'''simple docstring'''






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 lowercase_					(		unittest.TestCase ):







   """simple docstring"""

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

        _A								=     floats_tensor((batch_size, num_channels) + sizes,   rng=random.Random(0    )    ).to(UpperCamelCase__    )
        return image
   @property
   def   __UpperCAmelCase      (			self			: Dict    )     -> Tuple:
        torch.manual_seed(0    )
        _A								=     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   __UpperCAmelCase      (			self			: Optional[int]    )     -> int:
        torch.manual_seed(0    )
        _A								=     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   __UpperCAmelCase      (			self			: List[Any]    )     -> Union[str, Any]:
        torch.manual_seed(0    )
        _A								=     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(UpperCamelCase__    )
   @property
   def   __UpperCAmelCase      (			self			: Any    )     -> Any:
        def extract(*UpperCamelCase__			: Tuple,   **UpperCamelCase__			: Dict    ):







             class lowercase_					:







                """simple docstring"""

                def __init__(			self			: Union[str, Any]    )     -> List[Any]:
                     _A								=     torch.ones([0]    )

                def   __UpperCAmelCase      (			self			: Optional[int],   UpperCamelCase__			: Tuple    )     -> List[str]:
                     self.pixel_values.to(UpperCamelCase__    )
                     return self






             return Out()

        return extract
   def   __UpperCAmelCase      (			self			: int    )     -> str:
        _A								=     'cpu'  # ensure determinism for the device-dependent torch.Generator
        _A								=     self.dummy_cond_unet
        _A								=     DDIMScheduler(
            beta_start=0.00_085,   beta_end=0.012,   beta_schedule='scaled_linear',   clip_sample=UpperCamelCase__,   set_alpha_to_one=UpperCamelCase__,   )

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

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

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

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

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

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

        assert image.shape == (1, 64, 64, 3)
        _A								=     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   __UpperCAmelCase      (			self			: Optional[int]    )     -> int:
        _A								=     'cpu'  # ensure determinism for the device-dependent torch.Generator
        _A								=     self.dummy_cond_unet
        _A								=     PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
        _A								=     self.dummy_vae
        _A								=     self.dummy_text_encoder
        _A								=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

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

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

        _A								=     output.images

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

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

        assert image.shape == (1, 64, 64, 3)
        _A								=     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   __UpperCAmelCase      (			self			: List[Any]    )     -> Optional[int]:
        _A								=     StableDiffusionPipeline.from_pretrained(
            'hf-internal-testing/tiny-stable-diffusion-lms-pipe',   safety_checker=UpperCamelCase__    )
        assert isinstance(UpperCamelCase__,   UpperCamelCase__    )
        assert isinstance(pipe.scheduler,   UpperCamelCase__    )
        assert pipe.safety_checker is None

        _A								=     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(UpperCamelCase__    )
             _A								=     StableDiffusionPipeline.from_pretrained(UpperCamelCase__    )

        # sanity check that the pipeline still works
        assert pipe.safety_checker is None
        _A								=     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   __UpperCAmelCase      (			self			: Optional[int]    )     -> int:
        _A								=     self.dummy_cond_unet
        _A								=     PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
        _A								=     self.dummy_vae
        _A								=     self.dummy_text_encoder
        _A								=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

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

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

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

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







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







   """simple docstring"""

   def   __UpperCAmelCase      (			self			: str    )     -> Optional[int]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> int:
        _A								=     'cuda'
        _A								=     StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    )
        _A								=     pipe.to(UpperCamelCase__    )
        pipe.set_progress_bar_config(disable=UpperCamelCase__    )

        _A								=     'a photo of a cat'
        _A								=     {
            '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,
        }

        _A								=     3
        _A								=     7

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

        _A								=     output.images
        _A								=     image[0, -3:, -3:, -1]
        _A								=     [
            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(UpperCamelCase__    )
        _A								=     pipe(
            [prompt],   generator=UpperCamelCase__,   guidance_scale=UpperCamelCase__,   num_inference_steps=50,   output_type='np',   width=5_12,   height=5_12,   **UpperCamelCase__,   )

