



'''simple docstring'''



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


_lowercase       :						List[str]						="block_records.npy"


_lowercase       :						Optional[Any]						=logging.get_logger(__name__)


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

				snake_case__      :   Optional[Any]      =    tf.data.TFRecordDataset(UpperCamelCase__   ,				buffer_size=512 * 1024 * 1024			)
				snake_case__      :   Tuple      =    blocks_dataset.batch(UpperCamelCase__   ,				drop_remainder=UpperCamelCase__			)
				snake_case__      :   Union[str, Any]      =    next(blocks_dataset.take(1			).as_numpy_iterator()			)

				return np_record






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						Dict    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						Tuple=2    ,			__UpperCamelCase		:						Dict=1000    ,			__UpperCamelCase		:						List[str]=100    ,			__UpperCamelCase		:						Dict=100000    ,			)      ->      Optional[int]:
							"""simple docstring"""





							from scann.scann_ops.py.scann_ops_pybind import builder as Builder

							snake_case__      :   Tuple      =    Builder(db=__UpperCamelCase    ,			num_neighbors=__UpperCamelCase    ,			distance_measure='''dot_product'''			)
							snake_case__      :   List[str]      =    builder.tree(
							    num_leaves=__UpperCamelCase    ,			num_leaves_to_search=__UpperCamelCase    ,			training_sample_size=__UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    builder.score_ah(dimensions_per_block=__UpperCamelCase			)

							snake_case__      :   str      =    builder.build()







			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						str			)      ->      Dict:
							"""simple docstring"""





							snake_case__  ,      snake_case__      :   int      =    self.searcher.search_batched(question_projection.detach().cpu()			)
							return retrieved_block_ids.astype('''int64'''			)






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						Tuple    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Any			)      ->      Optional[int]:
							"""simple docstring"""





							super().__init__()
							snake_case__      :   Optional[Any]      =    block_records
							snake_case__      :   List[Any]      =    tokenizer



			def __call__(     self		:						Any    ,			__UpperCamelCase		:						Optional[Any]    ,			__UpperCamelCase		:						List[str]    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Tuple=None    ,			__UpperCamelCase		:						Union[str, Any]="pt"			)      ->      str:
							"""simple docstring"""





							snake_case__      :   Optional[int]      =    np.take(self.block_records    ,			indices=__UpperCamelCase    ,			axis=0			)

							snake_case__      :   Dict      =    self.tokenizer.decode(question_input_ids[0]    ,			skip_special_tokens=__UpperCamelCase			)

							snake_case__      :   Any      =    []
							snake_case__      :   Any      =    []
							for retrieved_block in retrieved_blocks:
											text.append(__UpperCamelCase			)
											text_pair.append(retrieved_block.decode()			)

							snake_case__      :   int      =    self.tokenizer(
							    __UpperCamelCase    ,			__UpperCamelCase    ,			padding=__UpperCamelCase    ,			truncation=__UpperCamelCase    ,			return_special_tokens_mask=__UpperCamelCase    ,			max_length=__UpperCamelCase			)
							snake_case__      :   int      =    concat_inputs.convert_to_tensors(__UpperCamelCase			)

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



			@classmethod
			def 				lowerCAmelCase (     cls		:						Tuple    ,			__UpperCamelCase		:						Optional[Union[str, os.PathLike]]    ,			*__UpperCamelCase		:						List[str]    ,			**__UpperCamelCase		:						List[str]			)      ->      int:
							"""simple docstring"""





							if os.path.isdir(__UpperCamelCase			):
											snake_case__      :   List[Any]      =    os.path.join(__UpperCamelCase    ,			_REALM_BLOCK_RECORDS_FILENAME			)
							else:
											snake_case__      :   Any      =    hf_hub_download(
											    repo_id=__UpperCamelCase    ,			filename=_REALM_BLOCK_RECORDS_FILENAME    ,			**__UpperCamelCase			)
							snake_case__      :   int      =    np.load(__UpperCamelCase    ,			allow_pickle=__UpperCamelCase			)

							snake_case__      :   List[Any]      =    AutoTokenizer.from_pretrained(__UpperCamelCase    ,			*__UpperCamelCase    ,			**__UpperCamelCase			)

							return cls(__UpperCamelCase    ,			__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						int			)      ->      Tuple:
							"""simple docstring"""





							np.save(os.path.join(__UpperCamelCase    ,			_REALM_BLOCK_RECORDS_FILENAME			)    ,			self.block_records			)
							# save tokenizer
							self.tokenizer.save_pretrained(__UpperCamelCase			)







			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						Optional[Any]    ,			__UpperCamelCase		:						List[str]			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   int      =    []
							snake_case__      :   List[str]      =    []
							snake_case__      :   Union[str, Any]      =    []
							snake_case__      :   int      =    0

							for input_id in concat_inputs.input_ids:
											snake_case__      :   Union[str, Any]      =    input_id.tolist()
											# Check answers between two [SEP] tokens
											snake_case__      :   List[str]      =    input_id_list.index(self.tokenizer.sep_token_id			)
											snake_case__      :   Optional[Any]      =    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    ,			__UpperCamelCase			):
																			if answer[0] == input_id_list[idx]:
																							if input_id_list[idx : idx + len(__UpperCamelCase			)] == answer:
																											start_pos[-1].append(__UpperCamelCase			)
																											end_pos[-1].append(idx + len(__UpperCamelCase			) - 1			)

											if len(start_pos[-1]			) == 0:
															has_answers.append(__UpperCamelCase			)
											else:
															has_answers.append(__UpperCamelCase			)
															if len(start_pos[-1]			) > max_answers:
																			snake_case__      :   Optional[int]      =    len(start_pos[-1]			)

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











'''simple docstring'''


import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute




def       A							(      _UpperCAmelCase   :				float = np.pi / 2						,_UpperCAmelCase   :				float = np.pi / 2						,_UpperCAmelCase   :				float = np.pi / 2					)  ->				qiskit.result.counts.Counts:


  '''simple docstring'''




  __lowerCAmelCase		:    Optional[int]			     =  QuantumRegister(3						,'qr'					)  # Define the number of quantum bits
  __lowerCAmelCase		:    Optional[int]			     =  ClassicalRegister(1						,'cr'					)  # Define the number of classical bits

  __lowerCAmelCase		:    List[Any]			     =  QuantumCircuit(_UpperCAmelCase						,_UpperCAmelCase					)  # Define the quantum circuit.

  # Build the circuit
  quantum_circuit.u(_UpperCAmelCase						,_UpperCAmelCase						,_UpperCAmelCase						,0					)  # Quantum State to teleport
  quantum_circuit.h(1					)  # add hadamard gate
  quantum_circuit.cx(
      1						,2					)  # add control gate with qubit 1 as control and 2 as target.
  quantum_circuit.cx(0						,1					)
  quantum_circuit.h(0					)
  quantum_circuit.cz(0						,2					)  # add control z gate.
  quantum_circuit.cx(1						,2					)

  quantum_circuit.measure([2]						,[0]					)  # measure the qubit.

  # Simulate the circuit using qasm simulator
  __lowerCAmelCase		:    List[str]			     =  Aer.get_backend('aer_simulator'					)
  __lowerCAmelCase		:    Optional[Any]			     =  execute(_UpperCAmelCase						,_UpperCAmelCase						,shots=1_0_0_0					)

  return job.result().get_counts(_UpperCAmelCase					)


if __name__ == "__main__":
    print(
        "Total count for teleported state is: "
        F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
    )





