


'''simple docstring'''

import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class        UpperCAmelCase       (				__SCREAMING_SNAKE_CASE						,	unittest.TestCase):
 _lowerCamelCase       :    Optional[Any]			    =    UnCLIPPipeline
 _lowerCamelCase       :    int			    =    TEXT_TO_IMAGE_PARAMS - {
     "negative_prompt",
     "height",
     "width",
     "negative_prompt_embeds",
     "guidance_scale",
     "prompt_embeds",
     "cross_attention_kwargs",
 }
 _lowerCamelCase       :    Union[str, Any]			    =    TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCamelCase       :    Dict			    =    [
     "generator",
     "return_dict",
     "prior_num_inference_steps",
     "decoder_num_inference_steps",
     "super_res_num_inference_steps",
 ]
 _lowerCamelCase       :    List[str]			    =    False

 @property
 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    return 32

 @property
 def 						lowercase_  (       self       :		int    ):





    """simple docstring"""



    return 32

 @property
 def 						lowercase_  (       self       :		List[str]    ):





    """simple docstring"""



    return self.time_input_dim

 @property
 def 						lowercase_  (       self       :		Tuple    ):





    """simple docstring"""



    return self.time_input_dim * 4

 @property
 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



    return 100

 @property
 def 						lowercase_  (       self       :		List[str]    ):





    """simple docstring"""



    UpperCamelCase__         =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
    return tokenizer

 @property
 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    torch.manual_seed(0    )
    UpperCamelCase__         =  CLIPTextConfig(
        bos_token_id=0,    eos_token_id=2,    hidden_size=self.text_embedder_hidden_size,    projection_dim=self.text_embedder_hidden_size,    intermediate_size=37,    layer_norm_eps=1e-0_5,    num_attention_heads=4,    num_hidden_layers=5,    pad_token_id=1,    vocab_size=1000,    )
    return CLIPTextModelWithProjection(a_    )

 @property
 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    torch.manual_seed(0    )

    UpperCamelCase__         =  {
        '''num_attention_heads''': 2,
        '''attention_head_dim''': 12,
        '''embedding_dim''': self.text_embedder_hidden_size,
        '''num_layers''': 1,
    }

    UpperCamelCase__         =  PriorTransformer(**a_    )
    return model

 @property
 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    torch.manual_seed(0    )

    UpperCamelCase__         =  {
        '''clip_embeddings_dim''': self.text_embedder_hidden_size,
        '''time_embed_dim''': self.time_embed_dim,
        '''cross_attention_dim''': self.cross_attention_dim,
    }

    UpperCamelCase__         =  UnCLIPTextProjModel(**a_    )
    return model

 @property
 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    torch.manual_seed(0    )

    UpperCamelCase__         =  {
        '''sample_size''': 32,
        # RGB in channels
        '''in_channels''': 3,
        # Out channels is double in channels because predicts mean and variance
        '''out_channels''': 6,
        '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
        '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
        '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
        '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
        '''layers_per_block''': 1,
        '''cross_attention_dim''': self.cross_attention_dim,
        '''attention_head_dim''': 4,
        '''resnet_time_scale_shift''': '''scale_shift''',
        '''class_embed_type''': '''identity''',
    }

    UpperCamelCase__         =  UNetaDConditionModel(**a_    )
    return model

 @property
 def 						lowercase_  (       self       :		Union[str, Any]    ):





    """simple docstring"""



    return {
        "sample_size": 64,
        "layers_per_block": 1,
        "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
        "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
        "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
        "in_channels": 6,
        "out_channels": 3,
    }

 @property
 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    torch.manual_seed(0    )

    UpperCamelCase__         =  UNetaDModel(**self.dummy_super_res_kwargs    )
    return model

 @property
 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



    torch.manual_seed(1    )

    UpperCamelCase__         =  UNetaDModel(**self.dummy_super_res_kwargs    )
    return model

 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  self.dummy_prior
    UpperCamelCase__         =  self.dummy_decoder
    UpperCamelCase__         =  self.dummy_text_proj
    UpperCamelCase__         =  self.dummy_text_encoder
    UpperCamelCase__         =  self.dummy_tokenizer
    UpperCamelCase__         =  self.dummy_super_res_first
    UpperCamelCase__         =  self.dummy_super_res_last

    UpperCamelCase__         =  UnCLIPScheduler(
        variance_type="fixed_small_log",    prediction_type="sample",    num_train_timesteps=1000,    clip_sample_range=5.0,    )

