






def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Dict       						= generate_pascal_triangle(a__			)
						for row_idx in range(a__			):
												# 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    UpperCamelCase						(	_A			):






						"""simple docstring"""







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

						__magic_name__      :       List[str]       						= []
						for current_row_idx in range(a__			):
												__magic_name__      :       Optional[int]       						= populate_current_row(a__,   a__			)
												triangle.append(a__			)
						return triangle

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







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

def    UpperCamelCase						(	_A,   _A,   _A,   _A,   ):






						"""simple docstring"""







						__magic_name__      :       int       						= triangle[current_row_idx - 1][current_col_idx - 1]
						__magic_name__      :       List[str]       						= triangle[current_row_idx - 1][current_col_idx]
						__magic_name__      :       Tuple       						= above_to_left_elt + above_to_right_elt

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







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

						__magic_name__      :       Union[str, Any]       						= [[1]]

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

						return result

def    UpperCamelCase						(	):






						"""simple docstring"""







						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(_A,   _A			) -> None:
												__magic_name__      :       Union[str, Any]       						= f'{func.__name__}({value})'
												__magic_name__      :       str       						= 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(a__,   a__			)
												print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()








import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 	snake_case__ (							tf.keras.layers.Layer				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													__magic_name__      :       List[Any]       						= max_length if max_length is not None else self.max_length

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

							return {"attention_mask": attention_mask, "input_ids": input_ids}
