



'''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 		_UpperCamelCase						(							UpperCamelCase__					):


			'''simple docstring'''

			_A   :	Any    					=	['''image_processor''', '''tokenizer''']
			_A   :	Optional[Any]    					=	'''Pix2StructImageProcessor'''
			_A   :	Tuple    					=	('''T5Tokenizer''', '''T5TokenizerFast''')

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				Dict							,  lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	False
					super().__init__(_a							,  _a		)

			def __call__(							self  :				Tuple							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				Dict = None							,  lowerCAmelCase__  :				Optional[Any] = True							,  lowerCAmelCase__  :				Optional[int] = False							,  lowerCAmelCase__  :				Dict = None							,  lowerCAmelCase__  :				int = None							,  lowerCAmelCase__  :				Dict = 2_0_4_8							,  lowerCAmelCase__  :				Any = 0							,  lowerCAmelCase__  :				Union[str, Any] = None							,  lowerCAmelCase__  :				str = None							,  lowerCAmelCase__  :				List[Any] = False							,  lowerCAmelCase__  :				List[Any] = False							,  lowerCAmelCase__  :				Optional[int] = False							,  lowerCAmelCase__  :				str = False							,  lowerCAmelCase__  :				Optional[Any] = False							,  lowerCAmelCase__  :				Any = True							,  lowerCAmelCase__  :				Optional[Any] = None							,  **lowerCAmelCase__  :				Tuple							,  ):





					"""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:
							__SCREAMING_SNAKE_CASE :	List[str]					 =	self.tokenizer
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.tokenizer(
							    text=_a							,  add_special_tokens=_a							,  padding=_a							,  truncation=_a							,  max_length=_a							,  stride=_a							,  pad_to_multiple_of=_a							,  return_attention_mask=_a							,  return_overflowing_tokens=_a							,  return_special_tokens_mask=_a							,  return_offsets_mapping=_a							,  return_token_type_ids=_a							,  return_length=_a							,  verbose=_a							,  return_tensors=_a							,  **_a							,  )
							return text_encoding

					if not self.image_processor.is_vqa:
							# add pixel_values
							__SCREAMING_SNAKE_CASE :	Dict					 =	self.image_processor(
							    _a							,  return_tensors=_a							,  max_patches=_a							,  **_a		)
					else:
							# add pixel_values and bbox
							__SCREAMING_SNAKE_CASE :	Dict					 =	self.image_processor(
							    _a							,  return_tensors=_a							,  max_patches=_a							,  header_text=_a							,  **_a		)

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

							if "attention_mask" in text_encoding:
									__SCREAMING_SNAKE_CASE :	List[str]					 =	text_encoding.pop("""attention_mask"""		)
							if "input_ids" in text_encoding:
									__SCREAMING_SNAKE_CASE :	str					 =	text_encoding.pop("""input_ids"""		)
					else:
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	None

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

					return encoding_image_processor

			def UpperCamelCase__      (							self  :				Any							,  *lowerCAmelCase__  :				List[Any]							,  **lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				str							,  *lowerCAmelCase__  :				int							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






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



			@property
			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






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




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ :			List[Any]												=				{
    '''configuration_time_series_transformer''': [
        '''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TimeSeriesTransformerConfig''',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :			List[Any]												=				[
						    '''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TimeSeriesTransformerForPrediction''',
						    '''TimeSeriesTransformerModel''',
						    '''TimeSeriesTransformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_time_series_transformer import (
						    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TimeSeriesTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_time_series_transformer import (
												    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TimeSeriesTransformerForPrediction,
												    TimeSeriesTransformerModel,
												    TimeSeriesTransformerPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :			Dict												=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)