"""simple docstring"""




from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
				import torch
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    List[str]    				=		['image_processor', 'tokenizer']
						lowerCAmelCase__	:    Optional[Any]    				=		'OneFormerImageProcessor'
						lowerCAmelCase__	:    List[Any]    				=		('CLIPTokenizer', 'CLIPTokenizerFast')





						def __init__(							self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase = 77		,__UpperCAmelCase = 77		,**__UpperCAmelCase  )       ->					Tuple:
													if image_processor is None:
																				raise ValueError('You need to specify an `image_processor`.'  )
													if tokenizer is None:
																				raise ValueError('You need to specify a `tokenizer`.'  )

													A__						  =				max_seq_length
													A__						  =				task_seq_length

													super().__init__(__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase=None		,__UpperCAmelCase=77  )       ->					int:
													if text_list is None:
																				raise ValueError('tokens cannot be None.'  )

													A__						  =				self.tokenizer(__UpperCAmelCase		,padding='max_length'		,max_length=__UpperCAmelCase		,truncation=__UpperCAmelCase  )

													A__  ,	A__						  =				tokens['attention_mask'], tokens['input_ids']

													A__						  =				[]
													for attn_mask, input_id in zip(__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				torch.tensor(__UpperCAmelCase  ) * torch.tensor(__UpperCAmelCase  )
																				token_inputs.append(token.unsqueeze(0  )  )

													A__						  =				torch.cat(__UpperCAmelCase		,dim=0  )
													return token_inputs





						def __call__(							self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,**__UpperCAmelCase  )       ->					Optional[Any]:

													if task_inputs is None:
																				raise ValueError('You have to specify the task_input. Found None.'  )
													elif images is None:
																				raise ValueError('You have to specify the image. Found None.'  )

													if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs  ):
																				raise ValueError('task_inputs must be semantic, instance, or panoptic.'  )

													A__						  =				self.image_processor(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase  )

													if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				[task_inputs]

													if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ) and all(isinstance(__UpperCAmelCase		,__UpperCAmelCase  ) for task_input in task_inputs  ):
																				A__						  =				[]
																				for task in task_inputs:
																											A__						  =				f'''the task is {task}'''
																											task_token_inputs.append(__UpperCAmelCase  )
																				A__						  =				self._preprocess_text(__UpperCAmelCase		,max_length=self.task_seq_length  )
													else:
																				raise TypeError('Task Inputs should be a string or a list of strings.'  )

													if hasattr(__UpperCAmelCase		,'text_inputs'  ):
																				A__						  =				encoded_inputs.text_inputs

																				A__						  =				[]
																				for texts in texts_list:
																											A__						  =				self._preprocess_text(__UpperCAmelCase		,max_length=self.max_seq_length  )
																											text_inputs.append(text_input_list.unsqueeze(0  )  )

																				A__						  =				torch.cat(__UpperCAmelCase		,dim=0  )

													return encoded_inputs





						def 					snake_case__						(							self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,**__UpperCAmelCase  )       ->					Dict:

													if task_inputs is None:
																				raise ValueError('You have to specify the task_input. Found None.'  )
													elif images is None:
																				raise ValueError('You have to specify the image. Found None.'  )

													if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs  ):
																				raise ValueError('task_inputs must be semantic, instance, or panoptic.'  )

													A__						  =				self.image_processor.encode_inputs(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,**__UpperCAmelCase  )

													if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				[task_inputs]

													if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ) and all(isinstance(__UpperCAmelCase		,__UpperCAmelCase  ) for task_input in task_inputs  ):
																				A__						  =				[]
																				for task in task_inputs:
																											A__						  =				f'''the task is {task}'''
																											task_token_inputs.append(__UpperCAmelCase  )
																				A__						  =				self._preprocess_text(__UpperCAmelCase		,max_length=self.task_seq_length  )
													else:
																				raise TypeError('Task Inputs should be a string or a list of strings.'  )

													if hasattr(__UpperCAmelCase		,'text_inputs'  ):
																				A__						  =				encoded_inputs.text_inputs

