



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 			A_      (	__a     :				List[str]		):

				"""simple docstring"""
				a__				      =		tmp_path / """file.csv"""
				a__				      =		textwrap.dedent(
				    """\
        header1,header2
        1,2
        10,20
        """		)
				with open(__a ,		"""w"""		) as f:
								f.write(__a		)
				return str(__a		)

@pytest.fixture
def 			A_      (	__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		tmp_path / """malformed_file.csv"""
				a__				      =		textwrap.dedent(
				    """\
        header1,header2
        1,2
        10,20,
        """		)
				with open(__a ,		"""w"""		) as f:
								f.write(__a		)
				return str(__a		)

@pytest.fixture
def 			A_      (	__a     :				Tuple ,		__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		tmp_path / """csv_with_image.csv"""
				a__				      =		textwrap.dedent(
				    F'''\
        image
        {image_file}
        '''		)
				with open(__a ,		"""w"""		) as f:
								f.write(__a		)
				return str(__a		)

@pytest.fixture
def 			A_      (	__a     :				int		):

				"""simple docstring"""
				a__				      =		tmp_path / """csv_with_label.csv"""
				a__				      =		textwrap.dedent(
				    """\
        label
        good
        bad
        good
        """		)
				with open(__a ,		"""w"""		) as f:
								f.write(__a		)
				return str(__a		)

@pytest.fixture
def 			A_      (	__a     :				Optional[Any]		):

				"""simple docstring"""
				a__				      =		tmp_path / """csv_with_int_list.csv"""
				a__				      =		textwrap.dedent(
				    """\
        int_list
        1 2 3
        4 5 6
        7 8 9
        """		)
				with open(__a ,		"""w"""		) as f:
								f.write(__a		)
				return str(__a		)

def 			A_      (	__a     :				Dict ,		__a     :				Any ,		__a     :				Optional[Any]		):

				"""simple docstring"""
				a__				      =		Csv()
				a__				      =		csv._generate_tables([[csv_file, malformed_csv_file]]		)
				with pytest.raises(__a ,		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(__a		) in record.message
				    for record in caplog.records		)

@require_pil
def 			A_      (	__a     :				int		):

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

def 			A_      (	__a     :				Optional[Any]		):

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




def 			A_      (	__a     :				str		):

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






# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase							 =					"""3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
	import torch

	print("""Torch version:""", torch.__version__)
	print("""Cuda available:""", torch.cuda.is_available())
	print("""Cuda version:""", torch.version.cuda)
	print("""CuDNN version:""", torch.backends.cudnn.version())
	print("""Number of GPUs available:""", torch.cuda.device_count())
	print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
	print("""Torch version:""", None)

try:
	import deepspeed

	print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
	print("""DeepSpeed version:""", None)

try:
	import tensorflow as tf

	print("""TensorFlow version:""", tf.__version__)
	print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
	print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
	print("""TensorFlow version:""", None)

