



import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil


@pytest.fixture
def 			UpperCamelCase(  __UpperCamelCase							:		str ):
		lowerCAmelCase_			:     List[str]									= tmp_path / '''file.csv'''
		lowerCAmelCase_			:     Tuple									= textwrap.dedent(
		    '''\
        header1,header2
        1,2
        10,20
        ''' )
		with open(__UpperCamelCase     ,'''w''' ) as f:
				f.write(__UpperCamelCase )
		return str(__UpperCamelCase )


@pytest.fixture
def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any] ):
		lowerCAmelCase_			:     Optional[Any]									= tmp_path / '''malformed_file.csv'''
		lowerCAmelCase_			:     Union[str, Any]									= textwrap.dedent(
		    '''\
        header1,header2
        1,2
        10,20,
        ''' )
		with open(__UpperCamelCase     ,'''w''' ) as f:
				f.write(__UpperCamelCase )
		return str(__UpperCamelCase )


@pytest.fixture
def 			UpperCamelCase(  __UpperCamelCase							:		str     ,__UpperCamelCase							:		Union[str, Any] ):
		lowerCAmelCase_			:     int									= tmp_path / '''csv_with_image.csv'''
		lowerCAmelCase_			:     List[Any]									= textwrap.dedent(
		    f"""\
        image
        {image_file}
        """ )
		with open(__UpperCamelCase     ,'''w''' ) as f:
				f.write(__UpperCamelCase )
		return str(__UpperCamelCase )


@pytest.fixture
def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     Tuple									= tmp_path / '''csv_with_label.csv'''
		lowerCAmelCase_			:     Dict									= textwrap.dedent(
		    '''\
        label
        good
        bad
        good
        ''' )
		with open(__UpperCamelCase     ,'''w''' ) as f:
				f.write(__UpperCamelCase )
		return str(__UpperCamelCase )


@pytest.fixture
def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any] ):
		lowerCAmelCase_			:     Any									= tmp_path / '''csv_with_int_list.csv'''
		lowerCAmelCase_			:     int									= textwrap.dedent(
		    '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        ''' )
		with open(__UpperCamelCase     ,'''w''' ) as f:
				f.write(__UpperCamelCase )
		return str(__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		str     ,__UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		List[Any] ):
		lowerCAmelCase_			:     List[str]									= Csv()
		lowerCAmelCase_			:     Any									= csv._generate_tables([[csv_file, malformed_csv_file]] )
		with pytest.raises(__UpperCamelCase     ,match='''Error tokenizing data''' ):
				for _ in generator:
						pass
		assert any(
		    record.levelname == '''ERROR'''
		    and '''Failed to read file''' in record.message
		    and os.path.basename(__UpperCamelCase ) in record.message
		    for record in caplog.records )


@require_pil
def 			UpperCamelCase(  __UpperCamelCase							:		Optional[Any] ):
		with open(__UpperCamelCase     ,encoding='''utf-8''' ) as f:
				lowerCAmelCase_			:     Union[str, Any]									= f.read().splitlines()[1]
		lowerCAmelCase_			:     Optional[Any]									= Csv(encoding='''utf-8'''     ,features=Features({'''image''': Image()} ) )
		lowerCAmelCase_			:     List[Any]									= csv._generate_tables([[csv_file_with_image]] )
		lowerCAmelCase_			:     Union[str, Any]									= pa.concat_tables([table for _, table in generator] )
		assert pa_table.schema.field('''image''' ).type == Image()()
		lowerCAmelCase_			:     Optional[int]									= pa_table.to_pydict()['''image''']
		assert generated_content == [{"path": image_file, "bytes": None}]


def 			UpperCamelCase(  __UpperCamelCase							:		Tuple ):
		with open(__UpperCamelCase     ,encoding='''utf-8''' ) as f:
				lowerCAmelCase_			:     int									= f.read().splitlines()[1:]
		lowerCAmelCase_			:     Optional[Any]									= Csv(encoding='''utf-8'''     ,features=Features({'''label''': ClassLabel(names=['''good''', '''bad'''] )} ) )
		lowerCAmelCase_			:     Dict									= csv._generate_tables([[csv_file_with_label]] )
		lowerCAmelCase_			:     Optional[int]									= pa.concat_tables([table for _, table in generator] )
		assert pa_table.schema.field('''label''' ).type == ClassLabel(names=['''good''', '''bad'''] )()
		lowerCAmelCase_			:     List[str]									= pa_table.to_pydict()['''label''']
		assert generated_content == [ClassLabel(names=['''good''', '''bad'''] ).straint(__UpperCamelCase ) for label in labels]





def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		lowerCAmelCase_			:     int									= Csv(encoding='''utf-8'''     ,sep=''','''     ,converters={'''int_list''': lambda __UpperCamelCase : [int(__UpperCamelCase ) for i in x.split()]} )
		lowerCAmelCase_			:     Union[str, Any]									= csv._generate_tables([[csv_file_with_int_list]] )
		lowerCAmelCase_			:     int									= pa.concat_tables([table for _, table in generator] )
		assert pa.types.is_list(pa_table.schema.field('''int_list''' ).type )
		lowerCAmelCase_			:     Optional[Any]									= pa_table.to_pydict()['''int_list''']
		assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


