

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class        A_		(       a__			,							unittest.TestCase				):
							_UpperCAmelCase    :  int						      =      KandinskyImgaImgPipeline
							_UpperCAmelCase    :  Union[str, Any]						      =      ["prompt", "image_embeds", "negative_image_embeds", "image"]
							_UpperCAmelCase    :  str						      =      [
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							]
							_UpperCAmelCase    :  str						      =      [
							    "generator",
							    "height",
							    "width",
							    "strength",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							_UpperCAmelCase    :  List[Any]						      =      False
							@property
							def     lowerCAmelCase     (		self		:			List[Any]):
								return 3_2
							@property
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								return 3_2
							@property
							def     lowerCAmelCase     (		self		:			List[str]):
								return self.time_input_dim
							@property
							def     lowerCAmelCase     (		self		:			str):
								return self.time_input_dim * 4
							@property
							def     lowerCAmelCase     (		self		:			Any):
								return 1_0_0
							@property
							def     lowerCAmelCase     (		self		:			List[str]):
								__lowerCamelCase				:				Dict					       = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
								return tokenizer
							@property
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								torch.manual_seed(0)
								__lowerCamelCase				:				str					       = MCLIPConfig(
								    numDims=self.cross_attention_dim   ,transformerDimensions=self.text_embedder_hidden_size   ,hidden_size=self.text_embedder_hidden_size   ,intermediate_size=3_7   ,num_attention_heads=4   ,num_hidden_layers=5   ,vocab_size=1_0_0_5   ,)

								__lowerCamelCase				:				int					       = MultilingualCLIP(SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				str					       = text_encoder.eval()

								return text_encoder
							@property
							def     lowerCAmelCase     (		self		:			List[Any]):
								torch.manual_seed(0)

								__lowerCamelCase				:				Optional[int]					       = {
								    'in_channels': 4,
								    # Out channels is double in channels because predicts mean and variance
								    'out_channels': 8,
								    'addition_embed_type': 'text_image',
								    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
								    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
								    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
								    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
								    'layers_per_block': 1,
								    'encoder_hid_dim': self.text_embedder_hidden_size,
								    'encoder_hid_dim_type': 'text_image_proj',
								    'cross_attention_dim': self.cross_attention_dim,
								    'attention_head_dim': 4,
								    'resnet_time_scale_shift': 'scale_shift',
								    'class_embed_type': None,
								}

								__lowerCamelCase				:				Optional[Any]					       = UNetaDConditionModel(**SCREAMING_SNAKE_CASE_)
								return model
							@property
							def     lowerCAmelCase     (		self		:			Optional[int]):
								return {
								    "block_out_channels": [3_2, 6_4],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}
							@property
							def     lowerCAmelCase     (		self		:			Optional[int]):
								torch.manual_seed(0)
								__lowerCamelCase				:				str					       = VQModel(**self.dummy_movq_kwargs)
								return model
							def     lowerCAmelCase     (		self		:			Tuple):
								__lowerCamelCase				:				Union[str, Any]					       = self.dummy_text_encoder
								__lowerCamelCase				:				Tuple					       = self.dummy_tokenizer
								__lowerCamelCase				:				Optional[int]					       = self.dummy_unet
								__lowerCamelCase				:				Union[str, Any]					       = self.dummy_movq

								__lowerCamelCase				:				Any					       = {
								    'num_train_timesteps': 1_0_0_0,
								    'beta_schedule': 'linear',
								    'beta_start': 0.00085,
								    'beta_end': 0.012,
								    'clip_sample': False,
								    'set_alpha_to_one': False,
								    'steps_offset': 0,
								    'prediction_type': 'epsilon',
								    'thresholding': False,
								}

