




'''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        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   str       =						["""image_processor""", """tokenizer"""]
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						"""Pix2StructImageProcessor"""
     SCREAMING_SNAKE_CASE_							:   List[str]       =						("""T5Tokenizer""", """T5TokenizerFast""")






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				False
          super().__init__(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def __call__(			self ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 2_048 ,_SCREAMING_SNAKE_CASE = 0 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      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:
               _snake_case							=				self.tokenizer
               _snake_case							=				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							=				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							=				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							=				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							=				text_encoding.pop("attention_mask"   )
               if "input_ids" in text_encoding:
                    _snake_case							=				text_encoding.pop("input_ids"   )
          else:
               _snake_case							=				None

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

          return encoding_image_processor






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      Tuple:
          return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      int:
          _snake_case							=				self.tokenizer.model_input_names
          _snake_case							=				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )








'''simple docstring'''





def      __a     (       _UpperCamelCase:   int   )						->			None:

     """simple docstring"""




     _snake_case							=				generate_pascal_triangle(_UpperCamelCase   )
     for row_idx in range(_UpperCamelCase   ):
          # Print left spaces
          for _ in range(num_rows - row_idx - 1   ):
               print(end=" "   )
          # Print row values
          for col_idx in range(row_idx + 1   ):
               if col_idx != row_idx:
                    print(triangle[row_idx][col_idx] ,						end=" "   )
               else:
                    print(triangle[row_idx][col_idx] ,						end=""   )
          print()





def      __a     (       _UpperCamelCase:   int   )						->			list[list[int]]:

     """simple docstring"""




     if not isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
          raise TypeError("The input value of 'num_rows' should be 'int'"   )

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              "The input value of 'num_rows' should be greater than or equal to 0"   )

     _snake_case							=				[]
     for current_row_idx in range(_UpperCamelCase   ):
          _snake_case							=				populate_current_row(_UpperCamelCase ,						_UpperCamelCase   )
          triangle.append(_UpperCamelCase   )
     return triangle





def      __a     (       _UpperCamelCase:   list[list[int]] ,						_UpperCamelCase:   int   )						->			list[int]:

     """simple docstring"""




     _snake_case							=				[-1] * (current_row_idx + 1)
     # first and last elements of current row are equal to 1
     _snake_case							, _snake_case							=				1, 1
     for current_col_idx in range(1 ,						_UpperCamelCase   ):
          calculate_current_element(
              _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
     return current_row





def      __a     (       _UpperCamelCase:   list[list[int]] ,						_UpperCamelCase:   list[int] ,						_UpperCamelCase:   int ,						_UpperCamelCase:   int ,						)						->			None:

     """simple docstring"""




     _snake_case							=				triangle[current_row_idx - 1][current_col_idx - 1]
     _snake_case							=				triangle[current_row_idx - 1][current_col_idx]
     _snake_case							=				above_to_left_elt + above_to_right_elt





def      __a     (       _UpperCamelCase:   int   )						->			list[list[int]]:

     """simple docstring"""




     if not isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
          raise TypeError("The input value of 'num_rows' should be 'int'"   )

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              "The input value of 'num_rows' should be greater than or equal to 0"   )

     _snake_case							=				[[1]]

     for row_index in range(1 ,						_UpperCamelCase   ):
          _snake_case							=				[0] + result[-1] + [0]
          _snake_case							=				row_index + 1
          # Calculate the number of distinct elements in a row
          _snake_case							=				sum(divmod(_UpperCamelCase ,						2   )   )
          _snake_case							=				[
              temp_row[i - 1] + temp_row[i] for i in range(1 ,						distinct_elements + 1   )
          ]
          _snake_case							=				row_first_half[: (row_index + 1) // 2]
          row_second_half.reverse()
          _snake_case							=				row_first_half + row_second_half
          result.append(_UpperCamelCase   )

     return result





def      __a     (       )						->			None:

     """simple docstring"""




     from collections.abc import Callable
     from timeit import timeit

     def benchmark_a_function(_UpperCamelCase:   Callable ,						_UpperCamelCase:   int   ) -> None:
          _snake_case							=				F"""{func.__name__}({value})"""
          _snake_case							=				timeit(F"""__main__.{call}""" ,						setup="import __main__"   )
          # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
          print(F"""{call:38} -- {timing:.4f} seconds"""   )

     for value in range(15   ):  # (1, 7, 14):
          for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
               benchmark_a_function(_UpperCamelCase ,						_UpperCamelCase   )
          print()


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()


