






from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Optional[Any],  lowerCamelCase						:    Collection[float] | None = None  ):

								'''simple docstring'''
								if components is None:
												lowercase__    =			[]
								lowercase__    =			list(lowerCamelCase  )




				def __len__(     self						:    str  ):

								'''simple docstring'''
								return len(self.__components  )




				def __str__(     self						:    Tuple  ):

								'''simple docstring'''
								return "(" + ",".join(map(lowerCamelCase,  self.__components  )  ) + ")"




				def __add__(     self						:    List[str],  lowerCamelCase						:    Vector  ):

								'''simple docstring'''
								lowercase__    =			len(self  )
								if size == len(lowerCamelCase  ):
												lowercase__    =			[self.__components[i] + other.component(lowerCamelCase  ) for i in range(lowerCamelCase  )]
												return Vector(lowerCamelCase  )
								else:
												raise Exception('''must have the same size'''  )




				def __sub__(     self						:    Optional[int],  lowerCamelCase						:    Vector  ):

								'''simple docstring'''
								lowercase__    =			len(self  )
								if size == len(lowerCamelCase  ):
												lowercase__    =			[self.__components[i] - other.component(lowerCamelCase  ) for i in range(lowerCamelCase  )]
												return Vector(lowerCamelCase  )
								else:  # error case
												raise Exception('''must have the same size'''  )




				@overload
				def __mul__(     self						:    str,  lowerCamelCase						:    float  ):

								'''simple docstring'''
								...




				@overload
				def __mul__(     self						:    Optional[Any],  lowerCamelCase						:    Vector  ):

								'''simple docstring'''
								...




				def __mul__(     self						:    Optional[Any],  lowerCamelCase						:    float | Vector  ):

								'''simple docstring'''
								if isinstance(lowerCamelCase,  (float, int)  ):
												lowercase__    =			[c * other for c in self.__components]
												return Vector(lowerCamelCase  )
								elif isinstance(lowerCamelCase,  lowerCamelCase  ) and len(self  ) == len(lowerCamelCase  ):
												lowercase__    =			len(self  )
												lowercase__    =			[self.__components[i] * other.component(lowerCamelCase  ) for i in range(lowerCamelCase  )]
												return sum(lowerCamelCase  )
								else:  # error case
												raise Exception('''invalid operand!'''  )




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								return Vector(self.__components  )




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								if isinstance(lowerCamelCase,  lowerCamelCase  ) and -len(self.__components  ) <= i < len(self.__components  ):
												return self.__components[i]
								else:
												raise Exception('''index out of range'''  )




				def 				lowercase__   (     self						:    Optional[int],  lowerCamelCase						:    int,  lowerCamelCase						:    float  ):

								'''simple docstring'''
								assert -len(self.__components  ) <= pos < len(self.__components  )
								lowercase__    =			value




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								if len(self.__components  ) == 0:
												raise Exception('''Vector is empty'''  )
								lowercase__    =			[c**2 for c in self.__components]
								return math.sqrt(sum(lowerCamelCase  )  )




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    Vector,  lowerCamelCase						:    bool = False  ):

								'''simple docstring'''
								lowercase__    =			self * other
								lowercase__    =			self.euclidean_length() * other.euclidean_length()
								if deg:
												return math.degrees(math.acos(num / den  )  )
								else:
												return math.acos(num / den  )






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_		,   lowerCamelCase_				)
				return Vector([0] * dimension				)






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_		,   lowerCamelCase_				) and (isinstance(lowerCamelCase_		,   lowerCamelCase_				))
				lowercase__    =			[0] * dimension
				lowercase__    =			1
				return Vector(lowerCamelCase_				)






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				assert (
				    isinstance(lowerCamelCase_		,   lowerCamelCase_				)
				    and isinstance(lowerCamelCase_		,   lowerCamelCase_				)
				    and (isinstance(lowerCamelCase_		,   (int, float)				))
				)
				return x * scalar + y






