



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.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu

class    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

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



       @property
       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    1
           __a							=    3
           __a							=    (32, 32)

           __a							=    floats_tensor((batch_size, num_channels) + sizes					,		rng=random.Random(0)).to(__SCREAMING_SNAKE_CASE)
           return image



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


           '''simple docstring'''

           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 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           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 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           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=1_000					,		)
           return CLIPTextModel(__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           def extract(*__SCREAMING_SNAKE_CASE							:					int					,		**__SCREAMING_SNAKE_CASE							:					Dict):

               class    _A	:



                      def __init__(		self							:					int):


                          '''simple docstring'''

                          __a							=    torch.ones([0])





                      def 		_lowerCamelCase      (		self							:					int					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


                          '''simple docstring'''

                          self.pixel_values.to(__SCREAMING_SNAKE_CASE)
                          return self


               return Out()

           return extract



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           __a							=    self.dummy_cond_unet
           __a							=    DDIMScheduler(
               beta_start=0.0_00_85					,		beta_end=0.0_12					,		beta_schedule='''scaled_linear'''					,		clip_sample=__SCREAMING_SNAKE_CASE					,		set_alpha_to_one=__SCREAMING_SNAKE_CASE					,		)

           __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=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		vae=__SCREAMING_SNAKE_CASE					,		text_encoder=__SCREAMING_SNAKE_CASE					,		tokenizer=__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		feature_extractor=self.dummy_extractor					,		)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

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

           __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=6.0					,		num_inference_steps=2					,		output_type='''np'''					,		return_dict=__SCREAMING_SNAKE_CASE					,		)[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([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64])

           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							:					Optional[Any]):


           '''simple docstring'''

           __a							=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           __a							=    self.dummy_cond_unet
           __a							=    PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
           __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=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		vae=__SCREAMING_SNAKE_CASE					,		text_encoder=__SCREAMING_SNAKE_CASE					,		tokenizer=__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		feature_extractor=self.dummy_extractor					,		)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

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

           __a							=    output.images

           __a							=    torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(0)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=6.0					,		num_inference_steps=2					,		output_type='''np'''					,		return_dict=__SCREAMING_SNAKE_CASE					,		)[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([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93])

           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							:					Optional[int]):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained(
               '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''					,		safety_checker=__SCREAMING_SNAKE_CASE)
           assert isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
           assert isinstance(pipe.scheduler					,		__SCREAMING_SNAKE_CASE)
           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(__SCREAMING_SNAKE_CASE)
               __a							=    StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE)

           # 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 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a							=    self.dummy_cond_unet
           __a							=    PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE)
           __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=__SCREAMING_SNAKE_CASE					,		scheduler=__SCREAMING_SNAKE_CASE					,		vae=__SCREAMING_SNAKE_CASE					,		text_encoder=__SCREAMING_SNAKE_CASE					,		tokenizer=__SCREAMING_SNAKE_CASE					,		safety_checker=__SCREAMING_SNAKE_CASE					,		feature_extractor=self.dummy_extractor					,		)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __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    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,		safety_checker=__SCREAMING_SNAKE_CASE)
           __a							=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    (
               '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
               ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
               ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
               ''' children from bahnhof zoo, detailed '''
           )
           __a							=    4_003_660_346
           __a							=    7

           # without safety guidance (sld_guidance_scale = 0)
           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=0					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

           assert image.shape == (1, 512, 512, 3)

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

           # without safety guidance (strong configuration)
           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=2_000					,		sld_warmup_steps=7					,		sld_threshold=0.0_25					,		sld_momentum_scale=0.5					,		sld_mom_beta=0.7					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

           assert image.shape == (1, 512, 512, 3)

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



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''					,		safety_checker=__SCREAMING_SNAKE_CASE)
           __a							=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    '''padme amidala taking a bath artwork, safe for work, no nudity'''
           __a							=    2_734_971_755
           __a							=    7

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=0					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

           assert image.shape == (1, 512, 512, 3)

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

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=2_000					,		sld_warmup_steps=7					,		sld_threshold=0.0_25					,		sld_momentum_scale=0.5					,		sld_mom_beta=0.7					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

           assert image.shape == (1, 512, 512, 3)

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





       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''')
           __a							=    sd_pipe.to(__SCREAMING_SNAKE_CASE)
           sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

           __a							=    (
               '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
               ''' leyendecker'''
           )
           __a							=    1_044_355_234
           __a							=    12

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=0					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-7

