
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


_A       =					"block_records.npy"


_A       =					logging.get_logger(__name__)






def        lowerCamelCase__    (    __lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			import tensorflow.compat.va as tf

			lowerCAmelCase_      =     tf.data.TFRecordDataset(__lowerCAmelCase   ,							buffer_size=512 * 1024 * 1024    )
			lowerCAmelCase_      =     blocks_dataset.batch(__lowerCAmelCase   ,							drop_remainder=__lowerCAmelCase    )
			lowerCAmelCase_      =     next(blocks_dataset.take(1    ).as_numpy_iterator()    )

			return np_record





class      _lowerCAmelCase :




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

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

							lowerCAmelCase_      =     builder.build()




				def 					__a						( self    ,    _UpperCamelCase					)    ->				List[Any]:
							lowerCAmelCase_     ,  lowerCAmelCase_      =     self.searcher.search_batched(question_projection.detach().cpu()					)
							return retrieved_block_ids.astype("int64"					)





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				List[Any]:
							super().__init__()
							lowerCAmelCase_      =     block_records
							lowerCAmelCase_      =     tokenizer




				def __call__( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase=None    ,    _UpperCamelCase="pt"					)    ->				str:
							lowerCAmelCase_      =     np.take(self.block_records    ,    indices=_UpperCamelCase    ,    axis=0					)

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

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

							lowerCAmelCase_      =     self.tokenizer(
							    _UpperCamelCase    ,    _UpperCamelCase    ,    padding=_UpperCamelCase    ,    truncation=_UpperCamelCase    ,    return_special_tokens_mask=_UpperCamelCase    ,    max_length=_UpperCamelCase					)
							lowerCAmelCase_      =     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 					__a						( cls    ,    _UpperCamelCase    ,    *_UpperCamelCase    ,    **_UpperCamelCase					)    ->				Dict:
							if os.path.isdir(_UpperCamelCase					):
										lowerCAmelCase_      =     os.path.join(_UpperCamelCase    ,    _REALM_BLOCK_RECORDS_FILENAME					)
							else:
										lowerCAmelCase_      =     hf_hub_download(
										    repo_id=_UpperCamelCase    ,    filename=_REALM_BLOCK_RECORDS_FILENAME    ,    **_UpperCamelCase					)
							lowerCAmelCase_      =     np.load(_UpperCamelCase    ,    allow_pickle=_UpperCamelCase					)

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

							return cls(_UpperCamelCase    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				int:
							# save block records
							np.save(os.path.join(_UpperCamelCase    ,    _REALM_BLOCK_RECORDS_FILENAME					)    ,    self.block_records					)
							# save tokenizer
							self.tokenizer.save_pretrained(_UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				List[str]:
							lowerCAmelCase_      =     []
							lowerCAmelCase_      =     []
							lowerCAmelCase_      =     []
							lowerCAmelCase_      =     0

							for input_id in concat_inputs.input_ids:
										lowerCAmelCase_      =     input_id.tolist()
										# Check answers between two [SEP] tokens
										lowerCAmelCase_      =     input_id_list.index(self.tokenizer.sep_token_id					)
										lowerCAmelCase_      =     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:
																lowerCAmelCase_      =     len(start_pos[-1]					)

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NatForImageClassification",
			    "NatModel",
			    "NatPreTrainedModel",
			    "NatBackbone",
			]

if TYPE_CHECKING:
			from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nat import (
						    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NatBackbone,
						    NatForImageClassification,
						    NatModel,
						    NatPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



