"""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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


_UpperCamelCase								=		logging.get_logger(__name__)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[Any]								=			b.T
							A__       :     int								=			np.sum(np.square(A_							)			,  axis=1							)
							A__       :     str								=			np.sum(np.square(A_							)			,  axis=0							)
							A__       :     Any								=			np.matmul(A_			,  A_							)
							A__       :     int								=			aa[:, None] - 2 * ab + ba[None, :]
							return d

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			x.reshape(-1			,  3							)
							A__       :     str								=			squared_euclidean_distance(A_			,  A_							)
							return np.argmin(A_			,  axis=1							)



class 						__UpperCAmelCase    (a_     ):







			'''simple docstring'''
			_UpperCamelCase  :						Tuple					     =				['pixel_values']





			def __init__(			self   ,      snake_case_ = None   ,      snake_case_ = True   ,      snake_case_ = None   ,      snake_case_ = PILImageResampling.BILINEAR   ,      snake_case_ = True   ,      snake_case_ = True   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**lowercase_							)
										A__       :     int								=			size if size is not None else {'''height''': 256, '''width''': 256}
										A__       :     Union[str, Any]								=			get_size_dict(lowercase_							)
										A__       :     Any								=			np.array(lowercase_							) if clusters is not None else None
										A__       :     int								=			do_resize
										A__       :     str								=			size
										A__       :     List[str]								=			resample
										A__       :     str								=			do_normalize
										A__       :     int								=			do_color_quantize





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_ = PILImageResampling.BILINEAR   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     Tuple								=			get_size_dict(lowercase_							)
										if "height" not in size or "width" not in size:
																	raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}'''							)
										return resize(
										    lowercase_   ,      size=(size["""height"""], size["""width"""])   ,      resample=lowercase_   ,      data_format=lowercase_   ,      **lowercase_							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None   ,      ):







										'''simple docstring'''


										A__       :     Dict								=			rescale(image=lowercase_   ,      scale=1 / 1_27.5   ,      data_format=lowercase_							)
										A__       :     Optional[int]								=			image - 1
										return image





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = ChannelDimension.FIRST   ,      **snake_case_   ,      ):







										'''simple docstring'''


										A__       :     Tuple								=			do_resize if do_resize is not None else self.do_resize
										A__       :     int								=			size if size is not None else self.size
										A__       :     Tuple								=			get_size_dict(lowercase_							)
										A__       :     str								=			resample if resample is not None else self.resample
										A__       :     Optional[int]								=			do_normalize if do_normalize is not None else self.do_normalize
										A__       :     Tuple								=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
										A__       :     Any								=			clusters if clusters is not None else self.clusters
										A__       :     Optional[Any]								=			np.array(lowercase_							)

										A__       :     Optional[int]								=			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 or resample is None:
																	raise ValueError("""Size and resample must be specified if do_resize is True."""							)

										if do_color_quantize and clusters is None:
																	raise ValueError("""Clusters must be specified if do_color_quantize is True."""							)

										# All transformations expect numpy arrays.
										A__       :     Optional[Any]								=			[to_numpy_array(lowercase_							) for image in images]

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

										if do_normalize:
																	A__       :     Dict								=			[self.normalize(image=lowercase_							) for image in images]

										if do_color_quantize:
																	A__       :     Optional[Any]								=			[to_channel_dimension_format(lowercase_   ,      ChannelDimension.LAST							) for image in images]
																	# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																	A__       :     int								=			np.array(lowercase_							)
																	A__       :     Tuple								=			color_quantize(lowercase_   ,      lowercase_							).reshape(images.shape[:-1]							)

																	# flatten to (batch_size, height*width)
																	A__       :     Optional[Any]								=			images.shape[0]
																	A__       :     Tuple								=			images.reshape(lowercase_   ,      -1							)

																	# We need to convert back to a list of images to keep consistent behaviour across processors.
																	A__       :     Tuple								=			list(lowercase_							)
										else:
																	A__       :     str								=			[to_channel_dimension_format(lowercase_   ,      lowercase_							) for image in images]

										A__       :     Optional[Any]								=			{'''input_ids''': images}
										return BatchFeature(data=lowercase_   ,      tensor_type=lowercase_							)








"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_albert import AlbertTokenizer
else:
							__UpperCamelCase    : Any				 =						None

__UpperCamelCase    : Optional[int]				 =						logging.get_logger(__name__)
__UpperCamelCase    : List[Any]				 =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase    : str				 =						{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

__UpperCamelCase    : Any				 =						{
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

__UpperCamelCase    : Union[str, Any]				 =						'''▁'''







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			VOCAB_FILES_NAMES
					lowercase__												=			PRETRAINED_VOCAB_FILES_MAP
					lowercase__												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__												=			AlbertTokenizer







					def __init__(			self     :							Union[str, Any]  ,lowercase_     :							List[str]=None  ,lowercase_     :							Union[str, Any]=None  ,lowercase_     :							str=True  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=False  ,lowercase_     :							Tuple="[CLS]"  ,lowercase_     :							Optional[int]="[SEP]"  ,lowercase_     :							Optional[Any]="<unk>"  ,lowercase_     :							List[Any]="[SEP]"  ,lowercase_     :							Optional[int]="<pad>"  ,lowercase_     :							List[Any]="[CLS]"  ,lowercase_     :							Optional[int]="[MASK]"  ,**lowercase_     :							Optional[int]  ,):
						# Mask token behave like a normal word, i.e. include the space before it and
						# is included in the raw text, there should be a match in a non-normalized sentence.
						lowerCAmelCase__      :		Optional[Any]    							=      (
						    AddedToken(lowercase_  ,lstrip=lowercase_  ,rstrip=lowercase_  ,normalized=lowercase_						)
						    if isinstance(lowercase_  ,lowercase_						)
						    else mask_token
						)

						super().__init__(
						    lowercase_  ,tokenizer_file=lowercase_  ,do_lower_case=lowercase_  ,remove_space=lowercase_  ,keep_accents=lowercase_  ,bos_token=lowercase_  ,eos_token=lowercase_  ,unk_token=lowercase_  ,sep_token=lowercase_  ,pad_token=lowercase_  ,cls_token=lowercase_  ,mask_token=lowercase_  ,**lowercase_  ,)

						lowerCAmelCase__      :		List[Any]    							=      do_lower_case
						lowerCAmelCase__      :		str    							=      remove_space
						lowerCAmelCase__      :		Optional[int]    							=      keep_accents
						lowerCAmelCase__      :		Dict    							=      vocab_file
						lowerCAmelCase__      :		Optional[int]    							=      False if not self.vocab_file else True







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[Any]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		Dict    							=      [self.cls_token_id]
						if token_ids_a is None:
							return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[str]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		List[Any]    							=      [self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
						return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							str  ,lowercase_     :							Optional[str] = None						):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''						)

						if not os.path.isdir(lowercase_						):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
							return
						lowerCAmelCase__      :		Union[str, Any]    							=      os.path.join(
						    lowercase_  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase_						):
							copyfile(self.vocab_file  ,lowercase_						)

						return (out_vocab_file,)




