






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

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
  if is_torch_available():
    import torch
  if is_tf_available():
    import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


__a      :       Dict				  =   logging.get_logger(__name__)

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

__a      :       Any				  =   {
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

__a      :       Tuple				  =   {
    "Salesforce/codegen-350M-mono": 2048,
}


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE   						=				PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE   						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE   						=				["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE   						=				CodeGenTokenizer




     def __init__(				self       :			Optional[Any] ,	UpperCamelCase_       :			Optional[Any]=None ,	UpperCamelCase_       :			List[str]=None ,	UpperCamelCase_       :			Optional[int]=None ,	UpperCamelCase_       :			List[str]="<|endoftext|>" ,	UpperCamelCase_       :			int="<|endoftext|>" ,	UpperCamelCase_       :			List[str]="<|endoftext|>" ,	UpperCamelCase_       :			Any=False ,	**UpperCamelCase_       :			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_ ,	)

         if kwargs.pop("""add_bos_token""" ,	UpperCamelCase_				):
             __A         =	kwargs.pop("""name_or_path""" ,	""""""				)
             raise ValueError(
                 """Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
                 """Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
                 F"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
                 F"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
                 """This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
                 """ so that the fast tokenizer works correctly."""				)

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

         __A         =	add_prefix_space




     def 		lowerCAmelCase_     (				self       :			str ,	*UpperCamelCase_       :			Optional[int] ,	**UpperCamelCase_       :			Optional[Any]				):


         """simple docstring"""



         __A         =	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       :			Any ,	*UpperCamelCase_       :			Dict ,	**UpperCamelCase_       :			str				):


         """simple docstring"""



         __A         =	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       :			Tuple ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Optional[str] = None				):


         """simple docstring"""



         __A         =	self._tokenizer.model.save(UpperCamelCase_ ,	name=UpperCamelCase_				)
         return tuple(UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"] ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			bool = None ,	UpperCamelCase_       :			Optional[List[str]] = None ,	**UpperCamelCase_       :			Any ,	):


         """simple docstring"""



         __A         =	super().decode(
             token_ids=UpperCamelCase_ ,	skip_special_tokens=UpperCamelCase_ ,	clean_up_tokenization_spaces=UpperCamelCase_ ,	**UpperCamelCase_ ,	)

         if truncate_before_pattern is not None and len(UpperCamelCase_				) > 0:
             __A         =	self.truncate(UpperCamelCase_ ,	UpperCamelCase_				)

         return decoded_text





     def 		lowerCAmelCase_     (				self       :			Optional[int] ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Any				):


         """simple docstring"""



         def find_re(UpperCamelCase_       :			int ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			str				):
             __A         =	pattern.search(UpperCamelCase_ ,	UpperCamelCase_				)
             return m.start() if m else -1

         __A         =	[re.compile(UpperCamelCase_ ,	re.MULTILINE				) for pattern in truncate_before_pattern]

         __A         =	list(re.finditer("""^print""" ,	UpperCamelCase_ ,	re.MULTILINE				)				)

         if len(UpperCamelCase_				) > 1:
             __A         =	completion[: prints[1].start()]

         __A         =	list(re.finditer("""^def""" ,	UpperCamelCase_ ,	re.MULTILINE				)				)

         if len(UpperCamelCase_				) > 1:
             __A         =	completion[: defs[1].start()]

         __A         =	0

         __A         =	[
             pos for pos in [find_re(UpperCamelCase_ ,	UpperCamelCase_ ,	UpperCamelCase_				) for terminal in terminals] if pos != -1
         ]

         if len(UpperCamelCase_				) > 0:
             return completion[: min(UpperCamelCase_				)]
         else:
             return completion












from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			float | Decimal			,				__lowercase   :			float = 1_0**-1_0    )						->      float:





    """simple docstring"""

    __A         =	a
    while True:
        __A         =	Decimal(__lowercase    ) - (
            Decimal(eval(__lowercase    )    ) / Decimal(eval(str(diff(__lowercase    )    )    )    )  # noqa: S307
        )
        # This number dictates the accuracy of the answer
        if abs(eval(__lowercase    )    ) < precision:  # noqa: S307
            return float(__lowercase    )


# 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
  print(f"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}""")
  # Find Square Root of 5
  print(f"""The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}""")
  # Exponential Roots
  print(f"""The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}""")




