





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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  _lowerCamelCase :    int  							= None


_lowerCamelCase :    Tuple  							= logging.get_logger(__name__)

_lowerCamelCase :    Tuple  							= {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase :    Tuple  							= {
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase :    int  							= {
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}


class 						lowerCamelCase       (__SCREAMING_SNAKE_CASE      ):







       """simple docstring"""




       UpperCAmelCase_      						=   VOCAB_FILES_NAMES
       UpperCAmelCase_      						=   PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase_      						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase_      						=   ["input_ids", "attention_mask"]
       UpperCAmelCase_      						=   TaTokenizer

       UpperCAmelCase_      						=   []






       def __init__( self     :					int,			_UpperCAmelCase     :					Union[str, Any]=None,			_UpperCAmelCase     :					str=None,			_UpperCAmelCase     :					List[Any]="</s>",			_UpperCAmelCase     :					Optional[Any]="<unk>",			_UpperCAmelCase     :					str="<pad>",			_UpperCAmelCase     :					Union[str, Any]=1_0_0,			_UpperCAmelCase     :					Dict=None,			**_UpperCAmelCase     :					Optional[int],			)				->			Optional[Any]:





              """simple docstring"""
              # Add extra_ids to the special token list
              if extra_ids > 0 and additional_special_tokens is None:
                     SCREAMING_SNAKE_CASE__       :      str     =						[F'''<extra_id_{i}>''' for i in range(__UpperCAmelCase						)]
              elif extra_ids > 0 and additional_special_tokens is not None:
                     # Check that we have the right number of extra special tokens
                     SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						len(set(filter(lambda _UpperCAmelCase						: bool("extra_id_" in str(__UpperCAmelCase						)						),			__UpperCAmelCase						)						)						)
                     if extra_tokens != extra_ids:
                            raise ValueError(
                                F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
                                " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                                " tokens"						)

              super().__init__(
                  __UpperCAmelCase,			tokenizer_file=__UpperCAmelCase,			eos_token=__UpperCAmelCase,			unk_token=__UpperCAmelCase,			pad_token=__UpperCAmelCase,			extra_ids=__UpperCAmelCase,			additional_special_tokens=__UpperCAmelCase,			**__UpperCAmelCase,			)

              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						vocab_file
              SCREAMING_SNAKE_CASE__       :      Any     =						False if not self.vocab_file else True
              SCREAMING_SNAKE_CASE__       :      Tuple     =						extra_ids






       @staticmethod
       def  A_     ( _UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					Dict						)				->			Union[str, Any]:





              """simple docstring"""
              if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
                     if init_max_model_length is not None and init_max_model_length != max_model_length:
                            return init_max_model_length
                     elif init_max_model_length is None:
                            warnings.warn(
                                "This tokenizer was incorrectly instantiated with a model max length of"
                                F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
                                " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                                " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                                F''' {pretrained_model_name_or_path} automatically truncating your input to'''
                                F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
                                F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
                                " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                                " instantiate this tokenizer with `model_max_length` set to your preferred value.",			__UpperCAmelCase,			)

              return max_model_length






       def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Any = 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(__UpperCAmelCase						):
                     logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
                     return
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						os.path.join(
                  __UpperCAmelCase,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

              if os.path.abspath(self.vocab_file						) != os.path.abspath(__UpperCAmelCase						):
                     copyfile(self.vocab_file,			__UpperCAmelCase						)
                     logger.info(F'''Copy vocab file to {out_vocab_file}'''						)

              return (out_vocab_file,)






       def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[str] = None						)				->			List[int]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Any     =						token_ids_a + [self.eos_token_id]
              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a
              else:
                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						token_ids_a + [self.eos_token_id]
                     return self.prefix_tokens + token_ids_a + token_ids_a






       def  A_     ( self     :					Dict,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Dict = None						)				->			List[int]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Dict     =						[self.eos_token_id]

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






       def  A_     ( self     :					Optional[Any]						)				->			int:





              """simple docstring"""
              return list(
                  set(filter(lambda _UpperCAmelCase						: bool(re.search(r"<extra_id_\d+>",			__UpperCAmelCase						)						) is not None,			self.additional_special_tokens						)						)						)






       def  A_     ( self     :					List[str]						)				->			Optional[Any]:





              """simple docstring"""
              return [self.convert_tokens_to_ids(__UpperCAmelCase						) for token in self.get_sentinel_tokens()]













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




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  complex			,				SCREAMING_SNAKE_CASE__      :  str = "x"			,				SCREAMING_SNAKE_CASE__      :  float = 10**-10			,				SCREAMING_SNAKE_CASE__      :  int = 1			,				)	->    complex:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						symbols(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						lambdify(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						lambdify(SCREAMING_SNAKE_CASE__			,				diff(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )       )

							SCREAMING_SNAKE_CASE__       :      Any     =						starting_point

							while True:
														if diff_function(SCREAMING_SNAKE_CASE__       ) != 0:
																					SCREAMING_SNAKE_CASE__       :      Any     =						prev_guess - multiplicity * func(SCREAMING_SNAKE_CASE__       ) / diff_function(
																					    SCREAMING_SNAKE_CASE__       )
														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

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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', 1_0, precision=0.005)}",
		)

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






