



"""simple docstring"""




from __future__ import annotations

import math

_lowerCAmelCase    : Dict							=							"2020.9.26"
_lowerCAmelCase    : Tuple							=							"xcodz-dot, cclaus, dhruvmanila"

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						float						,  SCREAMING_SNAKE_CASE__     :						float						,  SCREAMING_SNAKE_CASE__     :						float						,  SCREAMING_SNAKE_CASE__     :						float						,  SCREAMING_SNAKE_CASE__     :						float    )  ->       Union[str, Any]:





     '''simple docstring'''

     if not all(isinstance(SCREAMING_SNAKE_CASE__						,  (float, int)    ) for val in locals().values()    ):
          _UpperCAmelCase      :     List[str]        =   f'Input values must either be float or int: {list(locals().values()    )}'
          raise TypeError(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     List[Any]        =   ((x * distance) / (z + distance)) * scale
     _UpperCAmelCase      :     Dict        =   ((y * distance) / (z + distance)) * scale
     return projected_x, projected_y

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						float						,  SCREAMING_SNAKE_CASE__     :						float						,  SCREAMING_SNAKE_CASE__     :						float						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						float    )  ->       Tuple:





     '''simple docstring'''

     if not isinstance(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    ):
          raise TypeError("Axis must be a str"    )
     _UpperCAmelCase      :     Optional[int]        =   locals()
     del input_variables["axis"]
     if not all(isinstance(SCREAMING_SNAKE_CASE__						,  (float, int)    ) for val in input_variables.values()    ):
          _UpperCAmelCase      :     Dict        =   (
              'Input values except axis must either be float or int: '
              f'{list(input_variables.values()    )}'
          )
          raise TypeError(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     str        =   (angle % 360) / 450 * 180 / math.pi
     if axis == "z":
          _UpperCAmelCase      :     str        =   x * math.cos(SCREAMING_SNAKE_CASE__    ) - y * math.sin(SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Union[str, Any]        =   y * math.cos(SCREAMING_SNAKE_CASE__    ) + x * math.sin(SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Any        =   z
     elif axis == "x":
          _UpperCAmelCase      :     Any        =   y * math.cos(SCREAMING_SNAKE_CASE__    ) - z * math.sin(SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Tuple        =   z * math.cos(SCREAMING_SNAKE_CASE__    ) + y * math.sin(SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     List[str]        =   x
     elif axis == "y":
          _UpperCAmelCase      :     List[Any]        =   x * math.cos(SCREAMING_SNAKE_CASE__    ) - z * math.sin(SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Dict        =   z * math.cos(SCREAMING_SNAKE_CASE__    ) + x * math.sin(SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     List[Any]        =   y
     else:
          raise ValueError("not a valid axis, choose one of \'x\', \'y\', \'z\'"    )

     return new_x, new_y, new_z


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(F"{convert_to_ad(1.0, 2.0, 3.0, 1_0.0, 1_0.0) = }")
 print(F"{rotate(1.0, 2.0, 3.0, 'y', 9_0.0) = }")





"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_lowerCAmelCase    : List[str]							=							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase    : int							=							{
    "vocab_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
        ),
        "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-generator": (
            "https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
        ),
    },
}

_lowerCAmelCase    : List[Any]							=							{
    "google/electra-small-generator": 5_12,
    "google/electra-base-generator": 5_12,
    "google/electra-large-generator": 5_12,
    "google/electra-small-discriminator": 5_12,
    "google/electra-base-discriminator": 5_12,
    "google/electra-large-discriminator": 5_12,
}

_lowerCAmelCase    : Optional[Any]							=							{
    "google/electra-small-generator": {"do_lower_case": True},
    "google/electra-base-generator": {"do_lower_case": True},
    "google/electra-large-generator": {"do_lower_case": True},
    "google/electra-small-discriminator": {"do_lower_case": True},
    "google/electra-base-discriminator": {"do_lower_case": True},
    "google/electra-large-discriminator": {"do_lower_case": True},
}






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE						:       Any								=			PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE						:       List[str]								=			PRETRAINED_INIT_CONFIGURATION
      __SCREAMING_SNAKE_CASE						:       List[str]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE						:       Tuple								=			ElectraTokenizer



      def __init__(					self					:     Dict					,     A					:     Dict=None					,     A					:     Optional[int]=None					,     A					:     Dict=True					,     A					:     Optional[Any]="[UNK]"					,     A					:     Any="[SEP]"					,     A					:     str="[PAD]"					,     A					:     Tuple="[CLS]"					,     A					:     Optional[Any]="[MASK]"					,     A					:     Any=True					,     A					:     Tuple=None					,     **A					:     Any					,     ):
           super().__init__(
               A					,     tokenizer_file=A					,     do_lower_case=A					,     unk_token=A					,     sep_token=A					,     pad_token=A					,     cls_token=A					,     mask_token=A					,     tokenize_chinese_chars=A					,     strip_accents=A					,     **A					,     )

           _UpperCAmelCase      :     List[str]        =   json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
           if (
               normalizer_state.get("lowercase"					,     A						) != do_lower_case
               or normalizer_state.get("strip_accents"					,     A						) != strip_accents
               or normalizer_state.get("handle_chinese_chars"					,     A						) != tokenize_chinese_chars
           ):
                _UpperCAmelCase      :     Union[str, Any]        =   getattr(A					,     normalizer_state.pop("type"						)						)
                _UpperCAmelCase      :     Dict        =   do_lower_case
                _UpperCAmelCase      :     Optional[int]        =   strip_accents
                _UpperCAmelCase      :     Any        =   tokenize_chinese_chars
                _UpperCAmelCase      :     Optional[Any]        =   normalizer_class(**A						)

           _UpperCAmelCase      :     int        =   do_lower_case



      def 						snake_case_ (					self					:     Tuple					,     A					:     str					,     A					:     int=None						):
           _UpperCAmelCase      :     Dict        =   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                output += token_ids_a + [self.sep_token_id]

           return output



      def 						snake_case_ (					self					:     Any					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           _UpperCAmelCase      :     Any        =   [self.sep_token_id]
           _UpperCAmelCase      :     Dict        =   [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						) * [0] + len(token_ids_a + sep						) * [1]



      def 						snake_case_ (					self					:     Any					,     A					:     str					,     A					:     Optional[str] = None						):
           _UpperCAmelCase      :     List[Any]        =   self._tokenizer.model.save(A					,     name=A						)
           return tuple(A						)
