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 lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



@pytest.fixture
def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		tmp_path / '''malformed_file.csv'''
    __UpperCamelCase     :Tuple			  =		textwrap.dedent(
        '''\
        header1,header2
        1,2
        10,20,
        '''	)
    with open(SCREAMING_SNAKE_CASE  ,			'''w'''	) as f:
        f.write(SCREAMING_SNAKE_CASE	)
    return str(SCREAMING_SNAKE_CASE	)



@pytest.fixture
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		tmp_path / '''csv_with_image.csv'''
    __UpperCamelCase     :str			  =		textwrap.dedent(
        f"""\
        image
        {image_file}
        """	)
    with open(SCREAMING_SNAKE_CASE  ,			'''w'''	) as f:
        f.write(SCREAMING_SNAKE_CASE	)
    return str(SCREAMING_SNAKE_CASE	)



@pytest.fixture
def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



@pytest.fixture
def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		tmp_path / '''csv_with_int_list.csv'''
    __UpperCamelCase     :Optional[Any]			  =		textwrap.dedent(
        '''\
        int_list
        1 2 3
        4 5 6
        7 8 9
        '''	)
    with open(SCREAMING_SNAKE_CASE  ,			'''w'''	) as f:
        f.write(SCREAMING_SNAKE_CASE	)
    return str(SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		Csv()
    __UpperCamelCase     :Optional[int]			  =		csv._generate_tables([[csv_file, malformed_csv_file]]	)
    with pytest.raises(SCREAMING_SNAKE_CASE  ,			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(SCREAMING_SNAKE_CASE	) in record.message
        for record in caplog.records	)



@require_pil
def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						str						=      """ctrl"""
    a__	:						Dict						=      ["""past_key_values"""]
    a__	:						Tuple						=      {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(	self , __lowercase=246_534 , __lowercase=256 , __lowercase=1_280 , __lowercase=8_192 , __lowercase=48 , __lowercase=16 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=1E-6 , __lowercase=0.02 , __lowercase=True , **__lowercase , )       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		vocab_size
        __UpperCamelCase     :Optional[Any]			  =		n_positions
        __UpperCamelCase     :Dict			  =		n_embd
        __UpperCamelCase     :Dict			  =		n_layer
        __UpperCamelCase     :List[Any]			  =		n_head
        __UpperCamelCase     :int			  =		dff
        __UpperCamelCase     :Union[str, Any]			  =		resid_pdrop
        __UpperCamelCase     :Optional[int]			  =		embd_pdrop
        __UpperCamelCase     :List[Any]			  =		layer_norm_epsilon
        __UpperCamelCase     :Dict			  =		initializer_range

        __UpperCamelCase     :Any			  =		use_cache

        super().__init__(**__lowercase)





