

import os
import sys
import unittest


lowercase  :							Optional[int]	   =							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase  :							Any	   =							os.path.join(git_repo_path, "src", "transformers")

lowercase  :							Union[str, Any]	   =							"\n{0} = None\n"

lowercase  :							List[str]	   =							"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


lowercase  :							Union[str, Any]	   =							"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"
class 		SCREAMING_SNAKE_CASE__       (		unittest.TestCase ):







						"""simple docstring"""




						def __lowerCamelCase       (					self     )				->						Tuple:




									'''simple docstring'''


									__UpperCamelCase     :     str										=   find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"     )
									self.assertIsNone(_lowercase     )

									__UpperCamelCase     :     Tuple										=   find_backend("    if not is_tokenizers_available():"     )
									self.assertEqual(_lowercase			,			"tokenizers"     )

									__UpperCamelCase     :     Tuple										=   find_backend("    if not is_tensorflow_text_available():"     )
									self.assertEqual(_lowercase			,			"tensorflow_text"     )

									__UpperCamelCase     :     Any										=   find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"     )
									self.assertEqual(_lowercase			,			"sentencepiece_and_tokenizers"     )

									__UpperCamelCase     :     Optional[int]										=   find_backend(
									    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"     )
									self.assertEqual(_lowercase			,			"sentencepiece_and_tensorflow_text"     )

									__UpperCamelCase     :     Tuple										=   find_backend(
									    "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"     )
									self.assertEqual(_lowercase			,			"sentencepiece_and_tokenizers_and_vision"     )

						def __lowerCamelCase       (					self     )				->						List[str]:




									'''simple docstring'''


									__UpperCamelCase     :     Dict										=   read_init()
									# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
									self.assertIn("torch"			,			_lowercase     )
									self.assertIn("tensorflow_text"			,			_lowercase     )
									self.assertIn("sentencepiece_and_tokenizers"			,			_lowercase     )

									# Likewise, we can't assert on the exact content of a key
									self.assertIn("BertModel"			,			objects["torch"]     )
									self.assertIn("TFBertModel"			,			objects["tf"]     )
									self.assertIn("FlaxBertModel"			,			objects["flax"]     )
									self.assertIn("BertModel"			,			objects["torch"]     )
									self.assertIn("TFBertTokenizer"			,			objects["tensorflow_text"]     )
									self.assertIn("convert_slow_tokenizer"			,			objects["sentencepiece_and_tokenizers"]     )

						def __lowerCamelCase       (					self     )				->						List[Any]:




									'''simple docstring'''


									__UpperCamelCase     :     Tuple										=   create_dummy_object("CONSTANT"			,			"'torch'"     )
									self.assertEqual(_lowercase			,			"\nCONSTANT = None\n"     )

									__UpperCamelCase     :     Dict										=   create_dummy_object("function"			,			"'torch'"     )
									self.assertEqual(
									    _lowercase			,			"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"     )

									__UpperCamelCase     :     Optional[Any]										=   "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n"
									__UpperCamelCase     :     Union[str, Any]										=   create_dummy_object("FakeClass"			,			"'torch'"     )
									self.assertEqual(_lowercase			,			_lowercase     )




						def __lowerCamelCase       (					self     )				->						Dict:




									'''simple docstring'''


									__UpperCamelCase     :     int										=   "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n"
									__UpperCamelCase     :     List[str]										=   create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}     )
									self.assertEqual(dummy_files["torch"]			,			_lowercase     )




"""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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			str						    = tmp_path / "file.csv"
						snake_case   :			Tuple						    = textwrap.dedent(
						    "\\n        header1,header2\n        1,2\n        10,20\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			List[Any]						    = tmp_path / "malformed_file.csv"
						snake_case   :			Union[str, Any]						    = textwrap.dedent(
						    "\\n        header1,header2\n        1,2\n        10,20,\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






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

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = tmp_path / "csv_with_label.csv"
						snake_case   :			Tuple						    = textwrap.dedent(
						    "\\n        label\n        good\n        bad\n        good\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str]     ,		lowerCamelCase_:							Any     ,		lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			Tuple						    = Csv()
						snake_case   :			str						    = 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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






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



