




import math

import flax.linen as nn
import jax.numpy as jnp

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict     ,							lowercase     :	Union[str, Any]     ,							lowercase     :	Tuple = 1     ,							lowercase     :	Any = 1     ,							lowercase     :	int = 1.0e4     ,							lowercase     :	List[str] = False     ,							lowercase     :	Optional[Any] = 1.0     ,							):


						'''simple docstring'''



						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even"""
						lowerCamelCase_							=   float(embedding_dim // 2					)
						lowerCamelCase_							=   math.log(max_timescale / min_timescale					) / (num_timescales - freq_shift)
						lowerCamelCase_							=   min_timescale * jnp.exp(jnp.arange(lowercase__     ,							dtype=jnp.floataa					) * -log_timescale_increment					)
						lowerCamelCase_							=   jnp.expand_dims(lowercase__     ,							1					) * jnp.expand_dims(lowercase__     ,							0					)

						# scale embeddings
						lowerCamelCase_							=   scale * emb

						if flip_sin_to_cos:
												lowerCamelCase_							=   jnp.concatenate([jnp.cos(lowercase__					), jnp.sin(lowercase__					)]     ,							axis=1					)
						else:
												lowerCamelCase_							=   jnp.concatenate([jnp.sin(lowercase__					), jnp.cos(lowercase__					)]     ,							axis=1					)
						lowerCamelCase_							=   jnp.reshape(lowercase__     ,							[jnp.shape(lowercase__					)[0], embedding_dim]					)
						return signal







class   A(						nn.Module       ):



	'''simple docstring'''


	UpperCamelCase						       =						32
	UpperCamelCase						       =						jnp.floataa

	@nn.compact
	def __call__(    self :  Tuple						,							A_ :  int  )       ->     Optional[Any]:

							"""simple docstring"""






							lowerCamelCase_							=   nn.Dense(self.time_embed_dim						,							dtype=self.dtype						,							name='linear_1'  )(__SCREAMING_SNAKE_CASE  )
							lowerCamelCase_							=   nn.silu(__SCREAMING_SNAKE_CASE  )
							lowerCamelCase_							=   nn.Dense(self.time_embed_dim						,							dtype=self.dtype						,							name='linear_2'  )(__SCREAMING_SNAKE_CASE  )
							return temb







class   A(						nn.Module       ):



	'''simple docstring'''


	UpperCamelCase						       =						32
	UpperCamelCase						       =						False
	UpperCamelCase						       =						1

	@nn.compact
	def __call__(    self :  Optional[int]						,							A_ :  str  )       ->     List[Any]:

							"""simple docstring"""






							return get_sinusoidal_embeddings(
							    __SCREAMING_SNAKE_CASE						,							embedding_dim=self.dim						,							flip_sin_to_cos=self.flip_sin_to_cos						,							freq_shift=self.freq_shift  )









import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase    :		Union[str, Any]			   =       logging.get_logger(__name__)


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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	str     ,							lowercase     :	int     ,							lowercase     :	List[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:
            lowerCamelCase_							=   TOKENIZER_CLASSES
      else:
            lowerCamelCase_							=   {tokenizer_name: getattr(lowercase     ,							tokenizer_name + 'Fast'					)}

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

      for tokenizer_name in tokenizer_names:
            lowerCamelCase_							=   TOKENIZER_CLASSES[tokenizer_name]

            lowerCamelCase_							=   True
            if checkpoint_name is None:
                  lowerCamelCase_							=   list(tokenizer_class.max_model_input_sizes.keys()					)
            else:
                  lowerCamelCase_							=   [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
                  lowerCamelCase_							=   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:
                        lowerCamelCase_					,    lowerCamelCase_							=   checkpoint.split('/'					)
                        lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                  elif add_prefix:
                        lowerCamelCase_							=   checkpoint
                        lowerCamelCase_							=   dump_path
                  else:
                        lowerCamelCase_							=   None
                        lowerCamelCase_							=   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]:
                        lowerCamelCase_							=   list(tokenizer.pretrained_vocab_files_map.values()					)[0][checkpoint]
                        lowerCamelCase_							=   file_path.split(lowercase					)[-1][0]
                        if next_char == "/":
                              lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                              lowerCamelCase_							=   None

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

                  lowerCamelCase_							=   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__":
 lowerCamelCase    :		Tuple			   =       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    :		Union[str, Any]			   =       parser.parse_args()

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