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				random.seed(lowerCamelCase_				)
				lowercase__    =			[random.randint(lowerCamelCase_		,   lowerCamelCase_				) for _ in range(lowerCamelCase_				)]
				return Vector(lowerCamelCase_				)
class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Union[str, Any],  lowerCamelCase						:    list[list[float]],  lowerCamelCase						:    int,  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			matrix
								lowercase__    =			w
								lowercase__    =			h




				def __str__(     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			''''''
								for i in range(self.__height  ):
												ans += "|"
												for j in range(self.__width  ):
																if j < self.__width - 1:
																				ans += str(self.__matrix[i][j]  ) + ","
																else:
																				ans += str(self.__matrix[i][j]  ) + "|\n"
								return ans




				def __add__(     self						:    Any,  lowerCamelCase						:    Matrix  ):

								'''simple docstring'''
								if self.__width == other.width() and self.__height == other.height():
												lowercase__    =			[]
												for i in range(self.__height  ):
																lowercase__    =			[
																    self.__matrix[i][j] + other.component(lowerCamelCase,  lowerCamelCase  )
																    for j in range(self.__width  )
																]
																matrix.append(lowerCamelCase  )
												return Matrix(lowerCamelCase,  self.__width,  self.__height  )
								else:
												raise Exception('''matrix must have the same dimension!'''  )




				def __sub__(     self						:    Dict,  lowerCamelCase						:    Matrix  ):

								'''simple docstring'''
								if self.__width == other.width() and self.__height == other.height():
												lowercase__    =			[]
												for i in range(self.__height  ):
																lowercase__    =			[
																    self.__matrix[i][j] - other.component(lowerCamelCase,  lowerCamelCase  )
																    for j in range(self.__width  )
																]
																matrix.append(lowerCamelCase  )
												return Matrix(lowerCamelCase,  self.__width,  self.__height  )
								else:
												raise Exception('''matrices must have the same dimension!'''  )




				@overload
				def __mul__(     self						:    Optional[int],  lowerCamelCase						:    float  ):

								'''simple docstring'''
								...




				@overload
				def __mul__(     self						:    Optional[int],  lowerCamelCase						:    Vector  ):

								'''simple docstring'''
								...




				def __mul__(     self						:    Optional[int],  lowerCamelCase						:    float | Vector  ):

								'''simple docstring'''
								if isinstance(lowerCamelCase,  lowerCamelCase  ):  # matrix-vector
												if len(lowerCamelCase  ) == self.__width:
																lowercase__    =			zero_vector(self.__height  )
																for i in range(self.__height  ):
																				lowercase__    =			[
																				    self.__matrix[i][j] * other.component(lowerCamelCase  )
																				    for j in range(self.__width  )
																				]
																				ans.change_component(lowerCamelCase,  sum(lowerCamelCase  )  )
																return ans
												else:
																raise Exception(
																    '''vector must have the same size as the '''
																    '''number of columns of the matrix!'''  )
								elif isinstance(lowerCamelCase,  (int, float)  ):  # matrix-scalar
												lowercase__    =			[
												    [self.__matrix[i][j] * other for j in range(self.__width  )]
												    for i in range(self.__height  )
												]
												return Matrix(lowerCamelCase,  self.__width,  self.__height  )
								return None




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								return self.__height




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								return self.__width




				def 				lowercase__   (     self						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    int  ):

								'''simple docstring'''
								if 0 <= x < self.__height and 0 <= y < self.__width:
												return self.__matrix[x][y]
								else:
												raise Exception('''change_component: indices out of bounds'''  )




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    float  ):

								'''simple docstring'''
								if 0 <= x < self.__height and 0 <= y < self.__width:
												lowercase__    =			value
								else:
												raise Exception('''change_component: indices out of bounds'''  )




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    int,  lowerCamelCase						:    int  ):

								'''simple docstring'''
								if self.__height != self.__width:
												raise Exception('''Matrix is not square'''  )
								lowercase__    =			self.__matrix[:x] + self.__matrix[x + 1 :]
								for i in range(len(lowerCamelCase  )  ):
												lowercase__    =			minor[i][:y] + minor[i][y + 1 :]
								return Matrix(lowerCamelCase,  self.__width - 1,  self.__height - 1  ).determinant()




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    int,  lowerCamelCase						:    int  ):

								'''simple docstring'''
								if self.__height != self.__width:
												raise Exception('''Matrix is not square'''  )
								if 0 <= x < self.__height and 0 <= y < self.__width:
												return (-1) ** (x + y) * self.minor(lowerCamelCase,  lowerCamelCase  )
								else:
												raise Exception('''Indices out of bounds'''  )




