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				(							_UpperCamelCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	str								=	["image_processor", "tokenizer"]
						_UpperCamelCase  :	int								=	"Pix2StructImageProcessor"
						_UpperCamelCase  :	int								=	("T5Tokenizer", "T5TokenizerFast")







						def __init__(    self			:		Any ,				lowerCamelCase_			:		List[Any] ,				lowerCamelCase_			:		Any					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   False
										super().__init__(_UpperCAmelCase ,				_UpperCAmelCase					)







						def __call__(    self			:		Any ,				lowerCamelCase_			:		Dict=None ,				lowerCamelCase_			:		Optional[Any] = None ,				lowerCamelCase_			:		List[Any] = True ,				lowerCamelCase_			:		Tuple = False ,				lowerCamelCase_			:		Optional[Any] = None ,				lowerCamelCase_			:		int = None ,				lowerCamelCase_			:		Optional[Any] = 20_48 ,				lowerCamelCase_			:		Any = 0 ,				lowerCamelCase_			:		Optional[int] = None ,				lowerCamelCase_			:		Optional[int] = None ,				lowerCamelCase_			:		Any = False ,				lowerCamelCase_			:		Any = False ,				lowerCamelCase_			:		Optional[Any] = False ,				lowerCamelCase_			:		str = False ,				lowerCamelCase_			:		int = False ,				lowerCamelCase_			:		str = True ,				lowerCamelCase_			:		Optional[Any] = None ,				**lowerCamelCase_			:		Union[str, Any] ,				):





										'''simple docstring'''







										if images is None and text is None:
														raise ValueError('You have to specify either images or text.'					)

										# Get only text
										if images is None and not self.image_processor.is_vqa:
														_snake_case :			int  						=   self.tokenizer
														_snake_case :			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_attention_mask=_UpperCAmelCase ,				return_overflowing_tokens=_UpperCAmelCase ,				return_special_tokens_mask=_UpperCAmelCase ,				return_offsets_mapping=_UpperCAmelCase ,				return_token_type_ids=_UpperCAmelCase ,				return_length=_UpperCAmelCase ,				verbose=_UpperCAmelCase ,				return_tensors=_UpperCAmelCase ,				**_UpperCAmelCase ,				)
														return text_encoding

										if not self.image_processor.is_vqa:
														# add pixel_values
														_snake_case :			Optional[Any]  						=   self.image_processor(
														    _UpperCAmelCase ,				return_tensors=_UpperCAmelCase ,				max_patches=_UpperCAmelCase ,				**_UpperCAmelCase					)
										else:
														# add pixel_values and bbox
														_snake_case :			Dict  						=   self.image_processor(
														    _UpperCAmelCase ,				return_tensors=_UpperCAmelCase ,				max_patches=_UpperCAmelCase ,				header_text=_UpperCAmelCase ,				**_UpperCAmelCase					)

										if text is not None and not self.image_processor.is_vqa:
														_snake_case :			Dict  						=   self.tokenizer(
														    text=_UpperCAmelCase ,				add_special_tokens=_UpperCAmelCase ,				padding=_UpperCAmelCase ,				truncation=_UpperCAmelCase ,				max_length=_UpperCAmelCase ,				stride=_UpperCAmelCase ,				pad_to_multiple_of=_UpperCAmelCase ,				return_attention_mask=_UpperCAmelCase ,				return_overflowing_tokens=_UpperCAmelCase ,				return_special_tokens_mask=_UpperCAmelCase ,				return_offsets_mapping=_UpperCAmelCase ,				return_token_type_ids=_UpperCAmelCase ,				return_length=_UpperCAmelCase ,				verbose=_UpperCAmelCase ,				return_tensors=_UpperCAmelCase ,				**_UpperCAmelCase ,				)

														if "attention_mask" in text_encoding:
																		_snake_case :			List[str]  						=   text_encoding.pop('attention_mask'					)
														if "input_ids" in text_encoding:
																		_snake_case :			Dict  						=   text_encoding.pop('input_ids'					)
										else:
														_snake_case :			Optional[Any]  						=   None

										if text_encoding is not None:
														encoding_image_processor.update(_UpperCAmelCase					)

										return encoding_image_processor







						def 		__UpperCAmelCase       (    self			:		int ,				*lowerCamelCase_			:		List[str] ,				**lowerCamelCase_			:		List[Any]					):





										'''simple docstring'''







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







						def 		__UpperCAmelCase       (    self			:		Dict ,				*lowerCamelCase_			:		Optional[int] ,				**lowerCamelCase_			:		Any					):





										'''simple docstring'''







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







						@property
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   self.tokenizer.model_input_names
										_snake_case :			Dict  						=   self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_		:				Any				=						{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Optional[Any]				=						[
		    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CanineForMultipleChoice''',
		    '''CanineForQuestionAnswering''',
		    '''CanineForSequenceClassification''',
		    '''CanineForTokenClassification''',
		    '''CanineLayer''',
		    '''CanineModel''',
		    '''CaninePreTrainedModel''',
		    '''load_tf_weights_in_canine''',
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		lowercase_		:				str				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






