




import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_UpperCAmelCase		:  int	=   logging.get_logger(__name__)


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



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  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:
    lowercase			:int							=  TOKENIZER_CLASSES
  else:
    lowercase			:Optional[Any]							=  {tokenizer_name: getattr(lowerCamelCase,	tokenizer_name + "Fast"      )}

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

  for tokenizer_name in tokenizer_names:
    lowercase			:List[Any]							=  TOKENIZER_CLASSES[tokenizer_name]

    lowercase			:Union[str, Any]							=  True
    if checkpoint_name is None:
      lowercase			:int							=  list(tokenizer_class.max_model_input_sizes.keys()      )
    else:
      lowercase			:Dict							=  [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
      lowercase			:Any							=  tokenizer_class.from_pretrained(lowerCamelCase,	force_download=lowerCamelCase      )

      # 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:
        lowercase		,			lowercase			:Any							=  checkpoint.split("/"      )
        lowercase			:Dict							=  os.path.join(lowerCamelCase,	lowerCamelCase      )
      elif add_prefix:
        lowercase			:int							=  checkpoint
        lowercase			:List[Any]							=  dump_path
      else:
        lowercase			:Any							=  None
        lowercase			:Union[str, 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]:
        lowercase			:List[str]							=  list(tokenizer.pretrained_vocab_files_map.values()      )[0][checkpoint]
        lowercase			:Optional[int]							=  file_path.split(lowerCamelCase      )[-1][0]
        if next_char == "/":
          lowercase			:Union[str, Any]							=  os.path.join(lowerCamelCase,	lowerCamelCase      )
          lowercase			:Optional[Any]							=  None

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

      lowercase			:int							=  tokenizer.save_pretrained(
          lowerCamelCase,	legacy_format=lowerCamelCase,	filename_prefix=lowerCamelCase      )
      logger.info(F"=> File names {file_names}"      )

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


if __name__ == "__main__":
     _UpperCAmelCase		:  Optional[int]	=   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.",
     )
     _UpperCAmelCase		:  int	=   parser.parse_args()

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









import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor




class       __lowerCAmelCase				(							unittest.TestCase):






       def __init__(      self:    List[Any]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict=7				,    _lowerCAmelCase:    Optional[Any]=3				,    _lowerCAmelCase:    List[Any]=18				,    _lowerCAmelCase:    int=30				,    _lowerCAmelCase:    Union[str, Any]=4_00				,    _lowerCAmelCase:    Any=True				,    _lowerCAmelCase:    Union[str, Any]=32				,    _lowerCAmelCase:    int=True				,    ):
         lowercase			:Any							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:Any							=  num_channels
         lowercase			:Optional[Any]							=  image_size
         lowercase			:Optional[Any]							=  min_resolution
         lowercase			:str							=  max_resolution
         lowercase			:str							=  do_resize
         lowercase			:Optional[int]							=  size_divisor
         lowercase			:Tuple							=  do_rescale






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return {
             "do_resize": self.do_resize,
             "size_divisor": self.size_divisor,
             "do_rescale": self.do_rescale,
         }







@require_torch
@require_vision
class       __lowerCAmelCase				(							lowerCAmelCase					,       unittest.TestCase):
       _a           =  GLPNImageProcessor if is_vision_available() else None






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:int							=  GLPNImageProcessingTester(self )






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return self.image_processor_tester.prepare_image_processor_dict()






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:Optional[Any]							=  self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "do_resize" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "size_divisor" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "resample" ) )
         self.assertTrue(hasattr(_lowerCAmelCase				,    "do_rescale" ) )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         pass






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         # Initialize image_processing
         lowercase			:Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         lowercase			:List[str]							=  prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_lowerCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,    Image.Image )

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         lowercase			:str							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         # Initialize image_processing
         lowercase			:Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         lowercase			:Union[str, Any]							=  prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_lowerCAmelCase				,    numpify=_lowerCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,    np.ndarray )

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         lowercase			:Optional[int]							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         # Initialize image_processing
         lowercase			:Optional[Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         lowercase			:List[str]							=  prepare_image_inputs(self.image_processor_tester				,    equal_resolution=_lowerCAmelCase				,    torchify=_lowerCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase				,    torch.Tensor )

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         lowercase			:Dict							=  image_processing(image_inputs[0]				,    return_tensors="pt" ).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )



