





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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCamelCase				=       logging.get_logger(__name__)


if is_vision_available():
     import PIL




class  _lowerCamelCase       (			__SCREAMING_SNAKE_CASE			):



      """simple docstring"""






      UpperCAmelCase_  :					List[str]      			=["pixel_values"]







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






        '''simple docstring'''






        super().__init__(**UpperCAmelCase_      )
        __snake_case      : Optional[Any]						=				size if size is not None else {"shortest_edge": 224}
        __snake_case      : Optional[Any]						=				get_size_dict(UpperCAmelCase_ ,  default_to_square=UpperCAmelCase_      )
        __snake_case      : Optional[int]						=				crop_size if crop_size is not None else {"height": 224, "width": 224}
        __snake_case      : Tuple						=				get_size_dict(UpperCAmelCase_ ,  default_to_square=UpperCAmelCase_ ,  param_name="crop_size"      )

        __snake_case      : Union[str, Any]						=				do_resize
        __snake_case      : Union[str, Any]						=				size
        __snake_case      : Any						=				resample
        __snake_case      : List[str]						=				do_center_crop
        __snake_case      : List[Any]						=				crop_size
        __snake_case      : List[str]						=				do_rescale
        __snake_case      : List[str]						=				rescale_factor
        __snake_case      : str						=				do_normalize
        __snake_case      : Tuple						=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        __snake_case      : List[Any]						=				image_std if image_std is not None else OPENAI_CLIP_STD
        __snake_case      : int						=				do_convert_rgb







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase = PILImageResampling.BICUBIC ,  UpperCAmelCase = None ,  **UpperCAmelCase ,  )				->      np.ndarray:






        '''simple docstring'''






        __snake_case      : List[str]						=				get_size_dict(UpperCAmelCase_ ,  default_to_square=UpperCAmelCase_      )
        if "shortest_edge" not in size:
          raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""      )
        __snake_case      : Optional[int]						=				get_resize_output_image_size(UpperCAmelCase_ ,  size=size["shortest_edge"] ,  default_to_square=UpperCAmelCase_      )
        return resize(UpperCAmelCase_ ,  size=UpperCAmelCase_ ,  resample=UpperCAmelCase_ ,  data_format=UpperCAmelCase_ ,  **UpperCAmelCase_      )







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






        '''simple docstring'''






        __snake_case      : int						=				get_size_dict(UpperCAmelCase_      )
        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(UpperCAmelCase_ ,  size=(size["height"], size["width"]) ,  data_format=UpperCAmelCase_ ,  **UpperCAmelCase_      )







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase = None ,  **UpperCAmelCase ,  )				->      int:






        '''simple docstring'''






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







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






        '''simple docstring'''






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







      def   UpperCAmelCase						(       self ,  UpperCAmelCase ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = None ,  UpperCAmelCase = ChannelDimension.FIRST ,  **UpperCAmelCase ,  )				->      PIL.Image.Image:






        '''simple docstring'''






        __snake_case      : Tuple						=				do_resize if do_resize is not None else self.do_resize
        __snake_case      : List[Any]						=				size if size is not None else self.size
        __snake_case      : List[Any]						=				get_size_dict(UpperCAmelCase_ ,  param_name="size" ,  default_to_square=UpperCAmelCase_      )
        __snake_case      : Any						=				resample if resample is not None else self.resample
        __snake_case      : Any						=				do_center_crop if do_center_crop is not None else self.do_center_crop
        __snake_case      : Any						=				crop_size if crop_size is not None else self.crop_size
        __snake_case      : Union[str, Any]						=				get_size_dict(UpperCAmelCase_ ,  param_name="crop_size" ,  default_to_square=UpperCAmelCase_      )
        __snake_case      : Union[str, Any]						=				do_rescale if do_rescale is not None else self.do_rescale
        __snake_case      : List[str]						=				rescale_factor if rescale_factor is not None else self.rescale_factor
        __snake_case      : int						=				do_normalize if do_normalize is not None else self.do_normalize
        __snake_case      : Optional[Any]						=				image_mean if image_mean is not None else self.image_mean
        __snake_case      : Optional[int]						=				image_std if image_std is not None else self.image_std
        __snake_case      : Dict						=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        __snake_case      : Dict						=				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:
          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."      )

        if do_normalize and (image_mean is None or image_std is None):
          raise ValueError("Image mean and std must be specified if do_normalize is True."      )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
          __snake_case      : List[Any]						=				[convert_to_rgb(UpperCAmelCase_      ) for image in images]

        # All transformations expect numpy arrays.
        __snake_case      : str						=				[to_numpy_array(UpperCAmelCase_      ) for image in images]

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

        if do_center_crop:
          __snake_case      : Optional[Any]						=				[self.center_crop(image=UpperCAmelCase_ ,  size=UpperCAmelCase_      ) for image in images]

        if do_rescale:
          __snake_case      : Any						=				[self.rescale(image=UpperCAmelCase_ ,  scale=UpperCAmelCase_      ) for image in images]

        if do_normalize:
          __snake_case      : List[str]						=				[self.normalize(image=UpperCAmelCase_ ,  mean=UpperCAmelCase_ ,  std=UpperCAmelCase_      ) for image in images]

        __snake_case      : List[str]						=				[to_channel_dimension_format(UpperCAmelCase_ ,  UpperCAmelCase_      ) for image in images]

        __snake_case      : str						=				{"pixel_values": images}
        return BatchFeature(data=UpperCAmelCase_ ,  tensor_type=UpperCAmelCase_      )








import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


__A									=				get_tests_dir("fixtures/test_sentencepiece_bpe.model")



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			BartphoTokenizer
 lowercase_		=			False
 lowercase_		=			True

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Tuple:



   '''simple docstring'''



   super().setUp()

   lowerCamelCase__:      int			  =["▁This", "▁is", "▁a", "▁t", "est"]
   lowerCamelCase__:      Tuple			  =dict(zip(UpperCAmelCase_       , range(len(UpperCAmelCase_))))
   lowerCamelCase__:      List[Any]			  ={"unk_token": "<unk>"}

   lowerCamelCase__:      Dict			  =os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["monolingual_vocab_file"])
   with open(self.monolingual_vocab_file       , "w"       , encoding="utf-8") as fp:
     for token in vocab_tokens:
       fp.write(F"""{token} {vocab_tokens[token]}\n""")

   lowerCamelCase__:      Dict			  =BartphoTokenizer(UpperCAmelCase_       , self.monolingual_vocab_file       , **self.special_tokens_map)
   tokenizer.save_pretrained(self.tmpdirname)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , **UpperCAmelCase_				:   Optional[Any])       ->str:



   '''simple docstring'''



   kwargs.update(self.special_tokens_map)
   return BartphoTokenizer.from_pretrained(self.tmpdirname       , **UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any]       , UpperCAmelCase_				:   Optional[Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  ="This is a là test"
   lowerCamelCase__:      Optional[Any]			  ="This is a<unk><unk> test"
   return input_text, output_text







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =BartphoTokenizer(UpperCAmelCase_       , self.monolingual_vocab_file       , **self.special_tokens_map)
   lowerCamelCase__:      List[Any]			  ="This is a là test"
   lowerCamelCase__:      Optional[int]			  ="▁This ▁is ▁a ▁l à ▁t est".split()
   lowerCamelCase__:      Optional[int]			  =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_       , UpperCAmelCase_)

   lowerCamelCase__:      Tuple			  =tokens + [tokenizer.unk_token]
   lowerCamelCase__:      List[Any]			  =[4, 5, 6, 3, 3, 7, 8, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)       , UpperCAmelCase_)

