





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


UpperCAmelCase     :     Any							=	logging.get_logger(__name__)







@add_end_docstrings(
    UpperCamelCase__						,    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     __lowerCAmelCase	(   UpperCamelCase__):







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						np.ndarray:



        '''simple docstring'''




        if self.framework == "tf":
          a__     : Dict  =tf.where(input_ids == self.tokenizer.mask_token_id							).numpy()
        elif self.framework == "pt":
          a__     : List[Any]  =torch.nonzero(input_ids == self.tokenizer.mask_token_id		,				as_tuple=lowerCAmelCase__							)
        else:
          raise ValueError("Unsupported framework"							)
        return masked_index







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						np.ndarray:



        '''simple docstring'''




        a__     : int  =self.get_masked_index(lowerCAmelCase__							)
        a__     : Tuple  =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        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Any:



        '''simple docstring'''




        if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          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(lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__=None		,				**lowerCAmelCase__							)       ->						Dict[str, GenericTensor]:



        '''simple docstring'''




        if return_tensors is None:
          a__     : Tuple  =self.framework
        a__     : List[Any]  =self.tokenizer(lowerCAmelCase__		,				return_tensors=lowerCAmelCase__							)
        self.ensure_exactly_one_mask_token(lowerCAmelCase__							)
        return model_inputs







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.model(**lowerCAmelCase__							)
        a__     : Union[str, Any]  =model_inputs["input_ids"]
        return model_outputs







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__=5		,				lowerCAmelCase__=None							)       ->						Optional[Any]:



        '''simple docstring'''




        if target_ids is not None and target_ids.shape[0] < top_k:
          a__     : Dict  =target_ids.shape[0]
        a__     : str  =model_outputs["input_ids"][0]
        a__     : List[str]  =model_outputs["logits"]

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

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

          a__     : Optional[Any]  =outputs[0, masked_index, :]
          a__     : List[Any]  =stable_softmax(lowerCAmelCase__		,				axis=-1							)
          if target_ids is not None:
            a__     : List[str]  =tf.gather_nd(tf.squeeze(lowerCAmelCase__		,				0							)		,				target_ids.reshape(-1		,				1							)							)
            a__     : Any  =tf.expand_dims(lowerCAmelCase__		,				0							)

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

          a__     : int  =outputs[0, masked_index, :]
          a__     : Union[str, Any]  =logits.softmax(dim=-1							)
          if target_ids is not None:
            a__     : Optional[Any]  =probs[..., target_ids]

          a__ ,							a__     : str  =probs.topk(lowerCAmelCase__							)

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

            a__     : str  =p
            # Filter padding out:
            a__     : Tuple  =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__     : Dict  =self.tokenizer.decode(lowerCAmelCase__		,				skip_special_tokens=lowerCAmelCase__							)
            a__     : Dict  ={"score": v, "token": p, "token_str": self.tokenizer.decode([p]							), "sequence": sequence}
            row.append(lowerCAmelCase__							)
          result.append(lowerCAmelCase__							)
        if single_mask:
          return result[0]
        return result







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__=None							)       ->						Any:



        '''simple docstring'''




        if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          a__     : str  =[targets]
        try:
          a__     : Tuple  =self.tokenizer.get_vocab()
        except Exception:
          a__     : List[str]  ={}
        a__     : int  =[]
        for target in targets:
          a__     : Tuple  =vocab.get(lowerCAmelCase__		,				lowerCAmelCase__							)
          if id_ is None:
            a__     : Optional[int]  =self.tokenizer(
                lowerCAmelCase__		,				add_special_tokens=lowerCAmelCase__		,				return_attention_mask=lowerCAmelCase__		,				return_token_type_ids=lowerCAmelCase__		,				max_length=1		,				truncation=lowerCAmelCase__		,				)["input_ids"]
            if len(lowerCAmelCase__							) == 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__     : int  =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[Any]  =list(set(lowerCAmelCase__							)							)
        if len(lowerCAmelCase__							) == 0:
          raise ValueError("At least one target must be provided when passed."							)
        a__     : Tuple  =np.array(lowerCAmelCase__							)
        return target_ids







      def        _lowercase  (   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Union[str, Any]  ={}

        if targets is not None:
          a__     : Any  =self.get_target_ids(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : Union[str, Any]  =target_ids

        if top_k is not None:
          a__     : Any  =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		,				lowerCAmelCase__		,				*lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : List[str]  =super().__call__(lowerCAmelCase__		,				**lowerCAmelCase__							)
        if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							) and len(lowerCAmelCase__							) == 1:
          return outputs[0]
        return outputs













from __future__ import annotations







class     __lowerCAmelCase	:







      def __init__(   self		,				lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        a__     : int  =TypeError(
            "Matrices must be formed from a list of zero or more lists containing at "
            "least one and the same number of values, each of which must be of type "
            "int or float."							)
        if len(lowerCAmelCase__							) != 0:
          a__     : List[str]  =len(rows[0]							)
          if cols == 0:
            raise error
          for row in rows:
            if len(lowerCAmelCase__							) != cols:
              raise error
            for value in row:
              if not isinstance(lowerCAmelCase__		,				(int, float)							):
                raise error
          a__     : List[Any]  =rows
        else:
          a__     : str  =[]







      def        _lowercase  (   self							)       ->						list[list[int]]:



        '''simple docstring'''




        return [[row[i] for row in self.rows] for i in range(len(self.rows[0]							)							)]







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows							)







      @property
      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return len(self.rows[0]							)







      @property
      def        _lowercase  (   self							)       ->						tuple[int, int]:



        '''simple docstring'''




        return (self.num_rows, self.num_columns)







      @property
      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return self.order[0] == self.order[1]