    UpperCamelCase__         =  UnCLIPScheduler(
        variance_type="learned_range",    prediction_type="epsilon",    num_train_timesteps=1000,    )

    UpperCamelCase__         =  UnCLIPScheduler(
        variance_type="fixed_small_log",    prediction_type="epsilon",    num_train_timesteps=1000,    )

    UpperCamelCase__         =  {
        '''prior''': prior,
        '''decoder''': decoder,
        '''text_proj''': text_proj,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''super_res_first''': super_res_first,
        '''super_res_last''': super_res_last,
        '''prior_scheduler''': prior_scheduler,
        '''decoder_scheduler''': decoder_scheduler,
        '''super_res_scheduler''': super_res_scheduler,
    }

    return components

 def 						lowercase_  (       self       :		List[str],    a_       :		Tuple,    a_       :		Union[str, Any]=0    ):





    """simple docstring"""



    if str(a_    ).startswith("mps"    ):
       UpperCamelCase__         =  torch.manual_seed(a_    )
    else:
       UpperCamelCase__         =  torch.Generator(device=a_    ).manual_seed(a_    )
    UpperCamelCase__         =  {
        '''prompt''': '''horse''',
        '''generator''': generator,
        '''prior_num_inference_steps''': 2,
        '''decoder_num_inference_steps''': 2,
        '''super_res_num_inference_steps''': 2,
        '''output_type''': '''numpy''',
    }
    return inputs

 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  '''cpu'''

    UpperCamelCase__         =  self.get_dummy_components()

    UpperCamelCase__         =  self.pipeline_class(**a_    )
    UpperCamelCase__         =  pipe.to(a_    )

    pipe.set_progress_bar_config(disable=a_    )

    UpperCamelCase__         =  pipe(**self.get_dummy_inputs(a_    )    )
    UpperCamelCase__         =  output.images

    UpperCamelCase__         =  pipe(
        **self.get_dummy_inputs(a_    ),    return_dict=a_,    )[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(
        [
            0.9_997,
            0.9_988,
            0.0_028,
            0.9_997,
            0.9_984,
            0.9_965,
            0.0_029,
            0.9_986,
            0.0_025,
        ]    )

    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 						lowercase_  (       self       :		Tuple    ):





    """simple docstring"""



    UpperCamelCase__         =  torch.device("cpu"    )







    class        UpperCAmelCase       :
     _lowerCamelCase       :    Union[str, Any]			    =    1







    UpperCamelCase__         =  self.get_dummy_components()

    UpperCamelCase__         =  self.pipeline_class(**a_    )
    UpperCamelCase__         =  pipe.to(a_    )

    UpperCamelCase__         =  components['''prior''']
    UpperCamelCase__         =  components['''decoder''']
    UpperCamelCase__         =  components['''super_res_first''']
    UpperCamelCase__         =  components['''tokenizer''']
    UpperCamelCase__         =  components['''text_encoder''']

    UpperCamelCase__         =  torch.Generator(device=a_    ).manual_seed(0    )
    UpperCamelCase__         =  prior.dtype
    UpperCamelCase__         =  1

    UpperCamelCase__         =  (batch_size, prior.config.embedding_dim)
    UpperCamelCase__         =  pipe.prepare_latents(
        a_,    dtype=a_,    device=a_,    generator=a_,    latents=a_,    scheduler=DummyScheduler()    )
    UpperCamelCase__         =  (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
    UpperCamelCase__         =  pipe.prepare_latents(
        a_,    dtype=a_,    device=a_,    generator=a_,    latents=a_,    scheduler=DummyScheduler()    )

    UpperCamelCase__         =  (
        batch_size,
        super_res_first.config.in_channels // 2,
        super_res_first.config.sample_size,
        super_res_first.config.sample_size,
    )
    UpperCamelCase__         =  pipe.prepare_latents(
        a_,    dtype=a_,    device=a_,    generator=a_,    latents=a_,    scheduler=DummyScheduler()    )

    pipe.set_progress_bar_config(disable=a_    )

    UpperCamelCase__         =  '''this is a prompt example'''

    UpperCamelCase__         =  torch.Generator(device=a_    ).manual_seed(0    )
    UpperCamelCase__         =  pipe(
        [prompt],    generator=a_,    prior_num_inference_steps=2,    decoder_num_inference_steps=2,    super_res_num_inference_steps=2,    prior_latents=a_,    decoder_latents=a_,    super_res_latents=a_,    output_type="np",    )
    UpperCamelCase__         =  output.images

