

"""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


__UpperCamelCase						:   Optional[Any]							   =	logging.getLogger(__name__)





def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		Any						       =			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=10_00		,  help='''Batch size during training.'''		,  )
	parser.add_argument(
	    '''--vocab_size'''		,  type=_SCREAMING_SNAKE_CASE		,  default=1_00_48		,  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'''		,  )

	lowerCAmelCase__			:		Any						       =			parser.parse_args()
	return args





def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		Any						       =			datasets.load_dataset(args.dataset_name		,  args.dataset_config		,  split='''train'''				)

	if args.limit is not None:
		lowerCAmelCase__			:		Union[str, Any]						       =			min(len(_SCREAMING_SNAKE_CASE				)		,  args.limit				)
		lowerCAmelCase__			:		int						       =			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.
	lowerCAmelCase__			:		List[Any]						       =			Tokenizer(Unigram()				)
	lowerCAmelCase__			:		Union[str, Any]						       =			normalizers.Sequence([normalizers.Replace('''``'''		,  '''"'''				), normalizers.Replace('''\'\''''		,  '''"'''				)]				)
	lowerCAmelCase__			:		Optional[int]						       =			pre_tokenizers.Metaspace()

	# Prepare the trainer.
	lowerCAmelCase__			:		Dict						       =			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!'''				)

	lowerCAmelCase__			:		Optional[int]						       =			tokenizer.token_to_id('''[CLS]'''				)
	lowerCAmelCase__			:		int						       =			tokenizer.token_to_id('''[SEP]'''				)
	lowerCAmelCase__			:		List[Any]						       =			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),
	    ]		,  )
	lowerCAmelCase__			:		int						       =			decoders.Metaspace()

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


if __name__ == "__main__":
							__UpperCamelCase						:   Dict							   =	parse_args()
							main(args)







"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase						:   List[Any]							   =	logging.get_logger(__name__)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= ["pixel_values"]







					def __init__( self    :       Tuple     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Dict[str, int]] = None     ,lowercase_    :       PILImageResampling = PILImageResampling.BILINEAR     ,lowercase_    :       bool = True     ,lowercase_    :       Union[int, float] = 1 / 2_5_5     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,**lowercase_    :       Tuple     ,):
						super().__init__(**lowercase_	)
						lowerCAmelCase__			:		Any						       =			size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__			:		Tuple						       =			get_size_dict(lowercase_	)
						lowerCAmelCase__			:		Tuple						       =			do_resize
						lowerCAmelCase__			:		Tuple						       =			do_rescale
						lowerCAmelCase__			:		Union[str, Any]						       =			do_normalize
						lowerCAmelCase__			:		Optional[int]						       =			size
						lowerCAmelCase__			:		List[Any]						       =			resample
						lowerCAmelCase__			:		int						       =			rescale_factor
						lowerCAmelCase__			:		int						       =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__			:		Any						       =			image_std if image_std is not None else IMAGENET_STANDARD_STD







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       np.ndarray     ,lowercase_    :       Dict[str, int]     ,lowercase_    :       PILImageResampling = PILImageResampling.BILINEAR     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       str     ,):
						lowerCAmelCase__			:		Union[str, Any]						       =			get_size_dict(lowercase_	)
						if "height" not in size or "width" not in size:
							raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'	)
						return resize(
						    lowercase_     ,size=(size['''height'''], size['''width'''])     ,resample=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       np.ndarray     ,lowercase_    :       float     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       Tuple	):
						return rescale(lowercase_     ,scale=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       np.ndarray     ,lowercase_    :       Union[float, List[float]]     ,lowercase_    :       Union[float, List[float]]     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       Union[str, Any]     ,):
						return normalize(lowercase_     ,mean=lowercase_     ,std=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       ImageInput     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Dict[str, int] = None     ,lowercase_    :       PILImageResampling = None     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Optional[float] = None     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[str, TensorType]] = None     ,lowercase_    :       Union[str, ChannelDimension] = ChannelDimension.FIRST     ,**lowercase_    :       List[Any]     ,):
						lowerCAmelCase__			:		Optional[int]						       =			do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__			:		Any						       =			do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__			:		Dict						       =			do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__			:		Dict						       =			resample if resample is not None else self.resample
						lowerCAmelCase__			:		Dict						       =			rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__			:		Optional[Any]						       =			image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__			:		str						       =			image_std if image_std is not None else self.image_std

						lowerCAmelCase__			:		Any						       =			size if size is not None else self.size
						lowerCAmelCase__			:		Dict						       =			get_size_dict(lowercase_	)

						lowerCAmelCase__			:		Union[str, Any]						       =			make_list_of_images(lowercase_	)

						if not valid_images(lowercase_	):
							raise ValueError(
							    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
							    '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

						if do_resize and size is None:
							raise ValueError('''Size must be specified if do_resize is True.'''	)

						if do_rescale and rescale_factor is None:
							raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

						# All transformations expect numpy arrays.
						lowerCAmelCase__			:		Dict						       =			[to_numpy_array(lowercase_	) for image in images]

						if do_resize:
							lowerCAmelCase__			:		Optional[Any]						       =			[self.resize(image=lowercase_     ,size=lowercase_     ,resample=lowercase_	) for image in images]

						if do_rescale:
							lowerCAmelCase__			:		List[Any]						       =			[self.rescale(image=lowercase_     ,scale=lowercase_	) for image in images]

						if do_normalize:
							lowerCAmelCase__			:		Dict						       =			[self.normalize(image=lowercase_     ,mean=lowercase_     ,std=lowercase_	) for image in images]

						lowerCAmelCase__			:		Tuple						       =			[to_channel_dimension_format(lowercase_     ,lowercase_	) for image in images]

						lowerCAmelCase__			:		int						       =			{'''pixel_values''': images}
						return BatchFeature(data=lowercase_     ,tensor_type=lowercase_	)




