



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


__UpperCAmelCase						  =     '''block_records.npy'''


__UpperCAmelCase						  =     logging.get_logger(__name__)





def 						__lowerCamelCase       (      __magic_name__	:  Any     , __magic_name__	:  str						):
							import tensorflow.compat.va as tf

							a__:       Union[str, Any]											=tf.data.TFRecordDataset(__lowerCAmelCase     , buffer_size=512 * 1_024 * 1_024						)
							a__:       List[str]											=blocks_dataset.batch(__lowerCAmelCase     , drop_remainder=__lowerCAmelCase						)
							a__:       Any											=next(blocks_dataset.take(1						).as_numpy_iterator()						)

							return np_record


class 	lowerCamelCase__ :


						def __init__( self						:  str      ,       _a						:  str      ,       _a						:  Any      ,       _a						:  Optional[int]=2      ,       _a						:  Optional[Any]=1_0_0_0      ,       _a						:  Optional[Any]=1_0_0      ,       _a						:  int=1_0_0_0_0_0      ,       ):
													from scann.scann_ops.py.scann_ops_pybind import builder as Builder

													a__:       Any											=Builder(db=_a      ,       num_neighbors=_a      ,       distance_measure="dot_product"			)
													a__:       List[str]											=builder.tree(
													    num_leaves=_a      ,       num_leaves_to_search=_a      ,       training_sample_size=_a			)
													a__:       Tuple											=builder.score_ah(dimensions_per_block=_a			)

													a__:       Union[str, Any]											=builder.build()







						def   _lowerCamelCase			( self						:  List[str]      ,       _a						:  Union[str, Any]			):
													a__:       Tuple											=self.searcher.search_batched(question_projection.detach().cpu()			)
													return retrieved_block_ids.astype("int64"			)


class 	lowerCamelCase__ :


						def __init__( self						:  Any      ,       _a						:  Optional[int]      ,       _a						:  List[Any]			):
													super().__init__()
													a__:       str											=block_records
													a__:       List[str]											=tokenizer


						def __call__( self						:  Optional[Any]      ,       _a						:  List[Any]      ,       _a						:  Any      ,       _a						:  Optional[int]      ,       _a						:  int=None      ,       _a						:  Dict="pt"			):
													a__:       Union[str, Any]											=np.take(self.block_records      ,       indices=_a      ,       axis=0			)

													a__:       Dict											=self.tokenizer.decode(question_input_ids[0]      ,       skip_special_tokens=_a			)

													a__:       Dict											=[]
													a__:       Tuple											=[]
													for retrieved_block in retrieved_blocks:
																				text.append(_a			)
																				text_pair.append(retrieved_block.decode()			)

													a__:       List[str]											=self.tokenizer(
													    _a      ,       _a      ,       padding=_a      ,       truncation=_a      ,       return_special_tokens_mask=_a      ,       max_length=_a			)
													a__:       Dict											=concat_inputs.convert_to_tensors(_a			)

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


						@classmethod
						def   _lowerCamelCase			( cls						:  int      ,       _a						:  Optional[Union[str, os.PathLike]]      ,       *_a						:  Tuple      ,       **_a						:  Union[str, Any]			):






													if os.path.isdir(_a			):
																				a__:       Optional[Any]											=os.path.join(_a      ,       _REALM_BLOCK_RECORDS_FILENAME			)
													else:
																				a__:       List[str]											=hf_hub_download(
																				    repo_id=_a      ,       filename=_REALM_BLOCK_RECORDS_FILENAME      ,       **_a			)
													a__:       Union[str, Any]											=np.load(_a      ,       allow_pickle=_a			)

													a__:       List[Any]											=AutoTokenizer.from_pretrained(_a      ,       *_a      ,       **_a			)

													return cls(_a      ,       _a			)


						def   _lowerCamelCase			( self						:  int      ,       _a						:  Tuple			):
													np.save(os.path.join(_a      ,       _REALM_BLOCK_RECORDS_FILENAME			)      ,       self.block_records			)
													# save tokenizer
													self.tokenizer.save_pretrained(_a			)







						def   _lowerCamelCase			( self						:  int      ,       _a						:  List[str]      ,       _a						:  Any			):
													a__:       Optional[int]											=[]
													a__:       Optional[int]											=[]
													a__:       Any											=[]
													a__:       int											=0

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

																				if len(start_pos[-1]			) == 0:
																											has_answers.append(_a			)
																				else:
																											has_answers.append(_a			)
																											if len(start_pos[-1]			) > max_answers:
																																		a__:       str											=len(start_pos[-1]			)

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











from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
		from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline





