

import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil


@pytest.fixture
def 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			a_	:	Union[str, Any]          =   tmp_path / """text.txt"""
			a_	:	Dict          =   textwrap.dedent(
			    """\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        """)
			with open(a__     ,  """w"""     ,  encoding="""utf-8""") as f:
						f.write(a__)
			return str(a__)


@pytest.fixture
def 				_UpperCAmelCase							(	a__     ,  a__):

			'''simple docstring'''



			a_	:	List[Any]          =   tmp_path / """text_with_image.txt"""
			with open(a__     ,  """w"""     ,  encoding="""utf-8""") as f:
						f.write(a__)
			return str(a__)


@pytest.mark.parametrize("""keep_linebreaks"""     ,  [True, False])
def 				_UpperCAmelCase							(	a__     ,  a__):

			'''simple docstring'''



			with open(a__     ,  encoding="""utf-8""") as f:
						a_	:	List[Any]          =   f.read().splitlines(keepends=a__)
			a_	:	str          =   Text(keep_linebreaks=a__     ,  encoding="""utf-8""")
			a_	:	str          =   text._generate_tables([[text_file]])
			a_	:	List[str]          =   pa.concat_tables([table for _, table in generator]).to_pydict()["""text"""]
			assert generated_content == expected_content


@require_pil
def 				_UpperCAmelCase							(	a__):

			'''simple docstring'''



			with open(a__     ,  encoding="""utf-8""") as f:
						a_	:	Optional[Any]          =   f.read().splitlines()[0]
			a_	:	Any          =   Text(encoding="""utf-8"""     ,  features=Features({"""image""": Image()}))
			a_	:	Optional[int]          =   text._generate_tables([[text_file_with_image]])
			a_	:	List[str]          =   pa.concat_tables([table for _, table in generator])
			assert pa_table.schema.field("""image""").type == Image()()
			a_	:	List[Any]          =   pa_table.to_pydict()["""image"""]
			assert generated_content == [{"path": image_file, "bytes": None}]



@pytest.mark.parametrize("""sample_by"""     ,  ["""line""", """paragraph""", """document"""])
def 				_UpperCAmelCase							(	a__     ,  a__):

			'''simple docstring'''



			with open(a__     ,  encoding="""utf-8""") as f:
						a_	:	Any          =   f.read()
			if sample_by == "line":
						a_	:	Tuple          =   expected_content.splitlines()
			elif sample_by == "paragraph":
						a_	:	List[str]          =   expected_content.split("""\n\n""")
			elif sample_by == "document":
						a_	:	List[Any]          =   [expected_content]
			a_	:	Tuple          =   Text(sample_by=a__     ,  encoding="""utf-8"""     ,  chunksize=1_0_0)
			a_	:	List[str]          =   text._generate_tables([[text_file]])
			a_	:	List[str]          =   pa.concat_tables([table for _, table in generator]).to_pydict()["""text"""]
			assert generated_content == expected_content





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


