
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  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = ["""image_processor""", """tokenizer"""]
			__UpperCamelCase      :						Any					      = """Pix2StructImageProcessor"""
			__UpperCamelCase      :						Union[str, Any]					      = ("""T5Tokenizer""", """T5TokenizerFast""")

			def __init__(       self :       Tuple						,	snake_case_ :       List[Any]						,	snake_case_ :       Optional[Any]      ):
							UpperCamelCase_:							Optional[Any]	=						False
							super().__init__(snake_case_						,	snake_case_      )

			def __call__(       self :       Optional[Any]						,	snake_case_ :       List[Any]=None						,	snake_case_ :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,	snake_case_ :       bool = True						,	snake_case_ :       Union[bool, str, PaddingStrategy] = False						,	snake_case_ :       Union[bool, str, TruncationStrategy] = None						,	snake_case_ :       Optional[int] = None						,	snake_case_ :       Optional[int] = 2048						,	snake_case_ :       int = 0						,	snake_case_ :       Optional[int] = None						,	snake_case_ :       Optional[bool] = None						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = True						,	snake_case_ :       Optional[Union[str, TensorType]] = None						,	**snake_case_ :       Any						,	):
							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:
											UpperCamelCase_:							str	=						self.tokenizer
											UpperCamelCase_:							Tuple	=						self.tokenizer(
											    text=snake_case_						,	add_special_tokens=snake_case_						,	padding=snake_case_						,	truncation=snake_case_						,	max_length=snake_case_						,	stride=snake_case_						,	pad_to_multiple_of=snake_case_						,	return_attention_mask=snake_case_						,	return_overflowing_tokens=snake_case_						,	return_special_tokens_mask=snake_case_						,	return_offsets_mapping=snake_case_						,	return_token_type_ids=snake_case_						,	return_length=snake_case_						,	verbose=snake_case_						,	return_tensors=snake_case_						,	**snake_case_						,	)
											return text_encoding

							if not self.image_processor.is_vqa:
											# add pixel_values
											UpperCamelCase_:							Tuple	=						self.image_processor(
											    snake_case_						,	return_tensors=snake_case_						,	max_patches=snake_case_						,	**snake_case_      )
							else:
											# add pixel_values and bbox
											UpperCamelCase_:							Tuple	=						self.image_processor(
											    snake_case_						,	return_tensors=snake_case_						,	max_patches=snake_case_						,	header_text=snake_case_						,	**snake_case_      )

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

											if "attention_mask" in text_encoding:
															UpperCamelCase_:							Any	=						text_encoding.pop("""attention_mask"""      )
											if "input_ids" in text_encoding:
															UpperCamelCase_:							str	=						text_encoding.pop("""input_ids"""      )
							else:
											UpperCamelCase_:							int	=						None

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

							return encoding_image_processor

			def       lowerCAmelCase__	(       self :       List[Any]						,	*snake_case_ :       Tuple						,	**snake_case_ :       List[str]      ):
							return self.tokenizer.batch_decode(*snake_case_						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict						,	*snake_case_ :       Optional[int]						,	**snake_case_ :       List[Any]      ):
							return self.tokenizer.decode(*snake_case_						,	**snake_case_      )




			@property
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Union[str, Any]	=						self.tokenizer.model_input_names
							UpperCamelCase_:							List[str]	=						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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_						: Optional[Any]         =  {
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[Any]         =  ["""DistilBertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[int]         =  [
							    """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DistilBertForMaskedLM""",
							    """DistilBertForMultipleChoice""",
							    """DistilBertForQuestionAnswering""",
							    """DistilBertForSequenceClassification""",
							    """DistilBertForTokenClassification""",
							    """DistilBertModel""",
							    """DistilBertPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[str]         =  [
							    """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDistilBertForMaskedLM""",
							    """TFDistilBertForMultipleChoice""",
							    """TFDistilBertForQuestionAnswering""",
							    """TFDistilBertForSequenceClassification""",
							    """TFDistilBertForTokenClassification""",
							    """TFDistilBertMainLayer""",
							    """TFDistilBertModel""",
							    """TFDistilBertPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """FlaxDistilBertForMaskedLM""",
							    """FlaxDistilBertForMultipleChoice""",
							    """FlaxDistilBertForQuestionAnswering""",
							    """FlaxDistilBertForSequenceClassification""",
							    """FlaxDistilBertForTokenClassification""",
							    """FlaxDistilBertModel""",
							    """FlaxDistilBertPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_distilbert import (
							    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DistilBertConfig,
							    DistilBertOnnxConfig,
							)
							from .tokenization_distilbert import DistilBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_distilbert_fast import DistilBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_distilbert import (
														    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DistilBertForMaskedLM,
														    DistilBertForMultipleChoice,
														    DistilBertForQuestionAnswering,
														    DistilBertForSequenceClassification,
														    DistilBertForTokenClassification,
														    DistilBertModel,
														    DistilBertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_distilbert import (
														    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDistilBertForMaskedLM,
														    TFDistilBertForMultipleChoice,
														    TFDistilBertForQuestionAnswering,
														    TFDistilBertForSequenceClassification,
														    TFDistilBertForTokenClassification,
														    TFDistilBertMainLayer,
														    TFDistilBertModel,
														    TFDistilBertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_distilbert import (
														    FlaxDistilBertForMaskedLM,
														    FlaxDistilBertForMultipleChoice,
														    FlaxDistilBertForQuestionAnswering,
														    FlaxDistilBertForSequenceClassification,
														    FlaxDistilBertForTokenClassification,
														    FlaxDistilBertModel,
														    FlaxDistilBertPreTrainedModel,
														)

else:
							import sys

							lowerCamelCase_						: Optional[Any]         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






