






"""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__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["image_processor", "tokenizer"]
      __UpperCamelCase	 =  "Pix2StructImageProcessor"
      __UpperCamelCase	 =  ("T5Tokenizer", "T5TokenizerFast")
      def __init__(						self     :  Any						,	lowercase_     :  Dict						,	lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		False
          super().__init__(lowercase_						,	lowercase_)
      def __call__(						self     :  Dict						,	lowercase_     :  Optional[int]=None						,	lowercase_     :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,	lowercase_     :  bool = True						,	lowercase_     :  Union[bool, str, PaddingStrategy] = False						,	lowercase_     :  Union[bool, str, TruncationStrategy] = None						,	lowercase_     :  Optional[int] = None						,	lowercase_     :  Optional[int] = 2048						,	lowercase_     :  int = 0						,	lowercase_     :  Optional[int] = None						,	lowercase_     :  Optional[bool] = None						,	lowercase_     :  bool = False						,	lowercase_     :  bool = False						,	lowercase_     :  bool = False						,	lowercase_     :  bool = False						,	lowercase_     :  bool = False						,	lowercase_     :  bool = True						,	lowercase_     :  Optional[Union[str, TensorType]] = None						,	**lowercase_     :  List[str]						,	):




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

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

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

              if "attention_mask" in text_encoding:
                  SCREAMING_SNAKE_CASE_      :  List[Any]    						=		text_encoding.pop('''attention_mask''')
              if "input_ids" in text_encoding:
                  SCREAMING_SNAKE_CASE_      :  Dict    						=		text_encoding.pop('''input_ids''')
          else:
              SCREAMING_SNAKE_CASE_      :  str    						=		None

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

          return encoding_image_processor
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	*lowercase_     :  str						,	**lowercase_     :  List[str]):




          '''simple docstring'''


          return self.tokenizer.batch_decode(*lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	*lowercase_     :  List[Any]						,	**lowercase_     :  Any):




          '''simple docstring'''


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



      @property
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.tokenizer.model_input_names
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))








import re

from filelock import FileLock


try:
   import nltk

   lowerCamelCase__ :   str					=							True
except (ImportError, ModuleNotFoundError):
   lowerCamelCase__ :   Union[str, Any]					=							False

if NLTK_AVAILABLE:
   with FileLock('.lock') as lock:
      nltk.download('punkt', quiet=True)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> str:
      re.sub('<n>'			,			''			,			__UpperCAmelCase							)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(__UpperCAmelCase							)							)