           __a							=    torch.manual_seed(__SCREAMING_SNAKE_CASE)
           __a							=    sd_pipe(
               [prompt]					,		generator=__SCREAMING_SNAKE_CASE					,		guidance_scale=__SCREAMING_SNAKE_CASE					,		num_inference_steps=50					,		output_type='''np'''					,		width=512					,		height=512					,		sld_guidance_scale=2_000					,		sld_warmup_steps=7					,		sld_threshold=0.0_25					,		sld_momentum_scale=0.5					,		sld_mom_beta=0.7					,		)

           __a							=    output.images
           __a							=    image[0, -3:, -3:, -1]
           __a							=    np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61])
           assert image.shape == (1, 512, 512, 3)

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










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

import fsspec

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

class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					NestedDataStructureLike[PathLike]					,		__SCREAMING_SNAKE_CASE							:					Optional[NamedSplit] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[Features] = None					,		__SCREAMING_SNAKE_CASE							:					str = None					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		**__SCREAMING_SNAKE_CASE							:					Any					,		):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE					,		split=__SCREAMING_SNAKE_CASE					,		features=__SCREAMING_SNAKE_CASE					,		cache_dir=__SCREAMING_SNAKE_CASE					,		keep_in_memory=__SCREAMING_SNAKE_CASE					,		streaming=__SCREAMING_SNAKE_CASE					,		num_proc=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           __a							=    field
           __a							=    path_or_paths if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
           __a							=    Json(
               cache_dir=__SCREAMING_SNAKE_CASE					,		data_files=__SCREAMING_SNAKE_CASE					,		features=__SCREAMING_SNAKE_CASE					,		field=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)





       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           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=__SCREAMING_SNAKE_CASE					,		download_mode=__SCREAMING_SNAKE_CASE					,		verification_mode=__SCREAMING_SNAKE_CASE					,		base_path=__SCREAMING_SNAKE_CASE					,		num_proc=self.num_proc					,		)
               __a							=    self.builder.as_dataset(
                   split=self.split					,		verification_mode=__SCREAMING_SNAKE_CASE					,		in_memory=self.keep_in_memory)
           return dataset


class    _A	:



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Dataset					,		__SCREAMING_SNAKE_CASE							:					Union[PathLike, BinaryIO]					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           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_json_kwargs



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.to_json_kwargs.pop('''path_or_buf'''					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.to_json_kwargs.pop('''orient'''					,		'''records''')
           __a							=    self.to_json_kwargs.pop('''lines'''					,		True if orient == '''records''' else False)
           __a							=    self.to_json_kwargs.pop('''index'''					,		False if orient in ['''split''', '''table'''] else True)
           __a							=    self.to_json_kwargs.pop('''compression'''					,		__SCREAMING_SNAKE_CASE)

           if compression not in [None, "infer", "gzip", "bz2", "xz"]:
               raise NotImplementedError(F'`datasets` currently does not support {compression} compression')

           if isinstance(self.path_or_buf					,		(str, bytes, os.PathLike)):
               with fsspec.open(self.path_or_buf					,		'''wb'''					,		compression=__SCREAMING_SNAKE_CASE) as buffer:
                   __a							=    self._write(file_obj=__SCREAMING_SNAKE_CASE					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**self.to_json_kwargs)
           else:
               if compression:
                   raise NotImplementedError(
                       F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                       ''' was passed. Please provide a local path instead.''')
               __a							=    self._write(
                   file_obj=self.path_or_buf					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**self.to_json_kwargs)
           return written



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a	,       __a	,       __a	,       __a	,       __a							=    args

           __a							=    query_table(
               table=self.dataset.data					,		key=slice(__SCREAMING_SNAKE_CASE					,		offset + self.batch_size)					,		indices=self.dataset._indices					,		)
           __a							=    batch.to_pandas().to_json(
               path_or_buf=__SCREAMING_SNAKE_CASE					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           if not json_str.endswith('''\n'''):
               json_str += "\n"
           return json_str.encode(self.encoding)





       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					BinaryIO					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		):


           '''simple docstring'''

           __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 json from Arrow format'''					,		):
                   __a							=    self._batch_json((offset, orient, lines, index, to_json_kwargs))
                   written += file_obj.write(__SCREAMING_SNAKE_CASE)
           else:
               __a	,       __a							=    len(self.dataset), self.batch_size
               with multiprocessing.Pool(self.num_proc) as pool:
                   for json_str in logging.tqdm(
                       pool.imap(
                           self._batch_json					,		[(offset, orient, lines, index, to_json_kwargs) for offset in range(0					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)]					,		)					,		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 json from Arrow format'''					,		):
                       written += file_obj.write(__SCREAMING_SNAKE_CASE)

           return written





