





"""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()

lowercase__				:Dict				   =       logging.get_logger(__name__)


lowercase__				:Optional[Any]				   =       {name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->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:
    __UpperCAmelCase    :						Any       =						TOKENIZER_CLASSES
  else:
    __UpperCAmelCase    :						Optional[Any]       =						{tokenizer_name: getattr(UpperCAmelCase_  ,			tokenizer_name + '''Fast'''  )}

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

  for tokenizer_name in tokenizer_names:
    __UpperCAmelCase    :						Optional[Any]       =						TOKENIZER_CLASSES[tokenizer_name]

    __UpperCAmelCase    :						List[str]       =						True
    if checkpoint_name is None:
      __UpperCAmelCase    :						Optional[int]       =						list(tokenizer_class.max_model_input_sizes.keys()  )
    else:
      __UpperCAmelCase    :						Optional[int]       =						[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
      __UpperCAmelCase    :						List[str]       =						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:
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						checkpoint.split('''/'''  )
        __UpperCAmelCase    :						Any       =						os.path.join(UpperCAmelCase_  ,			UpperCAmelCase_  )
      elif add_prefix:
        __UpperCAmelCase    :						Dict       =						checkpoint
        __UpperCAmelCase    :						Tuple       =						dump_path
      else:
        __UpperCAmelCase    :						List[str]       =						None
        __UpperCAmelCase    :						Any       =						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]:
        __UpperCAmelCase    :						int       =						list(tokenizer.pretrained_vocab_files_map.values()  )[0][checkpoint]
        __UpperCAmelCase    :						Dict       =						file_path.split(UpperCAmelCase_  )[-1][0]
        if next_char == "/":
          __UpperCAmelCase    :						Tuple       =						os.path.join(UpperCAmelCase_  ,			UpperCAmelCase_  )
          __UpperCAmelCase    :						Dict       =						None

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

      __UpperCAmelCase    :						int       =						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__":
  lowercase__				:List[Any]				   =       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.',
  )
  lowercase__				:Optional[Any]				   =       parser.parse_args()

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






"""simple docstring"""





import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard




@slow
@require_flax
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      str ):


        '''simple docstring'''




        super().tearDown()
        gc.collect()

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						List[Any]       =						FlaxStableDiffusionPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2'''						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)

        __UpperCAmelCase    :						Dict       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						Any       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						replicate(__lowercase )
        __UpperCAmelCase    :						Optional[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						str       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						Dict       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						Tuple       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Any       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Union[str, Any]       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						str       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						List[Any]       =						jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2







      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						'''stabilityai/stable-diffusion-2'''
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						FlaxDPMSolverMultistepScheduler.from_pretrained(__lowercase						,				subfolder='''scheduler''' )
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						FlaxStableDiffusionPipeline.from_pretrained(
            __lowercase						,				scheduler=__lowercase						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)
        __UpperCAmelCase    :						Dict       =						scheduler_params

        __UpperCAmelCase    :						int       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						List[Any]       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						replicate(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						List[Any]       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						List[Any]       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Tuple       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Any       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						List[str]       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						Any       =						jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2