




import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						dataset
				SCREAMING_SNAKE_CASE_								=						process
				SCREAMING_SNAKE_CASE_								=						params





	def __len__(							self      )  ->					Tuple:




				"""simple docstring"""



				return len(self.dataset      )





	def __getitem__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.dataset[i]
				SCREAMING_SNAKE_CASE_								=						self.process(SCREAMING_SNAKE_CASE      ,     **self.params      )
				return processed




class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=None      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						loader
				SCREAMING_SNAKE_CASE_								=						infer
				SCREAMING_SNAKE_CASE_								=						params
				if loader_batch_size == 1:
							# Let's spare some time by deactivating altogether
							SCREAMING_SNAKE_CASE_								=						None
				SCREAMING_SNAKE_CASE_								=						loader_batch_size

				# Internal bookkeeping
				SCREAMING_SNAKE_CASE_								=						None
				SCREAMING_SNAKE_CASE_								=						None





	def __len__(							self      )  ->					List[Any]:




				"""simple docstring"""



				return len(self.loader      )





	def __iter__(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						iter(self.loader      )
				return self





	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				if isinstance(self._loader_batch_data      ,     torch.Tensor      ):
							# Batch data is simple tensor, just fetch the slice
							SCREAMING_SNAKE_CASE_								=						self._loader_batch_data[self._loader_batch_index]
				else:
							# Batch data is assumed to be BaseModelOutput (or dict)
							SCREAMING_SNAKE_CASE_								=						{}
							for k, element in self._loader_batch_data.items():
										if isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
													# Convert ModelOutput to tuple first
													SCREAMING_SNAKE_CASE_								=						element.to_tuple()
													if isinstance(element[0]      ,     torch.Tensor      ):
																SCREAMING_SNAKE_CASE_								=						tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
													elif isinstance(element[0]      ,     np.ndarray      ):
																SCREAMING_SNAKE_CASE_								=						tuple(np.expand_dims(el[self._loader_batch_index]      ,     0      ) for el in element      )
													continue
										if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
													# Those are stored as lists of tensors so need specific unbatching.
													if isinstance(element[0]      ,     torch.Tensor      ):
																SCREAMING_SNAKE_CASE_								=						tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
													elif isinstance(element[0]      ,     np.ndarray      ):
																SCREAMING_SNAKE_CASE_								=						tuple(np.expand_dims(el[self._loader_batch_index]      ,     0      ) for el in element      )
													continue
										if element is None:
													# This can happen for optional data that get passed around
													SCREAMING_SNAKE_CASE_								=						None
										elif isinstance(element[self._loader_batch_index]      ,     torch.Tensor      ):
													# Take correct batch data, but make it looked like batch_size=1
													# For compatibility with other methods within transformers

													SCREAMING_SNAKE_CASE_								=						element[self._loader_batch_index].unsqueeze(0      )
										elif isinstance(element[self._loader_batch_index]      ,     np.ndarray      ):
													# Take correct batch data, but make it looked like batch_size=1
													# For compatibility with other methods within transformers
													SCREAMING_SNAKE_CASE_								=						np.expand_dims(element[self._loader_batch_index]      ,     0      )
										else:
													# This is typically a list, so no need to `unsqueeze`.
													SCREAMING_SNAKE_CASE_								=						element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
							SCREAMING_SNAKE_CASE_								=						self._loader_batch_data.__class__(SCREAMING_SNAKE_CASE      )
				self._loader_batch_index += 1
				return result





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							# We are currently unrolling a batch so we just need to return
							# the current item within a batch
							return self.loader_batch_item()

				# We're out of items within a batch
				SCREAMING_SNAKE_CASE_								=						next(self.iterator      )
				SCREAMING_SNAKE_CASE_								=						self.infer(SCREAMING_SNAKE_CASE      ,     **self.params      )
				# We now have a batch of "inferred things".
				if self.loader_batch_size is not None:
							# Try to infer the size of the batch
							if isinstance(SCREAMING_SNAKE_CASE      ,     torch.Tensor      ):
										SCREAMING_SNAKE_CASE_								=						processed
							else:
										SCREAMING_SNAKE_CASE_								=						list(processed.keys()      )[0]
										SCREAMING_SNAKE_CASE_								=						processed[key]
							if isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
										SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE      )
							else:
										SCREAMING_SNAKE_CASE_								=						first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
										# could be last batch so we can't unroll as many
										# elements.
										SCREAMING_SNAKE_CASE_								=						observed_batch_size
							# Setting internal index to unwrap the batch
							SCREAMING_SNAKE_CASE_								=						processed
							SCREAMING_SNAKE_CASE_								=						0
							return self.loader_batch_item()
				else:
							# We're not unrolling batches
							return processed




class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=None      )  ->					Any:




				"""simple docstring"""



				super().__init__(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )





	def __iter__(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						iter(self.loader      )
				SCREAMING_SNAKE_CASE_								=						None
				return self





