"""simple docstring"""





import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ImageGPTImageProcessor




class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				def __init__(      self							:							Optional[int]      ,    _A							:							Dict      ,    _A							:							Tuple=7      ,    _A							:							Tuple=3      ,    _A							:							int=1_8      ,    _A							:							List[str]=3_0      ,    _A							:							Optional[int]=4_0_0      ,    _A							:							str=True      ,    _A							:							Dict=None      ,    _A							:							List[str]=True      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    size if size is not None else {"""height""": 1_8, """width""": 1_8}
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    parent
											_SCREAMING_SNAKE_CASE		:						str			  =    batch_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    num_channels
											_SCREAMING_SNAKE_CASE		:						str			  =    image_size
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    min_resolution
											_SCREAMING_SNAKE_CASE		:						int			  =    max_resolution
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    do_resize
											_SCREAMING_SNAKE_CASE		:						str			  =    size
											_SCREAMING_SNAKE_CASE		:						int			  =    do_normalize





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											return {
											    # here we create 2 clusters for the sake of simplicity
											    "clusters": np.asarray(
											        [
											            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
											            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
											        ]),
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											}




@require_torch
@require_vision
class  _snake_case		(    __lowercase   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						ImageGPTImageProcessor if is_vision_available() else None





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ImageGPTImageProcessingTester(self)





				@property
				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											return self.image_processor_tester.prepare_image_processor_dict()





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(_A      ,    """clusters"""))
											self.assertTrue(hasattr(_A      ,    """do_resize"""))
											self.assertTrue(hasattr(_A      ,    """size"""))
											self.assertTrue(hasattr(_A      ,    """do_normalize"""))





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size      ,    {"""height""": 1_8, """width""": 1_8})

											_SCREAMING_SNAKE_CASE		:						str			  =    self.image_processing_class.from_dict(self.image_processor_dict      ,    size=4_2)
											self.assertEqual(image_processor.size      ,    {"""height""": 4_2, """width""": 4_2})





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    self.image_processing_class(**self.image_processor_dict)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    json.loads(image_processor.to_json_string())
											for key, value in self.image_processor_dict.items():
																		if key == "clusters":
																									self.assertTrue(np.array_equal(_A      ,    obj[key]))
																		else:
																									self.assertEqual(obj[key]      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.image_processing_class(**self.image_processor_dict)

											with tempfile.TemporaryDirectory() as tmpdirname:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    os.path.join(_A      ,    """image_processor.json""")
																		image_processor_first.to_json_file(_A)
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.image_processing_class.from_json_file(_A).to_dict()

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    image_processor_first.to_dict()
											for key, value in image_processor_first.items():
																		if key == "clusters":
																									self.assertTrue(np.array_equal(_A      ,    image_processor_second[key]))
																		else:
																									self.assertEqual(image_processor_first[key]      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.image_processing_class(**self.image_processor_dict)

											with tempfile.TemporaryDirectory() as tmpdirname:
																		image_processor_first.save_pretrained(_A)
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.image_processing_class.from_pretrained(_A).to_dict()

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    image_processor_first.to_dict()
											for key, value in image_processor_first.items():
																		if key == "clusters":
																									self.assertTrue(np.array_equal(_A      ,    image_processor_second[key]))
																		else:
																									self.assertEqual(image_processor_first[key]      ,    _A)





				@unittest.skip("""ImageGPT requires clusters at initialization""")
				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											pass



def      lowerCamelCase_()->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    load_dataset("""hf-internal-testing/fixtures_image_utils"""     ,	split="""test"""  )

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    Image.open(dataset[4]["""file"""]  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    Image.open(dataset[5]["""file"""]  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    [imagea, imagea]

							return images




@require_vision
@require_torch
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@slow
				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small""")

											_SCREAMING_SNAKE_CASE		:						int			  =    prepare_images()

