



"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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        __snake_case       (	_lowercase  ,     unittest.TestCase):
  snake_case__				:   Any  			=						KandinskyVaaPipeline
  snake_case__				:   List[str]  			=						[
      "image_embeds",
      "negative_image_embeds",
  ]
  snake_case__				:   Optional[int]  			=						["image_embeds", "negative_image_embeds"]
  snake_case__				:   Any  			=						[
      "generator",
      "height",
      "width",
      "latents",
      "guidance_scale",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  snake_case__				:   Union[str, Any]  			=						False




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         return 3_2




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         return 3_2




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Dict		):






         """simple docstring"""






         return self.time_input_dim




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         return self.time_input_dim * 4




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         return 1_0_0




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         torch.manual_seed(0		)

         _lowerCamelCase						:				Any    			= {
             '''in_channels''': 4,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''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''': '''image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         _lowerCamelCase						:				Tuple    			= UNetaDConditionModel(**__lowerCAmelCase		)
         return model




  @property
  def 						SCREAMING_SNAKE_CASE				( self     :						Tuple		):






         """simple docstring"""






         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 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         torch.manual_seed(0		)
         _lowerCamelCase						:				Any    			= VQModel(**self.dummy_movq_kwargs		)
         return model




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[Any]    			= self.dummy_unet
         _lowerCamelCase						:				Optional[Any]    			= self.dummy_movq

         _lowerCamelCase						:				Optional[int]    			= DDIMScheduler(
             num_train_timesteps=1_0_0_0						,					beta_schedule='''linear'''						,					beta_start=0.0_00_85						,					beta_end=0.0_12						,					clip_sample=__lowerCAmelCase						,					set_alpha_to_one=__lowerCAmelCase						,					steps_offset=1						,					prediction_type='''epsilon'''						,					thresholding=__lowerCAmelCase						,					)

         _lowerCamelCase						:				Optional[int]    			= {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }
         return components




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]						,					__lowerCAmelCase     :						Tuple						,					__lowerCAmelCase     :						Optional[Any]=0		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= floats_tensor((1, self.text_embedder_hidden_size)						,					rng=random.Random(__lowerCAmelCase		)		).to(__lowerCAmelCase		)
         _lowerCamelCase						:				Union[str, Any]    			= floats_tensor((1, self.text_embedder_hidden_size)						,					rng=random.Random(seed + 1		)		).to(
             __lowerCAmelCase		)
         if str(__lowerCAmelCase		).startswith('''mps'''		):
                _lowerCamelCase						:				Union[str, Any]    			= torch.manual_seed(__lowerCAmelCase		)
         else:
                _lowerCamelCase						:				Any    			= torch.Generator(device=__lowerCAmelCase		).manual_seed(__lowerCAmelCase		)
         _lowerCamelCase						:				List[str]    			= {
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 6_4,
             '''width''': 6_4,
             '''guidance_scale''': 4.0,
             '''num_inference_steps''': 2,
             '''output_type''': '''np''',
         }
         return inputs




  def 						SCREAMING_SNAKE_CASE				( self     :						Any		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[Any]    			= '''cpu'''

         _lowerCamelCase						:				Any    			= self.get_dummy_components()

         _lowerCamelCase						:				Union[str, Any]    			= self.pipeline_class(**__lowerCAmelCase		)
         _lowerCamelCase						:				Tuple    			= pipe.to(__lowerCAmelCase		)

         pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:				Optional[int]    			= pipe(**self.get_dummy_inputs(__lowerCAmelCase		)		)
         _lowerCamelCase						:				List[Any]    			= output.images

         _lowerCamelCase						:				str    			= pipe(
             **self.get_dummy_inputs(__lowerCAmelCase		)						,					return_dict=__lowerCAmelCase						,					)[0]

         _lowerCamelCase						:				Any    			= image[0, -3:, -3:, -1]
         _lowerCamelCase						:				Optional[Any]    			= image_from_tuple[0, -3:, -3:, -1]

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

         _lowerCamelCase						:				int    			= np.array(
             [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]		)

         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        __snake_case       (	unittest.TestCase):




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




  def 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''		)

         _lowerCamelCase						:				Dict    			= KandinskyVaaPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-prior'''						,					torch_dtype=torch.floataa		)
         pipe_prior.to(__lowerCAmelCase		)

         _lowerCamelCase						:				Any    			= KandinskyVaaPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-decoder'''						,					torch_dtype=torch.floataa		)
         _lowerCamelCase						:				int    			= pipeline.to(__lowerCAmelCase		)
         pipeline.set_progress_bar_config(disable=__lowerCAmelCase		)

         _lowerCamelCase						:				int    			= '''red cat, 4k photo'''

         _lowerCamelCase						:				Any    			= torch.Generator(device='''cuda'''		).manual_seed(0		)
         _lowerCamelCase			,    _lowerCamelCase						:				int    			= pipe_prior(
             __lowerCAmelCase						,					generator=__lowerCAmelCase						,					num_inference_steps=5						,					negative_prompt=''''''						,					).to_tuple()

         _lowerCamelCase						:				List[Any]    			= torch.Generator(device='''cuda'''		).manual_seed(0		)
         _lowerCamelCase						:				Dict    			= pipeline(
             image_embeds=__lowerCAmelCase						,					negative_image_embeds=__lowerCAmelCase						,					generator=__lowerCAmelCase						,					num_inference_steps=1_0_0						,					output_type='''np'''						,					)

         _lowerCamelCase						:				Optional[int]    			= output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)

         assert_mean_pixel_difference(__lowerCAmelCase						,					__lowerCAmelCase		)






'''simple docstring'''
from pathlib import Path

import fire
from tqdm import tqdm


def 			a      (			__a="ro" ,					__a="en" ,					__a="wmt16" ,					__a=None   )  ->	None:



					'''simple docstring'''






					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''   )
					UpperCamelCase__   :int												= f'''{src_lang}-{tgt_lang}'''
					print(f'''Converting {dataset}-{pair}'''   )
					UpperCamelCase__   :Tuple												= datasets.load_dataset(__a ,					__a   )
					if save_dir is None:
										UpperCamelCase__   :Any												= f'''{dataset}-{pair}'''
					UpperCamelCase__   :Dict												= Path(__a   )
					save_dir.mkdir(exist_ok=__a   )

					for split in ds.keys():
										print(f'''Splitting {split} with {ds[split].num_rows} records'''   )

										# to save to val.source, val.target like summary datasets
										UpperCamelCase__   :Dict												= '''val''' if split == '''validation''' else split
										UpperCamelCase__   :List[Any]												= save_dir.joinpath(f'''{fn}.source'''   )
										UpperCamelCase__   :int												= save_dir.joinpath(f'''{fn}.target'''   )
										UpperCamelCase__   :Union[str, Any]												= src_path.open('''w+'''   )
										UpperCamelCase__   :Tuple												= tgt_path.open('''w+'''   )

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]   ):
															UpperCamelCase__   :Union[str, Any]												= x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''   )
															tgt_fp.write(ex[tgt_lang] + '''\n'''   )

					print(f'''Saved {dataset} dataset to {save_dir}'''   )


if __name__ == "__main__":
		fire.Fire(download_wmt_dataset)