import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__lowerCamelCase      :			Dict							       =		False







class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    str	,     _lowercase      :    Optional[Any]=32				):



     """simple docstring"""






     set_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				UNetaDModel(sample_size=_lowercase	,     in_channels=3	,     out_channels=3				)
     SCREAMING_SNAKE_CASE__      							=				torch.optim.SGD(model.parameters()	,     lr=0.00_01				)
     return model, optimizer




   @slow
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
     SCREAMING_SNAKE_CASE__      							=				DDPMScheduler(
         num_train_timesteps=10_00	,     beta_start=0.00_01	,     beta_end=0.02	,     beta_schedule="""linear"""	,     clip_sample=_lowercase	,     )
     SCREAMING_SNAKE_CASE__      							=				DDIMScheduler(
         num_train_timesteps=10_00	,     beta_start=0.00_01	,     beta_end=0.02	,     beta_schedule="""linear"""	,     clip_sample=_lowercase	,     )

     assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

     # shared batches for DDPM and DDIM
     set_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				[torch.randn((4, 3, 32, 32)				).clip(-1	,     1				).to(_lowercase				) for _ in range(4				)]
     SCREAMING_SNAKE_CASE__      							=				[torch.randn((4, 3, 32, 32)				).to(_lowercase				) for _ in range(4				)]
     SCREAMING_SNAKE_CASE__      							=				[torch.randint(0	,     10_00	,     (4,)				).long().to(_lowercase				) for _ in range(4				)]

     # train with a DDPM scheduler
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.get_model_optimizer(resolution=32				)
     model.train().to(_lowercase				)
     for i in range(4				):
       optimizer.zero_grad()
       SCREAMING_SNAKE_CASE__      							=				ddpm_scheduler.add_noise(clean_images[i]	,     noise[i]	,     timesteps[i]				)
       SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     timesteps[i]				).sample
       SCREAMING_SNAKE_CASE__      							=				torch.nn.functional.mse_loss(_lowercase	,     noise[i]				)
       loss.backward()
       optimizer.step()
     del model, optimizer

     # recreate the model and optimizer, and retry with DDIM
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self.get_model_optimizer(resolution=32				)
     model.train().to(_lowercase				)
     for i in range(4				):
       optimizer.zero_grad()
       SCREAMING_SNAKE_CASE__      							=				ddim_scheduler.add_noise(clean_images[i]	,     noise[i]	,     timesteps[i]				)
       SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     timesteps[i]				).sample
       SCREAMING_SNAKE_CASE__      							=				torch.nn.functional.mse_loss(_lowercase	,     noise[i]				)
       loss.backward()
       optimizer.step()
     del model, optimizer

     self.assertTrue(torch.allclose(_lowercase	,     _lowercase	,     atol=1E-5				)				)
     self.assertTrue(torch.allclose(_lowercase	,     _lowercase	,     atol=1E-5				)				)

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__lowerCamelCase      :			int							       =		logging.get_logger(__name__)


__lowerCamelCase      :			Tuple							       =		{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Optional[Any]     )							-> Union[str, Any]:







  """simple docstring"""





  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:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES
  else:
    SCREAMING_SNAKE_CASE__      							=				{tokenizer_name: getattr(__UpperCamelCase			,					tokenizer_name + """Fast"""     )}

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

  for tokenizer_name in tokenizer_names:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES[tokenizer_name]

    SCREAMING_SNAKE_CASE__      							=				True
    if checkpoint_name is None:
      SCREAMING_SNAKE_CASE__      							=				list(tokenizer_class.max_model_input_sizes.keys()     )
    else:
      SCREAMING_SNAKE_CASE__      							=				[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
      SCREAMING_SNAKE_CASE__      							=				tokenizer_class.from_pretrained(__UpperCamelCase			,					force_download=__UpperCamelCase     )

      # 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:
        SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				checkpoint.split("""/"""     )
        SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
      elif add_prefix:
        SCREAMING_SNAKE_CASE__      							=				checkpoint
        SCREAMING_SNAKE_CASE__      							=				dump_path
      else:
        SCREAMING_SNAKE_CASE__      							=				None
        SCREAMING_SNAKE_CASE__      							=				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]:
        SCREAMING_SNAKE_CASE__      							=				list(tokenizer.pretrained_vocab_files_map.values()     )[0][checkpoint]
        SCREAMING_SNAKE_CASE__      							=				file_path.split(__UpperCamelCase     )[-1][0]
        if next_char == "/":
          SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
          SCREAMING_SNAKE_CASE__      							=				None

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

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

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


if __name__ == "__main__":
   __lowerCamelCase      :			str							       =		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.''',
   )
   __lowerCamelCase      :			Any							       =		parser.parse_args()

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