






import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

a_					      = 0.25 + 0.0j
a_					      = -0.4 + 0.6j
a_					      = -0.1 + 0.651j
a_					      = -2.0
a_					      = 56
a_					      = 2.0
a_					      = 666





def      __lowercase  (		lowerCamelCase      :  complex						,						lowerCamelCase      :  numpy.ndarray			):
 return numpy.exp(lowerCamelCase			) + c_parameter





def      __lowercase  (		lowerCamelCase      :  complex						,						lowerCamelCase      :  numpy.ndarray			):
 return z_values * z_values + c_parameter





def      __lowercase  (		lowerCamelCase      :  float						,						lowerCamelCase      :  int			):
 UpperCamelCase_      :		List[str]				=      numpy.linspace(-window_size						,						lowerCamelCase						,						lowerCamelCase			)
 UpperCamelCase_      :		Optional[int]				=      x.reshape((nb_pixels, 1)			)
 UpperCamelCase_      :		Union[str, Any]				=      numpy.linspace(-window_size						,						lowerCamelCase						,						lowerCamelCase			)
 UpperCamelCase_      :		List[Any]				=      y.reshape((1, nb_pixels)			)
 return x + 1.0j * y





def      __lowercase  (		lowerCamelCase      :  Callable[[Any, numpy.ndarray], numpy.ndarray]						,						lowerCamelCase      :  Any						,						lowerCamelCase      :  int						,						lowerCamelCase      :  numpy.ndarray						,						lowerCamelCase      :  float | None = None						,						):
 UpperCamelCase_      :		List[Any]				=      z_a.astype('complex64'			)
 for _ in range(lowerCamelCase			):
  UpperCamelCase_      :		List[str]				=      eval_function(lowerCamelCase						,						lowerCamelCase			)
  if infinity is not None:
   numpy.nan_to_num(lowerCamelCase						,						copy=lowerCamelCase						,						nan=lowerCamelCase			)
   UpperCamelCase_      :		List[Any]				=      infinity
 return z_n





def      __lowercase  (		lowerCamelCase      :  str						,						lowerCamelCase      :  Any						,						lowerCamelCase      :  float						,						lowerCamelCase      :  numpy.ndarray						,						):
 UpperCamelCase_      :		Dict				=      (abs(lowerCamelCase			)).transpose()
 UpperCamelCase_      :		Optional[int]				=      abs_z_final[::-1, :]
 pyplot.matshow(abs_z_final < escape_radius			)
 pyplot.title(F"Julia set of ${function_label}$, $c={function_params}$"			)
 pyplot.show()





def      __lowercase  (		):
 warnings.filterwarnings(
     'ignore'						,						category=lowerCamelCase						,						message='overflow encountered in multiply'			)
 warnings.filterwarnings(
     'ignore'						,						category=lowerCamelCase						,						message='invalid value encountered in multiply'						,						)
 warnings.filterwarnings(
     'ignore'						,						category=lowerCamelCase						,						message='overflow encountered in absolute'			)
 warnings.filterwarnings(
     'ignore'						,						category=lowerCamelCase						,						message='overflow encountered in exp'			)


if __name__ == "__main__":
   a_					      = prepare_grid(window_size, nb_pixels)

   ignore_overflow_warnings()  # See file header for explanations

   a_					      = 24
   a_					      = 2 * abs(c_cauliflower) + 1
   a_					      = iterate_function(
       eval_quadratic_polynomial,
       c_cauliflower,
       nb_iterations,
       z_a,
       infinity=1.1 * escape_radius,
   )
   show_results('z^2+c', c_cauliflower, escape_radius, z_final)

   a_					      = 64
   a_					      = 2 * abs(c_polynomial_a) + 1
   a_					      = iterate_function(
       eval_quadratic_polynomial,
       c_polynomial_a,
       nb_iterations,
       z_a,
       infinity=1.1 * escape_radius,
   )
   show_results('z^2+c', c_polynomial_a, escape_radius, z_final)

