


'''simple docstring'''







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

from .. import config
from .logging import get_logger


_SCREAMING_SNAKE_CASE							: str										=			get_logger(__name__)






class 		_snake_case     (					enum.Enum		):






						'''simple docstring'''







						__snake_case				=   """all_checks"""
						__snake_case				=   """basic_checks"""
						__snake_case				=   """no_checks"""






class 		_snake_case     (					A__		):






						'''simple docstring'''







						pass






class 		_snake_case     (					A__		):






						'''simple docstring'''







						pass






class 		_snake_case     (					A__		):






						'''simple docstring'''







						pass






class 		_snake_case     (					A__		):






						'''simple docstring'''







						pass






def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__=None		):






				"""simple docstring"""



				if expected_checksums is None:
								logger.info("Unable to verify checksums."		)
								return
				if len(set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)		) > 0:
								raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)		)		)
				if len(set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)		) > 0:
								raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)		)		)
				__magic_name__					:       int    							=							[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
				__magic_name__					:       str    							=							" for " + verification_name if verification_name is not None else ""
				if len(SCREAMING_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 		_snake_case     (					A__		):






						'''simple docstring'''







						pass






class 		_snake_case     (					A__		):






						'''simple docstring'''







						pass






class 		_snake_case     (					A__		):






						'''simple docstring'''







						pass






class 		_snake_case     (					A__		):






						'''simple docstring'''







						pass






def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				if expected_splits is None:
								logger.info("Unable to verify splits sizes."		)
								return
				if len(set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)		) > 0:
								raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)		)		)
				if len(set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)		) > 0:
								raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE_		) - set(SCREAMING_SNAKE_CASE_		)		)		)
				__magic_name__					:       int    							=							[
				    {"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(SCREAMING_SNAKE_CASE_		) > 0:
								raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE_		)		)
				logger.info("All the splits matched successfully."		)



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__ = True		):






				"""simple docstring"""



				if record_checksum:
								__magic_name__					:       int    							=							shaaaa()
								with open(SCREAMING_SNAKE_CASE_  , "rb"		) as f:
												for chunk in iter(lambda: f.read(1 << 20		)  , B""		):
																m.update(SCREAMING_SNAKE_CASE_		)
												__magic_name__					:       Any    							=							m.hexdigest()
				else:
								__magic_name__					:       Any    							=							None
				return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE_		), "checksum": checksum}




def     _UpperCamelCase				(			UpperCamelCase__		):






				"""simple docstring"""



				if dataset_size and config.IN_MEMORY_MAX_SIZE:
								return dataset_size < config.IN_MEMORY_MAX_SIZE
				else:
								return False






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   ["""image_processor""", """tokenizer"""]
     __A	:  List[str]							  =   """BridgeTowerImageProcessor"""
     __A	:  str							  =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							_UpperCamelCase					)





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = 0				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.tokenizer(
            text=_UpperCamelCase				,							add_special_tokens=_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							max_length=_UpperCamelCase				,							stride=_UpperCamelCase				,							pad_to_multiple_of=_UpperCamelCase				,							return_token_type_ids=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							return_overflowing_tokens=_UpperCamelCase				,							return_special_tokens_mask=_UpperCamelCase				,							return_offsets_mapping=_UpperCamelCase				,							return_length=_UpperCamelCase				,							verbose=_UpperCamelCase				,							return_tensors=_UpperCamelCase				,							**_UpperCamelCase				,							)
        # add pixel_values + pixel_mask
        _UpperCAmelCase           =   self.image_processor(
            _UpperCamelCase				,							return_tensors=_UpperCamelCase				,							do_normalize=_UpperCamelCase				,							do_center_crop=_UpperCamelCase				,							**_UpperCamelCase					)
        encoding.update(_UpperCamelCase					)

        return encoding





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.batch_decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.tokenizer.model_input_names
        _UpperCAmelCase           =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)