

'''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   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:List[str]      	=					KandinskyVaaPipeline
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					[
     """image_embeds""",
     """negative_image_embeds""",
 ]
 __SCREAMING_SNAKE_CASE					:Tuple      	=					["""image_embeds""", """negative_image_embeds"""]
 __SCREAMING_SNAKE_CASE					:Tuple      	=					[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __SCREAMING_SNAKE_CASE					:List[str]      	=					False



 @property
 def 			snake_case__  (   self		:   List[Any]      ):
      return 32



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return 32



 @property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      return self.time_input_dim



 @property
 def 			snake_case__  (   self		:   Optional[Any]      ):
      return self.time_input_dim * 4



 @property
 def 			snake_case__  (   self		:   Dict      ):
      return 100



 @property
 def 			snake_case__  (   self		:   Tuple      ):
      torch.manual_seed(0      )

      __magic_name__						       =    {
          '''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,
      }

      __magic_name__						       =    UNetaDConditionModel(**a__      )
      return model



 @property
 def 			snake_case__  (   self		:   Any      ):
      return {
          "block_out_channels": [32, 64],
          "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": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }



 @property
 def 			snake_case__  (   self		:   str      ):
      torch.manual_seed(0      )
      __magic_name__						       =    VQModel(**self.dummy_movq_kwargs      )
      return model



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.dummy_unet
      __magic_name__						       =    self.dummy_movq

      __magic_name__						       =    DDIMScheduler(
          num_train_timesteps=1000			,				beta_schedule='''linear'''			,				beta_start=0.00_085			,				beta_end=0.012			,				clip_sample=a__			,				set_alpha_to_one=a__			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=a__			,				)

      __magic_name__						       =    {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''movq''': movq,
      }
      return components



 def 			snake_case__  (   self		:   Any			,				a__		:   Union[str, Any]			,				a__		:   Any=0      ):
      __magic_name__						       =    floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(a__      )      ).to(a__      )
      __magic_name__						       =    floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(seed + 1      )      ).to(
          a__      )
      if str(a__      ).startswith('''mps'''      ):
           __magic_name__						       =    torch.manual_seed(a__      )
      else:
           __magic_name__						       =    torch.Generator(device=a__      ).manual_seed(a__      )
      __magic_name__						       =    {
          '''image_embeds''': image_embeds,
          '''negative_image_embeds''': negative_image_embeds,
          '''generator''': generator,
          '''height''': 64,
          '''width''': 64,
          '''guidance_scale''': 4.0,
          '''num_inference_steps''': 2,
          '''output_type''': '''np''',
      }
      return inputs



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    '''cpu'''

      __magic_name__						       =    self.get_dummy_components()

      __magic_name__						       =    self.pipeline_class(**a__      )
      __magic_name__						       =    pipe.to(a__      )

      pipe.set_progress_bar_config(disable=a__      )

      __magic_name__						       =    pipe(**self.get_dummy_inputs(a__      )      )
      __magic_name__						       =    output.images

      __magic_name__						       =    pipe(
          **self.get_dummy_inputs(a__      )			,				return_dict=a__			,				)[0]

      __magic_name__						       =    image[0, -3:, -3:, -1]
      __magic_name__						       =    image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      __magic_name__						       =    np.array(
          [0.6_237_976, 1.0, 0.36_441_332, 1.0, 0.70_639_634, 0.29_877_186, 0.85_652_125, 0.5_216_843, 0.54_454_046]      )

      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   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   Any      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''      )

      __magic_name__						       =    KandinskyVaaPriorPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-2-prior'''			,				torch_dtype=torch.floataa      )
      pipe_prior.to(a__      )

      __magic_name__						       =    KandinskyVaaPipeline.from_pretrained(
          '''kandinsky-community/kandinsky-2-2-decoder'''			,				torch_dtype=torch.floataa      )
      __magic_name__						       =    pipeline.to(a__      )
      pipeline.set_progress_bar_config(disable=a__      )

      __magic_name__						       =    '''red cat, 4k photo'''

      __magic_name__						       =    torch.Generator(device='''cuda'''      ).manual_seed(0      )
      __magic_name__       ,  __magic_name__						       =    pipe_prior(
          a__			,				generator=a__			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

      __magic_name__						       =    torch.Generator(device='''cuda'''      ).manual_seed(0      )
      __magic_name__						       =    pipeline(
          image_embeds=a__			,				negative_image_embeds=a__			,				generator=a__			,				num_inference_steps=100			,				output_type='''np'''			,				)

      __magic_name__						       =    output.images[0]

      assert image.shape == (512, 512, 3)

      assert_mean_pixel_difference(a__			,				a__      )









'''simple docstring'''




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 						UpperCamelCase	(			a		,						a		,						a		,						a=1024			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__       ,  __magic_name__						       =    [], []

     __magic_name__						       =    list(zip(a		,						a			)			)
     __magic_name__       ,  __magic_name__						       =    sorted_examples[0]

     def is_too_big(a			):
          return tok(a		,						return_tensors='''pt'''			).input_ids.shape[1] > max_tokens

     for src, tgt in tqdm(sorted_examples[1:]			):
          __magic_name__						       =    new_src + ''' ''' + src
          __magic_name__						       =    new_tgt + ''' ''' + tgt
          if is_too_big(a			) or is_too_big(a			):  # cant fit, finalize example
               finished_src.append(a			)
               finished_tgt.append(a			)
               __magic_name__       ,  __magic_name__						       =    src, tgt
          else:  # can fit, keep adding
               __magic_name__       ,  __magic_name__						       =    cand_src, cand_tgt

    # cleanup
     if new_src:
          assert new_tgt
          finished_src.append(a			)
          finished_tgt.append(a			)
     return finished_src, finished_tgt




def 						UpperCamelCase	(			a		,						a		,						a		,						a			)				->     Any:






     '''simple docstring'''
     __magic_name__						       =    Path(a			)
     save_path.mkdir(exist_ok=a			)
     for split in ["train"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__						       =    [x.rstrip() for x in Path(a			).open().readlines()]
          __magic_name__       ,  __magic_name__						       =    pack_examples(a		,						a		,						a		,						a			)
          print(F'''packed {split} split from {len(a			)} examples -> {len(a			)}.'''			)
          Path(save_path / F'''{split}.source'''			).open('''w'''			).write('''\n'''.join(a			)			)
          Path(save_path / F'''{split}.target'''			).open('''w'''			).write('''\n'''.join(a			)			)
     for split in ["val", "test"]:
          __magic_name__       ,  __magic_name__						       =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
          shutil.copyfile(a		,						save_path / F'''{split}.source'''			)
          shutil.copyfile(a		,						save_path / F'''{split}.target'''			)




def 						UpperCamelCase	(			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    argparse.ArgumentParser()
     parser.add_argument('''--tok_name'''		,						type=a		,						help='''like facebook/bart-large-cnn,t5-base, etc.'''			)
     parser.add_argument('''--max_seq_len'''		,						type=a		,						default=128			)
     parser.add_argument('''--data_dir'''		,						type=a			)
     parser.add_argument('''--save_path'''		,						type=a			)
     __magic_name__						       =    parser.parse_args()
     __magic_name__						       =    AutoTokenizer.from_pretrained(args.tok_name			)
     return pack_data_dir(a		,						Path(args.data_dir			)		,						args.max_seq_len		,						args.save_path			)


if __name__ == "__main__":
      packer_cli()






