




'''simple docstring'''



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			(_lowerCamelCase				:  Any						)-> Any:




     '''simple docstring'''





     __snake_case            =	tmp_path / '''file.csv'''
     __snake_case            =	textwrap.dedent(
         '''\
        header1,header2
        1,2
        10,20
        '''						)
     with open(_lowerCamelCase ,						'''w'''						) as f:
          f.write(_lowerCamelCase						)
     return str(_lowerCamelCase						)







@pytest.fixture
def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     __snake_case            =	tmp_path / '''malformed_file.csv'''
     __snake_case            =	textwrap.dedent(
         '''\
        header1,header2
        1,2
        10,20,
        '''						)
     with open(_lowerCamelCase ,						'''w'''						) as f:
          f.write(_lowerCamelCase						)
     return str(_lowerCamelCase						)







@pytest.fixture
def       _UpperCamelCase			(_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Tuple						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	tmp_path / '''csv_with_image.csv'''
     __snake_case            =	textwrap.dedent(
         f'''\
        image
        {image_file}
        '''						)
     with open(_lowerCamelCase ,						'''w'''						) as f:
          f.write(_lowerCamelCase						)
     return str(_lowerCamelCase						)







@pytest.fixture
def       _UpperCamelCase			(_lowerCamelCase				:  Any						)-> Tuple:




     '''simple docstring'''





     __snake_case            =	tmp_path / '''csv_with_label.csv'''
     __snake_case            =	textwrap.dedent(
         '''\
        label
        good
        bad
        good
        '''						)
     with open(_lowerCamelCase ,						'''w'''						) as f:
          f.write(_lowerCamelCase						)
     return str(_lowerCamelCase						)







@pytest.fixture
def       _UpperCamelCase			(_lowerCamelCase				:  Any						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	tmp_path / '''csv_with_int_list.csv'''
     __snake_case            =	textwrap.dedent(
         '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''						)
     with open(_lowerCamelCase ,						'''w'''						) as f:
          f.write(_lowerCamelCase						)
     return str(_lowerCamelCase						)







def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Any						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	Csv()
     __snake_case            =	csv._generate_tables([[csv_file, malformed_csv_file]]						)
     with pytest.raises(_lowerCamelCase ,						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(_lowerCamelCase						) in record.message
         for record in caplog.records						)







@require_pil
def       _UpperCamelCase			(_lowerCamelCase				:  Dict						)-> Optional[Any]:




     '''simple docstring'''





     with open(_lowerCamelCase ,						encoding='''utf-8'''						) as f:
          __snake_case            =	f.read().splitlines()[1]
     __snake_case            =	Csv(encoding='''utf-8''' ,						features=Features({'''image''': Image()}						)						)
     __snake_case            =	csv._generate_tables([[csv_file_with_image]]						)
     __snake_case            =	pa.concat_tables([table for _, table in generator]						)
     assert pa_table.schema.field('''image'''						).type == Image()()
     __snake_case            =	pa_table.to_pydict()['''image''']
     assert generated_content == [{"path": image_file, "bytes": None}]







def       _UpperCamelCase			(_lowerCamelCase				:  Any						)-> int:




     '''simple docstring'''





     with open(_lowerCamelCase ,						encoding='''utf-8'''						) as f:
          __snake_case            =	f.read().splitlines()[1:]
     __snake_case            =	Csv(encoding='''utf-8''' ,						features=Features({'''label''': ClassLabel(names=['''good''', '''bad''']						)}						)						)
     __snake_case            =	csv._generate_tables([[csv_file_with_label]]						)
     __snake_case            =	pa.concat_tables([table for _, table in generator]						)
     assert pa_table.schema.field('''label'''						).type == ClassLabel(names=['''good''', '''bad''']						)()
     __snake_case            =	pa_table.to_pydict()['''label''']
     assert generated_content == [ClassLabel(names=['''good''', '''bad''']						).straint(_lowerCamelCase						) for label in labels]







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple						)-> Any:




     '''simple docstring'''





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










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A			:						List[str]        =  logging.get_logger(__name__)

A			:						Tuple        =  {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''ibert'''

					def __init__(self       :		int ,   _UpperCAmelCase       :		Union[str, Any]=3_0522 ,   _UpperCAmelCase       :		Any=768 ,   _UpperCAmelCase       :		Optional[Any]=12 ,   _UpperCAmelCase       :		Union[str, Any]=12 ,   _UpperCAmelCase       :		List[Any]=3072 ,   _UpperCAmelCase       :		List[Any]="gelu" ,   _UpperCAmelCase       :		List[str]=0.1 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		Optional[int]=512 ,   _UpperCAmelCase       :		str=2 ,   _UpperCAmelCase       :		Union[str, Any]=0.02 ,   _UpperCAmelCase       :		Dict=1E-1_2 ,   _UpperCAmelCase       :		int=1 ,   _UpperCAmelCase       :		str=0 ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		int="absolute" ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		List[Any]="none" ,   **_UpperCAmelCase       :		List[Any] ,   )		->  Any:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase ,   bos_token_id=_UpperCAmelCase ,   eos_token_id=_UpperCAmelCase ,   **_UpperCAmelCase						)

							lowercase__	     =						vocab_size
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_act
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						initializer_range
							lowercase__	     =						layer_norm_eps
							lowercase__	     =						position_embedding_type
							lowercase__	     =						quant_mode
							lowercase__	     =						force_dequant






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__	     =						{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__	     =						{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]						)

