



"""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


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

A:     str										=	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A:     List[Any]										=	{
    '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',
    },
}

A:     Optional[Any]										=	{
    'gpt2': 1_0_2_4,
    'gpt2-medium': 1_0_2_4,
    'gpt2-large': 1_0_2_4,
    'gpt2-xl': 1_0_2_4,
    'distilgpt2': 1_0_2_4,
}



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Union[str, Any]		=		VOCAB_FILES_NAMES
   __lowerCAmelCase						:     Optional[Any]		=		PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase						:     Union[str, Any]		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase						:     Optional[int]		=		['input_ids', 'attention_mask']
   __lowerCAmelCase						:     str		=		GPTaTokenizer

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE="<|endoftext|>"   ,  _SCREAMING_SNAKE_CASE="<|endoftext|>"   ,  _SCREAMING_SNAKE_CASE="<|endoftext|>"   ,  _SCREAMING_SNAKE_CASE=False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Optional[Any]:





    '''simple docstring'''




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

    UpperCAmelCase			:      Any			=			kwargs.pop("""add_bos_token"""   ,  __A		)

    UpperCAmelCase			:      Dict			=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
    if pre_tok_state.get("""add_prefix_space"""   ,  __A		) != add_prefix_space:
     UpperCAmelCase			:      Dict			=			getattr(__A   ,  pre_tok_state.pop("""type"""		)		)
     UpperCAmelCase			:      int			=			add_prefix_space
     UpperCAmelCase			:      Union[str, Any]			=			pre_tok_class(**__A		)

    UpperCAmelCase			:      Tuple			=			add_prefix_space

   def 			SCREAMING_SNAKE_CASE  (     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			kwargs.get("""is_split_into_words"""   ,  __A		)
    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(*__A   ,  **__A		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			kwargs.get("""is_split_into_words"""   ,  __A		)

    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(*__A   ,  **__A		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self._tokenizer.model.save(__A   ,  name=__A		)
    return tuple(__A		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			[]
    for is_user, text in conversation.iter_texts():
     input_ids.extend(self.encode(__A   ,  add_special_tokens=__A		) + [self.eos_token_id]		)

    if len(__A		) > self.model_max_length:
     UpperCAmelCase			:      Tuple			=			input_ids[-self.model_max_length :]
    return input_ids





"""simple docstring"""
from __future__ import annotations

from decimal import Decimal

from numpy import array

def    _snake_case		(		UpperCamelCase		:	list[list[float]]		):
 UpperCAmelCase			:      str			=			Decimal

 # Check if the provided matrix has 2 rows and 2 columns
 # since this implementation only works for 2x2 matrices
 if len(UpperCamelCase		) == 2 and len(matrix[0]		) == 2 and len(matrix[1]		) == 2:
  # Calculate the determinant of the matrix
  UpperCAmelCase			:      Optional[int]			=			float(
      d(matrix[0][0]		) * d(matrix[1][1]		) - d(matrix[1][0]		) * d(matrix[0][1]		)		)
  if determinant == 0:
   raise ValueError("""This matrix has no inverse."""		)

  # Creates a copy of the matrix with swapped positions of the elements
  UpperCAmelCase			:      Optional[Any]			=			[[0.0, 0.0], [0.0, 0.0]]
  UpperCAmelCase			,	UpperCAmelCase			:      Any			=			matrix[1][1], matrix[0][0]
  UpperCAmelCase			,	UpperCAmelCase			:      Union[str, Any]			=			-matrix[1][0], -matrix[0][1]

  # Calculate the inverse of the matrix
  return [
      [(float(d(UpperCamelCase		)		) / determinant) or 0.0 for n in row] for row in swapped_matrix
  ]
 elif (
     len(UpperCamelCase		) == 3
     and len(matrix[0]		) == 3
     and len(matrix[1]		) == 3
     and len(matrix[2]		) == 3
 ):
  # Calculate the determinant of the matrix using Sarrus rule
  UpperCAmelCase			:      str			=			float(
      (
          (d(matrix[0][0]		) * d(matrix[1][1]		) * d(matrix[2][2]		))
          + (d(matrix[0][1]		) * d(matrix[1][2]		) * d(matrix[2][0]		))
          + (d(matrix[0][2]		) * d(matrix[1][0]		) * d(matrix[2][1]		))
      )
      - (
          (d(matrix[0][2]		) * d(matrix[1][1]		) * d(matrix[2][0]		))
          + (d(matrix[0][1]		) * d(matrix[1][0]		) * d(matrix[2][2]		))
          + (d(matrix[0][0]		) * d(matrix[1][2]		) * d(matrix[2][1]		))
      )		)
  if determinant == 0:
   raise ValueError("""This matrix has no inverse."""		)

  # Creating cofactor matrix
  UpperCAmelCase			:      Optional[int]			=			[
      [d(0.0		), d(0.0		), d(0.0		)],
      [d(0.0		), d(0.0		), d(0.0		)],
      [d(0.0		), d(0.0		), d(0.0		)],
  ]
  UpperCAmelCase			:      str			=			(d(matrix[1][1]		) * d(matrix[2][2]		)) - (
      d(matrix[1][2]		) * d(matrix[2][1]		)
  )
  UpperCAmelCase			:      List[str]			=			-(
      (d(matrix[1][0]		) * d(matrix[2][2]		)) - (d(matrix[1][2]		) * d(matrix[2][0]		))
  )
  UpperCAmelCase			:      Optional[int]			=			(d(matrix[1][0]		) * d(matrix[2][1]		)) - (
      d(matrix[1][1]		) * d(matrix[2][0]		)
  )
  UpperCAmelCase			:      Optional[int]			=			-(
      (d(matrix[0][1]		) * d(matrix[2][2]		)) - (d(matrix[0][2]		) * d(matrix[2][1]		))
  )
  UpperCAmelCase			:      str			=			(d(matrix[0][0]		) * d(matrix[2][2]		)) - (
      d(matrix[0][2]		) * d(matrix[2][0]		)
  )
  UpperCAmelCase			:      Union[str, Any]			=			-(
      (d(matrix[0][0]		) * d(matrix[2][1]		)) - (d(matrix[0][1]		) * d(matrix[2][0]		))
  )
  UpperCAmelCase			:      Dict			=			(d(matrix[0][1]		) * d(matrix[1][2]		)) - (
      d(matrix[0][2]		) * d(matrix[1][1]		)
  )
  UpperCAmelCase			:      Optional[Any]			=			-(
      (d(matrix[0][0]		) * d(matrix[1][2]		)) - (d(matrix[0][2]		) * d(matrix[1][0]		))
  )
  UpperCAmelCase			:      str			=			(d(matrix[0][0]		) * d(matrix[1][1]		)) - (
      d(matrix[0][1]		) * d(matrix[1][0]		)
  )

  # Transpose the cofactor matrix (Adjoint matrix)
  UpperCAmelCase			:      str			=			array(UpperCamelCase		)
  for i in range(3		):
   for j in range(3		):
    UpperCAmelCase			:      Any			=			cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
  UpperCAmelCase			:      Tuple			=			array(UpperCamelCase		)
  for i in range(3		):
   for j in range(3		):
    inverse_matrix[i][j] /= d(UpperCamelCase		)

        # Calculate the inverse of the matrix
  return [[float(d(UpperCamelCase		)		) or 0.0 for n in row] for row in inverse_matrix]
 raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""		)
