


'''simple docstring'''



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

from .. import config
from .logging import get_logger


__A		=get_logger(__name__)


class 						_snake_case   (       enum.Enum		):
					lowerCAmelCase :Dict						=   """all_checks"""
					lowerCAmelCase :List[Any]						=   """basic_checks"""
					lowerCAmelCase :Optional[int]						=   """no_checks"""


class 						_snake_case   (       a__		):
					pass


class 						_snake_case   (       a__		):
					pass


class 						_snake_case   (       a__		):
					pass


class 						_snake_case   (       a__		):
					pass







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None   ) ->    Optional[Any]:





			if expected_checksums is None:
						logger.info("""Unable to verify checksums."""   )
						return
			if len(set(UpperCAmelCase_   ) - set(UpperCAmelCase_   )   ) > 0:
						raise ExpectedMoreDownloadedFiles(str(set(UpperCAmelCase_   ) - set(UpperCAmelCase_   )   )   )
			if len(set(UpperCAmelCase_   ) - set(UpperCAmelCase_   )   ) > 0:
						raise UnexpectedDownloadedFile(str(set(UpperCAmelCase_   ) - set(UpperCAmelCase_   )   )   )
			UpperCAmelCase__					:   List[str]       				=       [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
			UpperCAmelCase__					:   int       				=       ' for ' + verification_name if verification_name is not None else ''
			if len(UpperCAmelCase_   ) > 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__		):
					pass


class 						_snake_case   (       a__		):
					pass


class 						_snake_case   (       a__		):
					pass


class 						_snake_case   (       a__		):
					pass







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ) ->    str:





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







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__ = True   ) ->    dict:





			if record_checksum:
						UpperCAmelCase__					:   Dict       				=       shaaaa()
						with open(UpperCAmelCase_	, """rb"""   ) as f:
									for chunk in iter(lambda: f.read(1 << 2_0   )	, b""""""   ):
												m.update(UpperCAmelCase_   )
									UpperCAmelCase__					:   List[str]       				=       m.hexdigest()
			else:
						UpperCAmelCase__					:   Dict       				=       None
			return {"num_bytes": os.path.getsize(UpperCAmelCase_   ), "checksum": checksum}







def       _UpperCamelCase (						UpperCamelCase__   ) ->    Tuple:





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



'''simple docstring'''



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 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[int]						=   ['''image_processor''', '''tokenizer''']
					lowerCAmelCase :Optional[int]						=   '''BridgeTowerImageProcessor'''
					lowerCAmelCase :List[str]						=   ('''RobertaTokenizer''', '''RobertaTokenizerFast''')




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




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

								return encoding




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.tokenizer.model_input_names
								UpperCAmelCase__					:   str       				=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))