
import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Union[str, Any]				,    __UpperCamelCase			:    str							)			->							List[Any]:

				"""simple docstring"""







				assert isinstance(__UpperCamelCase				,    __UpperCamelCase							)
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''keep_in_memory'''				,    [False, True]							)
def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Union[str, Any]				,    __UpperCamelCase			:    List[Any]				,    __UpperCamelCase			:    int							)			->							Union[str, Any]:

				"""simple docstring"""







				A__	:						Union[str, Any]								=				tmp_path / '''cache'''
				A__	:						List[Any]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								A__	:						Union[str, Any]								=				CsvDatasetReader(__UpperCamelCase				,    cache_dir=__UpperCamelCase				,    keep_in_memory=__UpperCamelCase							).read()
				_check_csv_dataset(__UpperCamelCase				,    __UpperCamelCase							)

@pytest.mark.parametrize(
    '''features'''				,    [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]				,    )
def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    List[Any]				,    __UpperCamelCase			:    List[str]				,    __UpperCamelCase			:    Union[str, Any]							)			->							Any:

				"""simple docstring"""







				A__	:						Optional[Any]								=				tmp_path / '''cache'''
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				A__	:						str								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				A__	:						Optional[Any]								=				features.copy() if features else default_expected_features
				A__	:						List[Any]								=				(
				    Features({feature: Value(__UpperCamelCase							) for feature, dtype in features.items()}							) if features is not None else None
				)
				A__	:						Any								=				CsvDatasetReader(__UpperCamelCase				,    features=__UpperCamelCase				,    cache_dir=__UpperCamelCase							).read()
				_check_csv_dataset(__UpperCamelCase				,    __UpperCamelCase							)

@pytest.mark.parametrize('''split'''				,    [None, NamedSplit('''train'''							), '''train''', '''test''']							)
def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Dict				,    __UpperCamelCase			:    Union[str, Any]				,    __UpperCamelCase			:    Optional[int]							)			->							Union[str, Any]:

				"""simple docstring"""







				A__	:						Dict								=				tmp_path / '''cache'''
				A__	:						Tuple								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				A__	:						Union[str, Any]								=				CsvDatasetReader(__UpperCamelCase				,    cache_dir=__UpperCamelCase				,    split=__UpperCamelCase							).read()
				_check_csv_dataset(__UpperCamelCase				,    __UpperCamelCase							)
				assert dataset.split == split if split else "train"

@pytest.mark.parametrize('''path_type'''				,    [str, list]							)
def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Tuple				,    __UpperCamelCase			:    str				,    __UpperCamelCase			:    List[Any]							)			->							Union[str, Any]:

				"""simple docstring"""







				if issubclass(__UpperCamelCase				,    __UpperCamelCase							):
								A__	:						List[str]								=				csv_path
				elif issubclass(__UpperCamelCase				,    __UpperCamelCase							):
								A__	:						Union[str, Any]								=				[csv_path]
				A__	:						List[str]								=				tmp_path / '''cache'''
				A__	:						List[Any]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				A__	:						Union[str, Any]								=				CsvDatasetReader(__UpperCamelCase				,    cache_dir=__UpperCamelCase							).read()
				_check_csv_dataset(__UpperCamelCase				,    __UpperCamelCase							)

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Dict				,    __UpperCamelCase			:    Optional[int]				,    __UpperCamelCase			:    Dict=("train",)							)			->							str:

				"""simple docstring"""







				assert isinstance(__UpperCamelCase				,    __UpperCamelCase							)
				for split in splits:
								A__	:						int								=				dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize('''keep_in_memory'''				,    [False, True]							)
def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Any				,    __UpperCamelCase			:    Union[str, Any]				,    __UpperCamelCase			:    List[str]							)			->							int:

				"""simple docstring"""







				A__	:						Optional[int]								=				tmp_path / '''cache'''
				A__	:						List[str]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								A__	:						Optional[int]								=				CsvDatasetReader({'''train''': csv_path}				,    cache_dir=__UpperCamelCase				,    keep_in_memory=__UpperCamelCase							).read()
				_check_csv_datasetdict(__UpperCamelCase				,    __UpperCamelCase							)

@pytest.mark.parametrize(
    '''features'''				,    [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ]				,    )
def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Any				,    __UpperCamelCase			:    Tuple				,    __UpperCamelCase			:    Union[str, Any]							)			->							List[Any]:

				"""simple docstring"""







