


'''simple docstring'''
import warnings
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      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       ['image_processor', 'tokenizer']
		_a									=       'ViltImageProcessor'
		_a									=       ('BertTokenizer', 'BertTokenizerFast')




		def __init__(	self					,     UpperCamelCase_=None					,     UpperCamelCase_=None					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[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.'''					,     UpperCamelCase_					,     )
												UpperCamelCase__     :List[str]							      =				kwargs.pop('''feature_extractor'''							)

							UpperCamelCase__     :Tuple							      =				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__(UpperCamelCase_					,     UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				self.image_processor




		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_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				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__     :str							      =				self.image_processor(UpperCamelCase_					,     return_tensors=UpperCamelCase_							)
							encoding.update(UpperCamelCase_							)

							return encoding




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


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




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.tokenizer.model_input_names
							UpperCamelCase__     :Union[str, Any]							      =				self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case							   =  {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''ReformerTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''ReformerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  [
		    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ReformerAttention''',
		    '''ReformerForMaskedLM''',
		    '''ReformerForQuestionAnswering''',
		    '''ReformerForSequenceClassification''',
		    '''ReformerLayer''',
		    '''ReformerModel''',
		    '''ReformerModelWithLMHead''',
		    '''ReformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer import ReformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer_fast import ReformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_reformer import (
				    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ReformerAttention,
				    ReformerForMaskedLM,
				    ReformerForQuestionAnswering,
				    ReformerForSequenceClassification,
				    ReformerLayer,
				    ReformerModel,
				    ReformerModelWithLMHead,
				    ReformerPreTrainedModel,
				)

else:
		import sys

		__snake_case							   =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)