import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


lowerCAmelCase__  	=      "block_records.npy"


lowerCAmelCase__  	=      logging.get_logger(__name__)

def 	__lowerCamelCase							(				__a    : str      ,							__a    : int		)      ->       np.ndarray:
	import tensorflow.compat.va as tf

	_lowercase   =tf.data.TFRecordDataset(__a      ,							buffer_size=512 * 1_024 * 1_024		)
	_lowercase   =blocks_dataset.batch(__a      ,							drop_remainder=__a		)
	_lowercase   =next(blocks_dataset.take(1		).as_numpy_iterator()		)

	return np_record
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=1000       ,  lowerCAmelCase_=100       ,  lowerCAmelCase_=100000       ,  ):
		from scann.scann_ops.py.scann_ops_pybind import builder as Builder

		_lowercase   =Builder(db=lowerCAmelCase_       ,  num_neighbors=lowerCAmelCase_       ,  distance_measure="dot_product"       )
		_lowercase   =builder.tree(
		    num_leaves=lowerCAmelCase_       ,  num_leaves_to_search=lowerCAmelCase_       ,  training_sample_size=lowerCAmelCase_       )
		_lowercase   =builder.score_ah(dimensions_per_block=lowerCAmelCase_       )

		_lowercase   =builder.build()


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   =self.searcher.search_batched(question_projection.detach().cpu()       )
		return retrieved_block_ids.astype("int64"       )

class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		super().__init__()
		_lowercase   =block_records
		_lowercase   =tokenizer


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_="pt"       ):
		_lowercase   =np.take(self.block_records       ,  indices=lowerCAmelCase_       ,  axis=0       )

		_lowercase   =self.tokenizer.decode(question_input_ids[0]       ,  skip_special_tokens=lowerCAmelCase_       )

		_lowercase   =[]
		_lowercase   =[]
		for retrieved_block in retrieved_blocks:
			text.append(lowerCAmelCase_       )
			text_pair.append(retrieved_block.decode()       )

		_lowercase   =self.tokenizer(
		    lowerCAmelCase_       ,  lowerCAmelCase_       ,  padding=lowerCAmelCase_       ,  truncation=lowerCAmelCase_       ,  return_special_tokens_mask=lowerCAmelCase_       ,  max_length=lowerCAmelCase_       )
		_lowercase   =concat_inputs.convert_to_tensors(lowerCAmelCase_       )

		if answer_ids is not None:
			return self.block_has_answer(lowerCAmelCase_       ,  lowerCAmelCase_       ) + (concat_inputs_tensors,)
		else:
			return (None, None, None, concat_inputs_tensors)


	@classmethod
	def __lowerCAmelCase      (   cls       ,  lowerCAmelCase_       ,  *lowerCAmelCase_       ,  **lowerCAmelCase_       ):
		if os.path.isdir(lowerCAmelCase_       ):
			_lowercase   =os.path.join(lowerCAmelCase_       ,  _REALM_BLOCK_RECORDS_FILENAME       )
		else:
			_lowercase   =hf_hub_download(
			    repo_id=lowerCAmelCase_       ,  filename=_REALM_BLOCK_RECORDS_FILENAME       ,  **lowerCAmelCase_       )
		_lowercase   =np.load(lowerCAmelCase_       ,  allow_pickle=lowerCAmelCase_       )

		_lowercase   =AutoTokenizer.from_pretrained(lowerCAmelCase_       ,  *lowerCAmelCase_       ,  **lowerCAmelCase_       )

		return cls(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		# save block records
		np.save(os.path.join(lowerCAmelCase_       ,  _REALM_BLOCK_RECORDS_FILENAME       )       ,  self.block_records       )
		# save tokenizer
		self.tokenizer.save_pretrained(lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =[]
		_lowercase   =[]
		_lowercase   =[]
		_lowercase   =0

		for input_id in concat_inputs.input_ids:
			_lowercase   =input_id.tolist()
			# Check answers between two [SEP] tokens
			_lowercase   =input_id_list.index(self.tokenizer.sep_token_id       )
			_lowercase   =first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id       )

