


'''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__(       lowerCAmelCase	):
				__magic_name__    :				Union[str, Any]  		=	["image_processor", "tokenizer"]
				__magic_name__    :				int  		=	"Pix2StructImageProcessor"
				__magic_name__    :				str  		=	("T5Tokenizer", "T5TokenizerFast")




				def __init__(		self      :			Any				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			List[Any]				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     False
										super().__init__(lowerCAmelCase				,   lowerCAmelCase				)




				def __call__(		self      :			List[str]				,   lowerCAmelCase      :			str=None				,   lowerCAmelCase      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,   lowerCAmelCase      :			bool = True				,   lowerCAmelCase      :			Union[bool, str, PaddingStrategy] = False				,   lowerCAmelCase      :			Union[bool, str, TruncationStrategy] = None				,   lowerCAmelCase      :			Optional[int] = None				,   lowerCAmelCase      :			Optional[int] = 2048				,   lowerCAmelCase      :			int = 0				,   lowerCAmelCase      :			Optional[int] = None				,   lowerCAmelCase      :			Optional[bool] = None				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			bool = True				,   lowerCAmelCase      :			Optional[Union[str, TensorType]] = None				,   **lowerCAmelCase      :			List[str]				,   )->					BatchEncoding:

										"""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:
																UpperCAmelCase					    =     self.tokenizer
																UpperCAmelCase					    =     self.tokenizer(
																    text=lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				,   padding=lowerCAmelCase				,   truncation=lowerCAmelCase				,   max_length=lowerCAmelCase				,   stride=lowerCAmelCase				,   pad_to_multiple_of=lowerCAmelCase				,   return_attention_mask=lowerCAmelCase				,   return_overflowing_tokens=lowerCAmelCase				,   return_special_tokens_mask=lowerCAmelCase				,   return_offsets_mapping=lowerCAmelCase				,   return_token_type_ids=lowerCAmelCase				,   return_length=lowerCAmelCase				,   verbose=lowerCAmelCase				,   return_tensors=lowerCAmelCase				,   **lowerCAmelCase				,   )
																return text_encoding

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

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

																if "attention_mask" in text_encoding:
																						UpperCAmelCase					    =     text_encoding.pop('''attention_mask'''				)
																if "input_ids" in text_encoding:
																						UpperCAmelCase					    =     text_encoding.pop('''input_ids'''				)
										else:
																UpperCAmelCase					    =     None

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

										return encoding_image_processor




				def 					a__(		self      :			Optional[int]				,   *lowerCAmelCase      :			Dict				,   **lowerCAmelCase      :			Optional[Any]				)->					Optional[int]:

										"""simple docstring"""




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




				def 					a__(		self      :			Dict				,   *lowerCAmelCase      :			int				,   **lowerCAmelCase      :			Union[str, Any]				)->					Dict:

										"""simple docstring"""




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





				@property
				def 					a__(		self      :			Optional[int]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     self.tokenizer.model_input_names
										UpperCAmelCase					    =     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


_lowercase      :      List[Any]            =					{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[str]            =					[
	    """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XCLIPModel""",
	    """XCLIPPreTrainedModel""",
	    """XCLIPTextModel""",
	    """XCLIPVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_x_clip import (
	    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XCLIPConfig,
	    XCLIPTextConfig,
	    XCLIPVisionConfig,
	)
	from .processing_x_clip import XCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_x_clip import (
		    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XCLIPModel,
		    XCLIPPreTrainedModel,
		    XCLIPTextModel,
		    XCLIPVisionModel,
		)

else:
	import sys

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





