
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


_lowercase				:						str												= "block_records.npy"


_lowercase				:						str												= logging.get_logger(__name__)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				str   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						import tensorflow.compat.va as tf

						lowercase_			:  Union[str, Any]									= tf.data.TFRecordDataset(lowercase__   ,   buffer_size=512 * 1024 * 1024							)
						lowercase_			:  Any									= blocks_dataset.batch(lowercase__   ,   drop_remainder=lowercase__							)
						lowercase_			:  Optional[int]									= next(blocks_dataset.take(1							).as_numpy_iterator()							)

						return np_record






class 				lowerCAmelCase__     :






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE=2       ,							__SCREAMING_SNAKE_CASE=10_00       ,							__SCREAMING_SNAKE_CASE=1_00       ,							__SCREAMING_SNAKE_CASE=10_00_00       ,							):


											"""simple docstring"""





											from scann.scann_ops.py.scann_ops_pybind import builder as Builder

											lowercase_			:  Any									= Builder(db=__SCREAMING_SNAKE_CASE       ,							num_neighbors=__SCREAMING_SNAKE_CASE       ,							distance_measure='''dot_product'''	)
											lowercase_			:  Any									= builder.tree(
											    num_leaves=__SCREAMING_SNAKE_CASE       ,							num_leaves_to_search=__SCREAMING_SNAKE_CASE       ,							training_sample_size=__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Dict									= builder.score_ah(dimensions_per_block=__SCREAMING_SNAKE_CASE	)

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






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  int									= self.searcher.search_batched(question_projection.detach().cpu()	)
											return retrieved_block_ids.astype('''int64'''	)







class 				lowerCAmelCase__     :






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											super().__init__()
											lowercase_			:  Any									= block_records
											lowercase_			:  str									= tokenizer






					def __call__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE="pt"	):


											"""simple docstring"""





											lowercase_			:  Optional[int]									= np.take(self.block_records       ,							indices=__SCREAMING_SNAKE_CASE       ,							axis=0	)

											lowercase_			:  Dict									= self.tokenizer.decode(question_input_ids[0]       ,							skip_special_tokens=__SCREAMING_SNAKE_CASE	)

											lowercase_			:  List[str]									= []
											lowercase_			:  Optional[Any]									= []
											for retrieved_block in retrieved_blocks:
																	text.append(__SCREAMING_SNAKE_CASE	)
																	text_pair.append(retrieved_block.decode()	)

											lowercase_			:  Union[str, Any]									= self.tokenizer(
											    __SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							padding=__SCREAMING_SNAKE_CASE       ,							truncation=__SCREAMING_SNAKE_CASE       ,							return_special_tokens_mask=__SCREAMING_SNAKE_CASE       ,							max_length=__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Optional[int]									= concat_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE	)

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






					@classmethod
					def 			_snake_case					(     cls       ,							__SCREAMING_SNAKE_CASE       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if os.path.isdir(__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  Optional[Any]									= os.path.join(__SCREAMING_SNAKE_CASE       ,							_REALM_BLOCK_RECORDS_FILENAME	)
											else:
																	lowercase_			:  str									= hf_hub_download(
																	    repo_id=__SCREAMING_SNAKE_CASE       ,							filename=_REALM_BLOCK_RECORDS_FILENAME       ,							**__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Optional[int]									= np.load(__SCREAMING_SNAKE_CASE       ,							allow_pickle=__SCREAMING_SNAKE_CASE	)

											lowercase_			:  Union[str, Any]									= AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											return cls(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											np.save(os.path.join(__SCREAMING_SNAKE_CASE       ,							_REALM_BLOCK_RECORDS_FILENAME	)       ,							self.block_records	)
											# save tokenizer
											self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  Any									= []
											lowercase_			:  str									= []
											lowercase_			:  Any									= []
											lowercase_			:  List[Any]									= 0

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

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

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





'''simple docstring'''







from typing import List

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


if is_torch_available():
							import torch






class 				lowerCAmelCase__     (						lowerCamelCase_  ):
					lowerCAmelCase_       =  ['''image_processor''', '''tokenizer''']
					lowerCAmelCase_       =  '''OneFormerImageProcessor'''
					lowerCAmelCase_       =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE = 77       ,							__SCREAMING_SNAKE_CASE = 77       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											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`.'''	)

											lowercase_			:  Dict									= max_seq_length
											lowercase_			:  Dict									= task_seq_length

											super().__init__(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=77	):


											"""simple docstring"""





											if text_list is None:
																	raise ValueError('''tokens cannot be None.'''	)

											lowercase_			:  str									= self.tokenizer(__SCREAMING_SNAKE_CASE       ,							padding='''max_length'''       ,							max_length=__SCREAMING_SNAKE_CASE       ,							truncation=__SCREAMING_SNAKE_CASE	)

											lowercase_		,				lowercase_			:  List[str]									= tokens['''attention_mask'''], tokens['''input_ids''']

											lowercase_			:  List[str]									= []
											for attn_mask, input_id in zip(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  Optional[Any]									= torch.tensor(__SCREAMING_SNAKE_CASE	) * torch.tensor(__SCREAMING_SNAKE_CASE	)
																	token_inputs.append(token.unsqueeze(0	)	)

											lowercase_			:  str									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)
											return token_inputs






					def __call__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											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.'''	)

											lowercase_			:  Optional[int]									= self.image_processor(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[str]									= [task_inputs]

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

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Any									= encoded_inputs.text_inputs

																	lowercase_			:  int									= []
																	for texts in texts_list:
																							lowercase_			:  Any									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Tuple									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											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.'''	)

											lowercase_			:  Dict									= self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[Any]									= [task_inputs]

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

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Dict									= encoded_inputs.text_inputs

																	lowercase_			:  str									= []
																	for texts in texts_list:
																							lowercase_			:  List[str]									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Union[str, Any]									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)



