

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

from ..utils.generic import ModelOutput






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			def __init__(	self							:       Union[str, Any]					,					a							:       Optional[int]					,					a							:       List[str]					,					a							:       Union[str, Any]		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				dataset
				SCREAMING_SNAKE_CASE      :					str    =				process
				SCREAMING_SNAKE_CASE      :					List[str]    =				params
			def __len__(	self							:       int		)							->				int:

				"""simple docstring"""

				return len(self.dataset		)






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				self.dataset[i]
				SCREAMING_SNAKE_CASE      :					Tuple    =				self.process(a					,					**self.params		)
				return processed






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			def __init__(	self							:       Optional[int]					,					a							:       Any					,					a							:       Any					,					a							:       Union[str, Any]					,					a							:       Union[str, Any]=None		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				loader
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				infer
				SCREAMING_SNAKE_CASE      :					List[str]    =				params
				if loader_batch_size == 1:
					# Let's spare some time by deactivating altogether
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				None
				SCREAMING_SNAKE_CASE      :					List[Any]    =				loader_batch_size

				# Internal bookkeeping
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				None
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				None
			def __len__(	self							:       List[Any]		)							->				List[str]:

				"""simple docstring"""

				return len(self.loader		)
			def __iter__(	self							:       Optional[int]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				iter(self.loader		)
				return self
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				List[str]:

				"""simple docstring"""

				if isinstance(self._loader_batch_data					,					torch.Tensor		):
					# Batch data is simple tensor, just fetch the slice
					SCREAMING_SNAKE_CASE      :					str    =				self._loader_batch_data[self._loader_batch_index]
				else:
					# Batch data is assumed to be BaseModelOutput (or dict)
					SCREAMING_SNAKE_CASE      :					str    =				{}
					for k, element in self._loader_batch_data.items():
						if isinstance(a					,					a		):
							# Convert ModelOutput to tuple first
							SCREAMING_SNAKE_CASE      :					Optional[Any]    =				element.to_tuple()
							if isinstance(element[0]					,					torch.Tensor		):
								SCREAMING_SNAKE_CASE      :					str    =				tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
							elif isinstance(element[0]					,					np.ndarray		):
								SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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(a					,					a		):
							# Those are stored as lists of tensors so need specific unbatching.
							if isinstance(element[0]					,					torch.Tensor		):
								SCREAMING_SNAKE_CASE      :					Dict    =				tuple(el[self._loader_batch_index].unsqueeze(0		) for el in element		)
							elif isinstance(element[0]					,					np.ndarray		):
								SCREAMING_SNAKE_CASE      :					Dict    =				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      :					str    =				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      :					Optional[Any]    =				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      :					Optional[Any]    =				np.expand_dims(element[self._loader_batch_index]					,					0		)
						else:
							# This is typically a list, so no need to `unsqueeze`.
							SCREAMING_SNAKE_CASE      :					Optional[int]    =				element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
					SCREAMING_SNAKE_CASE      :					List[Any]    =				self._loader_batch_data.__class__(a		)
				self._loader_batch_index += 1
				return result






			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				Dict:

				"""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      :					Union[str, Any]    =				next(self.iterator		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.infer(a					,					**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(a					,					torch.Tensor		):
						SCREAMING_SNAKE_CASE      :					List[Any]    =				processed
					else:
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				list(processed.keys()		)[0]
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				processed[key]
					if isinstance(a					,					a		):
						SCREAMING_SNAKE_CASE      :					List[str]    =				len(a		)
					else:
						SCREAMING_SNAKE_CASE      :					int    =				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      :					Union[str, Any]    =				observed_batch_size
					# Setting internal index to unwrap the batch
					SCREAMING_SNAKE_CASE      :					List[Any]    =				processed
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				0
					return self.loader_batch_item()
				else:
					# We're not unrolling batches
					return processed






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			def __init__(	self							:       Any					,					a							:       Optional[int]					,					a							:       Union[str, Any]					,					a							:       List[Any]					,					a							:       List[str]=None		)							->				Dict:

				"""simple docstring"""

				super().__init__(a					,					a					,					a		)
			def __iter__(	self							:       int		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				iter(self.loader		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				None
				return self






			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Optional[Any]:

				"""simple docstring"""

				if self.subiterator is None:
					SCREAMING_SNAKE_CASE      :					int    =				self.infer(next(self.iterator		)					,					**self.params		)
				try:
					# Try to return next item
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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      :					Any    =				self.infer(next(self.iterator		)					,					**self.params		)
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				next(self.subiterator		)
				return processed






