

"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbart import MBartTokenizer
else:
  a    :int			=       None


a    :str			=       logging.get_logger(__name__)


a    :Dict			=       {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

a    :Tuple			=       {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

a    :Optional[Any]			=       {
    "facebook/mbart-large-en-ro": 1_024,
    "facebook/mbart-large-cc25": 1_024,
}

# fmt: off
a    :Any			=       ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]

class    __a							(__lowerCamelCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE  :int				=       VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE  :Dict				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE  :List[Any]				=       PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE  :Optional[int]				=       ["input_ids", "attention_mask"]
     _SCREAMING_SNAKE_CASE  :Dict				=       MBartTokenizer

     _SCREAMING_SNAKE_CASE  :List[int]				=       []
     _SCREAMING_SNAKE_CASE  :List[int]				=       []

     def __init__(  self		,			_a=None		,			_a=None		,			_a="<s>"		,			_a="</s>"		,			_a="</s>"		,			_a="<s>"		,			_a="<unk>"		,			_a="<pad>"		,			_a="<mask>"		,			_a=None		,			_a=None		,			_a=None		,			**_a		,			)     ->					Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Tuple											=		AddedToken(_a		,			lstrip=_a		,			rstrip=_a     ) if isinstance(_a		,			_a     ) else mask_token

           super().__init__(
               vocab_file=_a		,			tokenizer_file=_a		,			bos_token=_a		,			eos_token=_a		,			sep_token=_a		,			cls_token=_a		,			unk_token=_a		,			pad_token=_a		,			mask_token=_a		,			src_lang=_a		,			tgt_lang=_a		,			additional_special_tokens=_a		,			**_a		,			)

           SCREAMING_SNAKE_CASE__      :					Tuple											=		vocab_file
           SCREAMING_SNAKE_CASE__      :					int											=		False if not self.vocab_file else True

           SCREAMING_SNAKE_CASE__      :					Any											=		FAIRSEQ_LANGUAGE_CODES.copy()

           if additional_special_tokens is not None:
                 # Only add those special tokens if they are not already there.
                 _additional_special_tokens.extend(
                     [t for t in additional_special_tokens if t not in _additional_special_tokens]     )

           self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}     )
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		{
               lang_code: self.convert_tokens_to_ids(_a     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
           }

           SCREAMING_SNAKE_CASE__      :					List[str]											=		src_lang if src_lang is not None else '''en_XX'''
           SCREAMING_SNAKE_CASE__      :					str											=		self.convert_tokens_to_ids(self._src_lang     )
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		tgt_lang
           self.set_src_lang_special_tokens(self._src_lang     )

     @property
     def     _a	(  self     )     ->					str:


           """simple docstring"""


           return self._src_lang

     @src_lang.setter
     def     _a	(  self		,			_a     )     ->					None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					str											=		new_src_lang
           self.set_src_lang_special_tokens(self._src_lang     )

     def     _a	(  self		,			_a		,			_a = None     )     ->					List[int]:


           """simple docstring"""


           if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a + self.suffix_tokens
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

     def     _a	(  self		,			_a		,			_a = None     )     ->					List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Any											=		[self.sep_token_id]
           SCREAMING_SNAKE_CASE__      :					int											=		[self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep     ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]

     def     _a	(  self		,			_a		,			_a		,			_a		,			_a		,			**_a     )     ->					int:


           """simple docstring"""


           if src_lang is None or tgt_lang is None:
                 raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""     )
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		src_lang
           SCREAMING_SNAKE_CASE__      :					Tuple											=		self(_a		,			add_special_tokens=_a		,			return_tensors=_a		,			**_a     )
           SCREAMING_SNAKE_CASE__      :					List[str]											=		self.convert_tokens_to_ids(_a     )
           SCREAMING_SNAKE_CASE__      :					Any											=		tgt_lang_id
           return inputs

     def     _a	(  self		,			_a		,			_a = "en_XX"		,			_a = None		,			_a = "ro_RO"		,			**_a		,			)     ->					BatchEncoding:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					str											=		src_lang
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		tgt_lang
           return super().prepare_seqaseq_batch(_a		,			_a		,			**_a     )

     def     _a	(  self     )     ->					Union[str, Any]:


           """simple docstring"""


           return self.set_src_lang_special_tokens(self.src_lang     )

     def     _a	(  self     )     ->					Optional[int]:


           """simple docstring"""


           return self.set_tgt_lang_special_tokens(self.tgt_lang     )

     def     _a	(  self		,			_a     )     ->					None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					List[Any]											=		self.convert_tokens_to_ids(_a     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		[]
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		[self.eos_token_id, self.cur_lang_code]

           SCREAMING_SNAKE_CASE__      :					Tuple											=		self.convert_ids_to_tokens(self.prefix_tokens     )
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		self.convert_ids_to_tokens(self.suffix_tokens     )

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		processors.TemplateProcessing(
               single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str		,			pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str		,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,			self.prefix_tokens + self.suffix_tokens     )     )		,			)

     def     _a	(  self		,			_a     )     ->					None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					str											=		self.convert_tokens_to_ids(_a     )
           SCREAMING_SNAKE_CASE__      :					Dict											=		[]
           SCREAMING_SNAKE_CASE__      :					Dict											=		[self.eos_token_id, self.cur_lang_code]

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		self.convert_ids_to_tokens(self.prefix_tokens     )
           SCREAMING_SNAKE_CASE__      :					str											=		self.convert_ids_to_tokens(self.suffix_tokens     )

           SCREAMING_SNAKE_CASE__      :					Any											=		processors.TemplateProcessing(
               single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str		,			pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str		,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,			self.prefix_tokens + self.suffix_tokens     )     )		,			)





     def     _a	(  self		,			_a		,			_a = None     )     ->					Tuple[str]:


           """simple docstring"""


           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                     """tokenizer."""     )

           if not os.path.isdir(_a     ):
                 logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''     )
                 return
           SCREAMING_SNAKE_CASE__      :					str											=		os.path.join(
               _a		,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

           if os.path.abspath(self.vocab_file     ) != os.path.abspath(_a     ):
                 copyfile(self.vocab_file		,			_a     )

           return (out_vocab_file,)











from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			complex		,   __UpperCAmelCase:			str = "x"		,   __UpperCAmelCase:			float = 10**-10		,   __UpperCAmelCase:			int = 1		,   )      ->	complex:
    UpperCamelCase__							:				Optional[int]          =      symbols(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      lambdify(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				List[str]          =      lambdify(__UpperCAmelCase		,   diff(__UpperCAmelCase		,   __UpperCAmelCase      )      )

    UpperCamelCase__							:				int          =      starting_point

    while True:
        if diff_function(__UpperCAmelCase      ) != 0:
            UpperCamelCase__							:				Optional[int]          =      prev_guess - multiplicity * func(__UpperCAmelCase      ) / diff_function(
                __UpperCAmelCase      )
        else:
            raise ZeroDivisionError('''Could not find root'''      ) from None

        # Precision is checked by comparing the difference of consecutive guesses
        if abs(next_guess - prev_guess      ) < precision:
            return next_guess

        UpperCamelCase__							:				Tuple          =      next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')

  # Find root of polynomial
  # Find fourth Root of 5
  print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}''')

  # Find value of e
  print(
      'The root of log(y) - 1 = 0 is ',
      F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
  )

  # Exponential Roots
  print(
      'The root of exp(x) - 1 = 0 is',
      F'''{newton_raphson("exp(x) - 1", 10, precision=0.005)}''',
  )

  # Find root of cos(x)
  print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')

