
"""simple docstring"""




from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


snake_case			       =		logging.get_logger(__name__)  # pylint: disable=invalid-name




class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				List[Any]						  =    ["safety_checker", "feature_extractor"]


				def __init__(    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__ = True		,     )   ->   Optional[Any]:
									"""simple docstring"""







									super().__init__()
									if hasattr(scheduler.config		,     'steps_offset'      ) and scheduler.config.steps_offset != 1:
														SCREAMING_SNAKE_CASE           =     (
														    f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
														    f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
														    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
														    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
														    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
														    ' file'
														)
														deprecate('steps_offset!=1'		,     '1.0.0'		,     lowercase__		,     standard_warn=lowercase__      )
														SCREAMING_SNAKE_CASE           =     dict(scheduler.config      )
														SCREAMING_SNAKE_CASE           =     1
														SCREAMING_SNAKE_CASE           =     FrozenDict(lowercase__      )

									self.register_modules(
									    vae=lowercase__		,     text_encoder=lowercase__		,     tokenizer=lowercase__		,     unet=lowercase__		,     scheduler=lowercase__		,     safety_checker=lowercase__		,     feature_extractor=lowercase__		,     )
									self.register_to_config(requires_safety_checker=lowercase__      )


				@property
				def 							A (    self      )   ->   Dict[str, Any]:
									"""simple docstring"""







									return {k: getattr(self		,     lowercase__      ) for k in self.config.keys() if not k.startswith('_'      )}


				def 							A (    self		,     lowercase__ = "auto"      )   ->   str:
									"""simple docstring"""







									if slice_size == "auto":
														# half the attention head size is usually a good trade-off between
														# speed and memory
														SCREAMING_SNAKE_CASE           =     self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(lowercase__      )


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									self.enable_attention_slicing(lowercase__      )


				@torch.no_grad()
				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__ = 0.8		,     lowercase__ = 50		,     lowercase__ = 7.5		,     lowercase__ = None		,     lowercase__ = 1		,     lowercase__ = 0.0		,     lowercase__ = None		,     lowercase__ = "pil"		,     lowercase__ = True		,     lowercase__ = None		,     lowercase__ = 1		,     )   ->   List[str]:
									"""simple docstring"""







									return StableDiffusionInpaintPipelineLegacy(**self.components      )(
									    prompt=lowercase__		,     image=lowercase__		,     mask_image=lowercase__		,     strength=lowercase__		,     num_inference_steps=lowercase__		,     guidance_scale=lowercase__		,     negative_prompt=lowercase__		,     num_images_per_prompt=lowercase__		,     eta=lowercase__		,     generator=lowercase__		,     output_type=lowercase__		,     return_dict=lowercase__		,     callback=lowercase__		,     )


				@torch.no_grad()
				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__ = 0.8		,     lowercase__ = 50		,     lowercase__ = 7.5		,     lowercase__ = None		,     lowercase__ = 1		,     lowercase__ = 0.0		,     lowercase__ = None		,     lowercase__ = "pil"		,     lowercase__ = True		,     lowercase__ = None		,     lowercase__ = 1		,     **lowercase__		,     )   ->   int:
									"""simple docstring"""







									return StableDiffusionImgaImgPipeline(**self.components      )(
									    prompt=lowercase__		,     image=lowercase__		,     strength=lowercase__		,     num_inference_steps=lowercase__		,     guidance_scale=lowercase__		,     negative_prompt=lowercase__		,     num_images_per_prompt=lowercase__		,     eta=lowercase__		,     generator=lowercase__		,     output_type=lowercase__		,     return_dict=lowercase__		,     callback=lowercase__		,     callback_steps=lowercase__		,     )




