



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


__A             ="block_records.npy"


__A             =logging.get_logger(__name__)

def 	a						(				_UpperCAmelCase							: List[str]			,       _UpperCAmelCase							: Dict						):







		'''simple docstring'''





		import tensorflow.compat.va as tf

		__UpperCAmelCase   :					List[str]													=     tf.data.TFRecordDataset(SCREAMING_SNAKE_CASE_			,       buffer_size=5_12 * 10_24 * 10_24						)
		__UpperCAmelCase   :					int													=     blocks_dataset.batch(SCREAMING_SNAKE_CASE_			,       drop_remainder=SCREAMING_SNAKE_CASE_						)
		__UpperCAmelCase   :					Optional[int]													=     next(blocks_dataset.take(1						).as_numpy_iterator()						)

		return np_record

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			Any	,	a_							:			str	,	a_							:			Optional[Any]	,	a_							:			Tuple=2	,	a_							:			List[str]=10_00	,	a_							:			Any=1_00	,	a_							:			Union[str, Any]=10_00_00	,	):
					'''simple docstring'''





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

					__UpperCAmelCase   :					Union[str, Any]													=     Builder(db=lowercase__	,	num_neighbors=lowercase__	,	distance_measure='''dot_product'''			)
					__UpperCAmelCase   :					str													=     builder.tree(
					    num_leaves=lowercase__	,	num_leaves_to_search=lowercase__	,	training_sample_size=lowercase__			)
					__UpperCAmelCase   :					Tuple													=     builder.score_ah(dimensions_per_block=lowercase__			)

					__UpperCAmelCase   :					Any													=     builder.build()




			def 			snake_case__					(       self							:			Optional[int]	,	a_							:			Dict			):
					'''simple docstring'''





					__UpperCAmelCase							, __UpperCAmelCase   :					Tuple													=     self.searcher.search_batched(question_projection.detach().cpu()			)
					return retrieved_block_ids.astype('''int64'''			)

class 		UpperCAmelCase__      :




			'''simple docstring'''






			def __init__(       self							:			Optional[int]	,	a_							:			Optional[Any]	,	a_							:			Tuple			):
					'''simple docstring'''





					super().__init__()
					__UpperCAmelCase   :					Union[str, Any]													=     block_records
					__UpperCAmelCase   :					Tuple													=     tokenizer




			def __call__(       self							:			Optional[int]	,	a_							:			Optional[int]	,	a_							:			Union[str, Any]	,	a_							:			Optional[int]	,	a_							:			Optional[int]=None	,	a_							:			Optional[Any]="pt"			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     np.take(self.block_records	,	indices=lowercase__	,	axis=0			)

					__UpperCAmelCase   :					Union[str, Any]													=     self.tokenizer.decode(question_input_ids[0]	,	skip_special_tokens=lowercase__			)

					__UpperCAmelCase   :					Tuple													=     []
					__UpperCAmelCase   :					Optional[int]													=     []
					for retrieved_block in retrieved_blocks:
							text.append(lowercase__			)
							text_pair.append(retrieved_block.decode()			)

					__UpperCAmelCase   :					Dict													=     self.tokenizer(
					    lowercase__	,	lowercase__	,	padding=lowercase__	,	truncation=lowercase__	,	return_special_tokens_mask=lowercase__	,	max_length=lowercase__			)
					__UpperCAmelCase   :					Tuple													=     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 			snake_case__					(       cls							:			Dict	,	a_							:			Optional[Any]	,	*a_							:			Optional[int]	,	**a_							:			str			):
					'''simple docstring'''





					if os.path.isdir(lowercase__			):
							__UpperCAmelCase   :					Tuple													=     os.path.join(lowercase__	,	_REALM_BLOCK_RECORDS_FILENAME			)
					else:
							__UpperCAmelCase   :					Tuple													=     hf_hub_download(
							    repo_id=lowercase__	,	filename=_REALM_BLOCK_RECORDS_FILENAME	,	**lowercase__			)
					__UpperCAmelCase   :					str													=     np.load(lowercase__	,	allow_pickle=lowercase__			)

					__UpperCAmelCase   :					Optional[Any]													=     AutoTokenizer.from_pretrained(lowercase__	,	*lowercase__	,	**lowercase__			)

					return cls(lowercase__	,	lowercase__			)




			def 			snake_case__					(       self							:			Any	,	a_							:			int			):
					'''simple docstring'''





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




			def 			snake_case__					(       self							:			Optional[int]	,	a_							:			List[str]	,	a_							:			int			):
					'''simple docstring'''





					__UpperCAmelCase   :					Tuple													=     []
					__UpperCAmelCase   :					Optional[Any]													=     []
					__UpperCAmelCase   :					Dict													=     []
					__UpperCAmelCase   :					Optional[int]													=     0

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

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










def 	a						(				_UpperCAmelCase							: Optional[int]			,       _UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		# Create a stack and push index of fist element to it
		__UpperCAmelCase   :					List[Any]													=     []
		st.append(0						)

		# Span value of first element is always 1
		__UpperCAmelCase   :					Any													=     1

		# Calculate span values for rest of the elements
		for i in range(1			,       _UpperCAmelCase						):
				# Pop elements from stack while stack is not
				# empty and top of stack is smaller than price[i]
				while len(_UpperCAmelCase						) > 0 and price[st[0]] <= price[i]:
						st.pop()

				# If stack becomes empty, then price[i] is greater
				# than all elements on left of it, i.e. price[0],
				# price[1], ..price[i-1]. Else the price[i]  is
				# greater than elements after top of stack
				__UpperCAmelCase   :					int													=     i + 1 if len(_UpperCAmelCase						) <= 0 else (i - st[0])

				# Push this element to stack
				st.append(_UpperCAmelCase						)

def 	a						(				_UpperCAmelCase							: Dict			,       _UpperCAmelCase							: List[str]						):







		'''simple docstring'''





		for i in range(0			,       _UpperCAmelCase						):
				print(arr[i]			,       end=''' '''						)


# Driver program to test above function
__A             =[1_0, 4, 5, 9_0, 1_2_0, 8_0]
__A             =[0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)
print_array(S, len(price))





