





from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


@dataclass
class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Union[List[PIL.Image.Image], np.ndarray]
							lowerCamelCase     :      Optional[List[bool]]


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .pipeline_cycle_diffusion import CycleDiffusionPipeline
		from .pipeline_stable_diffusion import StableDiffusionPipeline
		from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
		from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
		from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
		from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
		from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
		from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
		from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
		from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
		from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
		from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
		from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
		from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
		from .pipeline_stable_unclip import StableUnCLIPPipeline
		from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
		from .safety_checker import StableDiffusionSafetyChecker
		from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
		from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
		if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.26.0''')):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import (
		    StableDiffusionDepthaImgPipeline,
		    StableDiffusionDiffEditPipeline,
		    StableDiffusionPixaPixZeroPipeline,
		)
else:
		from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
		from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
		from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
		if not (
		    is_torch_available()
		    and is_transformers_available()
		    and is_k_diffusion_available()
		    and is_k_diffusion_version('''>=''', '''0.0.12''')
		):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
		from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
		if not (is_transformers_available() and is_onnx_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_onnx_objects import *  # noqa F403
else:
		from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
		from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
		from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
		from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
		from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
		import flax


		@flax.struct.dataclass
		class  __UpperCamelCase       (		a__		):
									lowerCamelCase     :      np.ndarray
									lowerCamelCase     :      List[bool]
		from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
		from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
		from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
		from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
		from .safety_checker_flax import FlaxStableDiffusionSafetyChecker









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


a		:				int  			=    HfArgumentParser(PretokenizationArguments)
a		:				Optional[int]  			=    parser.parse_args()
if args.num_workers is None:
		a		:				Tuple  			=    multiprocessing.cpu_count()
a		:				Optional[int]  			=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

a		:				Dict  			=    time.time()
a		:				Tuple  			=    load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

a		:				Dict  			=    time.time()
a		:				Tuple  			=    ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

a		:				Tuple  			=    time.time()
ds.push_to_hub(args.tokenized_data_repo)







print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


