




"""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


a							      =     logging.get_logger(__name__)






def 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   str				)       ->   List[Any]:






      '''simple docstring'''


      _A     =  b.T
      _A     =  np.sum(np.square(_snake_case				)							,    axis=1				)
      _A     =  np.sum(np.square(_snake_case				)							,    axis=0				)
      _A     =  np.matmul(_snake_case							,    _snake_case				)
      _A     =  aa[:, None] - 2 * ab + ba[None, :]
      return d






def 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   Union[str, Any]				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A     =  x.reshape(-1							,    3				)
      _A     =  squared_euclidean_distance(_snake_case							,    _snake_case				)
      return np.argmin(_snake_case							,    axis=1				)







class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      ['''pixel_values''']

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			Optional[Union[List[List[int]], np.ndarray]] = None       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Dict[str, int] = None       ,				_UpperCAmelCase			:			PILImageResampling = PILImageResampling.BILINEAR       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			int       ,				):
       super().__init__(**_UpperCAmelCase						)
       _A     =  size if size is not None else {'height': 256, 'width': 256}
       _A     =  get_size_dict(_UpperCAmelCase						)
       _A     =  np.array(_UpperCAmelCase						) if clusters is not None else None
       _A     =  do_resize
       _A     =  size
       _A     =  resample
       _A     =  do_normalize
       _A     =  do_color_quantize

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			np.ndarray       ,				_UpperCAmelCase			:			Dict[str, int]       ,				_UpperCAmelCase			:			PILImageResampling = PILImageResampling.BILINEAR       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = None       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  get_size_dict(_UpperCAmelCase						)
       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(
           _UpperCAmelCase       ,				size=(size['height'], size['width'])       ,				resample=_UpperCAmelCase       ,				data_format=_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			np.ndarray       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = None       ,				):
       _A     =  rescale(image=_UpperCAmelCase       ,				scale=1 / 127.5       ,				data_format=_UpperCAmelCase						)
       _A     =  image - 1
       return image



 def      lowerCAmelCase_				(					self			:			Tuple       ,				_UpperCAmelCase			:			ImageInput       ,				_UpperCAmelCase			:			bool = None       ,				_UpperCAmelCase			:			Dict[str, int] = None       ,				_UpperCAmelCase			:			PILImageResampling = None       ,				_UpperCAmelCase			:			bool = None       ,				_UpperCAmelCase			:			Optional[bool] = None       ,				_UpperCAmelCase			:			Optional[Union[List[List[int]], np.ndarray]] = None       ,				_UpperCAmelCase			:			Optional[Union[str, TensorType]] = None       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST       ,				**_UpperCAmelCase			:			List[Any]       ,				):
       _A     =  do_resize if do_resize is not None else self.do_resize
       _A     =  size if size is not None else self.size
       _A     =  get_size_dict(_UpperCAmelCase						)
       _A     =  resample if resample is not None else self.resample
       _A     =  do_normalize if do_normalize is not None else self.do_normalize
       _A     =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
       _A     =  clusters if clusters is not None else self.clusters
       _A     =  np.array(_UpperCAmelCase						)

       _A     =  make_list_of_images(_UpperCAmelCase						)

       if not valid_images(_UpperCAmelCase						):
             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     =  [to_numpy_array(_UpperCAmelCase						) for image in images]

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

       if do_normalize:
             _A     =  [self.normalize(image=_UpperCAmelCase						) for image in images]

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

             # flatten to (batch_size, height*width)
             _A     =  images.shape[0]
             _A     =  images.reshape(_UpperCAmelCase       ,				-1						)

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

       _A     =  {'input_ids': images}
       return BatchFeature(data=_UpperCAmelCase       ,				tensor_type=_UpperCAmelCase						)





"""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_xlnet import XLNetTokenizer
else:
	A	:  Optional[Any]            =     None


A	:  Any            =     logging.get_logger(__name__)

A	:  Union[str, Any]            =     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A	:  List[Any]            =     {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

A	:  Union[str, Any]            =     {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

A	:  Optional[int]            =     "▁"

# Segments (not really needed)
A	:  str            =     0
A	:  Dict            =     1
A	:  List[str]            =     2
A	:  Tuple            =     3
A	:  Tuple            =     4
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =VOCAB_FILES_NAMES
							__UpperCAmelCase		:       Optional[int]							 =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:       Dict							 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:       List[Any]							 ="""left"""
							__UpperCAmelCase		:       List[str]							 =XLNetTokenizer

							def __init__(	self       ,  __a=None       ,  __a=None       ,  __a=False       ,  __a=True       ,  __a=False       ,  __a="<s>"       ,  __a="</s>"       ,  __a="<unk>"       ,  __a="<sep>"       ,  __a="<pad>"       ,  __a="<cls>"       ,  __a="<mask>"       ,  __a=["<eop>", "<eod>"]       ,  **__a       ,  ):
										# Mask token behave like a normal word, i.e. include the space before it
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else mask_token

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

										__lowerCAmelCase							       =     3
										__lowerCAmelCase							       =     do_lower_case
										__lowerCAmelCase							       =     remove_space
										__lowerCAmelCase							       =     keep_accents
										__lowerCAmelCase							       =     vocab_file
										__lowerCAmelCase							       =     False if not self.vocab_file else True

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [self.cls_token_id]
										if token_ids_a is None:
													return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [2]

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



							def      snake_case							(	self       ,  __a       ,  __a = 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(__a      ):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
													return
										__lowerCAmelCase							       =     os.path.join(
										    __a       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

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

										return (out_vocab_file,)




