
"""simple docstring"""







import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
  import torch


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE	       =      {"""vocab_file""": """spiece.model"""}

_SCREAMING_SNAKE_CASE	       =      {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

_SCREAMING_SNAKE_CASE	       =      {
    """AI-Sweden/gpt-sw3-126m""": 2_048,
    """AI-Sweden/gpt-sw3-350m""": 2_048,
    """AI-Sweden/gpt-sw3-1.6b""": 2_048,
    """AI-Sweden/gpt-sw3-6.7b""": 2_048,
    """AI-Sweden/gpt-sw3-20b""": 2_048,
}







class 							__magic_name__     (		lowercase__   ):
     _SCREAMING_SNAKE_CASE       :					List[str]     							=   VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE       :					int     							=   PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE       :					List[str]     							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   ['input_ids', 'attention_mask']





     def __init__(				self   :							Optional[int]							,						snake_case_   :							Dict							,						snake_case_   :							List[str]=False							,						snake_case_   :							Dict=False							,						snake_case_   :							Any=False							,						snake_case_   :							Dict=None							,						snake_case_   :							Dict=None							,						snake_case_   :							Dict=None							,						snake_case_   :							Dict=None							,						snake_case_   :							Optional[Dict[str, Any]] = None							,						**snake_case_   :							Tuple							,						):
            __snake_case     			=       {} if sp_model_kwargs is None else sp_model_kwargs

            __snake_case     			=       kwargs.get("name_or_path"    )
            if name_or_path is None:
                   logger.warning(
                       "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
                       " you are testing the model, this can safely be ignored"    )
                   __snake_case     			=       "None"

            # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
            __snake_case     			=       "<|endoftext|>" if eos_token is None else eos_token
            __snake_case     			=       "<unk>" if unk_token is None else unk_token
            if "gpt-sw3-7b" in name_or_path:
                   __snake_case     			=       unk_token if pad_token is None else pad_token
                   __snake_case     			=       eos_token if bos_token is None else bos_token
            else:
                   __snake_case     			=       "<pad>" if pad_token is None else pad_token
                   __snake_case     			=       "<s>" if bos_token is None else bos_token

            super().__init__(
                do_lower_case=snake_case_							,						remove_space=snake_case_							,						keep_accents=snake_case_							,						bos_token=snake_case_							,						eos_token=snake_case_							,						unk_token=snake_case_							,						pad_token=snake_case_							,						sp_model_kwargs=self.sp_model_kwargs							,						**snake_case_							,						)

            __snake_case     			=       do_lower_case
            __snake_case     			=       remove_space
            __snake_case     			=       keep_accents
            __snake_case     			=       vocab_file

            __snake_case     			=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
            self.sp_model.Load(snake_case_    )

            # Used for whitespace normalization in input texts
            # fmt : off
            __snake_case     			=       {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
            # fmt : on

            # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
            __snake_case     			=       re.compile(
                F'''[{"".join(map(snake_case_							,						list(range(0							,						9    )    ) + list(range(11							,						32    )    ) + list(range(127							,						160    )    ) + [160, 173, 8203]    )    )}]'''    )





     def __getstate__(				self   :							Any    ):
            __snake_case     			=       self.__dict__.copy()
            __snake_case     			=       None
            return state





     def __setstate__(				self   :							List[str]							,						snake_case_   :							Any    ):
            __snake_case     			=       d

            # for backward compatibility
            if not hasattr(self							,						"sp_model_kwargs"    ):
                   __snake_case     			=       {}

            __snake_case     			=       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
            self.sp_model.Load(self.vocab_file    )





     @property
     # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
     def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
            return len(self.sp_model    )





     def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str    ):
            __snake_case     			=       self.non_printing_characters_re.sub(""							,						snake_case_    )

            # Normalize whitespaces
            __snake_case     			=       "".join([char if char not in self.whitespaces else " " for char in text]    )