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								if self.__height != self.__width:
												raise Exception('''Matrix is not square'''  )
								if self.__height < 1:
												raise Exception('''Matrix has no element'''  )
								elif self.__height == 1:
												return self.__matrix[0][0]
								elif self.__height == 2:
												return (
												    self.__matrix[0][0] * self.__matrix[1][1]
												    - self.__matrix[0][1] * self.__matrix[1][0]
												)
								else:
												lowercase__    =			[
												    self.__matrix[0][y] * self.cofactor(0,  lowerCamelCase  ) for y in range(self.__width  )
												]
												return sum(lowerCamelCase  )






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[[0] * n for _ in range(lowerCamelCase_				)]
				return Matrix(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				)






def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				random.seed(lowerCamelCase_				)
				lowercase__    =			[
				    [random.randint(lowerCamelCase_		,   lowerCamelCase_				) for _ in range(lowerCamelCase_				)] for _ in range(lowerCamelCase_				)
				]
				return Matrix(lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				)












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__	:			Dict			   = 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__	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Optional[int],  lowerCamelCase						:    GenericTensor  ):

								'''simple docstring'''
								if self.framework == "tf":
												lowercase__    =			tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()
								elif self.framework == "pt":
												lowercase__    =			torch.nonzero(input_ids == self.tokenizer.mask_token_id,  as_tuple=lowerCamelCase  )
								else:
												raise ValueError('''Unsupported framework'''  )
								return masked_index




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    GenericTensor  ):

								'''simple docstring'''
								lowercase__    =			self.get_masked_index(lowerCamelCase  )
								lowercase__    =			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						:    Optional[Any],  lowerCamelCase						:    GenericTensor  ):

								'''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						:    List[str],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[int]=None,  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								if return_tensors is None:
												lowercase__    =			self.framework
								lowercase__    =			self.tokenizer(lowerCamelCase,  return_tensors=lowerCamelCase  )
								self.ensure_exactly_one_mask_token(lowerCamelCase  )
								return model_inputs




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.model(**lowerCamelCase  )
								lowercase__    =			model_inputs['''input_ids''']
								return model_outputs




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Tuple=5,  lowerCamelCase						:    List[Any]=None  ):

								'''simple docstring'''
								# Cap top_k if there are targets
								if target_ids is not None and target_ids.shape[0] < top_k:
												lowercase__    =			target_ids.shape[0]
								lowercase__    =			model_outputs['''input_ids'''][0]
								lowercase__    =			model_outputs['''logits''']

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

												lowercase__    =			outputs.numpy()

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

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

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

												lowercase__ ,						lowercase__    =			probs.topk(lowerCamelCase  )

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

																lowercase__    =			p
																# Filter padding out:
																lowercase__    =			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
																lowercase__    =			self.tokenizer.decode(lowerCamelCase,  skip_special_tokens=lowerCamelCase  )
																lowercase__    =			{'''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						:    int,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    Dict=None  ):

								'''simple docstring'''
								if isinstance(lowerCamelCase,  lowerCamelCase  ):
												lowercase__    =			[targets]
								try:
												lowercase__    =			self.tokenizer.get_vocab()
								except Exception:
												lowercase__    =			{}
								lowercase__    =			[]
								for target in targets:
												lowercase__    =			vocab.get(lowerCamelCase,  lowerCamelCase  )
												if id_ is None:
																lowercase__    =			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
																lowercase__    =			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_  )
								lowercase__    =			list(set(lowerCamelCase  )  )
								if len(lowerCamelCase  ) == 0:
												raise ValueError('''At least one target must be provided when passed.'''  )
								lowercase__    =			np.array(lowerCamelCase  )
								return target_ids




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    int=None,  lowerCamelCase						:    Any=None  ):

								'''simple docstring'''
								lowercase__    =			{}

								if targets is not None:
												lowercase__    =			self.get_target_ids(lowerCamelCase,  lowerCamelCase  )
												lowercase__    =			target_ids

								if top_k is not None:
												lowercase__    =			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[Any],  lowerCamelCase						:    Optional[Any],  *lowerCamelCase						:    Optional[Any],  **lowerCamelCase						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			super().__call__(lowerCamelCase,  **lowerCamelCase  )
								if isinstance(lowerCamelCase,  lowerCamelCase  ) and len(lowerCamelCase  ) == 1:
												return outputs[0]
								return outputs




