


'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			lowerCamelCase__ ,			unittest.TestCase ):
     UpperCAmelCase 					=				IFInpaintingPipeline
     UpperCAmelCase 					=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     UpperCAmelCase 					=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase 					=				PipelineTesterMixin.required_optional_params - {'''latents'''}






     def  a_					(			self		)       ->    int:
          return self._get_dummy_components()






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=0		)       ->    Any:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a						=		torch.manual_seed(__UpperCamelCase		)
          else:
               _a						=		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)

          _a						=		floats_tensor((1, 3, 32, 32)     ,							rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)
          _a						=		floats_tensor((1, 3, 32, 32)     ,							rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)

          _a						=		{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "mask_image": mask_image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs






     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()     ,							reason="XFormers attention is only available with CUDA and `xformers` installed"     ,							)
     def  a_					(			self		)       ->    List[str]:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)






     def  a_					(			self		)       ->    Optional[int]:
          self._test_save_load_optional_components()






     @unittest.skipIf(torch_device != "cuda"     ,							reason="float16 requires CUDA"		)
     def  a_					(			self		)       ->    Optional[int]:
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1		)






     def  a_					(			self		)       ->    Optional[Any]:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)






     def  a_					(			self		)       ->    int:
          self._test_save_load_local()






     def  a_					(			self		)       ->    int:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2     ,							)





'''simple docstring'''

import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def __UpperCamelCase   (			__lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Dict=1024				)      ->     str:







     '''simple docstring'''






     _a			,			_a						=		[], []

     _a						=		list(zip(__lowerCamelCase      ,  __lowerCamelCase				)				)
     _a			,			_a						=		sorted_examples[0]

     def is_too_big(__lowerCamelCase		: Union[str, Any]				):
          return tok(__lowerCamelCase      ,  return_tensors="pt"				).input_ids.shape[1] > max_tokens

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

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






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Path      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[int]				)      ->     Optional[int]:







     '''simple docstring'''






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






def __UpperCamelCase   (			)      ->     Any:







     '''simple docstring'''






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


if __name__ == "__main__":
   packer_cli()

