



'''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


lowerCAmelCase						:		str						      = '''block_records.npy'''


lowerCAmelCase						:		Union[str, Any]						      = 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=5_12 * 10_24 * 10_24       )
	__lowerCAmelCase       				=	blocks_dataset.batch(lowerCamelCase					,		drop_remainder=lowerCamelCase       )
	__lowerCAmelCase       				=	next(blocks_dataset.take(1       ).as_numpy_iterator()       )

	return np_record






class        UpperCAmelCase__						:



					def __init__(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase=2      ,					UpperCamelCase=1000      ,					UpperCamelCase=100      ,					UpperCamelCase=10_0000      ,					)  ->      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 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      Optional[int]:
						__lowerCAmelCase				,							__lowerCAmelCase       				=	self.searcher.search_batched(question_projection.detach().cpu()  )
						return retrieved_block_ids.astype("int64"  )






class        UpperCAmelCase__						:



					def __init__(     self      ,					UpperCamelCase      ,					UpperCamelCase  )  ->      str:
						super().__init__()
						__lowerCAmelCase       				=	block_records
						__lowerCAmelCase       				=	tokenizer



					def __call__(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase=None      ,					UpperCamelCase="pt"  )  ->      Union[str, Any]:
						__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 							UpperCAmelCase_			(     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 							UpperCAmelCase_			(     self      ,					UpperCamelCase  )  ->      Union[str, Any]:
						# save block records
						np.save(os.path.join(UpperCamelCase      ,					_REALM_BLOCK_RECORDS_FILENAME  )      ,					self.block_records  )
						# save tokenizer
						self.tokenizer.save_pretrained(UpperCamelCase  )



					def 							UpperCAmelCase_			(     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




'''simple docstring'''



lowerCAmelCase						:		Tuple						      = '''
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
'''

lowerCAmelCase						:		Tuple						      = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]