



"""simple docstring"""




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 ConvNextImageProcessor



class   a__		(						unittest.TestCase     ):


   def __init__(							self      :							int,     lowerCAmelCase      :							Dict,     lowerCAmelCase      :							Optional[int]=7,     lowerCAmelCase      :							Union[str, Any]=3,     lowerCAmelCase      :							int=18,     lowerCAmelCase      :							List[Any]=30,     lowerCAmelCase      :							Any=400,     lowerCAmelCase      :							int=True,     lowerCAmelCase      :							List[Any]=None,     lowerCAmelCase      :							int=0.875,     lowerCAmelCase      :							List[str]=True,     lowerCAmelCase      :							Optional[Any]=[0.5, 0.5, 0.5],     lowerCAmelCase      :							Tuple=[0.5, 0.5, 0.5],     )    ->       List[str]:
         lowercase  :      Tuple					=   size if size is not None else {'''shortest_edge''': 20}
         lowercase  :      List[Any]					=   parent
         lowercase  :      Optional[int]					=   batch_size
         lowercase  :      str					=   num_channels
         lowercase  :      Union[str, Any]					=   image_size
         lowercase  :      Optional[Any]					=   min_resolution
         lowercase  :      Union[str, Any]					=   max_resolution
         lowercase  :      Tuple					=   do_resize
         lowercase  :      List[Any]					=   size
         lowercase  :      Optional[int]					=   crop_pct
         lowercase  :      Any					=   do_normalize
         lowercase  :      Optional[int]					=   image_mean
         lowercase  :      Union[str, Any]					=   image_std






   def        lowercase				(							self      :							str			)    ->       str:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }




@require_torch
@require_vision
class   a__		(						__UpperCAmelCase,  unittest.TestCase     ):
   _lowerCamelCase												=				ConvNextImageProcessor if is_vision_available() else None


   def        lowercase				(							self      :							Optional[int]			)    ->       Dict:
         lowercase  :      List[Any]					=   ConvNextImageProcessingTester(self			)


   @property
   def        lowercase				(							self      :							List[str]			)    ->       Dict:
         return self.image_processor_tester.prepare_image_processor_dict()


   def        lowercase				(							self      :							Dict			)    ->       str:
         lowercase  :      List[Any]					=   self.image_processing_class(**self.image_processor_dict			)
         self.assertTrue(hasattr(lowercase_,     'do_resize'			)			)
         self.assertTrue(hasattr(lowercase_,     'size'			)			)
         self.assertTrue(hasattr(lowercase_,     'crop_pct'			)			)
         self.assertTrue(hasattr(lowercase_,     'do_normalize'			)			)
         self.assertTrue(hasattr(lowercase_,     'image_mean'			)			)
         self.assertTrue(hasattr(lowercase_,     'image_std'			)			)


   def        lowercase				(							self      :							int			)    ->       List[Any]:
         lowercase  :      int					=   self.image_processing_class.from_dict(self.image_processor_dict			)
         self.assertEqual(image_processor.size,     {'shortest_edge': 20}			)

         lowercase  :      Tuple					=   self.image_processing_class.from_dict(self.image_processor_dict,     size=42			)
         self.assertEqual(image_processor.size,     {'shortest_edge': 42}			)


   def        lowercase				(							self      :							List[Any]			)    ->       List[Any]:
         pass


   def        lowercase				(							self      :							Union[str, Any]			)    ->       int:
         lowercase  :      Tuple					=   self.image_processing_class(**self.image_processor_dict			)
         # create random PIL images
         lowercase  :      List[str]					=   prepare_image_inputs(self.image_processor_tester,     equal_resolution=lowercase_			)
         for image in image_inputs:
               self.assertIsInstance(lowercase_,     Image.Image			)

         # Test not batched input
         lowercase  :      List[str]					=   image_processing(image_inputs[0],     return_tensors='pt'			).pixel_values
         self.assertEqual(
             encoded_images.shape,     (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),     )

         # Test batched
         lowercase  :      int					=   image_processing(lowercase_,     return_tensors='pt'			).pixel_values
         self.assertEqual(
             encoded_images.shape,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),     )


   def        lowercase				(							self      :							Tuple			)    ->       Dict:
         lowercase  :      Tuple					=   self.image_processing_class(**self.image_processor_dict			)
         # create random numpy tensors
         lowercase  :      Tuple					=   prepare_image_inputs(self.image_processor_tester,     equal_resolution=lowercase_,     numpify=lowercase_			)
         for image in image_inputs:
               self.assertIsInstance(lowercase_,     np.ndarray			)

         # Test not batched input
         lowercase  :      List[str]					=   image_processing(image_inputs[0],     return_tensors='pt'			).pixel_values
         self.assertEqual(
             encoded_images.shape,     (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),     )

         # Test batched
         lowercase  :      Union[str, Any]					=   image_processing(lowercase_,     return_tensors='pt'			).pixel_values
         self.assertEqual(
             encoded_images.shape,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),     )






   def        lowercase				(							self      :							Optional[Any]			)    ->       Union[str, Any]:
         lowercase  :      Union[str, Any]					=   self.image_processing_class(**self.image_processor_dict			)
         # create random PyTorch tensors
         lowercase  :      Dict					=   prepare_image_inputs(self.image_processor_tester,     equal_resolution=lowercase_,     torchify=lowercase_			)
         for image in image_inputs:
               self.assertIsInstance(lowercase_,     torch.Tensor			)

         # Test not batched input
         lowercase  :      Union[str, Any]					=   image_processing(image_inputs[0],     return_tensors='pt'			).pixel_values
         self.assertEqual(
             encoded_images.shape,     (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),     )

         # Test batched
         lowercase  :      List[Any]					=   image_processing(lowercase_,     return_tensors='pt'			).pixel_values
         self.assertEqual(
             encoded_images.shape,     (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             ),     )





"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode


def   lowercase__     (			_UpperCAmelCase=10				)     ->			Any:






      '''simple docstring'''


      for _ in tqdm(range(0	,      args.n_examples	,      _UpperCAmelCase				)				):
            yield [next(_UpperCAmelCase				)[args.text_column] for _ in range(_UpperCAmelCase				)]


# Configuration
_UpperCamelCase:				Union[str, Any]    							=  HfArgumentParser(TokenizerTrainingArguments)
_UpperCamelCase:				Dict    							=  parser.parse_args()

# Base tokenizer
_UpperCamelCase:				List[Any]    							=  AutoTokenizer.from_pretrained(args.base_tokenizer)
_UpperCamelCase:				int    							=  list(bytes_to_unicode().values())

# Load dataset
_UpperCamelCase:				Union[str, Any]    							=  load_dataset(args.dataset_name, split='train', streaming=True)
_UpperCamelCase:				Union[str, Any]    							=  iter(dataset)


# Training and saving
_UpperCamelCase:				Union[str, Any]    							=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)





new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
