




import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



class 			a     (   unittest.TestCase			):

     '''simple docstring'''





     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
     @property
     def   lowerCamelCase_   (	self   :			List[str]				):
         UpperCAmelCase_							 =     1
         UpperCAmelCase_							 =     3
         UpperCAmelCase_							 =     (32, 32)

         UpperCAmelCase_							 =     floats_tensor((batch_size, num_channels) + sizes					,    rng=random.Random(0				)				).to(__snake_case				)
         return image
     @property
     def   lowerCamelCase_   (	self   :			List[str]				):
         torch.manual_seed(0				)
         UpperCAmelCase_							 =     UNetaDConditionModel(
             block_out_channels=(32, 64)					,    layers_per_block=2					,    sample_size=32					,    in_channels=4					,    out_channels=4					,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,    cross_attention_dim=32					,    )
         return model
     @property
     def   lowerCamelCase_   (	self   :			Dict				):
         torch.manual_seed(0				)
         UpperCAmelCase_							 =     AutoencoderKL(
             block_out_channels=[32, 64]					,    in_channels=3					,    out_channels=3					,    down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,    latent_channels=4					,    )
         return model
     @property
     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         torch.manual_seed(0				)
         UpperCAmelCase_							 =     CLIPTextConfig(
             bos_token_id=0					,    eos_token_id=2					,    hidden_size=32					,    intermediate_size=37					,    layer_norm_eps=1E-05					,    num_attention_heads=4					,    num_hidden_layers=5					,    pad_token_id=1					,    vocab_size=10_00					,    )
         return CLIPTextModel(__snake_case				)
     @property
     def   lowerCamelCase_   (	self   :			Dict				):
         def extract(*__snake_case   :			List[str]					,    **__snake_case   :			Optional[Any]				):



             class 			a     :

                  '''simple docstring'''





                  def __init__(	self   :			List[str]				):
                      UpperCAmelCase_							 =     torch.ones([0]				)




                  def   lowerCamelCase_   (	self   :			List[str]					,    __snake_case   :			Optional[Any]				):
                      self.pixel_values.to(__snake_case				)
                      return self


             return Out()

         return extract
     def   lowerCamelCase_   (	self   :			List[Any]				):
         UpperCAmelCase_							 =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_							 =     self.dummy_cond_unet
         UpperCAmelCase_							 =     DDIMScheduler(
             beta_start=0.00_085					,    beta_end=0.012					,    beta_schedule='''scaled_linear'''					,    clip_sample=__snake_case					,    set_alpha_to_one=__snake_case					,    )

         UpperCAmelCase_							 =     self.dummy_vae
         UpperCAmelCase_							 =     self.dummy_text_encoder
         UpperCAmelCase_							 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_							 =     StableDiffusionPipeline(
             unet=__snake_case					,    scheduler=__snake_case					,    vae=__snake_case					,    text_encoder=__snake_case					,    tokenizer=__snake_case					,    safety_checker=__snake_case					,    feature_extractor=self.dummy_extractor					,    )
         UpperCAmelCase_							 =     sd_pipe.to(__snake_case				)
         sd_pipe.set_progress_bar_config(disable=__snake_case				)

         UpperCAmelCase_							 =     '''A painting of a squirrel eating a burger'''

         UpperCAmelCase_							 =     torch.Generator(device=__snake_case				).manual_seed(0				)
         UpperCAmelCase_							 =     sd_pipe([prompt]					,    generator=__snake_case					,    guidance_scale=6.0					,    num_inference_steps=2					,    output_type='''np'''				)
         UpperCAmelCase_							 =     output.images

         UpperCAmelCase_							 =     torch.Generator(device=__snake_case				).manual_seed(0				)
         UpperCAmelCase_							 =     sd_pipe(
             [prompt]					,    generator=__snake_case					,    guidance_scale=6.0					,    num_inference_steps=2					,    output_type='''np'''					,    return_dict=__snake_case					,    )[0]

