





"""simple docstring"""



import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand







def 						__lowercase	(  snake_case_    :    List[str]		)  ->Dict:



    '''simple docstring'''







    return EnvironmentCommand()







def 						__lowercase	(  snake_case_    :    List[Any]		)  ->str:



    '''simple docstring'''







    return EnvironmentCommand(args.accelerate_config_file		)






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 @staticmethod
 def    UpperCamelCase__(       __lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Any							=	parser.add_parser('''env'''    )
     download_parser.set_defaults(func=__lowerCamelCase    )
     download_parser.add_argument(
         '''--accelerate-config_file'''   ,		default=__lowerCamelCase   ,		help='''The accelerate config file to use for the default values in the launching script.'''   ,		)
     download_parser.set_defaults(func=__lowerCamelCase    )

 def __init__(       self   ,		__lowerCamelCase   ,		*__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		List[str]							=	accelerate_config_file

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	'''not installed'''
     if is_safetensors_available():
         import safetensors

         __A       :		Union[str, Any]							=	safetensors.__version__
     elif importlib.util.find_spec('''safetensors'''    ) is not None:
         import safetensors

         __A       :		Dict							=	F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

     __A       :		List[str]							=	'''not installed'''
     __A       :		Any							=	'''not found'''
     if is_accelerate_available():
         import accelerate
         from accelerate.commands.config import default_config_file, load_config_from_file

         __A       :		Dict							=	accelerate.__version__
         # Get the default from the config file.
         if self._accelerate_config_file is not None or os.path.isfile(__lowerCamelCase    ):
             __A       :		Optional[int]							=	load_config_from_file(self._accelerate_config_file    ).to_dict()

         __A       :		int							=	(
             '''\n'''.join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]    )
             if isinstance(__lowerCamelCase   ,		__lowerCamelCase    )
             else F"""\t{accelerate_config}"""
         )

     __A       :		Optional[int]							=	'''not installed'''
     __A       :		Optional[int]							=	'''NA'''
     if is_torch_available():
         import torch

         __A       :		int							=	torch.__version__
         __A       :		Tuple							=	torch.cuda.is_available()

     __A       :		Tuple							=	'''not installed'''
     __A       :		Tuple							=	'''NA'''
     if is_tf_available():
         import tensorflow as tf

         __A       :		str							=	tf.__version__
         try:
             # deprecated in v2.1
             __A       :		Optional[int]							=	tf.test.is_gpu_available()
         except AttributeError:
             # returns list of devices, convert to bool
             __A       :		str							=	bool(tf.config.list_physical_devices('''GPU'''    )    )

     __A       :		int							=	'''not installed'''
     __A       :		List[Any]							=	'''not installed'''
     __A       :		List[Any]							=	'''not installed'''
     __A       :		Dict							=	'''NA'''
     if is_flax_available():
         import flax
         import jax
         import jaxlib

         __A       :		Dict							=	flax.__version__
         __A       :		str							=	jax.__version__
         __A       :		Tuple							=	jaxlib.__version__
         __A       :		List[Any]							=	jax.lib.xla_bridge.get_backend().platform

     __A       :		List[str]							=	{
         '''`transformers` version''': version,
         '''Platform''': platform.platform(),
         '''Python version''': platform.python_version(),
         '''Huggingface_hub version''': huggingface_hub.__version__,
         '''Safetensors version''': F"""{safetensors_version}""",
         '''Accelerate version''': F"""{accelerate_version}""",
         '''Accelerate config''': F"""{accelerate_config_str}""",
         '''PyTorch version (GPU?)''': F"""{pt_version} ({pt_cuda_available})""",
         '''Tensorflow version (GPU?)''': F"""{tf_version} ({tf_cuda_available})""",
         '''Flax version (CPU?/GPU?/TPU?)''': F"""{flax_version} ({jax_backend})""",
         '''Jax version''': F"""{jax_version}""",
         '''JaxLib version''': F"""{jaxlib_version}""",
         '''Using GPU in script?''': '''<fill in>''',
         '''Using distributed or parallel set-up in script?''': '''<fill in>''',
     }

     print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''    )
     print(self.format_dict(__lowerCamelCase    )    )

     return info







 @staticmethod
 def    UpperCamelCase__(       __lowerCamelCase    ):



     '''simple docstring'''




     return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]    ) + "\n"











"""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 						__lowercase	(  snake_case_    :    Optional[int]		)  ->str:



    '''simple docstring'''







    __A       :		Tuple							=	tmp_path / '''file.csv'''
    __A       :		Union[str, Any]							=	textwrap.dedent(
        '''\
        header1,header2
        1,2
        10,20
        '''		)
    with open(snake_case_							,'''w'''		) as f:
        f.write(snake_case_		)
    return str(snake_case_		)







@pytest.fixture
def 						__lowercase	(  snake_case_    :    str		)  ->Optional[Any]:



    '''simple docstring'''







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







@pytest.fixture
def 						__lowercase	(  snake_case_    :    Any							,snake_case_    :    Optional[int]		)  ->Optional[Any]:



    '''simple docstring'''







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







@pytest.fixture
def 						__lowercase	(  snake_case_    :    Optional[int]		)  ->Tuple:



    '''simple docstring'''







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







@pytest.fixture
def 						__lowercase	(  snake_case_    :    Dict		)  ->Optional[Any]:



    '''simple docstring'''







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







def 						__lowercase	(  snake_case_    :    List[str]							,snake_case_    :    Optional[int]							,snake_case_    :    Optional[Any]		)  ->Dict:



    '''simple docstring'''







    __A       :		str							=	Csv()
    __A       :		List[Any]							=	csv._generate_tables([[csv_file, malformed_csv_file]]		)
    with pytest.raises(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(snake_case_		) in record.message
        for record in caplog.records		)







@require_pil
def 						__lowercase	(  snake_case_    :    Optional[int]		)  ->Union[str, Any]:



    '''simple docstring'''







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







def 						__lowercase	(  snake_case_    :    Any		)  ->List[Any]:



    '''simple docstring'''







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







def 						__lowercase	(  snake_case_    :    str		)  ->Dict:



    '''simple docstring'''







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