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				if self.subiterator is None:
							SCREAMING_SNAKE_CASE_								=						self.infer(next(self.iterator      )      ,     **self.params      )
				try:
							# Try to return next item
							SCREAMING_SNAKE_CASE_								=						next(self.subiterator      )
				except StopIteration:
							# When a preprocess iterator ends, we can start lookig at the next item
							# ChunkIterator will keep feeding until ALL elements of iterator
							# all have created their subiterator and have been iterating against.
							#
							# Another way to look at it, is we're basically flattening lists of lists
							# into a single list, but with generators
							SCREAMING_SNAKE_CASE_								=						self.infer(next(self.iterator      )      ,     **self.params      )
							SCREAMING_SNAKE_CASE_								=						next(self.subiterator      )
				return processed




class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __iter__(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						iter(self.loader      )
				return self





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						False
				SCREAMING_SNAKE_CASE_								=						[]
				if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
							while self._loader_batch_index < self.loader_batch_size:
										SCREAMING_SNAKE_CASE_								=						self.loader_batch_item()
										SCREAMING_SNAKE_CASE_								=						item.pop('is_last'      )
										accumulator.append(SCREAMING_SNAKE_CASE      )
										if is_last:
													return accumulator

				while not is_last:
							SCREAMING_SNAKE_CASE_								=						self.infer(next(self.iterator      )      ,     **self.params      )
							if self.loader_batch_size is not None:
										if isinstance(SCREAMING_SNAKE_CASE      ,     torch.Tensor      ):
													SCREAMING_SNAKE_CASE_								=						processed
										else:
													SCREAMING_SNAKE_CASE_								=						list(processed.keys()      )[0]
													SCREAMING_SNAKE_CASE_								=						processed[key]
										if isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
													SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE      )
										else:
													SCREAMING_SNAKE_CASE_								=						first_tensor.shape[0]
										if 0 < observed_batch_size < self.loader_batch_size:
													# could be last batch so we can't unroll as many
													# elements.
													SCREAMING_SNAKE_CASE_								=						observed_batch_size
										SCREAMING_SNAKE_CASE_								=						processed
										SCREAMING_SNAKE_CASE_								=						0
										while self._loader_batch_index < self.loader_batch_size:
													SCREAMING_SNAKE_CASE_								=						self.loader_batch_item()
													SCREAMING_SNAKE_CASE_								=						item.pop('is_last'      )
													accumulator.append(SCREAMING_SNAKE_CASE      )
													if is_last:
																return accumulator
							else:
										SCREAMING_SNAKE_CASE_								=						processed
										SCREAMING_SNAKE_CASE_								=						item.pop('is_last'      )
										accumulator.append(SCREAMING_SNAKE_CASE      )
				return accumulator




class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						dataset
				SCREAMING_SNAKE_CASE_								=						key





	def __len__(							self      )  ->					str:




				"""simple docstring"""



				return len(self.dataset      )





	def __getitem__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				return self.dataset[i][self.key]




class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						dataset
				SCREAMING_SNAKE_CASE_								=						keya
				SCREAMING_SNAKE_CASE_								=						keya





	def __len__(							self      )  ->					Tuple:




				"""simple docstring"""



				return len(self.dataset      )





	def __getitem__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Optional[Any]:




				"""simple docstring"""



				return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}






from __future__ import annotations

from decimal import Decimal

from numpy import array

def lowercase    (  SCREAMING_SNAKE_CASE )			->							list[list[float]]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						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
						SCREAMING_SNAKE_CASE_								=						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
						SCREAMING_SNAKE_CASE_								=						[[0.0, 0.0], [0.0, 0.0]]
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						matrix[1][1], matrix[0][0]
						SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						-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
						SCREAMING_SNAKE_CASE_								=						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
						SCREAMING_SNAKE_CASE_								=						[
						    [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 )],
						]
						SCREAMING_SNAKE_CASE_								=						(d(matrix[1][1] ) * d(matrix[2][2] )) - (
						    d(matrix[1][2] ) * d(matrix[2][1] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[1][0] ) * d(matrix[2][1] )) - (
						    d(matrix[1][1] ) * d(matrix[2][0] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][0] ) * d(matrix[2][2] )) - (
						    d(matrix[0][2] ) * d(matrix[2][0] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][1] ) * d(matrix[1][2] )) - (
						    d(matrix[0][2] ) * d(matrix[1][1] )
						)
						SCREAMING_SNAKE_CASE_								=						-(
						    (d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
						)
						SCREAMING_SNAKE_CASE_								=						(d(matrix[0][0] ) * d(matrix[1][1] )) - (
						    d(matrix[0][1] ) * d(matrix[1][0] )
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						SCREAMING_SNAKE_CASE_								=						array(SCREAMING_SNAKE_CASE )
						for i in range(3 ):
									for j in range(3 ):
												SCREAMING_SNAKE_CASE_								=						cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						SCREAMING_SNAKE_CASE_								=						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.' )
