

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case							:			Any      		= {"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:			Union[str, Any]      		= [
	    """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """SEWDForCTC""",
	    """SEWDForSequenceClassification""",
	    """SEWDModel""",
	    """SEWDPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sew_d import (
		    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SEWDForCTC,
		    SEWDForSequenceClassification,
		    SEWDModel,
		    SEWDPreTrainedModel,
		)


else:
	import sys

	__snake_case							:			int      		= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





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


__snake_case							:			Optional[int]      		= """block_records.npy"""


__snake_case							:			List[Any]      		= logging.get_logger(__name__)


def 				_UpperCAmelCase							(	a__     ,  a__):

			'''simple docstring'''



			import tensorflow.compat.va as tf

			a_	:	List[Any]          =   tf.data.TFRecordDataset(a__     ,  buffer_size=5_1_2 * 1_0_2_4 * 1_0_2_4)
			a_	:	Optional[int]          =   blocks_dataset.batch(a__     ,  drop_remainder=a__)
			a_	:	Tuple          =   next(blocks_dataset.take(1).as_numpy_iterator())

			return np_record



class 					A__:
						"""simple docstring"""






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

									a_	:	Optional[int]          =   Builder(db=_lowercase     ,			num_neighbors=_lowercase     ,			distance_measure="""dot_product"""						)
									a_	:	Dict          =   builder.tree(
									    num_leaves=_lowercase     ,			num_leaves_to_search=_lowercase     ,			training_sample_size=_lowercase						)
									a_	:	Optional[Any]          =   builder.score_ah(dimensions_per_block=_lowercase						)

									a_	:	Optional[int]          =   builder.build()





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   str:
									a_       ,				a_	:	Union[str, Any]          =   self.searcher.search_batched(question_projection.detach().cpu()						)
									return retrieved_block_ids.astype("""int64"""						)







class 					A__:
						"""simple docstring"""






						def __init__(						self     ,			_lowercase     ,			_lowercase						)    ->   Optional[Any]:
									super().__init__()
									a_	:	List[Any]          =   block_records
									a_	:	Tuple          =   tokenizer





						def __call__(						self     ,			_lowercase     ,			_lowercase     ,			_lowercase     ,			_lowercase=None     ,			_lowercase="pt"						)    ->   Optional[Any]:
									a_	:	List[str]          =   np.take(self.block_records     ,			indices=_lowercase     ,			axis=0						)

									a_	:	Optional[Any]          =   self.tokenizer.decode(question_input_ids[0]     ,			skip_special_tokens=_lowercase						)

									a_	:	str          =   []
									a_	:	List[Any]          =   []
									for retrieved_block in retrieved_blocks:
												text.append(_lowercase						)
												text_pair.append(retrieved_block.decode()						)

									a_	:	str          =   self.tokenizer(
									    _lowercase     ,			_lowercase     ,			padding=_lowercase     ,			truncation=_lowercase     ,			return_special_tokens_mask=_lowercase     ,			max_length=_lowercase						)
									a_	:	Any          =   concat_inputs.convert_to_tensors(_lowercase						)

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





						@classmethod
						def  UpperCamelCase__				(						cls     ,			_lowercase     ,			*_lowercase     ,			**_lowercase						)    ->   Tuple:
									if os.path.isdir(_lowercase						):
												a_	:	Optional[Any]          =   os.path.join(_lowercase     ,			_REALM_BLOCK_RECORDS_FILENAME						)
									else:
												a_	:	Tuple          =   hf_hub_download(
												    repo_id=_lowercase     ,			filename=_REALM_BLOCK_RECORDS_FILENAME     ,			**_lowercase						)
									a_	:	int          =   np.load(_lowercase     ,			allow_pickle=_lowercase						)

									a_	:	Tuple          =   AutoTokenizer.from_pretrained(_lowercase     ,			*_lowercase     ,			**_lowercase						)

									return cls(_lowercase     ,			_lowercase						)





						def  UpperCamelCase__				(						self     ,			_lowercase						)    ->   List[Any]:
									# save block records
									np.save(os.path.join(_lowercase     ,			_REALM_BLOCK_RECORDS_FILENAME						)     ,			self.block_records						)
									# save tokenizer
									self.tokenizer.save_pretrained(_lowercase						)





						def  UpperCamelCase__				(						self     ,			_lowercase     ,			_lowercase						)    ->   Any:
									a_	:	List[Any]          =   []
									a_	:	str          =   []
									a_	:	List[str]          =   []
									a_	:	Optional[int]          =   0

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

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

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