            # NFC Unicode normalization
            __snake_case     			=       unicodedata.normalize("NFC"							,						snake_case_    )
            return text





     def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							str							,						**snake_case_   :							Optional[Any]    ):
            __snake_case     			=       self.preprocess_text(snake_case_    )
            return self.sp_model.encode(snake_case_							,						out_type=snake_case_    )





     def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str    ):
            return self.sp_model.PieceToId(snake_case_    )





     def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							int    ):
            return self.sp_model.IdToPiece(snake_case_    )





     @staticmethod
     def 							lowerCAmelCase      (				snake_case_   :							str    ):
            return out_string





     def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[str]    ):
            __snake_case     			=       []
            __snake_case     			=       ""
            __snake_case     			=       False
            for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                          # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                          if not prev_is_special:
                                 out_string += " "

                          out_string += self.sp_model.decode(snake_case_    ) + token
                          __snake_case     			=       True
                          __snake_case     			=       []
                   else:
                          current_sub_tokens.append(snake_case_    )
                          __snake_case     			=       False
            out_string += self.sp_model.decode(snake_case_    )

            return out_string





     def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
            __snake_case     			=       {self.convert_ids_to_tokens(snake_case_    ): i for i in range(self.vocab_size    )}
            vocab.update(self.added_tokens_encoder    )
            return vocab





     def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str							,						snake_case_   :							Optional[str] = None    ):
            if not os.path.isdir(snake_case_    ):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
                   return
            __snake_case     			=       os.path.join(
                snake_case_							,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

            if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case_    ) and os.path.isfile(self.vocab_file    ):
                   copyfile(self.vocab_file							,						snake_case_    )
            elif not os.path.isfile(self.vocab_file    ):
                   with open(snake_case_							,						"wb"    ) as fi:
                          __snake_case     			=       self.sp_model.serialized_model_proto()
                          fi.write(snake_case_    )

            return (out_vocab_file,)





     def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Union[str, List[str]]							,						snake_case_   :							Union[str, bool] = False    ):

            if isinstance(snake_case_							,						snake_case_    ):
                   __snake_case     			=       self.preprocess_text(snake_case_    )
                   __snake_case     			=       self.sp_model.encode(snake_case_    )
            else:
                   __snake_case     			=       [self.preprocess_text(snake_case_    ) for t in text]
                   __snake_case     			=       self.sp_model.encode(snake_case_    )

            if return_tensors is True or return_tensors == "pt":
                   __snake_case     			=       torch.tensor(snake_case_    )

            return token_ids





     def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Union[int, List[int]]    ):
            return self.sp_model.decode(snake_case_    )





     def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							"Conversation"    ):
            __snake_case     			=       [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
            __snake_case     			=       (
                F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(snake_case_    ) + F'''{self.bos_token}Bot:'''
            )
            return self.encode(text=snake_case_    )


"""simple docstring"""







from __future__ import annotations

from collections.abc import Callable
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 1_00							,    )  ->     float:







							"""simple docstring"""


							__snake_case     			=       x_start
							__snake_case     			=       fnc(SCREAMING_SNAKE_CASE )
							__snake_case     			=       0.0
							for _ in range(SCREAMING_SNAKE_CASE ):
														# Approximates small segments of curve as linear and solve
														# for trapezoidal area
														__snake_case     			=       (x_end - x_start) / steps + xa
														__snake_case     			=       fnc(SCREAMING_SNAKE_CASE )
														area += abs(fxa + fxa ) * (xa - xa) / 2
														# Increment step
														__snake_case     			=       xa
														__snake_case     			=       fxa
							return area


if __name__ == "__main__":
		def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







									"""simple docstring"""


									return x**3 + x**2

		print("""f(x) = x^3 + x^2""")
		print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
		_SCREAMING_SNAKE_CASE	       =      10
		while i <= 100_000:
				print(F"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
				i *= 10
