'''simple docstring'''

def __a (	__lowerCamelCase			:						int				)	->							None:
      '''simple docstring'''


      lowercase_     =      generate_pascal_triangle(__lowerCamelCase				)
      for row_idx in range(__lowerCamelCase				):
            # 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 (	__lowerCamelCase			:						int				)	->							list[list[int]]:
      '''simple docstring'''


      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				):
            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"				)

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


def __a (	__lowerCamelCase			:						list[list[int]] ,		__lowerCamelCase			:						int				)	->							list[int]:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						list[list[int]] ,		__lowerCamelCase			:						list[int] ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						int ,		)	->							None:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						int				)	->							list[list[int]]:
      '''simple docstring'''


      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				):
            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"				)

      lowercase_     =      [[1]]

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

      return result


def __a (	)	->							None:
      '''simple docstring'''


      from collections.abc import Callable
      from timeit import timeit

      def benchmark_a_function(__lowerCamelCase			:						Callable ,		__lowerCamelCase			:						int				) -> None:
            lowercase_     =      f'{func.__name__}({value})'
            lowercase_     =      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(__lowerCamelCase ,		__lowerCamelCase				)
            print()


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()







'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Any						  =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_ : Tuple						  =      {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_ : Dict						  =      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=GPTaTokenizer


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Any="<|endoftext|>"   ,		__lowerCAmelCase				:	Union[str, Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	List[Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	Optional[Any]=False   ,		**__lowerCAmelCase				:	Dict   ,		)      ->					int:
         super().__init__(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		tokenizer_file=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		add_prefix_space=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      kwargs.pop("add_bos_token"   ,		__lowerCAmelCase)

         lowercase_     =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space"   ,		__lowerCAmelCase) != add_prefix_space:
               lowercase_     =      getattr(__lowerCAmelCase   ,		pre_tok_state.pop("type"))
               lowercase_     =      add_prefix_space
               lowercase_     =      pre_tok_class(**__lowerCAmelCase)

         lowercase_     =      add_prefix_space


   def __UpperCAmelCase       (   self				:	List[str]   ,		*__lowerCAmelCase				:	List[str]   ,		**__lowerCAmelCase				:	str)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)
         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)

         assert self.add_prefix_space or not is_split_into_words, (
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(__lowerCAmelCase   ,		name=__lowerCAmelCase)
         return tuple(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	"Conversation")      ->					List[int]:
         lowercase_     =      []
         for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(__lowerCAmelCase   ,		add_special_tokens=__lowerCAmelCase) + [self.eos_token_id])

         if len(__lowerCAmelCase) > self.model_max_length:
               lowercase_     =      input_ids[-self.model_max_length :]
         return input_ids