								__lowerCamelCase				:				Union[str, Any]					       = DDIMScheduler(**SCREAMING_SNAKE_CASE_)

								__lowerCamelCase				:				str					       = {
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'unet': unet,
								    'scheduler': scheduler,
								    'movq': movq,
								}

								return components
							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			int=0):
								__lowerCamelCase				:				Union[str, Any]					       = floats_tensor((1, self.cross_attention_dim)   ,rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				Optional[int]					       = floats_tensor((1, self.cross_attention_dim)   ,rng=random.Random(seed + 1)).to(SCREAMING_SNAKE_CASE_)
								# create init_image
								__lowerCamelCase				:				List[Any]					       = floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				List[Any]					       = image.cpu().permute(0   ,2   ,3   ,1)[0]
								__lowerCamelCase				:				List[Any]					       = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_)).convert('RGB').resize((2_5_6, 2_5_6))

								if str(SCREAMING_SNAKE_CASE_).startswith('mps'):
									__lowerCamelCase				:				Tuple					       = torch.manual_seed(SCREAMING_SNAKE_CASE_)
								else:
									__lowerCamelCase				:				Dict					       = torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				List[str]					       = {
								    'prompt': 'horse',
								    'image': init_image,
								    'image_embeds': image_embeds,
								    'negative_image_embeds': negative_image_embeds,
								    'generator': generator,
								    'height': 6_4,
								    'width': 6_4,
								    'num_inference_steps': 1_0,
								    'guidance_scale': 7.0,
								    'strength': 0.2,
								    'output_type': 'np',
								}
								return inputs


							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase				:				Optional[Any]					       = 'cpu'

								__lowerCamelCase				:				Optional[Any]					       = self.get_dummy_components()

								__lowerCamelCase				:				int					       = self.pipeline_class(**SCREAMING_SNAKE_CASE_)
								__lowerCamelCase				:				List[str]					       = pipe.to(SCREAMING_SNAKE_CASE_)

								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

								__lowerCamelCase				:				Union[str, Any]					       = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_))
								__lowerCamelCase				:				Dict					       = output.images

								__lowerCamelCase				:				Any					       = pipe(
								    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_)   ,return_dict=SCREAMING_SNAKE_CASE_   ,)[0]

								__lowerCamelCase				:				Any					       = image[0, -3:, -3:, -1]
								__lowerCamelCase				:				int					       = image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)

								__lowerCamelCase				:				List[str]					       = np.array(
								    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233])
								assert (
								    np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
								), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2
								), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






@slow
@require_torch_gpu
class        A_		(       unittest.TestCase				):
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase				:				Optional[int]					       = load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/kandinsky/kandinsky_img2img_frog.npy')

								__lowerCamelCase				:				Tuple					       = load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
								__lowerCamelCase				:				Union[str, Any]					       = 'A red cartoon frog, 4k'

								__lowerCamelCase				:				Union[str, Any]					       = KandinskyPriorPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-1-prior'   ,torch_dtype=torch.floataa)
								pipe_prior.to(SCREAMING_SNAKE_CASE_)

								__lowerCamelCase				:				Any					       = KandinskyImgaImgPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-1'   ,torch_dtype=torch.floataa)
								__lowerCamelCase				:				Optional[int]					       = pipeline.to(SCREAMING_SNAKE_CASE_)

								pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

								__lowerCamelCase				:				List[str]					       = torch.Generator(device='cpu').manual_seed(0)
								__lowerCamelCase				:				Any					       = pipe_prior(
								    SCREAMING_SNAKE_CASE_   ,generator=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5   ,negative_prompt=''   ,).to_tuple()

								__lowerCamelCase				:				str					       = pipeline(
								    SCREAMING_SNAKE_CASE_   ,image=SCREAMING_SNAKE_CASE_   ,image_embeds=SCREAMING_SNAKE_CASE_   ,negative_image_embeds=SCREAMING_SNAKE_CASE_   ,generator=SCREAMING_SNAKE_CASE_   ,num_inference_steps=1_0_0   ,height=7_6_8   ,width=7_6_8   ,strength=0.2   ,output_type='np'   ,)

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

								assert image.shape == (7_6_8, 7_6_8, 3)

								assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_)









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()