			start_pos.append([]       )
			end_pos.append([]       )
			for answer in answer_ids:
				for idx in range(first_sep_idx + 1       ,  lowerCAmelCase_       ):
					if answer[0] == input_id_list[idx]:
						if input_id_list[idx : idx + len(lowerCAmelCase_       )] == answer:
							start_pos[-1].append(lowerCAmelCase_       )
							end_pos[-1].append(idx + len(lowerCAmelCase_       ) - 1       )

			if len(start_pos[-1]       ) == 0:
				has_answers.append(lowerCAmelCase_       )
			else:
				has_answers.append(lowerCAmelCase_       )
				if len(start_pos[-1]       ) > max_answers:
					_lowercase   =len(start_pos[-1]       )

        # Pad -1 to max_answers
		for start_pos_, end_pos_ in zip(lowerCAmelCase_       ,  lowerCAmelCase_       ):
			if len(start_pos_       ) < max_answers:
				_lowercase   =[-1] * (max_answers - len(start_pos_       ))
				start_pos_ += padded
				end_pos_ += padded
		return has_answers, start_pos, end_pos





import numpy as np
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_=5       ,  lowerCAmelCase_=5       ):
		_lowercase   =depth
		_lowercase   =0
		_lowercase   =None
		_lowercase   =None
		_lowercase   =min_leaf_size
		_lowercase   =None


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if labels.ndim != 1:
			print("Error: Input labels must be one dimensional"       )

		return np.mean((labels - prediction) ** 2       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if x.ndim != 1:
			print("Error: Input data set must be one dimensional"       )
			return
		if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
			print("Error: X and y have different lengths"       )
			return
		if y.ndim != 1:
			print("Error: Data set labels must be one dimensional"       )
			return

		if len(lowerCAmelCase_       ) < 2 * self.min_leaf_size:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		if self.depth == 1:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		_lowercase   =0
		_lowercase   =self.mean_squared_error(lowerCAmelCase_       ,  np.mean(lowerCAmelCase_       )       ) * 2
		for i in range(len(lowerCAmelCase_       )       ):
			if len(x[:i]       ) < self.min_leaf_size:
				continue
			elif len(x[i:]       ) < self.min_leaf_size:
				continue
			else:
				_lowercase   =self.mean_squared_error(x[:i]       ,  np.mean(y[:i]       )       )
				_lowercase   =self.mean_squared_error(x[i:]       ,  np.mean(y[i:]       )       )
				_lowercase   =error_left + error_right
				if error < min_error:
					_lowercase   =i
					_lowercase   =error

		if best_split != 0:
			_lowercase   =x[:best_split]
			_lowercase   =y[:best_split]
			_lowercase   =x[best_split:]
			_lowercase   =y[best_split:]

			_lowercase   =x[best_split]
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			self.left.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
			self.right.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
		else:
			_lowercase   =np.mean(lowerCAmelCase_       )

		return


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		if self.prediction is not None:
			return self.prediction
		elif self.left or self.right is not None:
			if x >= self.decision_boundary:
				return self.right.predict(lowerCAmelCase_       )
			else:
				return self.left.predict(lowerCAmelCase_       )
		else:
			print("Error: Decision tree not yet trained"       )
			return None

class 	_a  :



	"""simple docstring"""



	@staticmethod
	def __lowerCAmelCase      (   lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =float(0       )
		for label in labels:
			squared_error_sum += (label - prediction) ** 2

		return float(squared_error_sum / labels.size       )

def 	__lowerCamelCase							(				)      ->       Union[str, Any]:
	_lowercase   =np.arange(-1.0      ,							1.0      ,							0.005		)
	_lowercase   =np.sin(__a		)

	_lowercase   =DecisionTree(depth=10      ,							min_leaf_size=10		)
	tree.train(__a      ,							__a		)

	_lowercase   =(np.random.rand(10		) * 2) - 1
	_lowercase   =np.array([tree.predict(__a		) for x in test_cases]		)
	_lowercase   =np.mean((predictions - test_cases) ** 2		)

	print("Test values: " + str(__a		)		)
	print("Predictions: " + str(__a		)		)
	print("Average error: " + str(__a		)		)


if __name__ == "__main__":
						main()
						import doctest

						doctest.testmod(name="mean_squarred_error", verbose=True)




