'''simple docstring'''






import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


_UpperCAmelCase    :     Union[str, Any]         =       get_logger(__name__)







class lowercase_       (		enum.Enum			):







   """simple docstring"""

   __lowerCAmelCase         =   "all_checks"
   __lowerCAmelCase         =   "basic_checks"
   __lowerCAmelCase         =   "no_checks"







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""








class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""








class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""








class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""







def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[dict]   ,		__snake_case							:			dict   ,		__snake_case							:			str=None				):
     if expected_checksums is None:
          logger.info('Unable to verify checksums.'				)
          return
     if len(set(__snake_case				) - set(__snake_case				)				) > 0:
          raise ExpectedMoreDownloadedFiles(str(set(__snake_case				) - set(__snake_case				)				)				)
     if len(set(__snake_case				) - set(__snake_case				)				) > 0:
          raise UnexpectedDownloadedFile(str(set(__snake_case				) - set(__snake_case				)				)				)
     _A    		=  [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
     _A    		=  ' for ' + verification_name if verification_name is not None else ''
     if len(__snake_case				) > 0:
          raise NonMatchingChecksumError(
              F'Checksums didn\'t match{for_verification_name}:\n'
              F'{bad_urls}\n'
              'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'				)
     logger.info('All the checksums matched successfully' + for_verification_name				)







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""








class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""








class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""








class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""







def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[dict]   ,		__snake_case							:			dict				):
     if expected_splits is None:
          logger.info('Unable to verify splits sizes.'				)
          return
     if len(set(__snake_case				) - set(__snake_case				)				) > 0:
          raise ExpectedMoreSplits(str(set(__snake_case				) - set(__snake_case				)				)				)
     if len(set(__snake_case				) - set(__snake_case				)				) > 0:
          raise UnexpectedSplits(str(set(__snake_case				) - set(__snake_case				)				)				)
     _A    		=  [
         {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
         for name in expected_splits
         if expected_splits[name].num_examples != recorded_splits[name].num_examples
     ]
     if len(__snake_case				) > 0:
          raise NonMatchingSplitsSizesError(str(__snake_case				)				)
     logger.info('All the splits matched successfully.'				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			bool = True				):
     if record_checksum:
          _A    		=  shaaaa()
          with open(__snake_case   ,		'rb'				) as f:
               for chunk in iter(lambda: f.read(1 << 2_0				)   ,		B''				):
                    m.update(__snake_case				)
               _A    		=  m.hexdigest()
     else:
          _A    		=  None
     return {"num_bytes": os.path.getsize(__snake_case				), "checksum": checksum}





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     if dataset_size and config.IN_MEMORY_MAX_SIZE:
          return dataset_size < config.IN_MEMORY_MAX_SIZE
     else:
          return False





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['image_processor', 'tokenizer']
 __lowerCAmelCase	:  Union[str, Any]	   =      'AutoImageProcessor'
 __lowerCAmelCase	:  int	   =      'AutoTokenizer'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   self.image_processor


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none.""")

   if text is not None:
     lowercase__	: List[str]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if images is not None:
     lowercase__	: Optional[int]     		=   self.image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if text is not None and images is not None:
     lowercase__	: Union[str, Any]     		=   image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_)       ,	tensor_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)



 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return ["input_ids", "attention_mask", "pixel_values"]






