


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 MobileNetVaImageProcessor



class 							lowercase__(						unittest.TestCase			):






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=7   ,			__SCREAMING_SNAKE_CASE=3   ,			__SCREAMING_SNAKE_CASE=18   ,			__SCREAMING_SNAKE_CASE=30   ,			__SCREAMING_SNAKE_CASE=4_00   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=None   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=None   ,			)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=size if size is not None else {"shortest_edge": 20}
         UpperCamelCase__ :			Any 						=crop_size if crop_size is not None else {"height": 18, "width": 18}
         UpperCamelCase__ :			Optional[Any] 						=parent
         UpperCamelCase__ :			Optional[Any] 						=batch_size
         UpperCamelCase__ :			str 						=num_channels
         UpperCamelCase__ :			Dict 						=image_size
         UpperCamelCase__ :			Optional[int] 						=min_resolution
         UpperCamelCase__ :			int 						=max_resolution
         UpperCamelCase__ :			int 						=do_resize
         UpperCamelCase__ :			str 						=size
         UpperCamelCase__ :			Dict 						=do_center_crop
         UpperCamelCase__ :			str 						=crop_size




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }




@require_torch
@require_vision
class 							lowercase__(						snake_case__			,							unittest.TestCase			):






      '''simple docstring'''







      snake_case__       =					MobileNetVaImageProcessor if is_vision_available() else None




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=MobileNetVaImageProcessingTester(self)




      @property
      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=self.image_processing_class(**self.image_processor_dict)
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,			"do_resize"))
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,			"size"))
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,			"do_center_crop"))
         self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,			"center_crop"))




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=self.image_processing_class.from_dict(self.image_processor_dict)
         self.assertEqual(image_processor.size   ,			{"shortest_edge": 20})
         self.assertEqual(image_processor.crop_size   ,			{"height": 18, "width": 18})

         UpperCamelCase__ :			Union[str, Any] 						=self.image_processing_class.from_dict(self.image_processor_dict   ,			size=42   ,			crop_size=84)
         self.assertEqual(image_processor.size   ,			{"shortest_edge": 42})
         self.assertEqual(image_processor.crop_size   ,			{"height": 84, "width": 84})




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         pass




      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=self.image_processing_class(**self.image_processor_dict)
         # create random PIL images
         UpperCamelCase__ :			int 						=prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=__SCREAMING_SNAKE_CASE)
         for image in image_inputs:
            self.assertIsInstance(__SCREAMING_SNAKE_CASE   ,			Image.Image)

         # Test not batched input
         UpperCamelCase__ :			Optional[int] 						=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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)

         # Test batched
         UpperCamelCase__ :			List[str] 						=image_processing(__SCREAMING_SNAKE_CASE   ,			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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         UpperCamelCase__ :			Dict 						=prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=__SCREAMING_SNAKE_CASE   ,			numpify=__SCREAMING_SNAKE_CASE)
         for image in image_inputs:
            self.assertIsInstance(__SCREAMING_SNAKE_CASE   ,			np.ndarray)

         # Test not batched input
         UpperCamelCase__ :			Optional[int] 						=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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)

         # Test batched
         UpperCamelCase__ :			Tuple 						=image_processing(__SCREAMING_SNAKE_CASE   ,			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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         UpperCamelCase__ :			List[str] 						=prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=__SCREAMING_SNAKE_CASE   ,			torchify=__SCREAMING_SNAKE_CASE)
         for image in image_inputs:
            self.assertIsInstance(__SCREAMING_SNAKE_CASE   ,			torch.Tensor)

         # Test not batched input
         UpperCamelCase__ :			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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)

         # Test batched
         UpperCamelCase__ :			Tuple 						=image_processing(__SCREAMING_SNAKE_CASE   ,			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.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)





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 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






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


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  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)