         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         UpperCAmelCase_							 =     np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
     def   lowerCamelCase_   (	self   :			int				):
         UpperCAmelCase_							 =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_							 =     self.dummy_cond_unet
         UpperCAmelCase_							 =     PNDMScheduler(skip_prk_steps=__snake_case				)
         UpperCAmelCase_							 =     self.dummy_vae
         UpperCAmelCase_							 =     self.dummy_text_encoder
         UpperCAmelCase_							 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_							 =     StableDiffusionPipeline(
             unet=__snake_case					,    scheduler=__snake_case					,    vae=__snake_case					,    text_encoder=__snake_case					,    tokenizer=__snake_case					,    safety_checker=__snake_case					,    feature_extractor=self.dummy_extractor					,    )
         UpperCAmelCase_							 =     sd_pipe.to(__snake_case				)
         sd_pipe.set_progress_bar_config(disable=__snake_case				)

         UpperCAmelCase_							 =     '''A painting of a squirrel eating a burger'''
         UpperCAmelCase_							 =     torch.Generator(device=__snake_case				).manual_seed(0				)
         UpperCAmelCase_							 =     sd_pipe([prompt]					,    generator=__snake_case					,    guidance_scale=6.0					,    num_inference_steps=2					,    output_type='''np'''				)

         UpperCAmelCase_							 =     output.images

         UpperCAmelCase_							 =     torch.Generator(device=__snake_case				).manual_seed(0				)
         UpperCAmelCase_							 =     sd_pipe(
             [prompt]					,    generator=__snake_case					,    guidance_scale=6.0					,    num_inference_steps=2					,    output_type='''np'''					,    return_dict=__snake_case					,    )[0]

         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         UpperCAmelCase_							 =     np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
     def   lowerCamelCase_   (	self   :			List[str]				):
         UpperCAmelCase_							 =     StableDiffusionPipeline.from_pretrained(
             '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''					,    safety_checker=__snake_case				)
         assert isinstance(__snake_case					,    __snake_case				)
         assert isinstance(pipe.scheduler					,    __snake_case				)
         assert pipe.safety_checker is None

         UpperCAmelCase_							 =     pipe('''example prompt'''					,    num_inference_steps=2				).images[0]
         assert image is not None

         # check that there's no error when saving a pipeline with one of the models being None
         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(__snake_case				)
             UpperCAmelCase_							 =     StableDiffusionPipeline.from_pretrained(__snake_case				)

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         UpperCAmelCase_							 =     pipe('''example prompt'''					,    num_inference_steps=2				).images[0]
         assert image is not None




     @unittest.skipIf(torch_device != '''cuda'''					,    '''This test requires a GPU'''				)
     def   lowerCamelCase_   (	self   :			int				):
         UpperCAmelCase_							 =     self.dummy_cond_unet
         UpperCAmelCase_							 =     PNDMScheduler(skip_prk_steps=__snake_case				)
         UpperCAmelCase_							 =     self.dummy_vae
         UpperCAmelCase_							 =     self.dummy_text_encoder
         UpperCAmelCase_							 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

         # put models in fp16
         UpperCAmelCase_							 =     unet.half()
         UpperCAmelCase_							 =     vae.half()
         UpperCAmelCase_							 =     bert.half()

         # make sure here that pndm scheduler skips prk
         UpperCAmelCase_							 =     StableDiffusionPipeline(
             unet=__snake_case					,    scheduler=__snake_case					,    vae=__snake_case					,    text_encoder=__snake_case					,    tokenizer=__snake_case					,    safety_checker=__snake_case					,    feature_extractor=self.dummy_extractor					,    )
         UpperCAmelCase_							 =     sd_pipe.to(__snake_case				)
         sd_pipe.set_progress_bar_config(disable=__snake_case				)

         UpperCAmelCase_							 =     '''A painting of a squirrel eating a burger'''
         UpperCAmelCase_							 =     sd_pipe([prompt]					,    num_inference_steps=2					,    output_type='''np'''				).images

         assert image.shape == (1, 64, 64, 3)



@nightly
@require_torch_gpu
class 			a     (   unittest.TestCase			):

