



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 				lowerCAmelCase_		(  unittest.TestCase							):
       @property
       def 					snake_case_       (			self       )     ->		List[Any]:
          torch.manual_seed(0       )
          UpperCamelCase    :  Dict							=	UNetaDModel(
              block_out_channels=(32, 64),			layers_per_block=2,			sample_size=32,			in_channels=3,			out_channels=3,			down_block_types=('DownBlock2D', 'AttnDownBlock2D'),			up_block_types=('AttnUpBlock2D', 'UpBlock2D'),			)
          return model
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          torch.manual_seed(0       )
          UpperCamelCase    :  str							=	VQModel(
              block_out_channels=[32, 64],			in_channels=3,			out_channels=3,			down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],			up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],			latent_channels=3,			)
          return model
       @property
       def 					snake_case_       (			self       )     ->		List[str]:
          torch.manual_seed(0       )
          UpperCamelCase    :  Union[str, Any]							=	CLIPTextConfig(
              bos_token_id=0,			eos_token_id=2,			hidden_size=32,			intermediate_size=37,			layer_norm_eps=1e-05,			num_attention_heads=4,			num_hidden_layers=5,			pad_token_id=1,			vocab_size=1000,			)
          return CLIPTextModel(SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  Union[str, Any]							=	self.dummy_uncond_unet
          UpperCamelCase    :  Optional[Any]							=	DDIMScheduler()
          UpperCamelCase    :  List[Any]							=	self.dummy_vq_model

          UpperCamelCase    :  Tuple							=	LDMPipeline(unet=SCREAMING_SNAKE_CASE_,			vqvae=SCREAMING_SNAKE_CASE_,			scheduler=SCREAMING_SNAKE_CASE_       )
          ldm.to(SCREAMING_SNAKE_CASE_       )
          ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  str							=	torch.manual_seed(0       )
          UpperCamelCase    :  Dict							=	ldm(generator=SCREAMING_SNAKE_CASE_,			num_inference_steps=2,			output_type='numpy'       ).images

          UpperCamelCase    :  Tuple							=	torch.manual_seed(0       )
          UpperCamelCase    :  int							=	ldm(generator=SCREAMING_SNAKE_CASE_,			num_inference_steps=2,			output_type='numpy',			return_dict=SCREAMING_SNAKE_CASE_       )[0]

          UpperCamelCase    :  Dict							=	image[0, -3:, -3:, -1]
          UpperCamelCase    :  int							=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCamelCase    :  int							=	np.array([0.85_12, 0.8_18, 0.64_11, 0.68_08, 0.44_65, 0.56_18, 0.46, 0.62_31, 0.51_72]       )
          UpperCamelCase    :  Optional[Any]							=	1e-2 if torch_device != 'mps' else 3e-2

          assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance






@slow
@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  int							=	LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'       )
          ldm.to(SCREAMING_SNAKE_CASE_       )
          ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  int							=	torch.manual_seed(0       )
          UpperCamelCase    :  Optional[Any]							=	ldm(generator=SCREAMING_SNAKE_CASE_,			num_inference_steps=5,			output_type='numpy'       ).images

          UpperCamelCase    :  Optional[int]							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)
          UpperCamelCase    :  int							=	np.array([0.43_99, 0.4_49_75, 0.4_68_25, 0.4_74, 0.43_59, 0.45_81, 0.4_50_95, 0.43_41, 0.44_47]       )
          UpperCamelCase    :  Union[str, Any]							=	1e-2 if torch_device != 'mps' else 3e-2

          assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance








import argparse
import json

from tqdm import tqdm
def 		UpperCamelCase	(   )	->     Optional[int]:
   UpperCamelCase    :  List[Any]							=	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'       ,     )
   UpperCamelCase    :  int							=	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:
      UpperCamelCase    :  int							=	json.load(snake_case__	)
      for dpr_record in tqdm(snake_case__	):
         UpperCamelCase    :  Union[str, Any]							=	dpr_record['question']
         UpperCamelCase    :  Dict							=	[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()



