






"""simple docstring"""






from __future__ import annotations

from typing import Any




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Union[str, Any]						,	lowercase_     :  int						,	lowercase_     :  int						,	lowercase_     :  float = 0):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Dict    						=		row, column
          SCREAMING_SNAKE_CASE_      :  Any    						=		[[default_value for c in range(lowercase_)] for r in range(lowercase_)]
      def __str__(						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		F'Matrix consist of {self.row} rows and {self.column} columns\n'

          # Make string identifier
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		0
          for row_vector in self.array:
              for obj in row_vector:
                  SCREAMING_SNAKE_CASE_      :  str    						=		max(lowercase_						,	len(str(lowercase_)))
          SCREAMING_SNAKE_CASE_      :  str    						=		F'%{max_element_length}s'

          # Make string and return
          def single_line(lowercase_     :  list[float]) -> str:
              nonlocal string_format_identifier
              SCREAMING_SNAKE_CASE_      :  Any    						=		'''['''
              line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
              line += "]"
              return line

          s += "\n".join(single_line(lowercase_) for row_vector in self.array)
          return s
      def __repr__(						self     :  str):




          '''simple docstring'''


          return str(self)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  tuple[int, int]):




          '''simple docstring'''


          if not (isinstance(lowercase_						,	(list, tuple)) and len(lowercase_) == 2):
              return False
          elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
              return False
          else:
              return True
      def __getitem__(						self     :  Optional[int]						,	lowercase_     :  tuple[int, int]):




          '''simple docstring'''


          assert self.validate_indicies(lowercase_)
          return self.array[loc[0]][loc[1]]
      def __setitem__(						self     :  List[str]						,	lowercase_     :  tuple[int, int]						,	lowercase_     :  float):




          '''simple docstring'''


          assert self.validate_indicies(lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		value
      def __add__(						self     :  List[Any]						,	lowercase_     :  Matrix):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_)
          assert self.row == another.row and self.column == another.column

          # Add
          SCREAMING_SNAKE_CASE_      :  str    						=		Matrix(self.row						,	self.column)
          for r in range(self.row):
              for c in range(self.column):
                  SCREAMING_SNAKE_CASE_      :  Dict    						=		self[r, c] + another[r, c]
          return result
      def __neg__(						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		Matrix(self.row						,	self.column)
          for r in range(self.row):
              for c in range(self.column):
                  SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		-self[r, c]
          return result
      def __sub__(						self     :  Dict						,	lowercase_     :  Matrix):




          '''simple docstring'''


          return self + (-another)
      def __mul__(						self     :  Tuple						,	lowercase_     :  int | float | Matrix):




          '''simple docstring'''


          if isinstance(lowercase_						,	(int, float)):  # Scalar multiplication
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		Matrix(self.row						,	self.column)
              for r in range(self.row):
                  for c in range(self.column):
                      SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self[r, c] * another
              return result
          elif isinstance(lowercase_						,	lowercase_):  # Matrix multiplication
              assert self.column == another.row
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		Matrix(self.row						,	another.column)
              for r in range(self.row):
                  for c in range(another.column):
                      for i in range(self.column):
                          result[r, c] += self[r, i] * another[i, c]
              return result
          else:
              SCREAMING_SNAKE_CASE_      :  str    						=		F'Unsupported type given for another ({type(lowercase_)})'
              raise TypeError(lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		Matrix(self.column						,	self.row)
          for r in range(self.row):
              for c in range(self.column):
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self[r, c]
          return result



      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  Matrix						,	lowercase_     :  Matrix):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)
          assert self.row == self.column == u.row == v.row  # u, v should be column vector
          assert u.column == v.column == 1  # u, v should be column vector

          # Calculate
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		v.transpose()
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		(v_t * self * u)[0, 0] + 1
          if numerator_factor == 0:
              return None  # It's not invertable
          return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



  def 					_A			() ->       None:
      """simple docstring"""






      SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		Matrix(3    ,				3    ,				0							)
      for i in range(3							):
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		1
      print(f'a^(-1) is {ainv}'							)
      # u, v
      SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		Matrix(3    ,				1    ,				0							)
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		1, 2, -3
      SCREAMING_SNAKE_CASE_      :  str    						=		Matrix(3    ,				1    ,				0							)
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		4, -2, 5
      print(f'u is {u}'							)
      print(f'v is {v}'							)
      print(f'uv^T is {u * v.transpose()}'							)
      # Sherman Morrison
      print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(__a    ,				__a							)}'							)



  def 					_A			() ->       None:
      """simple docstring"""






      import doctest

      doctest.testmod()





  testa()










"""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


UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)

UpperCAmelCase_   : Dict   			= {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_   : Dict   			= {
    """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""",
    },
}

UpperCAmelCase_   : List[str]   			= {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  VOCAB_FILES_NAMES
      __UpperCamelCase	 =  PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase	 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase	 =  ["input_ids", "attention_mask"]
      __UpperCamelCase	 =  GPTaTokenizer
      def __init__(						self     :  Optional[int]						,	lowercase_     :  int=None						,	lowercase_     :  List[str]=None						,	lowercase_     :  Union[str, Any]=None						,	lowercase_     :  Tuple="<|endoftext|>"						,	lowercase_     :  str="<|endoftext|>"						,	lowercase_     :  Dict="<|endoftext|>"						,	lowercase_     :  Tuple=False						,	**lowercase_     :  Optional[int]						,	):




          '''simple docstring'''


          super().__init__(
              lowercase_						,	lowercase_						,	tokenizer_file=lowercase_						,	unk_token=lowercase_						,	bos_token=lowercase_						,	eos_token=lowercase_						,	add_prefix_space=lowercase_						,	**lowercase_						,	)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		kwargs.pop('''add_bos_token'''						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get('''add_prefix_space'''						,	lowercase_) != add_prefix_space:
              SCREAMING_SNAKE_CASE_      :  int    						=		getattr(lowercase_						,	pre_tok_state.pop('''type'''))
              SCREAMING_SNAKE_CASE_      :  str    						=		add_prefix_space
              SCREAMING_SNAKE_CASE_      :  Dict    						=		pre_tok_class(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		add_prefix_space
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	*lowercase_     :  List[Any]						,	**lowercase_     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kwargs.get('''is_split_into_words'''						,	lowercase_)
          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(*lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	*lowercase_     :  List[str]						,	**lowercase_     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		kwargs.get('''is_split_into_words'''						,	lowercase_)

          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(*lowercase_						,	**lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  str						,	lowercase_     :  Optional[str] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self._tokenizer.model.save(lowercase_						,	name=lowercase_)
          return tuple(lowercase_)



      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  "Conversation"):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		[]
          for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(lowercase_						,	add_special_tokens=lowercase_) + [self.eos_token_id])

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


