    f'No ControlNets found under {os.path.dirname(_lowerCamelCase					)}. Expected at least {pretrained_model_path + "_0"}.'					)

									return cls(_lowerCamelCase					)














"""simple docstring"""



import argparse
import json

from tqdm import tqdm
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       argparse.ArgumentParser()

  # Required parameters
  parser.add_argument(
      '--src_path'		,					type=__snake_case		,					default='biencoder-nq-dev.json'		,					help='Path to raw DPR training data'		,					)
  parser.add_argument(
      '--evaluation_set'		,					type=__snake_case		,					help='where to store parsed evaluation_set file'		,					)
  parser.add_argument(
      '--gold_data_path'		,					type=__snake_case		,					help='where to store parsed gold_data_path file'		,					)
  lowercase							    =       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:
    lowercase							    =       json.load(__snake_case    )
    for dpr_record in tqdm(__snake_case    ):
      lowercase							    =       dpr_record['question']
      lowercase							    =       [context['title'] for context in dpr_record['positive_ctxs']]
      eval_file.write(question + '\n'    )
      gold_file.write('\t'.join(__snake_case    ) + '\n'    )


if __name__ == "__main__":
    main()