        _A								=     output.images
        _A								=     image[0, -3:, -3:, -1]
        _A								=     [
            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   __UpperCAmelCase      (			self			: List[str]    )     -> Dict:
        _A								=     'cuda'
        _A								=     StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    )
        _A								=     pipe.to(UpperCamelCase__    )
        pipe.set_progress_bar_config(disable=UpperCamelCase__    )

        _A								=     'an image of a crowded boulevard, realistic, 4k'
        _A								=     {
            '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,
        }

        _A								=     9
        _A								=     7

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

        _A								=     output.images
        _A								=     image[0, -3:, -3:, -1]
        _A								=     [
            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(UpperCamelCase__    )
        _A								=     pipe(
            [prompt],   generator=UpperCamelCase__,   guidance_scale=UpperCamelCase__,   num_inference_steps=50,   output_type='np',   width=5_12,   height=5_12,   **UpperCamelCase__,   )

        _A								=     output.images
        _A								=     image[0, -3:, -3:, -1]
        _A								=     [
            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   __UpperCAmelCase      (			self			: List[str]    )     -> List[str]:
        _A								=     'cuda'
        _A								=     StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'    )
        _A								=     pipe.to(UpperCamelCase__    )
        pipe.set_progress_bar_config(disable=UpperCamelCase__    )

        _A								=     'a castle next to a river'
        _A								=     {
            '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,
        }

        _A								=     48
        _A								=     7

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

        _A								=     output.images
        _A								=     image[0, -3:, -3:, -1]
        _A								=     [
            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(UpperCamelCase__    )
        _A								=     pipe(
            [prompt],   generator=UpperCamelCase__,   guidance_scale=UpperCamelCase__,   num_inference_steps=50,   output_type='np',   width=5_12,   height=5_12,   **UpperCamelCase__,   )

        _A								=     output.images
        _A								=     image[0, -3:, -3:, -1]
        _A								=     [
            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   __UpperCAmelCase      (			self			: Tuple    )     -> Optional[Any]:
        _A								=     'cuda'
        _A								=     StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5',   torch_dtype=torch.floataa    )
        _A								=     pipe.to(UpperCamelCase__    )
        pipe.set_progress_bar_config(disable=UpperCamelCase__    )

        _A								=     'a photo of a cat'
        _A								=     {
            '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,
        }

        _A								=     3
        _A								=     7

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

        _A								=     output.images
        _A								=     image[0, -3:, -3:, -1]
        _A								=     [
            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(UpperCamelCase__    )
        _A								=     pipe(
            [prompt],   generator=UpperCamelCase__,   guidance_scale=UpperCamelCase__,   num_inference_steps=50,   output_type='np',   width=5_12,   height=5_12,   **UpperCamelCase__,   )

        _A								=     output.images
        _A								=     image[0, -3:, -3:, -1]
        _A								=     [
            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




'''simple docstring'''






import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   def __init__(			self			: Any,   UpperCamelCase__			: NestedDataStructureLike[PathLike],   UpperCamelCase__			: Optional[NamedSplit] = None,   UpperCamelCase__			: Optional[Features] = None,   UpperCamelCase__			: str = None,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: bool = False,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Optional[Any],   )     -> Tuple:
        super().__init__(
            UpperCamelCase__,   split=UpperCamelCase__,   features=UpperCamelCase__,   cache_dir=UpperCamelCase__,   keep_in_memory=UpperCamelCase__,   streaming=UpperCamelCase__,   num_proc=UpperCamelCase__,   **UpperCamelCase__,   )
        _A								=     path_or_paths if isinstance(UpperCamelCase__,   UpperCamelCase__    ) else {self.split: path_or_paths}
        _A								=     Csv(
            cache_dir=UpperCamelCase__,   data_files=UpperCamelCase__,   features=UpperCamelCase__,   **UpperCamelCase__,   )

