

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


__snake_case							:			Optional[int]      		= """block_records.npy"""


__snake_case							:			List[Any]      		= logging.get_logger(__name__)


def 				_UpperCAmelCase							(	a__     ,  a__):

			'''simple docstring'''



			import tensorflow.compat.va as tf

			a_	:	List[Any]          =   tf.data.TFRecordDataset(a__     ,  buffer_size=5_1_2 * 1_0_2_4 * 1_0_2_4)
			a_	:	Optional[int]          =   blocks_dataset.batch(a__     ,  drop_remainder=a__)
			a_	:	Tuple          =   next(blocks_dataset.take(1).as_numpy_iterator())

			return np_record



class 					A__:
						"""simple docstring"""






						def __init__(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase=2     ,			_lowercase=1_000     ,			_lowercase=100     ,			_lowercase=100_000     ,			)    ->   Optional[Any]:
									from scann.scann_ops.py.scann_ops_pybind import builder as Builder

									a_	:	Optional[int]          =   Builder(db=_lowercase     ,			num_neighbors=_lowercase     ,			distance_measure="""dot_product"""						)
									a_	:	Dict          =   builder.tree(
									    num_leaves=_lowercase     ,			num_leaves_to_search=_lowercase     ,			training_sample_size=_lowercase						)
									a_	:	Optional[Any]          =   builder.score_ah(dimensions_per_block=_lowercase						)

									a_	:	Optional[int]          =   builder.build()





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   str:
									a_       ,				a_	:	Union[str, Any]          =   self.searcher.search_batched(question_projection.detach().cpu()						)
									return retrieved_block_ids.astype("""int64"""						)







class 					A__:
						"""simple docstring"""






						def __init__(						self     ,			_lowercase     ,			_lowercase						)    ->   Optional[Any]:
									super().__init__()
									a_	:	List[Any]          =   block_records
									a_	:	Tuple          =   tokenizer





						def __call__(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase     ,			_lowercase=None     ,			_lowercase="pt"						)    ->   Optional[Any]:
									a_	:	List[str]          =   np.take(self.block_records     ,			indices=_lowercase     ,			axis=0						)

									a_	:	Optional[Any]          =   self.tokenizer.decode(question_input_ids[0]     ,			skip_special_tokens=_lowercase						)

									a_	:	str          =   []
									a_	:	List[Any]          =   []
									for retrieved_block in retrieved_blocks:
												text.append(_lowercase						)
												text_pair.append(retrieved_block.decode()						)

									a_	:	str          =   self.tokenizer(
									    _lowercase     ,			_lowercase     ,			padding=_lowercase     ,			truncation=_lowercase     ,			return_special_tokens_mask=_lowercase     ,			max_length=_lowercase						)
									a_	:	Any          =   concat_inputs.convert_to_tensors(_lowercase						)

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





						@classmethod
						def  UpperCamelCase__				(						cls     ,			_lowercase     ,			*_lowercase     ,			**_lowercase						)    ->   Tuple:
									if os.path.isdir(_lowercase						):
												a_	:	Optional[Any]          =   os.path.join(_lowercase     ,			_REALM_BLOCK_RECORDS_FILENAME						)
									else:
												a_	:	Tuple          =   hf_hub_download(
												    repo_id=_lowercase     ,			filename=_REALM_BLOCK_RECORDS_FILENAME     ,			**_lowercase						)
									a_	:	int          =   np.load(_lowercase     ,			allow_pickle=_lowercase						)

									a_	:	Tuple          =   AutoTokenizer.from_pretrained(_lowercase     ,			*_lowercase     ,			**_lowercase						)

									return cls(_lowercase     ,			_lowercase						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   List[Any]:
									# save block records
									np.save(os.path.join(_lowercase     ,			_REALM_BLOCK_RECORDS_FILENAME						)     ,			self.block_records						)
									# save tokenizer
									self.tokenizer.save_pretrained(_lowercase						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase						)    ->   Any:
									a_	:	List[Any]          =   []
									a_	:	str          =   []
									a_	:	List[str]          =   []
									a_	:	Optional[int]          =   0

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

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

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





import argparse
import os


__snake_case							:			List[Any]      		= {
    """pytorch""": {
        """1.13""": {
            """torch""": """1.13.1""",
            """torchvision""": """0.14.1""",
            """torchaudio""": """0.13.1""",
            """python""": 3.9,
            """cuda""": """cu116""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu116"""
            ),
            """base_image""": """nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.12""": {
            """torch""": """1.12.1""",
            """torchvision""": """0.13.1""",
            """torchaudio""": """0.12.1""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.11""": {
            """torch""": """1.11.0""",
            """torchvision""": """0.12.0""",
            """torchaudio""": """0.11.0""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.10""": {
            """torch""": """1.10.2""",
            """torchvision""": """0.11.3""",
            """torchaudio""": """0.10.2""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        """1.9""": {
            """torch""": """1.9.1""",
            """torchvision""": """0.10.1""",
            """torchaudio""": """0.9.1""",
            """python""": 3.9,
            """cuda""": """cu111""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu111"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
    """tensorflow""": {
        """2.11""": {
            """tensorflow""": """2.11.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.11.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.10""": {
            """tensorflow""": """2.10.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.10.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.9""": {
            """tensorflow""": """2.9.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.9.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.8""": {
            """tensorflow""": """2.8.2""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.8.2""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.7""": {
            """tensorflow""": """2.7.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.7.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.6""": {
            """tensorflow""": """2.6.5""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.6.5""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.5""": {
            """tensorflow""": """2.5.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.5.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
}


if __name__ == "__main__":
	__snake_case							:			List[str]      		= argparse.ArgumentParser("""Choose the framework and version to install""")
	parser.add_argument(
	    """--framework""", help="""The framework to install. Should be `torch` or `tensorflow`""", type=str, required=True
	)
	parser.add_argument("""--version""", help="""The version of the framework to install.""", type=str, required=True)
	__snake_case							:			int      		= parser.parse_args()

	__snake_case							:			Optional[Any]      		= past_versions_testing[args.framework][args.version]

	os.system(F"""echo \"export INSTALL_CMD=\'{info["install"]}\'\" >> ~/.profile""")
	print(F"""echo \"export INSTALL_CMD=\'{info["install"]}\'\" >> ~/.profile""")

	__snake_case							:			Dict      		= """"""
	if args.framework == "pytorch":
		__snake_case							:			Union[str, Any]      		= info["""cuda"""]
	os.system(F"""echo \"export CUDA='{cuda}'\" >> ~/.profile""")
	print(F"""echo \"export CUDA='{cuda}'\" >> ~/.profile""")