				@torch.no_grad()
				def 							A (    self		,     lowercase__		,     lowercase__ = 512		,     lowercase__ = 512		,     lowercase__ = 50		,     lowercase__ = 7.5		,     lowercase__ = None		,     lowercase__ = 1		,     lowercase__ = 0.0		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = "pil"		,     lowercase__ = True		,     lowercase__ = None		,     lowercase__ = 1		,     )   ->   Any:
									"""simple docstring"""







									return StableDiffusionPipeline(**self.components      )(
									    prompt=lowercase__		,     height=lowercase__		,     width=lowercase__		,     num_inference_steps=lowercase__		,     guidance_scale=lowercase__		,     negative_prompt=lowercase__		,     num_images_per_prompt=lowercase__		,     eta=lowercase__		,     generator=lowercase__		,     latents=lowercase__		,     output_type=lowercase__		,     return_dict=lowercase__		,     callback=lowercase__		,     callback_steps=lowercase__		,     )


"""simple docstring"""




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


snake_case			       =		logging.getLogger(__name__)

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'      )
					parser.add_argument(
					    '--dataset_name',					type=SCREAMING_SNAKE_CASE_,					default='wikitext',					help='Name of the training. Explore datasets at: hf.co/datasets.',					)
					parser.add_argument(
					    '--dataset_config',					type=SCREAMING_SNAKE_CASE_,					default='wikitext-103-raw-v1',					help='Configuration name of the dataset.'      )
					parser.add_argument(
					    '--batch_size',					type=SCREAMING_SNAKE_CASE_,					default=1_0_0_0,					help='Batch size during training.',					)
					parser.add_argument(
					    '--vocab_size',					type=SCREAMING_SNAKE_CASE_,					default=1_0_0_4_8,					help='Size of the desired vocabulary.',					)
					parser.add_argument(
					    '--limit',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					help='Limit the number of shards (used for debugging).',					)
					parser.add_argument(
					    '--export_to_hub',					action='store_true',					)

					SCREAMING_SNAKE_CASE           =     parser.parse_args()
					return args

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     datasets.load_dataset(args.dataset_name,					args.dataset_config,					split='train'      )

					if args.limit is not None:
										SCREAMING_SNAKE_CASE           =     min(len(SCREAMING_SNAKE_CASE_      ),					args.limit      )
										SCREAMING_SNAKE_CASE           =     dataset.select(range(SCREAMING_SNAKE_CASE_      )      )
										logger.info(f'''Limiting the dataset to {args.limit} entries.'''      )

					def batch_iterator():
										for i in range(0,					len(SCREAMING_SNAKE_CASE_      ),					args.batch_size      ):
															yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
					SCREAMING_SNAKE_CASE           =     Tokenizer(Unigram()      )
					SCREAMING_SNAKE_CASE           =     normalizers.Sequence([normalizers.Replace('``',					'"'      ), normalizers.Replace('\'\'',					'"'      )]      )
					SCREAMING_SNAKE_CASE           =     pre_tokenizers.Metaspace()

					# Prepare the trainer.
					SCREAMING_SNAKE_CASE           =     UnigramTrainer(
					    unk_token='<unk>',					special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]'],					vocab_size=args.vocab_size,					)

					logger.info('Training the tokenizer.'      )
					tokenizer.train_from_iterator(batch_iterator(),					trainer=SCREAMING_SNAKE_CASE_      )
					logger.info('Tokenizer training complete!'      )

					SCREAMING_SNAKE_CASE           =     tokenizer.token_to_id('[CLS]'      )
					SCREAMING_SNAKE_CASE           =     tokenizer.token_to_id('[SEP]'      )
					SCREAMING_SNAKE_CASE           =     processors.TemplateProcessing(
					    single='[CLS]:0 $A:0 [SEP]:0',					pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1',					special_tokens=[
					        ('[CLS]', cls_token_id),
					        ('[SEP]', sep_token_id),
					    ],					)
					SCREAMING_SNAKE_CASE           =     decoders.Metaspace()

					if args.export_to_hub:
										logger.info('Exporting the trained tokenzier to Hub.'      )
										SCREAMING_SNAKE_CASE           =     AlbertTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_      )
										new_tokenizer.push_to_hub('unigram-tokenizer-dataset'      )


if __name__ == "__main__":
	snake_case			       =		parse_args()
	main(args)