   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        # Build iterable dataset
        if self.streaming:
             _A								=     self.builder.as_streaming_dataset(split=self.split    )
        # Build regular (map-style) dataset
        else:
             _A								=     None
             _A								=     None
             _A								=     None
             _A								=     None

             self.builder.download_and_prepare(
                 download_config=UpperCamelCase__,   download_mode=UpperCamelCase__,   verification_mode=UpperCamelCase__,   base_path=UpperCamelCase__,   num_proc=self.num_proc,   )
             _A								=     self.builder.as_dataset(
                 split=self.split,   verification_mode=UpperCamelCase__,   in_memory=self.keep_in_memory    )
        return dataset







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: Optional[Any],   UpperCamelCase__			: Dataset,   UpperCamelCase__			: Union[PathLike, BinaryIO],   UpperCamelCase__			: Optional[int] = None,   UpperCamelCase__			: Optional[int] = None,   **UpperCamelCase__			: Dict,   )     -> Any:
        if num_proc is not None and num_proc <= 0:
             raise ValueError(f'num_proc {num_proc} must be an integer > 0.'    )

        _A								=     dataset
        _A								=     path_or_buf
        _A								=     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _A								=     num_proc
        _A								=     'utf-8'
        _A								=     to_csv_kwargs
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> int:
        _A								=     self.to_csv_kwargs.pop('path_or_buf',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('header',   UpperCamelCase__    )
        _A								=     self.to_csv_kwargs.pop('index',   UpperCamelCase__    )

        if isinstance(self.path_or_buf,   (str, bytes, os.PathLike)    ):
             with open(self.path_or_buf,   'wb+'    ) as buffer:
                  _A								=     self._write(file_obj=UpperCamelCase__,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        else:
             _A								=     self._write(file_obj=self.path_or_buf,   header=UpperCamelCase__,   index=UpperCamelCase__,   **self.to_csv_kwargs    )
        return written
   def   __UpperCAmelCase      (			self			: str,   UpperCamelCase__			: Optional[int]    )     -> str:
        _A		,					_A		,					_A		,					_A								=     args

        _A								=     query_table(
            table=self.dataset.data,   key=slice(UpperCamelCase__,   offset + self.batch_size    ),   indices=self.dataset._indices,   )
        _A								=     batch.to_pandas().to_csv(
            path_or_buf=UpperCamelCase__,   header=header if (offset == 0) else False,   index=UpperCamelCase__,   **UpperCamelCase__    )
        return csv_str.encode(self.encoding    )

   def   __UpperCAmelCase      (			self			: Union[str, Any],   UpperCamelCase__			: BinaryIO,   UpperCamelCase__			: List[str],   UpperCamelCase__			: Dict,   **UpperCamelCase__			: Optional[int]    )     -> int:
        _A								=     0

        if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0,   len(self.dataset    ),   self.batch_size    ),   unit='ba',   disable=not logging.is_progress_bar_enabled(),   desc='Creating CSV from Arrow format',   ):
                  _A								=     self._batch_csv((offset, header, index, to_csv_kwargs)    )
                  written += file_obj.write(UpperCamelCase__    )

        else:
             _A		,					_A								=     len(self.dataset    ), self.batch_size
             with multiprocessing.Pool(self.num_proc    ) as pool:
                  for csv_str in logging.tqdm(
                      pool.imap(
                          self._batch_csv,   [(offset, header, index, to_csv_kwargs) for offset in range(0,   UpperCamelCase__,   UpperCamelCase__    )],   ),   total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size,   unit='ba',   disable=not logging.is_progress_bar_enabled(),   desc='Creating CSV from Arrow format',   ):
                       written += file_obj.write(UpperCamelCase__    )

        return written