    UpperCamelCase__         =  tokenizer(
        a_,    padding="max_length",    max_length=tokenizer.model_max_length,    return_tensors="pt",    )
    UpperCamelCase__         =  text_encoder(text_inputs.input_ids    )
    UpperCamelCase__         =  text_inputs.attention_mask

    UpperCamelCase__         =  torch.Generator(device=a_    ).manual_seed(0    )
    UpperCamelCase__         =  pipe(
        generator=a_,    prior_num_inference_steps=2,    decoder_num_inference_steps=2,    super_res_num_inference_steps=2,    prior_latents=a_,    decoder_latents=a_,    super_res_latents=a_,    text_model_output=a_,    text_attention_mask=a_,    output_type="np",    )[0]

    # make sure passing text embeddings manually is identical
    assert np.abs(image - image_from_text    ).max() < 1e-4

 @skip_mps
 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  torch_device == '''cpu'''

    self._test_attention_slicing_forward_pass(test_max_difference=a_,    expected_max_diff=0.01    )

 @skip_mps
 def 						lowercase_  (       self       :		List[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  torch_device == '''cpu'''
    UpperCamelCase__         =  True
    UpperCamelCase__         =  [
        '''prior_num_inference_steps''',
        '''decoder_num_inference_steps''',
        '''super_res_num_inference_steps''',
    ]

    self._test_inference_batch_single_identical(
        test_max_difference=a_,    relax_max_difference=a_,    additional_params_copy_to_batched_inputs=a_,    )

 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  [
        '''prior_num_inference_steps''',
        '''decoder_num_inference_steps''',
        '''super_res_num_inference_steps''',
    ]

    if torch_device == "mps":
       # TODO: MPS errors with larger batch sizes
       UpperCamelCase__         =  [2, 3]
       self._test_inference_batch_consistent(
           batch_sizes=a_,    additional_params_copy_to_batched_inputs=a_,    )
    else:
       self._test_inference_batch_consistent(
           additional_params_copy_to_batched_inputs=a_    )

 @skip_mps
 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    return super().test_dict_tuple_outputs_equivalent()

 @skip_mps
 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    return super().test_save_load_local()

 @skip_mps
 def 						lowercase_  (       self       :		int    ):





    """simple docstring"""



    return super().test_save_load_optional_components()







@nightly
class        UpperCAmelCase       (				unittest.TestCase):

 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/unclip/karlo_v1_alpha_horse_cpu.npy"    )

    UpperCamelCase__         =  UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"    )
    pipeline.set_progress_bar_config(disable=a_    )

    UpperCamelCase__         =  torch.manual_seed(0    )
    UpperCamelCase__         =  pipeline(
        "horse",    num_images_per_prompt=1,    generator=a_,    output_type="np",    )

    UpperCamelCase__         =  output.images[0]

    assert image.shape == (256, 256, 3)
    assert np.abs(expected_image - image    ).max() < 1e-1







@slow
@require_torch_gpu
class        UpperCAmelCase       (				unittest.TestCase):

 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

 def 						lowercase_  (       self       :		Tuple    ):





    """simple docstring"""



    UpperCamelCase__         =  load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/unclip/karlo_v1_alpha_horse_fp16.npy"    )

    UpperCamelCase__         =  UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha",    torch_dtype=torch.floataa    )
    UpperCamelCase__         =  pipeline.to(a_    )
    pipeline.set_progress_bar_config(disable=a_    )

    UpperCamelCase__         =  torch.Generator(device="cpu"    ).manual_seed(0    )
    UpperCamelCase__         =  pipeline(
        "horse",    generator=a_,    output_type="np",    )

    UpperCamelCase__         =  output.images[0]

    assert image.shape == (256, 256, 3)

    assert_mean_pixel_difference(a_,    a_    )

 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()

    UpperCamelCase__         =  UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha",    torch_dtype=torch.floataa    )
    UpperCamelCase__         =  pipe.to(a_    )
    pipe.set_progress_bar_config(disable=a_    )
    pipe.enable_attention_slicing()
    pipe.enable_sequential_cpu_offload()

    UpperCamelCase__         =  pipe(
        "horse",    num_images_per_prompt=1,    prior_num_inference_steps=2,    decoder_num_inference_steps=2,    super_res_num_inference_steps=2,    output_type="np",    )

    UpperCamelCase__         =  torch.cuda.max_memory_allocated()
    # make sure that less than 7 GB is allocated
    assert mem_bytes < 7 * 10**9





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       )