											# test non-batched
											_SCREAMING_SNAKE_CASE		:						Any			  =    image_processing(images[0]      ,    return_tensors="""pt""")

											self.assertIsInstance(encoding.input_ids      ,    torch.LongTensor)
											self.assertEqual(encoding.input_ids.shape      ,    (1, 1_0_2_4))

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    [3_0_6, 1_9_1, 1_9_1]
											self.assertEqual(encoding.input_ids[0, :3].tolist()      ,    _A)

											# test batched
											_SCREAMING_SNAKE_CASE		:						str			  =    image_processing(_A      ,    return_tensors="""pt""")

											self.assertIsInstance(encoding.input_ids      ,    torch.LongTensor)
											self.assertEqual(encoding.input_ids.shape      ,    (2, 1_0_2_4))

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [3_0_3, 1_3, 1_3]
											self.assertEqual(encoding.input_ids[1, -3:].tolist()      ,    _A)



"""simple docstring"""





import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							assert isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	keep_in_memory=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



@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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						int			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						str			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    features.copy() if features else default_expected_features
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    (
							    Features({feature: Value(__SCREAMING_SNAKE_CASE  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



@pytest.mark.parametrize("""split"""     ,	[None, NamedSplit("""train"""  ), """train""", """test"""]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						int			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	split=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							assert dataset.split == split if split else "train"



@pytest.mark.parametrize("""path_type"""     ,	[str, list]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    str:
							if issubclass(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Any			  =    parquet_path
							elif issubclass(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    [parquet_path]
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						str			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_dataset(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=("train",)  )->    Union[str, Any]:
							assert isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							for split in splits:
														_SCREAMING_SNAKE_CASE		:						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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    ParquetDatasetReader(
														    {"""train""": parquet_path}     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	keep_in_memory=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



@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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    features.copy() if features else default_expected_features
							_SCREAMING_SNAKE_CASE		:						str			  =    (
							    Features({feature: Value(__SCREAMING_SNAKE_CASE  ) for feature, dtype in features.items()}  ) if features is not None else None
							)
							_SCREAMING_SNAKE_CASE		:						int			  =    ParquetDatasetReader({"""train""": parquet_path}     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



@pytest.mark.parametrize("""split"""     ,	[None, NamedSplit("""train"""  ), """train""", """test"""]  )
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							if split:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    {split: parquet_path}
							else:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    """train"""
														_SCREAMING_SNAKE_CASE		:						Any			  =    {"""train""": parquet_path, """test""": parquet_path}
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    tmp_path / """cache"""
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ParquetDatasetReader(__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE  ).read()
							_check_parquet_datasetdict(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	splits=list(path.keys()  )  )
							assert all(dataset[split].split == split for split in path.keys()  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    ParquetDatasetWriter(__SCREAMING_SNAKE_CASE     ,	tmp_path / """foo.parquet"""  )
							assert writer.write() > 0
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    pq.ParquetFile(tmp_path / """foo.parquet"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    pf.read()
							assert dataset.data.table == output_table



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Dict			  =    str(shared_datadir / """test_image_rgb.jpg"""  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {"""image""": [image_path]}
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    Features({"""image""": Image()}  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    Dataset.from_dict(__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    ParquetDatasetWriter(__SCREAMING_SNAKE_CASE     ,	tmp_path / """foo.parquet"""  )
							assert writer.write() > 0

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    Dataset.from_parquet(str(tmp_path / """foo.parquet"""  )  )
							assert dataset.features == reloaded_dataset.features

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ParquetDatasetReader(str(tmp_path / """foo.parquet"""  )     ,	streaming=__SCREAMING_SNAKE_CASE  ).read()
							assert dataset.features == reloaded_iterable_dataset.features



@pytest.mark.parametrize(
    """feature, expected"""     ,	[
        (Features({"""foo""": Value("""int32"""  )}  ), None),
        (Features({"""image""": Image(), """foo""": Value("""int32"""  )}  ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio()  )}  ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]     ,	)
def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    int:
							assert get_writer_batch_size(__SCREAMING_SNAKE_CASE  ) == expected


