
import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def 				a						(							snake_case__:					List[str]					):


				'''simple docstring'''
				lowercase_       =					{}
				lowercase_       =					tokenizer(example['''content''']						,   truncation=snake_case__					)['''input_ids''']
				lowercase_       =					len(example['''content''']					) / len(output['''input_ids''']					)
				return output


__a  			=					HfArgumentParser(PretokenizationArguments)
__a  			=					parser.parse_args()
if args.num_workers is None:
						__a  			=					multiprocessing.cpu_count()
__a  			=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

__a  			=					time.time()
__a  			=					load_dataset(args.dataset_name, split='train')
print(f"Dataset loaded in {time.time()-t_start:.2f}s")

__a  			=					time.time()
__a  			=					ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(f"Dataset tokenized in {time.time()-t_start:.2f}s")

__a  			=					time.time()
ds.push_to_hub(args.tokenized_data_repo)


print(f"Data pushed to the hub in {time.time()-t_start:.2f}s")











'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class   A_	(  unittest.TestCase			):




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   tempfile.mkdtemp()

      _UpperCAmelCase     							=   BlipImageProcessor()
      _UpperCAmelCase     							=   BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"    )

      _UpperCAmelCase     							=   BlipProcessor(snake_case_	,			snake_case_    )

      processor.save_pretrained(self.tmpdirname    )




 def      lowercase	(					self       :    Tuple	,			**snake_case_       :    int    ):
      return AutoProcessor.from_pretrained(self.tmpdirname	,			**snake_case_    ).tokenizer




 def      lowercase	(					self       :    Dict	,			**snake_case_       :    Any    ):
      return AutoProcessor.from_pretrained(self.tmpdirname	,			**snake_case_    ).image_processor




 def      lowercase	(					self       :    int    ):
      shutil.rmtree(self.tmpdirname    )




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   [np.random.randint(2_5_5	,			size=(3, 3_0, 4_0_0)	,			dtype=np.uinta    )]

      _UpperCAmelCase     							=   [Image.fromarray(np.moveaxis(snake_case_	,			0	,			-1    )    ) for x in image_inputs]

      return image_inputs




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   BlipProcessor(tokenizer=self.get_tokenizer()	,			image_processor=self.get_image_processor()    )
      processor.save_pretrained(self.tmpdirname    )

      _UpperCAmelCase     							=   self.get_tokenizer(bos_token="(BOS)"	,			eos_token="(EOS)"    )
      _UpperCAmelCase     							=   self.get_image_processor(do_normalize=snake_case_	,			padding_value=1.0    )

      _UpperCAmelCase     							=   BlipProcessor.from_pretrained(
          self.tmpdirname	,			bos_token="(BOS)"	,			eos_token="(EOS)"	,			do_normalize=snake_case_	,			padding_value=1.0    )

      self.assertEqual(processor.tokenizer.get_vocab()	,			tokenizer_add_kwargs.get_vocab()    )
      self.assertIsInstance(processor.tokenizer	,			snake_case_    )

      self.assertEqual(processor.image_processor.to_json_string()	,			image_processor_add_kwargs.to_json_string()    )
      self.assertIsInstance(processor.image_processor	,			snake_case_    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="np"    )
      _UpperCAmelCase     							=   processor(images=snake_case_	,			return_tensors="np"    )

      for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()	,			input_processor[key].sum()	,			delta=1e-2    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"

      _UpperCAmelCase     							=   processor(text=snake_case_    )

      _UpperCAmelCase     							=   tokenizer(snake_case_	,			return_token_type_ids=snake_case_    )

      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]	,			encoded_processor[key]    )




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"
      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   processor(text=snake_case_	,			images=snake_case_    )

      self.assertListEqual(list(inputs.keys()    )	,			["pixel_values", "input_ids", "attention_mask"]    )

      # test if it raises when no input is passed
      with pytest.raises(snake_case_    ):
           processor()




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _UpperCAmelCase     							=   processor.batch_decode(snake_case_    )
      _UpperCAmelCase     							=   tokenizer.batch_decode(snake_case_    )

      self.assertListEqual(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"
      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   processor(text=snake_case_	,			images=snake_case_    )

      # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
      self.assertListEqual(list(inputs.keys()    )	,			["pixel_values", "input_ids", "attention_mask"]    )




