
'''simple docstring'''





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

def      A (       A_							:					str    ,							A_							:					complex    ,							A_							:					str = "x"    ,							A_							:					float = 10**-10    ,							A_							:					int = 1    ,							):
						snake_case       :     Dict =    symbols(A_       )
						snake_case       :     Union[str, Any] =    lambdify(A_    ,							A_       )
						snake_case       :     List[Any] =    lambdify(A_    ,							diff(A_    ,							A_       )       )

						snake_case       :     Optional[int] =    starting_point

						while True:
												if diff_function(A_       ) != 0:
																		snake_case       :     List[Any] =    prev_guess - multiplicity * func(A_       ) / diff_function(
																		    A_       )
												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

												snake_case       :     List[Any] =    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)}''')



'''simple docstring'''





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


UpperCAmelCase    					=   logging.get_logger(__name__)

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

UpperCAmelCase    					=   {
    "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"
        ),
    },
}

UpperCAmelCase    					=   {
    "Salesforce/codegen-350M-mono": 2_048,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = CodeGenTokenizer




				def __init__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Dict=None,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=None,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=None,    SCREAMING_SNAKE_CASE_	:				Optional[int]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				List[Any]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				List[str]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				int=False,    **SCREAMING_SNAKE_CASE_	:				Optional[int],    ):
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										if kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Dict =    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.'''      )

										snake_case       :     int =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     Optional[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Union[str, Any] =    add_prefix_space
																snake_case       :     Optional[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     int =    add_prefix_space




				def 	__snake_case							(							self	:				List[Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Dict =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )
										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(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[str],    *SCREAMING_SNAKE_CASE_	:				str,    **SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Dict =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )

										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(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     Optional[int] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],    SCREAMING_SNAKE_CASE_	:				bool = False,    SCREAMING_SNAKE_CASE_	:				bool = None,    SCREAMING_SNAKE_CASE_	:				Optional[List[str]] = None,    **SCREAMING_SNAKE_CASE_	:				Union[str, Any],    ):
										snake_case       :     Dict =    super().decode(
										    token_ids=SCREAMING_SNAKE_CASE_,    skip_special_tokens=SCREAMING_SNAKE_CASE_,    clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										if truncate_before_pattern is not None and len(SCREAMING_SNAKE_CASE_      ) > 0:
																snake_case       :     Optional[int] =    self.truncate(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )

										return decoded_text




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										def find_re(SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
																snake_case       :     Optional[Any] =    pattern.search(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																return m.start() if m else -1

										snake_case       :     Union[str, Any] =    [re.compile(SCREAMING_SNAKE_CASE_,    re.MULTILINE      ) for pattern in truncate_before_pattern]

										snake_case       :     Union[str, Any] =    list(re.finditer('''^print''',    SCREAMING_SNAKE_CASE_,    re.MULTILINE      )      )

										if len(SCREAMING_SNAKE_CASE_      ) > 1:
																snake_case       :     Tuple =    completion[: prints[1].start()]

										snake_case       :     List[str] =    list(re.finditer('''^def''',    SCREAMING_SNAKE_CASE_,    re.MULTILINE      )      )

										if len(SCREAMING_SNAKE_CASE_      ) > 1:
																snake_case       :     Optional[Any] =    completion[: defs[1].start()]

										snake_case       :     Tuple =    0

										snake_case       :     List[Any] =    [
										    pos for pos in [find_re(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) for terminal in terminals] if pos != -1
										]

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

