






from importlib import import_module

from .logging import get_logger


UpperCamelCase        =				get_logger(__name__)

class        lowerCAmelCase_				:







				"""simple docstring"""




				def __init__( self     :Dict  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :str=None     ):
					UpperCamelCase__ :Tuple						=      attrs or []
					if module is not None:
						for key in module.__dict__:
							if key in attrs or not key.startswith("""__"""     ):
								setattr(self  ,     A_  ,     getattr(A_  ,     A_     )     )
					UpperCamelCase__ :Optional[int]						=      module._original_module if isinstance(A_  ,     _PatchedModuleObj     ) else module

class        lowerCAmelCase_				:







				"""simple docstring"""




				_snake_case				:   Union[str, Any]        =			[]







				def __init__( self     :Optional[int]  ,     lowerCamelCase__     :List[Any]  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :Any=None     ):
					UpperCamelCase__ :Dict						=      obj
					UpperCamelCase__ :Dict						=      target
					UpperCamelCase__ :List[str]						=      new
					UpperCamelCase__ :Optional[Any]						=      target.split("""."""     )[0]
					UpperCamelCase__ :int						=      {}
					UpperCamelCase__ :Dict						=      attrs or []







				def __enter__( self     :Optional[int]     ):
					*UpperCamelCase__				,		UpperCamelCase__ :int						=      self.target.split("""."""     )

					# Patch modules:
					# it's used to patch attributes of submodules like "os.path.join";
					# in this case we need to patch "os" and "os.path"

					for i in range(len(A_     )     ):
						try:
							UpperCamelCase__ :Optional[Any]						=      import_module(""".""".join(submodules[: i + 1]     )     )
						except ModuleNotFoundError:
							continue
						# We iterate over all the globals in self.obj in case we find "os" or "os.path"
						for attr in self.obj.__dir__():
							UpperCamelCase__ :List[str]						=      getattr(self.obj  ,     A_     )
							# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
							# This allows to patch renamed modules like "from os import path as ospath".
							if obj_attr is submodule or (
							    (isinstance(A_  ,     _PatchedModuleObj     ) and obj_attr._original_module is submodule)
							):
								UpperCamelCase__ :Any						=      obj_attr
								# patch at top level
								setattr(self.obj  ,     A_  ,     _PatchedModuleObj(A_  ,     attrs=self.attrs     )     )
								UpperCamelCase__ :Dict						=      getattr(self.obj  ,     A_     )
								# construct lower levels patches
								for key in submodules[i + 1 :]:
									setattr(A_  ,     A_  ,     _PatchedModuleObj(getattr(A_  ,     A_  ,     A_     )  ,     attrs=self.attrs     )     )
									UpperCamelCase__ :List[str]						=      getattr(A_  ,     A_     )
								# finally set the target attribute
								setattr(A_  ,     A_  ,     self.new     )

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

					if submodules:  # if it's an attribute of a submodule like "os.path.join"
						try:
							UpperCamelCase__ :int						=      getattr(import_module(""".""".join(A_     )     )  ,     A_     )
						except (AttributeError, ModuleNotFoundError):
							return
						# We iterate over all the globals in self.obj in case we find "os.path.join"
						for attr in self.obj.__dir__():
							# We don't check for the name of the global, but rather if its value *is* "os.path.join".
							# This allows to patch renamed attributes like "from os.path import join as pjoin".
							if getattr(self.obj  ,     A_     ) is attr_value:
								UpperCamelCase__ :List[Any]						=      getattr(self.obj  ,     A_     )
								setattr(self.obj  ,     A_  ,     self.new     )
					elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
						UpperCamelCase__ :List[Any]						=      globals()["""__builtins__"""][target_attr]
						setattr(self.obj  ,     A_  ,     self.new     )
					else:
						raise RuntimeError(f"""Tried to patch attribute {target_attr} instead of a submodule."""     )







				def __exit__( self     :Optional[int]  ,     *lowerCamelCase__     :Optional[int]     ):
					for attr in list(self.original     ):
						setattr(self.obj  ,     A_  ,     self.original.pop(A_     )     )







				def 				__a   ( self     :int     ):
					self.__enter__()
					self._active_patches.append(self     )







				def 				__a   ( self     :str     ):
					try:
						self._active_patches.remove(self     )
					except ValueError:
						# If the patch hasn't been started this will fail
						return None

					return self.__exit__()

"""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 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     ['''image_processor''', '''tokenizer''']
						_lowerCamelCase:     Optional[int]   =     '''Pix2StructImageProcessor'''
						_lowerCamelCase:     Dict   =     ('''T5Tokenizer''', '''T5TokenizerFast''')


						def __init__(			self  :   Optional[int]	,A_  :   List[str]	,A_  :   Optional[int]   )  -> int:
							A     = False
							super().__init__(A_	,A_   )


						def __call__(			self  :   Any	,A_  :   List[str]=None	,A_  :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,A_  :   bool = True	,A_  :   Union[bool, str, PaddingStrategy] = False	,A_  :   Union[bool, str, TruncationStrategy] = None	,A_  :   Optional[int] = None	,A_  :   Optional[int] = 2048	,A_  :   int = 0	,A_  :   Optional[int] = None	,A_  :   Optional[bool] = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = True	,A_  :   Optional[Union[str, TensorType]] = None	,**A_  :   Tuple	,)  -> BatchEncoding:
							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:
								A     = self.tokenizer
								A     = 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
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,**A_   )
							else:
								# add pixel_values and bbox
								A     = 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:
								A     = 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:
									A     = text_encoding.pop('attention_mask'   )
								if "input_ids" in text_encoding:
									A     = text_encoding.pop('input_ids'   )
							else:
								A     = None

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

							return encoding_image_processor


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,*A_  :   Optional[Any]	,**A_  :   Dict   )  -> Union[str, Any]:
							return self.tokenizer.batch_decode(*A_	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,*A_  :   Tuple	,**A_  :   List[str]   )  -> Any:
							return self.tokenizer.decode(*A_	,**A_   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Any:
							A     = self.tokenizer.model_input_names
							A     = self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )