


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

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE :   int							      =				get_logger(__name__)






class _lowerCamelCase(		enum.Enum     ):
       lowercase_				:			Tuple									=    """all_checks"""
       lowercase_				:			Union[str, Any]									=    """basic_checks"""
       lowercase_				:			Tuple									=    """no_checks"""






class _lowerCamelCase(		_a     ):
       pass






class _lowerCamelCase(		_a     ):
       pass






class _lowerCamelCase(		_a     ):
       pass






class _lowerCamelCase(		_a     ):
       pass



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_=None				)					->		Optional[Any]:
    if expected_checksums is None:
        logger.info('Unable to verify checksums.'				)
        return
    if len(set(lowerCamelCase_				) - set(lowerCamelCase_				)				) > 0:
        raise ExpectedMoreDownloadedFiles(str(set(lowerCamelCase_				) - set(lowerCamelCase_				)				)				)
    if len(set(lowerCamelCase_				) - set(lowerCamelCase_				)				) > 0:
        raise UnexpectedDownloadedFile(str(set(lowerCamelCase_				) - set(lowerCamelCase_				)				)				)
    _lowercase :							List[Any]   	=	[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
    _lowercase :							Optional[Any]   	=	' for ' + verification_name if verification_name is not None else ''
    if len(lowerCamelCase_				) > 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 _lowerCamelCase(		_a     ):
       pass






class _lowerCamelCase(		_a     ):
       pass






class _lowerCamelCase(		_a     ):
       pass






class _lowerCamelCase(		_a     ):
       pass



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		str:
    if expected_splits is None:
        logger.info('Unable to verify splits sizes.'				)
        return
    if len(set(lowerCamelCase_				) - set(lowerCamelCase_				)				) > 0:
        raise ExpectedMoreSplits(str(set(lowerCamelCase_				) - set(lowerCamelCase_				)				)				)
    if len(set(lowerCamelCase_				) - set(lowerCamelCase_				)				) > 0:
        raise UnexpectedSplits(str(set(lowerCamelCase_				) - set(lowerCamelCase_				)				)				)
    _lowercase :							List[str]   	=	[
        {'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(lowerCamelCase_				) > 0:
        raise NonMatchingSplitsSizesError(str(lowerCamelCase_				)				)
    logger.info('All the splits matched successfully.'				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_ = True				)					->		dict:
    if record_checksum:
        _lowercase :							Dict   	=	shaaaa()
        with open(lowerCamelCase_							,				'rb'				) as f:
            for chunk in iter(lambda: f.read(1 << 20				)							,				B''				):
                m.update(lowerCamelCase_				)
            _lowercase :							Any   	=	m.hexdigest()
    else:
        _lowercase :							str   	=	None
    return {"num_bytes": os.path.getsize(lowerCamelCase_				), "checksum": checksum}



def        UpperCamelCase_(   lowerCamelCase_				)					->		List[Any]:
    if dataset_size and config.IN_MEMORY_MAX_SIZE:
        return dataset_size < config.IN_MEMORY_MAX_SIZE
    else:
        return False






import warnings

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






class _lowerCamelCase(		_a     ):
       lowercase_				:			int									=    ["""image_processor""", """tokenizer"""]
       lowercase_				:			List[str]									=    """CLIPImageProcessor"""
       lowercase_				:			Union[str, Any]									=    ("""CLIPTokenizer""", """CLIPTokenizerFast""")
       def __init__(      self,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.',   lowerCamelCase,   )
               _lowercase :							Dict   	=	kwargs.pop('feature_extractor')

           _lowercase :							int   	=	image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('You need to specify an `image_processor`.')
           if tokenizer is None:
               raise ValueError('You need to specify a `tokenizer`.')

           super().__init__(lowerCamelCase,   lowerCamelCase)
       def __call__(      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     Tuple:




           """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 :							Optional[Any]   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)

           if images is not None:
               _lowercase :							List[Any]   	=	self.image_processor(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)

           if text is not None and images is not None:
               _lowercase :							int   	=	image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowerCamelCase),   tensor_type=lowerCamelCase)
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return self.tokenizer.batch_decode(*lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     int:




           """simple docstring"""

           return self.tokenizer.decode(*lowerCamelCase,   **lowerCamelCase)
       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							int   	=	self.tokenizer.model_input_names
           _lowercase :							Dict   	=	self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
       @property
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           warnings.warn(
               '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',   lowerCamelCase,   )
           return self.image_processor_class




       @property
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',   lowerCamelCase,   )
           return self.image_processor


