





'''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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				str											=       ["pixel_values"]


	def __init__(self     ,_lowerCamelCase = True     ,_lowerCamelCase = None     ,_lowerCamelCase = PILImageResampling.BICUBIC     ,_lowerCamelCase = True     ,_lowerCamelCase = True     ,_lowerCamelCase = 1 / 255     ,_lowerCamelCase = None     ,_lowerCamelCase = True     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,**_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				super().__init__(**_lowerCamelCase )
				__lowercase										=							size if size is not None else {'''height''': 224, '''width''': 224}
				__lowercase										=							get_size_dict(_lowerCamelCase )
				__lowercase										=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
				__lowercase										=							get_size_dict(_lowerCamelCase     ,default_to_square=_lowerCamelCase     ,param_name='''crop_size''' )

				__lowercase										=							do_resize
				__lowercase										=							do_rescale
				__lowercase										=							do_normalize
				__lowercase										=							do_center_crop
				__lowercase										=							crop_size
				__lowercase										=							size
				__lowercase										=							resample
				__lowercase										=							rescale_factor
				__lowercase										=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				__lowercase										=							image_std if image_std is not None else IMAGENET_DEFAULT_STD


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = PILImageResampling.BILINEAR     ,_lowerCamelCase = None     ,**_lowerCamelCase     ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase										=							get_size_dict(_lowerCamelCase )

				if "shortest_edge" in size:
							__lowercase										=							get_resize_output_image_size(_lowerCamelCase     ,size=size['''shortest_edge''']     ,default_to_square=_lowerCamelCase )
							# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
				elif "height" in size and "width" in size:
							__lowercase										=							(size['''height'''], size['''width'''])
				else:
							raise ValueError(f"Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}" )
				return resize(_lowerCamelCase     ,size=_lowerCamelCase     ,resample=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase     ,)							->							np.ndarray:







				'''simple docstring'''





				__lowercase										=							get_size_dict(_lowerCamelCase )
				if "height" not in size or "width" not in size:
							raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
				return center_crop(_lowerCamelCase     ,size=(size['''height'''], size['''width'''])     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





				return rescale(_lowerCamelCase     ,scale=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase     ,)							->							np.ndarray:







				'''simple docstring'''





				return normalize(_lowerCamelCase     ,mean=_lowerCamelCase     ,std=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = ChannelDimension.FIRST     ,**_lowerCamelCase     ,)							->							BatchFeature:







				'''simple docstring'''





				__lowercase										=							do_resize if do_resize is not None else self.do_resize
				__lowercase										=							do_rescale if do_rescale is not None else self.do_rescale
				__lowercase										=							do_normalize if do_normalize is not None else self.do_normalize
				__lowercase										=							do_center_crop if do_center_crop is not None else self.do_center_crop
				__lowercase										=							crop_size if crop_size is not None else self.crop_size
				__lowercase										=							get_size_dict(_lowerCamelCase     ,param_name='''crop_size'''     ,default_to_square=_lowerCamelCase )
				__lowercase										=							resample if resample is not None else self.resample
				__lowercase										=							rescale_factor if rescale_factor is not None else self.rescale_factor
				__lowercase										=							image_mean if image_mean is not None else self.image_mean
				__lowercase										=							image_std if image_std is not None else self.image_std

				__lowercase										=							size if size is not None else self.size
				__lowercase										=							get_size_dict(_lowerCamelCase )

				if not is_batched(_lowerCamelCase ):
							__lowercase										=							[images]

				if not valid_images(_lowerCamelCase ):
							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_center_crop and crop_size is None:
							raise ValueError('''Crop size must be specified if do_center_crop 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.
				__lowercase										=							[to_numpy_array(_lowerCamelCase ) for image in images]

				if do_resize:
							__lowercase										=							[self.resize(image=_lowerCamelCase     ,size=_lowerCamelCase     ,resample=_lowerCamelCase ) for image in images]

				if do_center_crop:
							__lowercase										=							[self.center_crop(image=_lowerCamelCase     ,size=_lowerCamelCase ) for image in images]

				if do_rescale:
							__lowercase										=							[self.rescale(image=_lowerCamelCase     ,scale=_lowerCamelCase ) for image in images]

				if do_normalize:
							__lowercase										=							[self.normalize(image=_lowerCamelCase     ,mean=_lowerCamelCase     ,std=_lowerCamelCase ) for image in images]

				__lowercase										=							[to_channel_dimension_format(_lowerCamelCase     ,_lowerCamelCase ) for image in images]

				__lowercase										=							{'''pixel_values''': images}
				return BatchFeature(data=_lowerCamelCase     ,tensor_type=_lowerCamelCase )






'''simple docstring'''



import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase = "▁"     ,_lowerCamelCase = True     ,_lowerCamelCase = "<unk>"     ,_lowerCamelCase = "</s>"     ,_lowerCamelCase = "<pad>"     ,)							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							{
				    '''pad''': {'''id''': 0, '''token''': pad_token},
				    '''eos''': {'''id''': 1, '''token''': eos_token},
				    '''unk''': {'''id''': 2, '''token''': unk_token},
				}

				__lowercase										=							[None] * len(self.special_tokens )
				for token_dict in self.special_tokens.values():
							__lowercase										=							token_dict['''token''']

				__lowercase										=							Tokenizer(Unigram() )

				__lowercase										=							normalizers.Sequence(
				    [
				        normalizers.Nmt(),
				        normalizers.NFKC(),
				        normalizers.Replace(Regex(''' {2,}''' )     ,''' ''' ),
				        normalizers.Lowercase(),
				    ] )
				__lowercase										=							pre_tokenizers.Sequence(
				    [
				        pre_tokenizers.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase ),
				        pre_tokenizers.Digits(individual_digits=_lowerCamelCase ),
				        pre_tokenizers.Punctuation(),
				    ] )
				__lowercase										=							decoders.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase )

				__lowercase										=							TemplateProcessing(
				    single=f"$A {self.special_tokens['eos']['token']}"     ,special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]     ,)

				__lowercase										=							{
				    '''model''': '''SentencePieceUnigram''',
				    '''replacement''': replacement,
				    '''add_prefix_space''': add_prefix_space,
				}

				super().__init__(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
							__lowercase										=							[files]
				self._tokenizer.train(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							List[str]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				self._tokenizer.train_from_iterator(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							json.loads(self._tokenizer.to_str() )

				__lowercase										=							self.special_tokens['''unk''']['''id''']

				__lowercase										=							Tokenizer.from_str(json.dumps(_lowerCamelCase ) )





