


'''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__    	=     logging.get_logger(__name__)

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

lowerCamelCase__    	=     {
    "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__    	=     {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}






class 	lowerCAmelCase__			(			__lowerCAmelCase	):
			lowerCAmelCase :	str          =     VOCAB_FILES_NAMES
			lowerCAmelCase :	List[Any]          =     PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase :	Dict          =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase :	Any          =     ["input_ids", "attention_mask"]
			lowerCAmelCase :	Dict          =     GPTaTokenizer




			def __init__(					self  :     Any	,  lowerCamelCase__  :     Union[str, Any]=None	,  lowerCamelCase__  :     Union[str, Any]=None	,  lowerCamelCase__  :     int=None	,  lowerCamelCase__  :     Tuple="<|endoftext|>"	,  lowerCamelCase__  :     Optional[Any]="<|endoftext|>"	,  lowerCamelCase__  :     List[str]="<|endoftext|>"	,  lowerCamelCase__  :     Optional[int]=False	,  **lowerCamelCase__  :     Dict	,  )   ->List[str]:



					'''simple docstring'''







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

					_UpperCAmelCase		:       Optional[Any]								=      kwargs.pop("add_bos_token"	,  _UpperCamelCase )

					_UpperCAmelCase		:       Optional[Any]								=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
					if pre_tok_state.get("add_prefix_space"	,  _UpperCamelCase ) != add_prefix_space:
							_UpperCAmelCase		:       Optional[int]								=      getattr(_UpperCamelCase	,  pre_tok_state.pop("type" ) )
							_UpperCAmelCase		:       Dict								=      add_prefix_space
							_UpperCAmelCase		:       List[Any]								=      pre_tok_class(**_UpperCamelCase )

					_UpperCAmelCase		:       Optional[Any]								=      add_prefix_space




			def 							lowerCAmelCase__    (					self  :     List[Any]	,  *lowerCamelCase__  :     Optional[int]	,  **lowerCamelCase__  :     Optional[int] )   ->BatchEncoding:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      kwargs.get("is_split_into_words"	,  _UpperCamelCase )
					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(*_UpperCamelCase	,  **_UpperCamelCase )




			def 							lowerCAmelCase__    (					self  :     Dict	,  *lowerCamelCase__  :     Dict	,  **lowerCamelCase__  :     Dict )   ->BatchEncoding:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      kwargs.get("is_split_into_words"	,  _UpperCamelCase )

					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(*_UpperCamelCase	,  **_UpperCamelCase )




			def 							lowerCAmelCase__    (					self  :     Any	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     Optional[str] = None )   ->Tuple[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      self._tokenizer.model.save(_UpperCamelCase	,  name=_UpperCamelCase )
					return tuple(_UpperCamelCase )






			def 							lowerCAmelCase__    (					self  :     Optional[int]	,  lowerCamelCase__  :     "Conversation" )   ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      []
					for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(_UpperCamelCase	,  add_special_tokens=_UpperCamelCase ) + [self.eos_token_id] )

					if len(_UpperCamelCase ) > self.model_max_length:
							_UpperCAmelCase		:       str								=      input_ids[-self.model_max_length :]
					return input_ids







'''simple docstring'''


from math import factorial






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     str )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      real
					if isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Any								=      [1] * rank
					else:
							_UpperCAmelCase		:       Dict								=      rank




			def __repr__(		self  :     str )   ->List[str]:



					'''simple docstring'''







					return (
					    F"""{self.real}+"""
					    F"""{'+'.join(str(lowerCamelCase__ )+'E'+str(n+1 )for n,dual in enumerate(self.duals ) )}"""
					)




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      self.duals.copy()
					while cur[-1] == 0:
							cur.pop(-1 )
					return Dual(self.real	,  lowerCamelCase__ )




			def __add__(		self  :     Dict	,  lowerCamelCase__  :     List[Any] )   ->Any:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							return Dual(self.real + other	,  self.duals )
					_UpperCAmelCase		:       Optional[int]								=      self.duals.copy()
					_UpperCAmelCase		:       Optional[int]								=      other.duals.copy()
					if len(lowerCamelCase__ ) > len(lowerCamelCase__ ):
							o_dual.extend([1] * (len(lowerCamelCase__ ) - len(lowerCamelCase__ )) )
					elif len(lowerCamelCase__ ) < len(lowerCamelCase__ ):
							s_dual.extend([1] * (len(lowerCamelCase__ ) - len(lowerCamelCase__ )) )
					_UpperCAmelCase		:       Union[str, Any]								=      []
					for i in range(len(lowerCamelCase__ ) ):
							new_duals.append(s_dual[i] + o_dual[i] )
					return Dual(self.real + other.real	,  lowerCamelCase__ )

			lowerCAmelCase :	Tuple          =     __add__




			def __sub__(		self  :     List[Any]	,  lowerCamelCase__  :     Union[str, Any] )   ->Dict:



					'''simple docstring'''







					return self + other * -1




			def __mul__(		self  :     List[str]	,  lowerCamelCase__  :     Optional[Any] )   ->Union[str, Any]:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Optional[int]								=      []
							for i in self.duals:
									new_duals.append(i * other )
							return Dual(self.real * other	,  lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      [0] * (len(self.duals ) + len(other.duals ) + 1)
					for i, item in enumerate(self.duals ):
							for j, jtem in enumerate(other.duals ):
									new_duals[i + j + 1] += item * jtem
					for k in range(len(self.duals ) ):
							new_duals[k] += self.duals[k] * other.real
					for index in range(len(other.duals ) ):
							new_duals[index] += other.duals[index] * self.real
					return Dual(self.real * other.real	,  lowerCamelCase__ )

			lowerCAmelCase :	Union[str, Any]          =     __mul__




			def __truediv__(		self  :     Optional[Any]	,  lowerCamelCase__  :     List[Any] )   ->Union[str, Any]:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Union[str, Any]								=      []
							for i in self.duals:
									new_duals.append(i / other )
							return Dual(self.real / other	,  lowerCamelCase__ )
					raise ValueError




			def __floordiv__(		self  :     str	,  lowerCamelCase__  :     str )   ->List[str]:



					'''simple docstring'''







					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Tuple								=      []
							for i in self.duals:
									new_duals.append(i // other )
							return Dual(self.real // other	,  lowerCamelCase__ )
					raise ValueError






			def __pow__(		self  :     Tuple	,  lowerCamelCase__  :     Optional[Any] )   ->Optional[int]:



					'''simple docstring'''







					if n < 0 or isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							raise ValueError("power must be a positive integer" )
					if n == 0:
							return 1
					if n == 1:
							return self
					_UpperCAmelCase		:       str								=      self
					for _ in range(n - 1 ):
							x *= self
					return x






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		if not callable(__lowerCAmelCase   ):
				raise ValueError("differentiate() requires a function as input for func"   )
		if not isinstance(__lowerCAmelCase      , (float, int)   ):
				raise ValueError("differentiate() requires a float as input for position"   )
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ):
				raise ValueError("differentiate() requires an int as input for order"   )
		_UpperCAmelCase		:       int								=      Dual(__lowerCAmelCase      , 1   )
		_UpperCAmelCase		:       Optional[int]								=      func(__lowerCAmelCase   )
		if order == 0:
				return result.real
		return result.duals[order - 1] * factorial(__lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()






						def  __lowerCAmelCase (__lowerCAmelCase   ):
								return y**2 * y**4





						print(differentiate(f, 9, 2))