     '''simple docstring'''





     def   lowerCamelCase_   (	self   :			List[str]				):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
     def   lowerCamelCase_   (	self   :			Dict				):
         UpperCAmelCase_							 =     '''cuda'''
         UpperCAmelCase_							 =     StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				)
         UpperCAmelCase_							 =     pipe.to(__snake_case				)
         pipe.set_progress_bar_config(disable=__snake_case				)

         UpperCAmelCase_							 =     '''a photo of a cat'''
         UpperCAmelCase_							 =     {
             '''editing_prompt''': ['''sunglasses'''],
             '''reverse_editing_direction''': [False],
             '''edit_warmup_steps''': 10,
             '''edit_guidance_scale''': 6,
             '''edit_threshold''': 0.95,
             '''edit_momentum_scale''': 0.5,
             '''edit_mom_beta''': 0.6,
         }

         UpperCAmelCase_							 =     3
         UpperCAmelCase_							 =     7

         # no sega enabled
         UpperCAmelCase_							 =     torch.Generator(__snake_case				)
         generator.manual_seed(__snake_case				)
         UpperCAmelCase_							 =     pipe(
             [prompt]					,    generator=__snake_case					,    guidance_scale=__snake_case					,    num_inference_steps=50					,    output_type='''np'''					,    width=5_12					,    height=5_12					,    )

         UpperCAmelCase_							 =     output.images
         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     [
             0.34_673_113,
             0.38_492_733,
             0.37_597_352,
             0.34_086_335,
             0.35_650_748,
             0.35_579_205,
             0.3_384_763,
             0.34_340_236,
             0.3_573_271,
         ]

         assert image.shape == (1, 5_12, 5_12, 3)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(__snake_case				)
         UpperCAmelCase_							 =     pipe(
             [prompt]					,    generator=__snake_case					,    guidance_scale=__snake_case					,    num_inference_steps=50					,    output_type='''np'''					,    width=5_12					,    height=5_12					,    **__snake_case					,    )

         UpperCAmelCase_							 =     output.images
         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     [
             0.41_887_826,
             0.37_728_766,
             0.30_138_272,
             0.41_416_335,
             0.41_664_985,
             0.36_283_392,
             0.36_191_246,
             0.43_364_465,
             0.43_001_732,
         ]

         assert image.shape == (1, 5_12, 5_12, 3)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
     def   lowerCamelCase_   (	self   :			str				):
         UpperCAmelCase_							 =     '''cuda'''
         UpperCAmelCase_							 =     StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				)
         UpperCAmelCase_							 =     pipe.to(__snake_case				)
         pipe.set_progress_bar_config(disable=__snake_case				)

         UpperCAmelCase_							 =     '''an image of a crowded boulevard, realistic, 4k'''
         UpperCAmelCase_							 =     {
             '''editing_prompt''': '''crowd, crowded, people''',
             '''reverse_editing_direction''': True,
             '''edit_warmup_steps''': 10,
             '''edit_guidance_scale''': 8.3,
             '''edit_threshold''': 0.9,
             '''edit_momentum_scale''': 0.5,
             '''edit_mom_beta''': 0.6,
         }

         UpperCAmelCase_							 =     9
         UpperCAmelCase_							 =     7

         # no sega enabled
         UpperCAmelCase_							 =     torch.Generator(__snake_case				)
         generator.manual_seed(__snake_case				)
         UpperCAmelCase_							 =     pipe(
             [prompt]					,    generator=__snake_case					,    guidance_scale=__snake_case					,    num_inference_steps=50					,    output_type='''np'''					,    width=5_12					,    height=5_12					,    )

         UpperCAmelCase_							 =     output.images
         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     [
             0.43_497_998,
             0.91_814_065,
             0.7_540_739,
             0.55_580_205,
             0.8_467_265,
             0.5_389_691,
             0.62_574_506,
             0.58_897_763,
             0.50_926_757,
         ]

         assert image.shape == (1, 5_12, 5_12, 3)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(__snake_case				)
         UpperCAmelCase_							 =     pipe(
             [prompt]					,    generator=__snake_case					,    guidance_scale=__snake_case					,    num_inference_steps=50					,    output_type='''np'''					,    width=5_12					,    height=5_12					,    **__snake_case					,    )

