




"""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  lowercase(					_lowerCAmelCase			):







 '''simple docstring'''





 lowercase__			 = ["image_processor", "tokenizer"]
 lowercase__			 = "Pix2StructImageProcessor"
 lowercase__			 = ("T5Tokenizer", "T5TokenizerFast")


 def __init__(    self:		Any,				a_:		Optional[int],				a_:		str		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  False
   super().__init__(SCREAMING_SNAKE_CASE_,				SCREAMING_SNAKE_CASE_		)


 def __call__(    self:		int,				a_:		Optional[Any]=None,				a_:		Optional[Any] = None,				a_:		Any = True,				a_:		Any = False,				a_:		List[str] = None,				a_:		Optional[int] = None,				a_:		str = 2_048,				a_:		List[str] = 0,				a_:		Optional[int] = None,				a_:		List[str] = None,				a_:		Dict = False,				a_:		List[str] = False,				a_:		str = False,				a_:		Any = False,				a_:		int = False,				a_:		Union[str, Any] = True,				a_:		str = None,				**a_:		int,				):




   '''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:
     _snake_case							:		Optional[Any]								=  self.tokenizer
     _snake_case							:		Optional[Any]								=  self.tokenizer(
         text=SCREAMING_SNAKE_CASE_,				add_special_tokens=SCREAMING_SNAKE_CASE_,				padding=SCREAMING_SNAKE_CASE_,				truncation=SCREAMING_SNAKE_CASE_,				max_length=SCREAMING_SNAKE_CASE_,				stride=SCREAMING_SNAKE_CASE_,				pad_to_multiple_of=SCREAMING_SNAKE_CASE_,				return_attention_mask=SCREAMING_SNAKE_CASE_,				return_overflowing_tokens=SCREAMING_SNAKE_CASE_,				return_special_tokens_mask=SCREAMING_SNAKE_CASE_,				return_offsets_mapping=SCREAMING_SNAKE_CASE_,				return_token_type_ids=SCREAMING_SNAKE_CASE_,				return_length=SCREAMING_SNAKE_CASE_,				verbose=SCREAMING_SNAKE_CASE_,				return_tensors=SCREAMING_SNAKE_CASE_,				**SCREAMING_SNAKE_CASE_,				)
     return text_encoding

   if not self.image_processor.is_vqa:
     # add pixel_values
     _snake_case							:		Union[str, Any]								=  self.image_processor(
         SCREAMING_SNAKE_CASE_,				return_tensors=SCREAMING_SNAKE_CASE_,				max_patches=SCREAMING_SNAKE_CASE_,				**SCREAMING_SNAKE_CASE_		)
   else:
     # add pixel_values and bbox
     _snake_case							:		Any								=  self.image_processor(
         SCREAMING_SNAKE_CASE_,				return_tensors=SCREAMING_SNAKE_CASE_,				max_patches=SCREAMING_SNAKE_CASE_,				header_text=SCREAMING_SNAKE_CASE_,				**SCREAMING_SNAKE_CASE_		)

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

     if "attention_mask" in text_encoding:
       _snake_case							:		Optional[Any]								=  text_encoding.pop("""attention_mask"""		)
     if "input_ids" in text_encoding:
       _snake_case							:		List[str]								=  text_encoding.pop("""input_ids"""		)
   else:
     _snake_case							:		Tuple								=  None

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

   return encoding_image_processor


 def 			UpperCamelCase_       (    self:		int,				*a_:		Tuple,				**a_:		Tuple		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		Optional[Any],				*a_:		Optional[int],				**a_:		Tuple		):




   '''simple docstring'''



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


 @property
 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  self.tokenizer.model_input_names
   _snake_case							:		Dict								=  self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)








"""simple docstring"""


from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)


