



import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()



class 				lowerCAmelCase_		(  a__       ,    a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Tuple        =  StableUnCLIPImgaImgPipeline
       UpperCAmelCase__				:					Dict        =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS
       UpperCAmelCase__				:					Dict        =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       UpperCAmelCase__				:					List[Any]        =  frozenset(
           []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       UpperCAmelCase__				:					str        =  frozenset([]							)
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  str							=	32
          UpperCamelCase    :  Dict							=	embedder_hidden_size

          # image encoding components

          UpperCamelCase    :  Any							=	CLIPImageProcessor(crop_size=32,			size=32       )

          torch.manual_seed(0       )
          UpperCamelCase    :  str							=	CLIPVisionModelWithProjection(
              CLIPVisionConfig(
                  hidden_size=SCREAMING_SNAKE_CASE_,			projection_dim=SCREAMING_SNAKE_CASE_,			num_hidden_layers=5,			num_attention_heads=4,			image_size=32,			intermediate_size=37,			patch_size=1,			)       )

          # regular denoising components

          torch.manual_seed(0       )
          UpperCamelCase    :  Tuple							=	StableUnCLIPImageNormalizer(embedding_dim=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	DDPMScheduler(beta_schedule='squaredcos_cap_v2'       )

          torch.manual_seed(0       )
          UpperCamelCase    :  Optional[int]							=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

          torch.manual_seed(0       )
          UpperCamelCase    :  Optional[Any]							=	CLIPTextModel(
              CLIPTextConfig(
                  bos_token_id=0,			eos_token_id=2,			hidden_size=SCREAMING_SNAKE_CASE_,			projection_dim=32,			intermediate_size=37,			layer_norm_eps=1e-05,			num_attention_heads=4,			num_hidden_layers=5,			pad_token_id=1,			vocab_size=1000,			)       )

          torch.manual_seed(0       )
          UpperCamelCase    :  List[Any]							=	UNetaDConditionModel(
              sample_size=32,			in_channels=4,			out_channels=4,			down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D'),			up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D'),			block_out_channels=(32, 64),			attention_head_dim=(2, 4),			class_embed_type='projection',			projection_class_embeddings_input_dim=embedder_projection_dim * 2,			cross_attention_dim=SCREAMING_SNAKE_CASE_,			layers_per_block=1,			upcast_attention=SCREAMING_SNAKE_CASE_,			use_linear_projection=SCREAMING_SNAKE_CASE_,			)

          torch.manual_seed(0       )
          UpperCamelCase    :  Any							=	DDIMScheduler(
              beta_schedule='scaled_linear',			beta_start=0.0_00_85,			beta_end=0.0_12,			prediction_type='v_prediction',			set_alpha_to_one=SCREAMING_SNAKE_CASE_,			steps_offset=1,			)

          torch.manual_seed(0       )
          UpperCamelCase    :  Union[str, Any]							=	AutoencoderKL()

          UpperCamelCase    :  str							=	{
              # image encoding components
              'feature_extractor': feature_extractor,
              'image_encoder': image_encoder.eval(),
              # image noising components
              'image_normalizer': image_normalizer.eval(),
              'image_noising_scheduler': image_noising_scheduler,
              # regular denoising components
              'tokenizer': tokenizer,
              'text_encoder': text_encoder.eval(),
              'unet': unet.eval(),
              'scheduler': scheduler,
              'vae': vae.eval(),
          }

          return components
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=True       )     ->		Optional[int]:
          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             UpperCamelCase    :  Optional[int]							=	torch.manual_seed(SCREAMING_SNAKE_CASE_       )
          else:
             UpperCamelCase    :  List[str]							=	torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  str							=	floats_tensor((1, 3, 32, 32),			rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )

          if pil_image:
             UpperCamelCase    :  int							=	input_image * 0.5 + 0.5
             UpperCamelCase    :  Dict							=	input_image.clamp(0,			1       )
             UpperCamelCase    :  Optional[int]							=	input_image.cpu().permute(0,			2,			3,			1       ).float().numpy()
             UpperCamelCase    :  Optional[Any]							=	DiffusionPipeline.numpy_to_pil(SCREAMING_SNAKE_CASE_       )[0]