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			def __iter__(	self							:       Union[str, Any]		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				iter(self.loader		)
				return self






			def 							__UpperCamelCase       (	self							:       str		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				False
				SCREAMING_SNAKE_CASE      :					List[str]    =				[]
				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      :					Any    =				self.loader_batch_item()
						SCREAMING_SNAKE_CASE      :					List[str]    =				item.pop("is_last"		)
						accumulator.append(a		)
						if is_last:
							return accumulator

				while not is_last:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.infer(next(self.iterator		)					,					**self.params		)
					if self.loader_batch_size is not None:
						if isinstance(a					,					torch.Tensor		):
							SCREAMING_SNAKE_CASE      :					Optional[int]    =				processed
						else:
							SCREAMING_SNAKE_CASE      :					List[Any]    =				list(processed.keys()		)[0]
							SCREAMING_SNAKE_CASE      :					List[Any]    =				processed[key]
						if isinstance(a					,					a		):
							SCREAMING_SNAKE_CASE      :					Any    =				len(a		)
						else:
							SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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      :					Union[str, Any]    =				observed_batch_size
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				processed
						SCREAMING_SNAKE_CASE      :					Dict    =				0
						while self._loader_batch_index < self.loader_batch_size:
							SCREAMING_SNAKE_CASE      :					Tuple    =				self.loader_batch_item()
							SCREAMING_SNAKE_CASE      :					Dict    =				item.pop("is_last"		)
							accumulator.append(a		)
							if is_last:
								return accumulator
					else:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				processed
						SCREAMING_SNAKE_CASE      :					Optional[Any]    =				item.pop("is_last"		)
						accumulator.append(a		)
				return accumulator






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			def __init__(	self							:       List[str]					,					a							:       Dataset					,					a							:       str		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				dataset
				SCREAMING_SNAKE_CASE      :					List[Any]    =				key
			def __len__(	self							:       Union[str, Any]		)							->				Dict:

				"""simple docstring"""

				return len(self.dataset		)






			def __getitem__(	self							:       Dict					,					a							:       List[Any]		)							->				List[str]:

				"""simple docstring"""

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






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			def __init__(	self							:       Optional[int]					,					a							:       Dataset					,					a							:       str					,					a							:       str		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				dataset
				SCREAMING_SNAKE_CASE      :					Dict    =				keya
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				keya
			def __len__(	self							:       Optional[Any]		)							->				Any:

				"""simple docstring"""

				return len(self.dataset		)






			def __getitem__(	self							:       Tuple					,					a							:       Optional[int]		)							->				List[str]:

				"""simple docstring"""

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





"""simple docstring"""






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







def      _snake_case			(				lowercase__							,    lowercase__		):
				return math.sqrt(sum(pow(a - b							,    2		) for a, b in zip(lowercase__							,    lowercase__		)		)		)







def      _snake_case			(				lowercase__							,    lowercase__		):

				if dataset.ndim != value_array.ndim:
								_lowerCamelCase     :			Tuple    =    (
								    'Wrong input data\'s dimensions... '
								    f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
								)
								raise ValueError(lowercase__		)

				try:
								if dataset.shape[1] != value_array.shape[1]:
												_lowerCamelCase     :			Optional[int]    =    (
												    'Wrong input data\'s shape... '
												    f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
												)
												raise ValueError(lowercase__		)
				except IndexError:
								if dataset.ndim != value_array.ndim:
												raise TypeError('Wrong shape'		)

				if dataset.dtype != value_array.dtype:
								_lowerCamelCase     :			int    =    (
								    'Input data have different datatype... '
								    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
								)
								raise TypeError(lowercase__		)

				_lowerCamelCase     :			Optional[int]    =    []

				for value in value_array:
								_lowerCamelCase     :			Tuple    =    euclidean(lowercase__							,    dataset[0]		)
								_lowerCamelCase     :			Union[str, Any]    =    dataset[0].tolist()

								for dataset_value in dataset[1:]:
												_lowerCamelCase     :			Optional[Any]    =    euclidean(lowercase__							,    lowercase__		)

												if dist > temp_dist:
																_lowerCamelCase     :			List[Any]    =    temp_dist
																_lowerCamelCase     :			List[str]    =    dataset_value.tolist()

								answer.append([vector, dist]		)

				return answer







def      _snake_case			(				lowercase__							,    lowercase__		):
				return np.dot(lowercase__							,    lowercase__		) / (norm(lowercase__		) * norm(lowercase__		))


if __name__ == "__main__":
			import doctest

			doctest.testmod()