



'''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


__a          =		logging.get_logger(__name__)







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







    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     GenericTensor						):
          if self.framework == "tf":
                snake_case__  :    Optional[Any]    						=      tf.where(input_ids == self.tokenizer.mask_token_id						).numpy()
          elif self.framework == "pt":
                snake_case__  :    Tuple    						=      torch.nonzero(input_ids == self.tokenizer.mask_token_id ,     as_tuple=snake_case_						)
          else:
                raise ValueError("""Unsupported framework"""						)
          return masked_index




    def lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     GenericTensor						):
          snake_case__  :    List[Any]    						=      self.get_masked_index(snake_case_						)
          snake_case__  :    List[Any]    						=      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 lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     GenericTensor						):
          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 lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Any ,     snake_case_					:     Optional[int]=None ,     **snake_case_					:     Optional[Any]						):
          if return_tensors is None:
                snake_case__  :    Tuple    						=      self.framework
          snake_case__  :    Optional[Any]    						=      self.tokenizer(snake_case_ ,     return_tensors=snake_case_						)
          self.ensure_exactly_one_mask_token(snake_case_						)
          return model_inputs




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     str						):
          snake_case__  :    Union[str, Any]    						=      self.model(**snake_case_						)
          snake_case__  :    Dict    						=      model_inputs["""input_ids"""]
          return model_outputs




    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     str=5 ,     snake_case_					:     List[Any]=None						):
          # Cap top_k if there are targets
          if target_ids is not None and target_ids.shape[0] < top_k:
                snake_case__  :    Any    						=      target_ids.shape[0]
          snake_case__  :    List[Any]    						=      model_outputs["""input_ids"""][0]
          snake_case__  :    Optional[Any]    						=      model_outputs["""logits"""]

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

                snake_case__  :    Optional[int]    						=      outputs.numpy()

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

                snake_case__  :    int    						=      tf.math.top_k(snake_case_ ,     k=snake_case_						)
                snake_case__						,				snake_case__  :    Any    						=      topk.values.numpy(), topk.indices.numpy()
          else:
                snake_case__  :    List[Any]    						=      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

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

                snake_case__						,				snake_case__  :    List[str]    						=      probs.topk(snake_case_						)

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

                      snake_case__  :    Union[str, Any]    						=      p
                      # Filter padding out:
                      snake_case__  :    List[str]    						=      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
                      snake_case__  :    str    						=      self.tokenizer.decode(snake_case_ ,     skip_special_tokens=snake_case_						)
                      snake_case__  :    Union[str, Any]    						=      {"""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 lowerCamelCase       (    self					:     int ,     snake_case_					:     Any ,     snake_case_					:     str=None						):
          if isinstance(snake_case_ ,     snake_case_						):
                snake_case__  :    Union[str, Any]    						=      [targets]
          try:
                snake_case__  :    Any    						=      self.tokenizer.get_vocab()
          except Exception:
                snake_case__  :    str    						=      {}
          snake_case__  :    List[Any]    						=      []
          for target in targets:
                snake_case__  :    List[str]    						=      vocab.get(snake_case_ ,     snake_case_						)
                if id_ is None:
                      snake_case__  :    int    						=      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
                      snake_case__  :    Optional[Any]    						=      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_						)
          snake_case__  :    Optional[Any]    						=      list(set(snake_case_						)						)
          if len(snake_case_						) == 0:
                raise ValueError("""At least one target must be provided when passed."""						)
          snake_case__  :    Dict    						=      np.array(snake_case_						)
          return target_ids




    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     Tuple=None ,     snake_case_					:     Union[str, Any]=None						):
          snake_case__  :    Union[str, Any]    						=      {}

          if targets is not None:
                snake_case__  :    List[str]    						=      self.get_target_ids(snake_case_ ,     snake_case_						)
                snake_case__  :    Union[str, Any]    						=      target_ids

          if top_k is not None:
                snake_case__  :    Optional[int]    						=      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					:     List[str] ,     snake_case_					:     Union[str, Any] ,     *snake_case_					:     Tuple ,     **snake_case_					:     List[Any]						):
          snake_case__  :    Optional[int]    						=      super().__call__(snake_case_ ,     **snake_case_						)
          if isinstance(snake_case_ ,     snake_case_						) and len(snake_case_						) == 1:
                return outputs[0]
          return outputs






import functools


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if not isinstance(_A     ,	_A							) or not all(isinstance(_A     ,	_A							) for day in days							):
		raise ValueError("The parameter days should be a list of integers"							)

	if len(_A							) != 3 or not all(isinstance(_A     ,	_A							) for cost in costs							):
		raise ValueError("The parameter costs should be a list of three integers"							)

	if len(_A							) == 0:
		return 0

	if min(_A							) <= 0:
		raise ValueError("All days elements should be greater than 0"							)

	if max(_A							) >= 366:
		raise ValueError("All days elements should be less than 366"							)

	snake_case_		    =     set(_A							)

	@functools.cache
	def dynamic_programming(_A							) -> int:
		if index > 365:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1							)

		return min(
		    costs[0] + dynamic_programming(index + 1							)     ,	costs[1] + dynamic_programming(index + 7							)     ,	costs[2] + dynamic_programming(index + 30							)     ,	)

	return dynamic_programming(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
