

"""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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Tuple  				=	tempfile.mkdtemp()

						lowerCAmelCase__	:						List[Any]  				=	BlipImageProcessor()
						lowerCAmelCase__	:						Dict  				=	GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''		)

						lowerCAmelCase__	:						Optional[Any]  				=	BlipaProcessor(lowercase_   ,lowercase_		)

						processor.save_pretrained(self.tmpdirname		)







					def        __lowerCAmelCase (       self			:				Dict   ,**lowercase_			:				Dict		):
						return AutoProcessor.from_pretrained(self.tmpdirname   ,**lowercase_		).tokenizer







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,**lowercase_			:				Tuple		):
						return AutoProcessor.from_pretrained(self.tmpdirname   ,**lowercase_		).image_processor







					def        __lowerCAmelCase (       self			:				List[Any]		):
						shutil.rmtree(self.tmpdirname		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	[np.random.randint(2_5_5   ,size=(3, 3_0, 4_0_0)   ,dtype=np.uinta		)]

						lowerCAmelCase__	:						Any  				=	[Image.fromarray(np.moveaxis(lowercase_   ,0   ,-1		)		) for x in image_inputs]

						return image_inputs







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						str  				=	BlipaProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						lowerCAmelCase__	:						str  				=	self.get_tokenizer(bos_token='''(BOS)'''   ,eos_token='''(EOS)'''		)
						lowerCAmelCase__	:						Optional[int]  				=	self.get_image_processor(do_normalize=lowercase_   ,padding_value=1.0		)

						lowerCAmelCase__	:						List[str]  				=	BlipaProcessor.from_pretrained(
						    self.tmpdirname   ,bos_token='''(BOS)'''   ,eos_token='''(EOS)'''   ,do_normalize=lowercase_   ,padding_value=1.0		)

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

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







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Tuple  				=	self.get_image_processor()
						lowerCAmelCase__	:						List[Any]  				=	self.get_tokenizer()

						lowerCAmelCase__	:						Tuple  				=	BlipaProcessor(tokenizer=lowercase_   ,image_processor=lowercase_		)

						lowerCAmelCase__	:						int  				=	self.prepare_image_inputs()

						lowerCAmelCase__	:						Dict  				=	image_processor(lowercase_   ,return_tensors='''np'''		)
						lowerCAmelCase__	:						Dict  				=	processor(images=lowercase_   ,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        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	self.get_image_processor()
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_tokenizer()

						lowerCAmelCase__	:						Optional[Any]  				=	BlipaProcessor(tokenizer=lowercase_   ,image_processor=lowercase_		)

						lowerCAmelCase__	:						Any  				=	'''lower newer'''

						lowerCAmelCase__	:						Optional[Any]  				=	processor(text=lowercase_		)

						lowerCAmelCase__	:						Dict  				=	tokenizer(lowercase_   ,return_token_type_ids=lowercase_		)

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







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						int  				=	self.get_image_processor()
						lowerCAmelCase__	:						Optional[Any]  				=	self.get_tokenizer()

						lowerCAmelCase__	:						Optional[Any]  				=	BlipaProcessor(tokenizer=lowercase_   ,image_processor=lowercase_		)

						lowerCAmelCase__	:						Any  				=	'''lower newer'''
						lowerCAmelCase__	:						Optional[int]  				=	self.prepare_image_inputs()

						lowerCAmelCase__	:						List[Any]  				=	processor(text=lowercase_   ,images=lowercase_		)

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

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







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	self.get_image_processor()
						lowerCAmelCase__	:						Tuple  				=	self.get_tokenizer()

						lowerCAmelCase__	:						List[Any]  				=	BlipaProcessor(tokenizer=lowercase_   ,image_processor=lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase__	:						Dict  				=	processor.batch_decode(lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	tokenizer.batch_decode(lowercase_		)

						self.assertListEqual(lowercase_   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_image_processor()
						lowerCAmelCase__	:						Dict  				=	self.get_tokenizer()

						lowerCAmelCase__	:						Union[str, Any]  				=	BlipaProcessor(tokenizer=lowercase_   ,image_processor=lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	'''lower newer'''
						lowerCAmelCase__	:						Tuple  				=	self.prepare_image_inputs()

						lowerCAmelCase__	:						List[Any]  				=	processor(text=lowercase_   ,images=lowercase_		)

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







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    {}
							snake_case_							=    tokenizer(example['content']   ,							truncation=UpperCamelCase__      )['input_ids']
							snake_case_							=    len(example['content']      ) / len(output['input_ids']      )
							return output


_UpperCAmelCase		:   Dict										=       HfArgumentParser(PretokenizationArguments)
_UpperCAmelCase		:   List[Any]										=       parser.parse_args()
if args.num_workers is None:
					_UpperCAmelCase		:   Union[str, Any]										=       multiprocessing.cpu_count()
_UpperCAmelCase		:   int										=       AutoTokenizer.from_pretrained(args.tokenizer_dir)

_UpperCAmelCase		:   Optional[int]										=       time.time()
_UpperCAmelCase		:   List[str]										=       load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   Union[str, Any]										=       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''')

_UpperCAmelCase		:   Dict										=       time.time()
ds.push_to_hub(args.tokenized_data_repo)


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




