"""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_0_4_8,
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Union[str, Any]  				=				VOCAB_FILES_NAMES
    _snake_case  :	Dict  				=				PRETRAINED_VOCAB_FILES_MAP
    _snake_case  :	Tuple  				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _snake_case  :	Optional[int]  				=				['input_ids', 'attention_mask']
    _snake_case  :	str  				=				CodeGenTokenizer


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

      if kwargs.pop("""add_bos_token""" ,					__lowerCAmelCase       ):
        _UpperCAmelCase    			=   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."""       )

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

      _UpperCAmelCase    			=   add_prefix_space


    def   lowerCAmelCase_	(       self						:	int ,					*__lowerCAmelCase						:	Optional[Any] ,					**__lowerCAmelCase						:	Any       ):
      _UpperCAmelCase    			=   kwargs.get("""is_split_into_words""" ,					__lowerCAmelCase       )
      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(*__lowerCAmelCase ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					*__lowerCAmelCase						:	List[Any] ,					**__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   kwargs.get("""is_split_into_words""" ,					__lowerCAmelCase       )

      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(*__lowerCAmelCase ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Optional[str] = None       ):
      _UpperCAmelCase    			=   self._tokenizer.model.save(__lowerCAmelCase ,					name=__lowerCAmelCase       )
      return tuple(__lowerCAmelCase       )


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

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

      return decoded_text


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	Dict       ):
      def find_re(__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	Optional[Any]       ):
        _UpperCAmelCase    			=   pattern.search(__lowerCAmelCase ,					__lowerCAmelCase       )
        return m.start() if m else -1

      _UpperCAmelCase    			=   [re.compile(__lowerCAmelCase ,					re.MULTILINE       ) for pattern in truncate_before_pattern]

      _UpperCAmelCase    			=   list(re.finditer("""^print""" ,					__lowerCAmelCase ,					re.MULTILINE       )       )

      if len(__lowerCAmelCase       ) > 1:
        _UpperCAmelCase    			=   completion[: prints[1].start()]

      _UpperCAmelCase    			=   list(re.finditer("""^def""" ,					__lowerCAmelCase ,					re.MULTILINE       )       )

      if len(__lowerCAmelCase       ) > 1:
        _UpperCAmelCase    			=   completion[: defs[1].start()]

      _UpperCAmelCase    			=   0

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

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

"""simple docstring"""


from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase = False		):







  """simple docstring"""
  if radian_mode:
    return [magnitude * cos(lowercase		), magnitude * sin(lowercase		)]
  return [magnitude * cos(radians(lowercase		)		), magnitude * sin(radians(lowercase		)		)]






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase = 10**-1		):







  """simple docstring"""
  _UpperCAmelCase    			=   cross(lowercase ,lowercase		)
  _UpperCAmelCase    			=   sum(lowercase		)
  return abs(lowercase		) < eps


if __name__ == "__main__":
    # Test to check if it works
    UpperCAmelCase__			=    array(
        [
            polar_force(718.4, 1_8_0 - 3_0),
            polar_force(879.54, 4_5),
            polar_force(1_0_0, -9_0),
        ]
    )

    UpperCAmelCase__			=    array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem 1 in image_data/2D_problems.jpg
    UpperCAmelCase__			=    array(
        [
            polar_force(3_0 * 9.81, 1_5),
            polar_force(2_1_5, 1_8_0 - 4_5),
            polar_force(2_6_4, 9_0 - 3_0),
        ]
    )

    UpperCAmelCase__			=    array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem in image_data/2D_problems_1.jpg
    UpperCAmelCase__			=    array([[0, -2_0_0_0], [0, -1_2_0_0], [0, 1_5_6_0_0], [0, -1_2_4_0_0]])

    UpperCAmelCase__			=    array([[0, 0], [6, 0], [1_0, 0], [1_2, 0]])

    assert in_static_equilibrium(forces, location)

    import doctest

    doctest.testmod()