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : str  =[
            [0 if column_num != row_num else 1 for column_num in range(self.num_rows							)]
            for row_num in range(self.num_rows							)
        ]
        return Matrix(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        if not self.is_square:
          return 0
        if self.order == (0, 0):
          return 1
        if self.order == (1, 1):
          return int(self.rows[0][0]							)
        if self.order == (2, 2):
          return int(
              (self.rows[0][0] * self.rows[1][1])
              - (self.rows[0][1] * self.rows[1][0])							)
        else:
          return sum(
              self.rows[0][column] * self.cofactors().rows[0][column]
              for column in range(self.num_columns							)							)







      def        _lowercase  (   self							)       ->						bool:



        '''simple docstring'''




        return bool(self.determinant()							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        a__     : List[str]  =[
            [
                self.rows[other_row][other_column]
                for other_column in range(self.num_columns							)
                if other_column != column
            ]
            for other_row in range(self.num_rows							)
            if other_row != row
        ]
        return Matrix(lowerCAmelCase__							).determinant()







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        if (row + column) % 2 == 0:
          return self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							)
        return -1 * self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        return Matrix(
            [
                [self.get_minor(lowerCAmelCase__		,				lowerCAmelCase__							) for column in range(self.num_columns							)]
                for row in range(self.num_rows							)
            ]							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        return Matrix(
            [
                [
                    self.minors().rows[row][column]
                    if (row + column) % 2 == 0
                    else self.minors().rows[row][column] * -1
                    for column in range(self.minors().num_columns							)
                ]
                for row in range(self.minors().num_rows							)
            ]							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Dict  =[
            [self.cofactors().rows[column][row] for column in range(self.num_columns							)]
            for row in range(self.num_rows							)
        ]
        return Matrix(lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Matrix:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.determinant()
        if not determinant:
          raise TypeError("Only matrices with a non-zero determinant have an inverse"							)
        return self.adjugate() * (1 / determinant)







      def __repr__(   self							)       ->						str:



        '''simple docstring'''




        return str(self.rows							)







      def __str__(   self							)       ->						str:



        '''simple docstring'''




        if self.num_rows == 0:
          return "[]"
        if self.num_rows == 1:
          return "[[" + ". ".join(str(self.rows[0]							)							) + "]]"
        return (
            "["
            + "\n ".join(
                [
                    "[" + ". ".join([str(lowerCAmelCase__							) for value in row]							) + ".]"
                    for row in self.rows
                ]							)
            + "]"
        )







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : List[str]  =TypeError("Row must be a list containing all ints and/or floats"							)
        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise type_error
        for value in row:
          if not isinstance(lowerCAmelCase__		,				(int, float)							):
            raise type_error
        if len(lowerCAmelCase__							) != self.num_columns:
          raise ValueError(
              "Row must be equal in length to the other rows in the matrix"							)
        if position is None:
          self.rows.append(lowerCAmelCase__							)
        else:
          a__     : Tuple  =self.rows[0:position] + [row] + self.rows[position:]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None							)       ->						None:



        '''simple docstring'''




        a__     : str  =TypeError(
            "Column must be a list containing all ints and/or floats"							)
        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise type_error
        for value in column:
          if not isinstance(lowerCAmelCase__		,				(int, float)							):
            raise type_error
        if len(lowerCAmelCase__							) != self.num_rows:
          raise ValueError(
              "Column must be equal in length to the other columns in the matrix"							)
        if position is None:
          a__     : Optional[Any]  =[self.rows[i] + [column[i]] for i in range(self.num_rows							)]
        else:
          a__     : Any  =[
              self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
              for i in range(self.num_rows							)
          ]







      def __eq__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          return NotImplemented
        return self.rows == other.rows







      def __ne__(   self		,				lowerCAmelCase__							)       ->						bool:



        '''simple docstring'''




        return not self == other







      def __neg__(   self							)       ->						Matrix:



        '''simple docstring'''




        return self * -1







      def __add__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if self.order != other.order:
          raise ValueError("Addition requires matrices of the same order"							)
        return Matrix(
            [
                [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns							)]
                for i in range(self.num_rows							)
            ]							)







      def __sub__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if self.order != other.order:
          raise ValueError("Subtraction requires matrices of the same order"							)
        return Matrix(
            [
                [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns							)]
                for i in range(self.num_rows							)
            ]							)







      def __mul__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if isinstance(lowerCAmelCase__		,				(int, float)							):
          return Matrix(
              [[int(element * other							) for element in row] for row in self.rows]							)
        elif isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          if self.num_columns != other.num_rows:
            raise ValueError(
                "The number of columns in the first matrix must "
                "be equal to the number of rows in the second"							)
          return Matrix(
              [
                  [Matrix.dot_product(lowerCAmelCase__		,				lowerCAmelCase__							) for column in other.columns()]
                  for row in self.rows
              ]							)
        else:
          raise TypeError(
              "A Matrix can only be multiplied by an int, float, or another matrix"							)







      def __pow__(   self		,				lowerCAmelCase__							)       ->						Matrix:



        '''simple docstring'''




        if not isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
          raise TypeError("A Matrix can only be raised to the power of an int"							)
        if not self.is_square:
          raise ValueError("Only square matrices can be raised to a power"							)
        if other == 0:
          return self.identity()
        if other < 0:
          if self.is_invertable():
            return self.inverse() ** (-other)
          raise ValueError(
              "Only invertable matrices can be raised to a negative power"							)
        a__     : Tuple  =self
        for _ in range(other - 1							):
          result *= self
        return result







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						int:



        '''simple docstring'''




        return sum(row[i] * column[i] for i in range(len(lowerCAmelCase__							)							)							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