				A__	:						int								=				tmp_path / '''cache'''
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				A__	:						Optional[int]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				A__	:						Dict								=				features.copy() if features else default_expected_features
				A__	:						str								=				(
				    Features({feature: Value(__UpperCamelCase							) for feature, dtype in features.items()}							) if features is not None else None
				)
				A__	:						Dict								=				CsvDatasetReader({'''train''': csv_path}				,    features=__UpperCamelCase				,    cache_dir=__UpperCamelCase							).read()
				_check_csv_datasetdict(__UpperCamelCase				,    __UpperCamelCase							)

@pytest.mark.parametrize('''split'''				,    [None, NamedSplit('''train'''							), '''train''', '''test''']							)
def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Any				,    __UpperCamelCase			:    Optional[int]				,    __UpperCamelCase			:    int							)			->							int:

				"""simple docstring"""







				if split:
								A__	:						Dict								=				{split: csv_path}
				else:
								A__	:						Optional[Any]								=				{'''train''': csv_path, '''test''': csv_path}
				A__	:						Tuple								=				tmp_path / '''cache'''
				A__	:						Union[str, Any]								=				{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
				A__	:						Optional[Any]								=				CsvDatasetReader(__UpperCamelCase				,    cache_dir=__UpperCamelCase							).read()
				_check_csv_datasetdict(__UpperCamelCase				,    __UpperCamelCase				,    splits=list(path.keys()							)							)
				assert all(dataset[split].split == split for split in path.keys()							)

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Optional[Any]							)			->							int:

				"""simple docstring"""







				with open(__UpperCamelCase				,    encoding='''utf-8'''							) as csvfile:
								yield from csv.reader(__UpperCamelCase							)

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Optional[int]				,    __UpperCamelCase			:    List[str]							)			->							str:

				"""simple docstring"""







				A__	:						Any								=				tmp_path / '''cache'''
				A__	:						Union[str, Any]								=				os.path.join(__UpperCamelCase				,    '''tmp.csv'''							)
				A__	:						Any								=				CsvDatasetReader({'''train''': csv_path}				,    cache_dir=__UpperCamelCase							).read()
				CsvDatasetWriter(dataset['''train''']				,    __UpperCamelCase				,    num_proc=1							).write()

				A__	:						Optional[Any]								=				iter_csv_file(__UpperCamelCase							)
				A__	:						Tuple								=				iter_csv_file(__UpperCamelCase							)

				for rowa, rowa in zip(__UpperCamelCase				,    __UpperCamelCase							):
								assert rowa == rowa

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Tuple				,    __UpperCamelCase			:    Any							)			->							Any:

				"""simple docstring"""







				A__	:						Union[str, Any]								=				tmp_path / '''cache'''
				A__	:						Optional[int]								=				os.path.join(__UpperCamelCase				,    '''tmp.csv'''							)
				A__	:						Union[str, Any]								=				CsvDatasetReader({'''train''': csv_path}				,    cache_dir=__UpperCamelCase							).read()
				CsvDatasetWriter(dataset['''train''']				,    __UpperCamelCase				,    num_proc=2							).write()

				A__	:						Union[str, Any]								=				iter_csv_file(__UpperCamelCase							)
				A__	:						str								=				iter_csv_file(__UpperCamelCase							)

				for rowa, rowa in zip(__UpperCamelCase				,    __UpperCamelCase							):
								assert rowa == rowa



def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int				,    __UpperCamelCase			:    Dict							)			->							Optional[Any]:

				"""simple docstring"""







				A__	:						str								=				tmp_path / '''cache'''
				A__	:						str								=				os.path.join(__UpperCamelCase				,    '''tmp.csv'''							)
				A__	:						Tuple								=				CsvDatasetReader({'''train''': csv_path}				,    cache_dir=__UpperCamelCase							).read()
				with pytest.raises(__UpperCamelCase							):
								CsvDatasetWriter(dataset['''train''']				,    __UpperCamelCase				,    num_proc=0							)

import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
					from PIL import Image







class 			UpperCamelCase__		(     unittest.TestCase,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''





	def 	__snake_case					(      self		):
					A__	:						Dict								=				load_tool('''image-question-answering'''		)
					self.tool.setup()
					A__	:						List[Any]								=				load_tool('''image-question-answering'''					,							remote=UpperCamelCase__		)





	def 	__snake_case					(      self		):
					A__	:						str								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						List[str]								=				self.tool(UpperCamelCase__					,							'''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						Dict								=				self.remote_tool(UpperCamelCase__					,							'''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Dict								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						int								=				self.tool(image=UpperCamelCase__					,							question='''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						str								=				self.remote_tool(image=UpperCamelCase__					,							question='''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)