   a_					      = 161
   a_					      = 2 * abs(c_polynomial_a) + 1
   a_					      = iterate_function(
       eval_quadratic_polynomial,
       c_polynomial_a,
       nb_iterations,
       z_a,
       infinity=1.1 * escape_radius,
   )
   show_results('z^2+c', c_polynomial_a, escape_radius, z_final)

   a_					      = 12
   a_					      = 10_000.0
   a_					      = iterate_function(
       eval_exponential,
       c_exponential,
       nb_iterations,
       z_a + 2,
       infinity=1.0E10,
   )
   show_results('e^z+c', c_exponential, escape_radius, z_final)




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


a_					      = logging.get_logger(__name__)

a_					      = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a_					      = {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

a_					      = {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

a_					      = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}





class 	_lowercase		(      snake_case_ ):
 lowercase         =     VOCAB_FILES_NAMES
 lowercase         =     PRETRAINED_VOCAB_FILES_MAP
 lowercase         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase         =     ['input_ids', 'attention_mask', 'token_type_ids']
 lowercase         =     DebertaTokenizer

 def __init__(				self			:							Optional[int]     ,   snake_case			:							Any=None     ,   snake_case			:							Union[str, Any]=None     ,   snake_case			:							Optional[Any]=None     ,   snake_case			:							List[str]="replace"     ,   snake_case			:							int="[CLS]"     ,   snake_case			:							Any="[SEP]"     ,   snake_case			:							int="[SEP]"     ,   snake_case			:							Union[str, Any]="[CLS]"     ,   snake_case			:							List[Any]="[UNK]"     ,   snake_case			:							List[str]="[PAD]"     ,   snake_case			:							Tuple="[MASK]"     ,   snake_case			:							int=False     ,   **snake_case			:							List[str]     ,   )     ->		Any:

  """simple docstring"""





  super().__init__(
      snake_case     ,   snake_case     ,   tokenizer_file=snake_case     ,   errors=snake_case     ,   bos_token=snake_case     ,   eos_token=snake_case     ,   unk_token=snake_case     ,   sep_token=snake_case     ,   cls_token=snake_case     ,   pad_token=snake_case     ,   mask_token=snake_case     ,   add_prefix_space=snake_case     ,   **snake_case     ,   )
  UpperCamelCase_      :		str				=      kwargs.pop('add_bos_token'     ,   snake_case    )

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

  UpperCamelCase_      :		int				=      add_prefix_space

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		str:

  """simple docstring"""





  if self._mask_token is None:
   if self.verbose:
    logger.error('Using mask_token, but it is not set yet.'    )
   return None
  return str(self._mask_token    )

 @mask_token.setter
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							Optional[int]    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      AddedToken(snake_case     ,   lstrip=snake_case     ,   rstrip=snake_case    ) if isinstance(snake_case     ,   snake_case    ) else value
  UpperCamelCase_      :		Optional[int]				=      value

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  UpperCamelCase_      :		Dict				=      [self.cls_token_id]
  UpperCamelCase_      :		Tuple				=      [self.sep_token_id]
  return cls + token_ids_a + sep + token_ids_a + sep

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      [self.sep_token_id]
  UpperCamelCase_      :		List[str]				=      [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 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   *snake_case			:							Any     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      kwargs.get('is_split_into_words'     ,   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(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   *snake_case			:							List[str]     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      kwargs.get('is_split_into_words'     ,   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(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							str     ,   snake_case			:							Optional[str] = None    )     ->		Tuple[str]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      self._tokenizer.model.save(snake_case     ,   name=snake_case    )
  return tuple(snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							"Conversation"    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      []
  for is_user, text in conversation.iter_texts():
   input_ids.extend(self.encode(snake_case     ,   add_special_tokens=snake_case    ) + [self.eos_token_id]    )

  if len(snake_case    ) > self.model_max_length:
   UpperCamelCase_      :		Optional[int]				=      input_ids[-self.model_max_length :]
  return input_ids



