




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 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       ["""image_processor""", """tokenizer"""]
						UpperCamelCase      =       """Pix2StructImageProcessor"""
						UpperCamelCase      =       ("""T5Tokenizer""", """T5TokenizerFast""")

						def __init__(		self :Optional[Any]     ,			__snake_case :int     ,			__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=False
										super().__init__(__snake_case     ,			__snake_case			)

						def __call__(		self :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] = 20_48     ,			__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     ,			):
										'''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:
														__magic_name__						:						Any										=self.tokenizer
														__magic_name__						:						Dict										=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
														__magic_name__						:						Dict										=self.image_processor(
														    __snake_case     ,			return_tensors=__snake_case     ,			max_patches=__snake_case     ,			**__snake_case			)
										else:
														# add pixel_values and bbox
														__magic_name__						:						Optional[int]										=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:
														__magic_name__						:						int										=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:
																		__magic_name__						:						Tuple										=text_encoding.pop("""attention_mask"""			)
														if "input_ids" in text_encoding:
																		__magic_name__						:						List[str]										=text_encoding.pop("""input_ids"""			)
										else:
														__magic_name__						:						Any										=None

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

										return encoding_image_processor

						def      A__    (		self :List[Any]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

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

						def      A__    (		self :Optional[Any]     ,			*__snake_case :Optional[int]     ,			**__snake_case :str			):
										'''simple docstring'''

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


						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						int										=self.tokenizer.model_input_names
										__magic_name__						:						Any										=self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)











'''simple docstring'''




import unittest

from transformers import DonutProcessor


__UpperCAmelCase            =       """naver-clova-ix/donut-base"""




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			DonutProcessor.from_pretrained(lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			{
							    """name""": """John Doe""",
							    """age""": """99""",
							    """city""": """Atlanta""",
							    """state""": """GA""",
							    """zip""": """30301""",
							    """phone""": """123-4567""",
							    """nicknames""": [{"""nickname""": """Johnny"""}, {"""nickname""": """JD"""}],
							}

							SCREAMING_SNAKE_CASE :				List[Any]		=			(
							    """<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"""
							    """<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"""
							    """<s_nicknames><s_nickname>Johnny</s_nickname>"""
							    """<sep/><s_nickname>JD</s_nickname></s_nicknames>"""
							)
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.processor.tokenajson(lowerCamelCase_						)

							self.assertDictEqual(lowerCamelCase_      ,						lowerCamelCase_						)