         UpperCAmelCase_							 =     output.images
         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     [
             0.3_089_719,
             0.30_500_144,
             0.29_016_042,
             0.30_630_964,
             0.325_687,
             0.29_419_225,
             0.2_908_091,
             0.28_723_598,
             0.27_696_294,
         ]

         assert image.shape == (1, 5_12, 5_12, 3)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
     def   lowerCamelCase_   (	self   :			Union[str, Any]				):
         UpperCAmelCase_							 =     '''cuda'''
         UpperCAmelCase_							 =     StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				)
         UpperCAmelCase_							 =     pipe.to(__snake_case				)
         pipe.set_progress_bar_config(disable=__snake_case				)

         UpperCAmelCase_							 =     '''a castle next to a river'''
         UpperCAmelCase_							 =     {
             '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
             '''reverse_editing_direction''': False,
             '''edit_warmup_steps''': [15, 18],
             '''edit_guidance_scale''': 6,
             '''edit_threshold''': [0.9, 0.8],
             '''edit_momentum_scale''': 0.5,
             '''edit_mom_beta''': 0.6,
         }

         UpperCAmelCase_							 =     48
         UpperCAmelCase_							 =     7

         # no sega enabled
         UpperCAmelCase_							 =     torch.Generator(__snake_case				)
         generator.manual_seed(__snake_case				)
         UpperCAmelCase_							 =     pipe(
             [prompt]					,    generator=__snake_case					,    guidance_scale=__snake_case					,    num_inference_steps=50					,    output_type='''np'''					,    width=5_12					,    height=5_12					,    )

         UpperCAmelCase_							 =     output.images
         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     [
             0.75_163_555,
             0.76_037_145,
             0.61_785,
             0.9_189_673,
             0.8_627_701,
             0.85_189_694,
             0.8_512_813,
             0.87_012_076,
             0.8_312_857,
         ]

         assert image.shape == (1, 5_12, 5_12, 3)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(__snake_case				)
         UpperCAmelCase_							 =     pipe(
             [prompt]					,    generator=__snake_case					,    guidance_scale=__snake_case					,    num_inference_steps=50					,    output_type='''np'''					,    width=5_12					,    height=5_12					,    **__snake_case					,    )

         UpperCAmelCase_							 =     output.images
         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     [
             0.73_553_365,
             0.7_537_271,
             0.74_341_905,
             0.66_480_356,
             0.6_472_925,
             0.63_039_416,
             0.64_812_905,
             0.6_749_717,
             0.6_517_102,
         ]

         assert image.shape == (1, 5_12, 5_12, 3)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2




     def   lowerCamelCase_   (	self   :			Dict				):
         UpperCAmelCase_							 =     '''cuda'''
         UpperCAmelCase_							 =     StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,    torch_dtype=torch.floataa				)
         UpperCAmelCase_							 =     pipe.to(__snake_case				)
         pipe.set_progress_bar_config(disable=__snake_case				)

         UpperCAmelCase_							 =     '''a photo of a cat'''
         UpperCAmelCase_							 =     {
             '''editing_prompt''': ['''sunglasses'''],
             '''reverse_editing_direction''': [False],
             '''edit_warmup_steps''': 10,
             '''edit_guidance_scale''': 6,
             '''edit_threshold''': 0.95,
             '''edit_momentum_scale''': 0.5,
             '''edit_mom_beta''': 0.6,
         }

         UpperCAmelCase_							 =     3
         UpperCAmelCase_							 =     7

         # no sega enabled
         UpperCAmelCase_							 =     torch.Generator(__snake_case				)
         generator.manual_seed(__snake_case				)
         UpperCAmelCase_							 =     pipe(
             [prompt]					,    generator=__snake_case					,    guidance_scale=__snake_case					,    num_inference_steps=50					,    output_type='''np'''					,    width=5_12					,    height=5_12					,    )

