

'''simple docstring'''




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   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    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		:   Any      ):
      torch.manual_seed(0      )
      __magic_name__						       =    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		:   Optional[int]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    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(a_      )



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    self.dummy_uncond_unet
      __magic_name__						       =    DDIMScheduler()
      __magic_name__						       =    self.dummy_vq_model

      __magic_name__						       =    LDMPipeline(unet=a_			,				vqvae=a_			,				scheduler=a_      )
      ldm.to(a_      )
      ldm.set_progress_bar_config(disable=a_      )

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    ldm(generator=a_			,				num_inference_steps=2			,				output_type='''numpy'''      ).images

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    ldm(generator=a_			,				num_inference_steps=2			,				output_type='''numpy'''			,				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([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172]      )
      __magic_name__						       =    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   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''      )
      ldm.to(a_      )
      ldm.set_progress_bar_config(disable=a_      )

      __magic_name__						       =    torch.manual_seed(0      )
      __magic_name__						       =    ldm(generator=a_			,				num_inference_steps=5			,				output_type='''numpy'''      ).images

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

      assert image.shape == (1, 256, 256, 3)
      __magic_name__						       =    np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447]      )
      __magic_name__						       =    1E-2 if torch_device != '''mps''' else 3E-2

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









'''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()






