






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


A_			:List[str]	    =		logging.getLogger(__name__)






def 				A	(			)	->		Union[str, Any]:
					__UpperCamelCase :   List[Any]									=argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'   )
					parser.add_argument(
					    '--dataset_name'				,type=a_				,default='wikitext'				,help='Name of the training. Explore datasets at: hf.co/datasets.'				,)
					parser.add_argument(
					    '--dataset_config'				,type=a_				,default='wikitext-103-raw-v1'				,help='Configuration name of the dataset.'   )
					parser.add_argument(
					    '--batch_size'				,type=a_				,default=1_000				,help='Batch size during training.'				,)
					parser.add_argument(
					    '--vocab_size'				,type=a_				,default=10_048				,help='Size of the desired vocabulary.'				,)
					parser.add_argument(
					    '--limit'				,default=a_				,type=a_				,help='Limit the number of shards (used for debugging).'				,)
					parser.add_argument(
					    '--export_to_hub'				,action='store_true'				,)

					__UpperCamelCase :   Union[str, Any]									=parser.parse_args()
					return args






def 				A	(			a_   )	->		Dict:
					__UpperCamelCase :   Optional[int]									=datasets.load_dataset(args.dataset_name				,args.dataset_config				,split='train'   )

					if args.limit is not None:
										__UpperCamelCase :   Union[str, Any]									=min(len(a_   )				,args.limit   )
										__UpperCamelCase :   int									=dataset.select(range(a_   )   )
										logger.info(F'Limiting the dataset to {args.limit} entries.'   )

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

    # Prepare the tokenizer.
					__UpperCamelCase :   Optional[Any]									=Tokenizer(Unigram()   )
					__UpperCamelCase :   Dict									=normalizers.Sequence([normalizers.Replace('``'				,'"'   ), normalizers.Replace('\'\''				,'"'   )]   )
					__UpperCamelCase :   Any									=pre_tokenizers.Metaspace()

					# Prepare the trainer.
					__UpperCamelCase :   List[Any]									=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=a_   )
					logger.info('Tokenizer training complete!'   )

					__UpperCamelCase :   Dict									=tokenizer.token_to_id('[CLS]'   )
					__UpperCamelCase :   Any									=tokenizer.token_to_id('[SEP]'   )
					__UpperCamelCase :   int									=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),
					    ]				,)
					__UpperCamelCase :   str									=decoders.Metaspace()

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


if __name__ == "__main__":
							A_			:List[Any]	    =		parse_args()
							main(args)









import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
							from .testing_utils import (
							    floats_tensor,
							    load_hf_numpy,
							    load_image,
							    load_numpy,
							    load_pt,
							    nightly,
							    parse_flag_from_env,
							    print_tensor_test,
							    require_torch_a,
							    require_torch_gpu,
							    skip_mps,
							    slow,
							    torch_all_close,
							    torch_device,
							)
							from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_			:List[str]	    =		get_logger(__name__)






def 				A	(			a_   )	->		Tuple:
					if version.parse(a_   ) < version.parse(a_   ):
										if "dev" in min_version:
															__UpperCamelCase :   List[str]									=(
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															__UpperCamelCase :   Tuple									=F'This example requires a minimum version of {min_version},'
										error_message += F' but the version found is {__version__}.\n'
										raise ImportError(a_   )