         UpperCAmelCase_							 =     output.images
         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     [
             0.34_887_695,
             0.3_876_953,
             0.375,
             0.34_423_828,
             0.3_581_543,
             0.35_717_773,
             0.3_383_789,
             0.34_570_312,
             0.359_375,
         ]

         assert image.shape == (1, 5_12, 5_12, 3)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(__snake_case				)
         UpperCAmelCase_							 =     pipe(
             [prompt]					,    generator=__snake_case					,    guidance_scale=__snake_case					,    num_inference_steps=50					,    output_type='''np'''					,    width=5_12					,    height=5_12					,    **__snake_case					,    )

         UpperCAmelCase_							 =     output.images
         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     [
             0.42_285_156,
             0.36_914_062,
             0.29_077_148,
             0.42_041_016,
             0.41_918_945,
             0.35_498_047,
             0.3_618_164,
             0.4_423_828,
             0.43_115_234,
         ]

         assert image.shape == (1, 5_12, 5_12, 3)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2











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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Any:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							tmp_path / '''cache'''
					__magic_name__					:							Tuple		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__					:							str		   =							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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Tuple:






					"""simple docstring"""






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







@pytest.mark.parametrize('''split''',	[None, NamedSplit('''train'''       ), '''train''', '''test''']       )
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[Any]:






					"""simple docstring"""






					__magic_name__					:							str		   =							tmp_path / '''cache'''
					__magic_name__					:							List[str]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							int		   =							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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					if issubclass(UpperCAmelCase,	UpperCAmelCase       ):
										__magic_name__					:							str		   =							csv_path
					elif issubclass(UpperCAmelCase,	UpperCAmelCase       ):
										__magic_name__					:							str		   =							[csv_path]
					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Any		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Tuple		   =							CsvDatasetReader(UpperCAmelCase,	cache_dir=UpperCAmelCase       ).read()
					_check_csv_dataset(UpperCAmelCase,	UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase=("train",)       )  ->Tuple:






					"""simple docstring"""






					assert isinstance(UpperCAmelCase,	UpperCAmelCase       )
					for split in splits:
										__magic_name__					:							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  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Tuple		   =							tmp_path / '''cache'''
					__magic_name__					:							int		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__magic_name__					:							Optional[Any]		   =							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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






					__magic_name__					:							Any		   =							tmp_path / '''cache'''
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__magic_name__					:							Dict		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							Dict		   =							features.copy() if features else default_expected_features
					__magic_name__					:							Any		   =							(
					    Features({feature: Value(UpperCAmelCase       ) for feature, dtype in features.items()}       ) if features is not None else None
					)
					__magic_name__					:							List[str]		   =							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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






					if split:
										__magic_name__					:							Dict		   =							{split: csv_path}
					else:
										__magic_name__					:							Any		   =							{'''train''': csv_path, '''test''': csv_path}
					__magic_name__					:							str		   =							tmp_path / '''cache'''
					__magic_name__					:							Union[str, Any]		   =							{'''col_1''': '''int64''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					__magic_name__					:							int		   =							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  lowerCAmelCase  (	UpperCAmelCase       )  ->int:






					"""simple docstring"""






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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							tmp_path / '''cache'''
					__magic_name__					:							List[Any]		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							int		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=1       ).write()

					__magic_name__					:							Optional[int]		   =							iter_csv_file(UpperCAmelCase       )
					__magic_name__					:							Any		   =							iter_csv_file(UpperCAmelCase       )

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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->Union[str, Any]:






					"""simple docstring"""






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

					__magic_name__					:							Tuple		   =							iter_csv_file(UpperCAmelCase       )
					__magic_name__					:							List[str]		   =							iter_csv_file(UpperCAmelCase       )

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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							tmp_path / '''cache'''
					__magic_name__					:							Any		   =							os.path.join(UpperCAmelCase,	'''tmp.csv'''       )
					__magic_name__					:							Any		   =							CsvDatasetReader({'''train''': csv_path},	cache_dir=UpperCAmelCase       ).read()
					with pytest.raises(UpperCAmelCase       ):
										CsvDatasetWriter(dataset['''train'''],	UpperCAmelCase,	num_proc=0       )



