
'''simple docstring'''
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__   :		List[str] 		=logging.get_logger(__name__)

A__   :		List[str] 		={
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''git_vision_model'''
 def __init__(   self			:    List[Any]				,   lowerCamelCase			:    str=7_68				,   lowerCamelCase			:    Union[str, Any]=30_72				,   lowerCamelCase			:    List[str]=12				,   lowerCamelCase			:    Optional[int]=12				,   lowerCamelCase			:    Any=3				,   lowerCamelCase			:    List[str]=2_24				,   lowerCamelCase			:    Union[str, Any]=16				,   lowerCamelCase			:    Union[str, Any]="quick_gelu"				,   lowerCamelCase			:    Any=1e-5				,   lowerCamelCase			:    List[str]=0.0				,   lowerCamelCase			:    List[str]=0.02				,   **lowerCamelCase			:    Dict				,   ):



   """simple docstring"""





   super().__init__(**lowerCamelCase       )

   __A		:     List[str]												=		hidden_size
   __A		:     Tuple												=		intermediate_size
   __A		:     Optional[int]												=		num_hidden_layers
   __A		:     Any												=		num_attention_heads
   __A		:     int												=		num_channels
   __A		:     Any												=		patch_size
   __A		:     List[Any]												=		image_size
   __A		:     Optional[int]												=		initializer_range
   __A		:     Optional[Any]												=		attention_dropout
   __A		:     str												=		layer_norm_eps
   __A		:     Union[str, Any]												=		hidden_act






 @classmethod
 def 		lowercase_(   cls			:    int				,   lowerCamelCase			:    Union[str, os.PathLike]				,   **lowerCamelCase			:    Any       ):



   """simple docstring"""





   cls._set_token_in_kwargs(lowerCamelCase       )

   __A		:     Optional[Any]												=		cls.get_config_dict(lowerCamelCase				,   **lowerCamelCase       )

   # get the vision config dict if we are loading from GITConfig
   if config_dict.get("""model_type"""       ) == "git":
     __A		:     Any												=		config_dict["""vision_config"""]

   if "model_type" in config_dict and hasattr(cls				,   """model_type"""       ) and config_dict["model_type"] != cls.model_type:
     logger.warning(
         f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
         f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

   return cls.from_dict(lowerCamelCase				,   **lowerCamelCase       )







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''git'''
 def __init__(   self			:    List[str]				,   lowerCamelCase			:    Union[str, Any]=None				,   lowerCamelCase			:    str=3_05_22				,   lowerCamelCase			:    List[Any]=7_68				,   lowerCamelCase			:    int=6				,   lowerCamelCase			:    Union[str, Any]=12				,   lowerCamelCase			:    List[Any]=30_72				,   lowerCamelCase			:    Optional[Any]="gelu"				,   lowerCamelCase			:    int=0.1				,   lowerCamelCase			:    Optional[int]=0.1				,   lowerCamelCase			:    Dict=10_24				,   lowerCamelCase			:    Optional[int]=0.02				,   lowerCamelCase			:    List[str]=1e-1_2				,   lowerCamelCase			:    List[Any]=0				,   lowerCamelCase			:    Optional[int]="absolute"				,   lowerCamelCase			:    str=True				,   lowerCamelCase			:    Optional[int]=False				,   lowerCamelCase			:    str=1_01				,   lowerCamelCase			:    List[str]=1_02				,   lowerCamelCase			:    Dict=None				,   **lowerCamelCase			:    Tuple				,   ):



   """simple docstring"""





   super().__init__(bos_token_id=lowerCamelCase				,   eos_token_id=lowerCamelCase				,   pad_token_id=lowerCamelCase				,   **lowerCamelCase       )

   if vision_config is None:
     __A		:     List[Any]												=		{}
     logger.info("""vision_config is None. initializing the GitVisionConfig with default values."""       )

   __A		:     str												=		GitVisionConfig(**lowerCamelCase       )
   __A		:     List[str]												=		vocab_size
   __A		:     Optional[Any]												=		hidden_size
   __A		:     Dict												=		num_hidden_layers
   __A		:     str												=		num_attention_heads
   __A		:     Any												=		hidden_act
   __A		:     str												=		intermediate_size
   __A		:     Dict												=		hidden_dropout_prob
   __A		:     Optional[int]												=		attention_probs_dropout_prob
   __A		:     Dict												=		max_position_embeddings
   __A		:     Dict												=		initializer_range
   __A		:     Any												=		layer_norm_eps
   __A		:     List[Any]												=		position_embedding_type
   __A		:     Union[str, Any]												=		use_cache
   __A		:     List[Any]												=		tie_word_embeddings
   __A		:     List[str]												=		num_image_with_embedding

   __A		:     Optional[int]												=		bos_token_id
   __A		:     List[str]												=		eos_token_id






 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   __A		:     Union[str, Any]												=		copy.deepcopy(self.__dict__       )
   __A		:     int												=		self.vision_config.to_dict()
   __A		:     str												=		self.__class__.model_type
   return output





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       None:





  """simple docstring"""

  __A		:     Tuple												=		generate_pascal_triangle(__SCREAMING_SNAKE_CASE )
  for row_idx in range(__SCREAMING_SNAKE_CASE ):
    # 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_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    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""" )

  __A		:     list[list[int]]												=		[]
  for current_row_idx in range(__SCREAMING_SNAKE_CASE ):
    __A		:     Tuple												=		populate_current_row(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    triangle.append(__SCREAMING_SNAKE_CASE )
  return triangle







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  int )				->       list[int]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		[-1] * (current_row_idx + 1)
  # first and last elements of current row are equal to 1
  __A							,					__A		:     Tuple												=		1, 1
  for current_col_idx in range(1			,     __SCREAMING_SNAKE_CASE ):
    calculate_current_element(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  return current_row







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  list[int]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int			,     )				->       None:





  """simple docstring"""

  __A		:     str												=		triangle[current_row_idx - 1][current_col_idx - 1]
  __A		:     Union[str, Any]												=		triangle[current_row_idx - 1][current_col_idx]
  __A		:     Any												=		above_to_left_elt + above_to_right_elt







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    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""" )

  __A		:     list[list[int]]												=		[[1]]

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

  return result







def        A_     (						)				->       None:





  """simple docstring"""

  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(__SCREAMING_SNAKE_CASE						:  Callable			,     __SCREAMING_SNAKE_CASE						:  int ) -> None:
    __A		:     List[Any]												=		F"{func.__name__}({value})"
    __A		:     Dict												=		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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



