

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_									=    "block_records.npy"


lowercase_									=    logging.get_logger(__name__)
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      int )				-> np.ndarray:





    '''simple docstring'''






    import tensorflow.compat.va as tf

    A__        =  tf.data.TFRecordDataset(SCREAMING_SNAKE_CASE__       ,     buffer_size=512 * 1024 * 1024 )
    A__        =  blocks_dataset.batch(SCREAMING_SNAKE_CASE__       ,     drop_remainder=SCREAMING_SNAKE_CASE__ )
    A__        =  next(blocks_dataset.take(1 ).as_numpy_iterator() )

    return np_record


class 					A		:







    """simple docstring"""





    def __init__( self					:			Optional[Any],lowercase_					:			int,lowercase_					:			Tuple,lowercase_					:			Dict=2,lowercase_					:			Optional[int]=1_0_0_0,lowercase_					:			Any=1_0_0,lowercase_					:			Tuple=1_0_0_0_0_0,)->       Union[str, Any]:





        '''simple docstring'''







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

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

        A__        =  builder.build()


    def 		snake_case__	( self					:			Any,lowercase_					:			Tuple						)->       int:





        '''simple docstring'''







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




class 					A		:







    """simple docstring"""





    def __init__( self					:			str,lowercase_					:			int,lowercase_					:			str						)->       List[str]:





        '''simple docstring'''







        super().__init__()
        A__        =  block_records
        A__        =  tokenizer


    def __call__( self					:			List[Any],lowercase_					:			Dict,lowercase_					:			int,lowercase_					:			Union[str, Any],lowercase_					:			str=None,lowercase_					:			List[str]="pt"						)->       Any:





        '''simple docstring'''







        A__        =  np.take(self.block_records,indices=lowercase_,axis=0						)

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

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

        A__        =  self.tokenizer(
            lowercase_,lowercase_,padding=lowercase_,truncation=lowercase_,return_special_tokens_mask=lowercase_,max_length=lowercase_						)
        A__        =  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,lowercase_					:			Optional[Union[str, os.PathLike]],*lowercase_					:			str,**lowercase_					:			Dict						)->       Optional[Any]:





        '''simple docstring'''







        if os.path.isdir(lowercase_						):
            A__        =  os.path.join(lowercase_,_REALM_BLOCK_RECORDS_FILENAME						)
        else:
            A__        =  hf_hub_download(
                repo_id=lowercase_,filename=_REALM_BLOCK_RECORDS_FILENAME,**lowercase_						)
        A__        =  np.load(lowercase_,allow_pickle=lowercase_						)

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

        return cls(lowercase_,lowercase_						)


    def 		snake_case__	( self					:			List[str],lowercase_					:			str						)->       Any:





        '''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					:			Union[str, Any],lowercase_					:			int,lowercase_					:			Union[str, Any]						)->       int:





        '''simple docstring'''







        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,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__        =  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__        =  [-1] * (max_answers - len(start_pos_						))
                start_pos_ += padded
                end_pos_ += padded
        return has_answers, start_pos, end_pos




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str )				-> str:





    '''simple docstring'''






    A__        =  min(re.search('^ +'       ,     SCREAMING_SNAKE_CASE__ ).end() if t.startswith(' ' ) else 0 for t in string.splitlines() )
    return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(SCREAMING_SNAKE_CASE__ )] )


lowercase_									=    "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

lowercase_									=    "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


lowercase_									=    "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			List[Any]						)->       str:





        '''simple docstring'''







        with tempfile.TemporaryDirectory() as tmp_dir:
            A__        =  Path(lowercase_						) / 'README.md'
            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertEqual(lowercase_,{}						)


    def 		snake_case__	( self					:			Tuple						)->       List[Any]:





        '''simple docstring'''







        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '						)
        assert DatasetMetadata.from_yaml_string(lowercase_						)

        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '						)
        with self.assertRaises(lowercase_						):
            DatasetMetadata.from_yaml_string(lowercase_						)

        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '						)
        assert DatasetMetadata.from_yaml_string(lowercase_						)

