





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	:       Tuple    	=							logging.get_logger(__name__)







@add_end_docstrings(
    __lowerCamelCase       ,      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    __snake_case	(							__lowerCamelCase      ):





							'''simple docstring'''







							def 							UpperCAmelCase__			(  self			:   Tuple							,    A			:   GenericTensor		):
									if self.framework == "tf":
											__snake_case:						List[Any]			  =						tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()
									elif self.framework == "pt":
											__snake_case:						List[Any]			  =						torch.nonzero(input_ids == self.tokenizer.mask_token_id							,    as_tuple=__A		)
									else:
											raise ValueError("""Unsupported framework"""		)
									return masked_index




							def 							UpperCAmelCase__			(  self			:   Dict							,    A			:   GenericTensor		):
									__snake_case:						int			  =						self.get_masked_index(__A		)
									__snake_case:						Union[str, 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 							UpperCAmelCase__			(  self			:   str							,    A			:   GenericTensor		):
									if isinstance(__A							,    __A		):
											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(__A		)




							def 							UpperCAmelCase__			(  self			:   int							,    A			:   str							,    A			:   Union[str, Any]=None							,    **A			:   Optional[Any]		):
									if return_tensors is None:
											__snake_case:						List[str]			  =						self.framework
									__snake_case:						Any			  =						self.tokenizer(__A							,    return_tensors=__A		)
									self.ensure_exactly_one_mask_token(__A		)
									return model_inputs




							def 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   int		):
									__snake_case:						List[Any]			  =						self.model(**__A		)
									__snake_case:						Optional[int]			  =						model_inputs["""input_ids"""]
									return model_outputs




							def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   Union[str, Any]							,    A			:   Optional[Any]=5							,    A			:   Tuple=None		):
									# Cap top_k if there are targets
									if target_ids is not None and target_ids.shape[0] < top_k:
											__snake_case:						str			  =						target_ids.shape[0]
									__snake_case:						Optional[Any]			  =						model_outputs["""input_ids"""][0]
									__snake_case:						Optional[int]			  =						model_outputs["""logits"""]

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

											__snake_case:						Dict			  =						outputs.numpy()

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

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

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

											__snake_case  ,       __snake_case:						Dict			  =						probs.topk(__A		)

									__snake_case:						Dict			  =						[]
									__snake_case:						Dict			  =						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:						Union[str, Any]			  =						input_ids.numpy().copy()
													if target_ids is not None:
															__snake_case:						str			  =						target_ids[p].tolist()

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




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




							def 							UpperCAmelCase__			(  self			:   Tuple							,    A			:   str=None							,    A			:   Tuple=None		):
									__snake_case:						List[str]			  =						{}

									if targets is not None:
											__snake_case:						int			  =						self.get_target_ids(__A							,    __A		)
											__snake_case:						int			  =						target_ids

									if top_k is not None:
											__snake_case:						str			  =						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			:   Any							,    A			:   Union[str, Any]							,    *A			:   str							,    **A			:   str		):
									__snake_case:						Union[str, Any]			  =						super().__call__(__A							,    **__A		)
									if isinstance(__A							,    __A		) and len(__A		) == 1:
											return outputs[0]
									return outputs













from __future__ import annotations

from decimal import Decimal

from numpy import array







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   list[list[float]]:
  __snake_case:						Any			  =						Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(SCREAMING_SNAKE_CASE__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
    # Calculate the determinant of the matrix
    __snake_case:						Tuple			  =						float(
        d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creates a copy of the matrix with swapped positions of the elements
    __snake_case:						Optional[int]			  =						[[0.0, 0.0], [0.0, 0.0]]
    __snake_case  ,       __snake_case:						Optional[Any]			  =						matrix[1][1], matrix[0][0]
    __snake_case  ,       __snake_case:						Union[str, Any]			  =						-matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(SCREAMING_SNAKE_CASE__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(SCREAMING_SNAKE_CASE__) == 3
      and len(matrix[0]) == 3
      and len(matrix[1]) == 3
      and len(matrix[2]) == 3
  ):
    # Calculate the determinant of the matrix using Sarrus rule
    __snake_case:						Any			  =						float(
        (
            (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
            + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
            + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
        )
        - (
            (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
            + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
            + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
        ))
    if determinant == 0:
      raise ValueError("""This matrix has no inverse.""")

    # Creating cofactor matrix
    __snake_case:						Tuple			  =						[
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
        [d(0.0), d(0.0), d(0.0)],
    ]
    __snake_case:						Dict			  =						(d(matrix[1][1]) * d(matrix[2][2])) - (
        d(matrix[1][2]) * d(matrix[2][1])
    )
    __snake_case:						Tuple			  =						-(
        (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[1][0]) * d(matrix[2][1])) - (
        d(matrix[1][1]) * d(matrix[2][0])
    )
    __snake_case:						Union[str, Any]			  =						-(
        (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
    )
    __snake_case:						str			  =						(d(matrix[0][0]) * d(matrix[2][2])) - (
        d(matrix[0][2]) * d(matrix[2][0])
    )
    __snake_case:						List[Any]			  =						-(
        (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[Any]			  =						(d(matrix[0][1]) * d(matrix[1][2])) - (
        d(matrix[0][2]) * d(matrix[1][1])
    )
    __snake_case:						List[str]			  =						-(
        (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[0][0]) * d(matrix[1][1])) - (
        d(matrix[0][1]) * d(matrix[1][0])
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        __snake_case:						Tuple			  =						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        inverse_matrix[i][j] /= d(SCREAMING_SNAKE_CASE__)

        # Calculate the inverse of the matrix
    return [[float(d(SCREAMING_SNAKE_CASE__)) or 0.0 for n in row] for row in inverse_matrix]
  raise ValueError("""Please provide a matrix of size 2x2 or 3x3.""")