A__  :  str								=					logging.get_logger(__name__)

A__  :  Any								=					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__  :  str								=					{
    '''vocab_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
        ),
        '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-reader''': (
            '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-reader''': (
            '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
        ),
    },
}

A__  :  Union[str, Any]								=					{
    '''google/realm-cc-news-pretrained-embedder''': 512,
    '''google/realm-cc-news-pretrained-encoder''': 512,
    '''google/realm-cc-news-pretrained-scorer''': 512,
    '''google/realm-cc-news-pretrained-openqa''': 512,
    '''google/realm-orqa-nq-openqa''': 512,
    '''google/realm-orqa-nq-reader''': 512,
    '''google/realm-orqa-wq-openqa''': 512,
    '''google/realm-orqa-wq-reader''': 512,
}

A__  :  Dict								=					{
    '''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = VOCAB_FILES_NAMES
							_a			      = PRETRAINED_VOCAB_FILES_MAP
							_a			      = PRETRAINED_INIT_CONFIGURATION
							_a			      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_a			      = RealmTokenizer




							def __init__(       self     :       int				,				A_     :       Optional[int]=None				,				A_     :       Optional[Any]=None				,				A_     :       Optional[Any]=True				,				A_     :       Optional[int]="[UNK]"				,				A_     :       List[Any]="[SEP]"				,				A_     :       List[Any]="[PAD]"				,				A_     :       Optional[Any]="[CLS]"				,				A_     :       Dict="[MASK]"				,				A_     :       List[Any]=True				,				A_     :       List[str]=None				,				**A_     :       List[str]				,				):
									super().__init__(
									    A_				,				tokenizer_file=A_				,				do_lower_case=A_				,				unk_token=A_				,				sep_token=A_				,				pad_token=A_				,				cls_token=A_				,				mask_token=A_				,				tokenize_chinese_chars=A_				,				strip_accents=A_				,				**A_				,				)

									lowerCAmelCase_			:     Optional[int]									= json.loads(self.backend_tokenizer.normalizer.__getstate__())
									if (
									    normalizer_state.get('''lowercase'''				,				A_) != do_lower_case
									    or normalizer_state.get('''strip_accents'''				,				A_) != strip_accents
									    or normalizer_state.get('''handle_chinese_chars'''				,				A_) != tokenize_chinese_chars
									):
											lowerCAmelCase_			:     int									= getattr(A_				,				normalizer_state.pop('''type'''))
											lowerCAmelCase_			:     str									= do_lower_case
											lowerCAmelCase_			:     Dict									= strip_accents
											lowerCAmelCase_			:     Optional[Any]									= tokenize_chinese_chars
											lowerCAmelCase_			:     Union[str, Any]									= normalizer_class(**A_)

									lowerCAmelCase_			:     Any									= do_lower_case




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Optional[Any]				,				**A_     :       Tuple):
									lowerCAmelCase_			:     List[str]									= PaddingStrategy.MAX_LENGTH

									lowerCAmelCase_			:     str									= text
									lowerCAmelCase_			:     int									= kwargs.pop('''text_pair'''				,				A_)
									lowerCAmelCase_			:     str									= kwargs.pop('''return_tensors'''				,				A_)

									lowerCAmelCase_			:     int									= {
									    '''input_ids''': [],
									    '''attention_mask''': [],
									    '''token_type_ids''': [],
									}

									for idx, candidate_text in enumerate(A_):
											if batch_text_pair is not None:
													lowerCAmelCase_			:     List[Any]									= batch_text_pair[idx]
											else:
													lowerCAmelCase_			:     List[Any]									= None

											lowerCAmelCase_			:     int									= super().__call__(A_				,				A_				,				return_tensors=A_				,				**A_)

											lowerCAmelCase_			:     Optional[Any]									= encoded_candidates.get('''input_ids''')
											lowerCAmelCase_			:     List[str]									= encoded_candidates.get('''attention_mask''')
											lowerCAmelCase_			:     Optional[Any]									= encoded_candidates.get('''token_type_ids''')

											if encoded_input_ids is not None:
													output_data["input_ids"].append(A_)
											if encoded_attention_mask is not None:
													output_data["attention_mask"].append(A_)
											if encoded_token_type_ids is not None:
													output_data["token_type_ids"].append(A_)

									lowerCAmelCase_			:     List[str]									= {key: item for key, item in output_data.items() if len(A_) != 0}

									return BatchEncoding(A_				,				tensor_type=A_)




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Tuple				,				A_     :       List[Any]=None):
									lowerCAmelCase_			:     Optional[Any]									= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

									return output




							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       List[int]				,				A_     :       Optional[List[int]] = None):
									lowerCAmelCase_			:     Tuple									= [self.sep_token_id]
									lowerCAmelCase_			:     Union[str, Any]									= [self.cls_token_id]
									if token_ids_a is None:
											return len(cls + token_ids_a + sep) * [0]
									return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]







							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       str				,				A_     :       Optional[str] = None):
									lowerCAmelCase_			:     List[str]									= self._tokenizer.model.save(A_				,				name=A_)
									return tuple(A_)