          return {
              "prompt": "An anime racoon running a marathon",
              "image": input_image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "np",
          }
       @skip_mps
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  List[Any]							=	'cpu'  # ensure determinism for the device-dependent torch.Generator
          UpperCamelCase    :  Optional[Any]							=	self.get_dummy_components()
          UpperCamelCase    :  Any							=	StableUnCLIPImgaImgPipeline(**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	sd_pipe.to(SCREAMING_SNAKE_CASE_       )
          sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
          inputs.update({'image_embeds': None}       )
          UpperCamelCase    :  int							=	sd_pipe(**SCREAMING_SNAKE_CASE_       ).images
          UpperCamelCase    :  int							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          UpperCamelCase    :  Optional[int]							=	np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78]       )

          assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-3
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Tuple							=	torch_device in ['cpu', 'mps']

          self._test_attention_slicing_forward_pass(test_max_difference=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  int							=	torch_device in ['cpu', 'mps']

          self._test_inference_batch_single_identical(test_max_difference=SCREAMING_SNAKE_CASE_       )






       @unittest.skipIf(
           torch_device != 'cuda' or not is_xformers_available(),			reason='XFormers attention is only available with CUDA and `xformers` installed',			)
       def 					snake_case_       (			self       )     ->		Dict:
          self._test_xformers_attention_forwardGenerator_pass(test_max_difference=SCREAMING_SNAKE_CASE_       )






@slow
@require_torch_gpu
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Optional[Any]							=	load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'       )

          UpperCamelCase    :  Optional[Any]							=	load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'       )

          UpperCamelCase    :  List[Any]							=	StableUnCLIPImgaImgPipeline.from_pretrained(
              'fusing/stable-unclip-2-1-l-img2img',			torch_dtype=torch.floataa       )
          pipe.to(SCREAMING_SNAKE_CASE_       )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )
          # stable unclip will oom when integration tests are run on a V100,
          # so turn on memory savings
          pipe.enable_attention_slicing()
          pipe.enable_sequential_cpu_offload()

          UpperCamelCase    :  List[str]							=	torch.Generator(device='cpu'       ).manual_seed(0       )
          UpperCamelCase    :  List[str]							=	pipe(SCREAMING_SNAKE_CASE_,			'anime turle',			generator=SCREAMING_SNAKE_CASE_,			output_type='np'       )

          UpperCamelCase    :  Optional[Any]							=	output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Any							=	load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'       )

          UpperCamelCase    :  str							=	load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'       )

          UpperCamelCase    :  int							=	StableUnCLIPImgaImgPipeline.from_pretrained(
              'fusing/stable-unclip-2-1-h-img2img',			torch_dtype=torch.floataa       )
          pipe.to(SCREAMING_SNAKE_CASE_       )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )
          # stable unclip will oom when integration tests are run on a V100,
          # so turn on memory savings
          pipe.enable_attention_slicing()
          pipe.enable_sequential_cpu_offload()

          UpperCamelCase    :  int							=	torch.Generator(device='cpu'       ).manual_seed(0       )
          UpperCamelCase    :  Any							=	pipe(SCREAMING_SNAKE_CASE_,			'anime turle',			generator=SCREAMING_SNAKE_CASE_,			output_type='np'       )

          UpperCamelCase    :  Dict							=	output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  List[Any]							=	load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'       )

          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          UpperCamelCase    :  Tuple							=	StableUnCLIPImgaImgPipeline.from_pretrained(
              'fusing/stable-unclip-2-1-h-img2img',			torch_dtype=torch.floataa       )
          UpperCamelCase    :  Any							=	pipe.to(SCREAMING_SNAKE_CASE_       )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )
          pipe.enable_attention_slicing()
          pipe.enable_sequential_cpu_offload()

          UpperCamelCase    :  str							=	pipe(
              SCREAMING_SNAKE_CASE_,			'anime turtle',			num_inference_steps=2,			output_type='np',			)

          UpperCamelCase    :  Dict							=	torch.cuda.max_memory_allocated()
          # make sure that less than 7 GB is allocated
          assert mem_bytes < 7 * 10**9








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()