																				A__						  =				[]
																				for texts in texts_list:
																											A__						  =				self._preprocess_text(__UpperCAmelCase		,max_length=self.max_seq_length  )
																											text_inputs.append(text_input_list.unsqueeze(0  )  )

																				A__						  =				torch.cat(__UpperCAmelCase		,dim=0  )

													return encoded_inputs





						def 					snake_case__						(							self		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					Any:
													return self.image_processor.post_process_semantic_segmentation(*__UpperCAmelCase		,**__UpperCAmelCase  )





						def 					snake_case__						(							self		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					Any:
													return self.image_processor.post_process_instance_segmentation(*__UpperCAmelCase		,**__UpperCAmelCase  )





						def 					snake_case__						(							self		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					Dict:
													return self.image_processor.post_process_panoptic_segmentation(*__UpperCAmelCase		,**__UpperCAmelCase  )






"""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									=	"block_records.npy"


__lowerCamelCase									=	logging.get_logger(__name__)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							import tensorflow.compat.va as tf

							A__						  =				tf.data.TFRecordDataset(UpperCamelCase__							,     buffer_size=512 * 1_024 * 1_024					)
							A__						  =				blocks_dataset.batch(UpperCamelCase__							,     drop_remainder=UpperCamelCase__					)
							A__						  =				next(blocks_dataset.take(1					).as_numpy_iterator()					)

							return np_record
class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=2		,__UpperCAmelCase=10_00		,__UpperCAmelCase=1_00		,__UpperCAmelCase=10_00_00		,)       ->					Optional[int]:
													from scann.scann_ops.py.scann_ops_pybind import builder as Builder

													A__						  =				Builder(db=__UpperCAmelCase		,num_neighbors=__UpperCAmelCase		,distance_measure='dot_product'  )
													A__						  =				builder.tree(
													    num_leaves=__UpperCAmelCase		,num_leaves_to_search=__UpperCAmelCase		,training_sample_size=__UpperCAmelCase  )
													A__						  =				builder.score_ah(dimensions_per_block=__UpperCAmelCase  )

													A__						  =				builder.build()





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					Tuple:
													A__  ,	A__						  =				self.searcher.search_batched(question_projection.detach().cpu()  )
													return retrieved_block_ids.astype('int64'  )






class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					List[str]:
													super().__init__()
													A__						  =				block_records
													A__						  =				tokenizer





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

													A__						  =				self.tokenizer.decode(question_input_ids[0]		,skip_special_tokens=__UpperCAmelCase  )

													A__						  =				[]
													A__						  =				[]
													for retrieved_block in retrieved_blocks:
																				text.append(__UpperCAmelCase  )
																				text_pair.append(retrieved_block.decode()  )

													A__						  =				self.tokenizer(
													    __UpperCAmelCase		,__UpperCAmelCase		,padding=__UpperCAmelCase		,truncation=__UpperCAmelCase		,return_special_tokens_mask=__UpperCAmelCase		,max_length=__UpperCAmelCase  )
													A__						  =				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 					snake_case__						(							cls		,__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					List[Any]:
													if os.path.isdir(__UpperCAmelCase  ):
																				A__						  =				os.path.join(__UpperCAmelCase		,_REALM_BLOCK_RECORDS_FILENAME  )
													else:
																				A__						  =				hf_hub_download(
																				    repo_id=__UpperCAmelCase		,filename=_REALM_BLOCK_RECORDS_FILENAME		,**__UpperCAmelCase  )
													A__						  =				np.load(__UpperCAmelCase		,allow_pickle=__UpperCAmelCase  )

													A__						  =				AutoTokenizer.from_pretrained(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase  )

													return cls(__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					Tuple:
													# save block records
													np.save(os.path.join(__UpperCAmelCase		,_REALM_BLOCK_RECORDS_FILENAME  )		,self.block_records  )
													# save tokenizer
													self.tokenizer.save_pretrained(__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					List[Any]:
													A__						  =				[]
													A__						  =				[]
													A__						  =				[]
													A__						  =				0

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

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





