




"""simple docstring"""



import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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      UpperCAmelCase_  (						A_,       A_,       unittest.TestCase							):
    lowercase__						 =				IFInpaintingSuperResolutionPipeline
    lowercase__						 =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
    lowercase__						 =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}							)
    lowercase__						 =				PipelineTesterMixin.required_optional_params - {'''latents'''}




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     return self._get_superresolution_dummy_components()




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	List[str]     ,			snake_case_						:	Any=0				)				->							Optional[int]:




     '''simple docstring'''

     if str(snake_case_				).startswith("mps"				):
      A__	=    torch.manual_seed(snake_case_				)
     else:
      A__	=    torch.Generator(device=snake_case_				).manual_seed(snake_case_				)

     A__	=    floats_tensor((1, 3, 16, 16)     ,			rng=random.Random(snake_case_				)				).to(snake_case_				)
     A__	=    floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(snake_case_				)				).to(snake_case_				)
     A__	=    floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(snake_case_				)				).to(snake_case_				)

     A__	=    {
         "prompt": "A painting of a squirrel eating a burger",
         "image": image,
         "original_image": original_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 							__magic_name__							(   self						:	Tuple				)				->							Dict:




     '''simple docstring'''

     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)




    def 							__magic_name__							(   self						:	str				)				->							List[Any]:




     '''simple docstring'''

     self._test_save_load_optional_components()




    @unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
    def 							__magic_name__							(   self						:	Optional[int]				)				->							Optional[Any]:




     '''simple docstring'''

     super().test_save_load_floataa(expected_max_diff=1e-1				)




    def 							__magic_name__							(   self						:	str				)				->							str:




     '''simple docstring'''

     self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)




    def 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

     self._test_save_load_local()




    def 							__magic_name__							(   self						:	str				)				->							List[str]:




     '''simple docstring'''

     self._test_inference_batch_single_identical(
         expected_max_diff=1e-2     ,			)











"""simple docstring"""



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE    =	{name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)						->		Any:
 if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
  raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()						)}."""						)

 if tokenizer_name is None:
  A__	=    TOKENIZER_CLASSES
 else:
  A__	=    {tokenizer_name: getattr(lowercase_      ,			tokenizer_name + "Fast"						)}

 logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""						)

 for tokenizer_name in tokenizer_names:
  A__	=    TOKENIZER_CLASSES[tokenizer_name]

  A__	=    True
  if checkpoint_name is None:
   A__	=    list(tokenizer_class.max_model_input_sizes.keys()						)
  else:
   A__	=    [checkpoint_name]

  logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""						)

  for checkpoint in checkpoint_names:
   logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""						)

   # Load tokenizer
   A__	=    tokenizer_class.from_pretrained(lowercase_      ,			force_download=lowercase_						)

   # Save fast tokenizer
   logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""						)

   # For organization names we create sub-directories
   if "/" in checkpoint:
    A__,				A__	=    checkpoint.split("/"						)
    A__	=    os.path.join(lowercase_      ,			lowercase_						)
   elif add_prefix:
    A__	=    checkpoint
    A__	=    dump_path
   else:
    A__	=    None
    A__	=    dump_path

   logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""						)

   if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()						)[0]:
    A__	=    list(tokenizer.pretrained_vocab_files_map.values()						)[0][checkpoint]
    A__	=    file_path.split(lowercase_						)[-1][0]
    if next_char == "/":
     A__	=    os.path.join(lowercase_      ,			lowercase_						)
     A__	=    None

    logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""						)

   A__	=    tokenizer.save_pretrained(
       lowercase_      ,			legacy_format=lowercase_      ,			filename_prefix=lowercase_						)
   logger.info(f"""=> File names {file_names}"""						)

   for file_name in file_names:
    if not file_name.endswith("tokenizer.json"						):
     os.remove(lowercase_						)
     logger.info(f"""=> removing {file_name}"""						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
      )
      parser.add_argument(
          "--tokenizer_name",
          default=None,
          type=str,
          help=(
              f'Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '
              "download and convert all the checkpoints from AWS."
          ),
      )
      parser.add_argument(
          "--checkpoint_name",
          default=None,
          type=str,
          help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
      )
      parser.add_argument(
          "--force_download",
          action="store_true",
          help="Re-download checkpoints.",
      )
      SCREAMING_SNAKE_CASE    =	parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)





