




"""simple docstring"""



from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
      import tensorflow as tf

      from ..tf_utils import stable_softmax


if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)




@add_end_docstrings(
    A_,       r'''
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    ''',       )
class      UpperCAmelCase_  (						A_							):




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	GenericTensor				)				->							np.ndarray:




     '''simple docstring'''

     if self.framework == "tf":
      A__	=    tf.where(input_ids == self.tokenizer.mask_token_id				).numpy()
     elif self.framework == "pt":
      A__	=    torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,			as_tuple=snake_case_				)
     else:
      raise ValueError("Unsupported framework"				)
     return masked_index




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	GenericTensor				)				->							np.ndarray:




     '''simple docstring'''

     A__	=    self.get_masked_index(snake_case_				)
     A__	=    np.prod(masked_index.shape				)
     if numel < 1:
      raise PipelineException(
          "fill-mask"     ,			self.model.base_model_prefix     ,			F"""No mask_token ({self.tokenizer.mask_token}) found on the input"""     ,			)




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	GenericTensor				)				->							Dict:




     '''simple docstring'''

     if isinstance(snake_case_     ,			snake_case_				):
      for model_input in model_inputs:
       self._ensure_exactly_one_mask_token(model_input["input_ids"][0]				)
     else:
      for input_ids in model_inputs["input_ids"]:
       self._ensure_exactly_one_mask_token(snake_case_				)




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	Union[str, Any]=None     ,			**snake_case_						:	Dict				)				->							Dict[str, GenericTensor]:




     '''simple docstring'''

     if return_tensors is None:
      A__	=    self.framework
     A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_				)
     self.ensure_exactly_one_mask_token(snake_case_				)
     return model_inputs




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.model(**snake_case_				)
     A__	=    model_inputs["input_ids"]
     return model_outputs




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Dict=5     ,			snake_case_						:	Any=None				)				->							str:




     '''simple docstring'''

     if target_ids is not None and target_ids.shape[0] < top_k:
      A__	=    target_ids.shape[0]
     A__	=    model_outputs["input_ids"][0]
     A__	=    model_outputs["logits"]

     if self.framework == "tf":
      A__	=    tf.where(input_ids == self.tokenizer.mask_token_id				).numpy()[:, 0]

      A__	=    outputs.numpy()

      A__	=    outputs[0, masked_index, :]
      A__	=    stable_softmax(snake_case_     ,			axis=-1				)
      if target_ids is not None:
       A__	=    tf.gather_nd(tf.squeeze(snake_case_     ,			0				)     ,			target_ids.reshape(-1     ,			1				)				)
       A__	=    tf.expand_dims(snake_case_     ,			0				)

      A__	=    tf.math.top_k(snake_case_     ,			k=snake_case_				)
      A__,				A__	=    topk.values.numpy(), topk.indices.numpy()
     else:
      A__	=    torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,			as_tuple=snake_case_				).squeeze(-1				)
      # Fill mask pipeline supports only one ${mask_token} per sample

      A__	=    outputs[0, masked_index, :]
      A__	=    logits.softmax(dim=-1				)
      if target_ids is not None:
       A__	=    probs[..., target_ids]

      A__,				A__	=    probs.topk(snake_case_				)

     A__	=    []
     A__	=    values.shape[0] == 1
     for i, (_values, _predictions) in enumerate(zip(values.tolist()     ,			predictions.tolist()				)				):
      A__	=    []
      for v, p in zip(_values     ,			_predictions				):
       # Copy is important since we're going to modify this array in place
       A__	=    input_ids.numpy().copy()
       if target_ids is not None:
        A__	=    target_ids[p].tolist()

       A__	=    p
       # Filter padding out:
       A__	=    tokens[np.where(tokens != self.tokenizer.pad_token_id				)]
       # Originally we skip special tokens to give readable output.
       # For multi masks though, the other [MASK] would be removed otherwise
       # making the output look odd, so we add them back
       A__	=    self.tokenizer.decode(snake_case_     ,			skip_special_tokens=snake_case_				)
       A__	=    {"score": v, "token": p, "token_str": self.tokenizer.decode([p]				), "sequence": sequence}
       row.append(snake_case_				)
      result.append(snake_case_				)
     if single_mask:
      return result[0]
     return result




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[Any]=None				)				->							int:




     '''simple docstring'''

     if isinstance(snake_case_     ,			snake_case_				):
      A__	=    [targets]
     try:
      A__	=    self.tokenizer.get_vocab()
     except Exception:
      A__	=    {}
     A__	=    []
     for target in targets:
      A__	=    vocab.get(snake_case_     ,			snake_case_				)
      if id_ is None:
       A__	=    self.tokenizer(
           snake_case_     ,			add_special_tokens=snake_case_     ,			return_attention_mask=snake_case_     ,			return_token_type_ids=snake_case_     ,			max_length=1     ,			truncation=snake_case_     ,			)["input_ids"]
       if len(snake_case_				) == 0:
        logger.warning(
            F"""The specified target token `{target}` does not exist in the model vocabulary. """
            "We cannot replace it with anything meaningful, ignoring it"				)
        continue
       A__	=    input_ids[0]
       # XXX: If users encounter this pass
       # it becomes pretty slow, so let's make sure
       # The warning enables them to fix the input to
       # get faster performance.
       logger.warning(
           F"""The specified target token `{target}` does not exist in the model vocabulary. """
           F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_				)}`."""				)
      target_ids.append(id_				)
     A__	=    list(set(snake_case_				)				)
     if len(snake_case_				) == 0:
      raise ValueError("At least one target must be provided when passed."				)
     A__	=    np.array(snake_case_				)
     return target_ids




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Tuple=None     ,			snake_case_						:	List[str]=None				)				->							List[Any]:




     '''simple docstring'''

     A__	=    {}

     if targets is not None:
      A__	=    self.get_target_ids(snake_case_     ,			snake_case_				)
      A__	=    target_ids

     if top_k is not None:
      A__	=    top_k

     if self.tokenizer.mask_token_id is None:
      raise PipelineException(
          "fill-mask"     ,			self.model.base_model_prefix     ,			"The tokenizer does not define a `mask_token`."				)
     return {}, {}, postprocess_params




    def __call__(   self						:	Dict     ,			snake_case_						:	Tuple     ,			*snake_case_						:	Dict     ,			**snake_case_						:	Optional[int]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    super().__call__(snake_case_     ,			**snake_case_				)
     if isinstance(snake_case_     ,			snake_case_				) and len(snake_case_				) == 1:
      return outputs[0]
     return outputs











"""simple docstring"""



import random

from .binary_exp_mod import bin_exp_mod







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=10_00						)						->		Optional[Any]:
 if n < 2:
  return False

 if n % 2 == 0:
  return n == 2

 # this means n is odd
 A__	=    n - 1
 A__	=    0
 while d % 2 == 0:
  d /= 2
  exp += 1

 # n - 1=d*(2**exp)
 A__	=    0
 while count < prec:
  A__	=    random.randint(2      ,			n - 1						)
  A__	=    bin_exp_mod(lowercase_      ,			lowercase_      ,			lowercase_						)
  if b != 1:
   A__	=    True
   for _ in range(lowercase_						):
    if b == n - 1:
     A__	=    False
     break
    A__	=    b * b
    b %= n
   if flag:
    return False
   count += 1
 return True


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	abs(int(input("Enter bound : ").strip()))
      print("Here's the list of primes:")
      print(", ".join(str(i) for i in range(n + 1) if is_prime_big(i)))





