





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__A									=				logging.get_logger(__name__)



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			["pixel_values"]

 def __init__(self				:   Dict       , UpperCAmelCase_				:   bool = True       , UpperCAmelCase_				:   Union[int, float] = 1 / 255       , UpperCAmelCase_				:   bool = True       , UpperCAmelCase_				:   int = 8       , **UpperCAmelCase_				:   Optional[int]       , )       ->None:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)

   lowerCamelCase__:      Dict			  =do_rescale
   lowerCamelCase__:      Any			  =rescale_factor
   lowerCamelCase__:      List[str]			  =do_pad
   lowerCamelCase__:      List[Any]			  =pad_size

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   np.ndarray       , UpperCAmelCase_				:   float       , UpperCAmelCase_				:   Optional[Union[str, ChannelDimension]] = None       , **UpperCAmelCase_				:   List[str])       ->np.ndarray:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any]       , UpperCAmelCase_				:   np.ndarray       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   Optional[Union[str, ChannelDimension]] = None)       ->str:



   '''simple docstring'''



   lowerCamelCase__  ,					lowerCamelCase__:      Tuple			  =get_image_size(UpperCAmelCase_)
   lowerCamelCase__:      Any			  =(old_height // size + 1) * size - old_height
   lowerCamelCase__:      Optional[int]			  =(old_width // size + 1) * size - old_width

   return pad(UpperCAmelCase_       , ((0, pad_height), (0, pad_width))       , mode="symmetric"       , data_format=UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_					(self				:   str       , UpperCAmelCase_				:   ImageInput       , UpperCAmelCase_				:   Optional[bool] = None       , UpperCAmelCase_				:   Optional[float] = None       , UpperCAmelCase_				:   Optional[bool] = None       , UpperCAmelCase_				:   Optional[int] = None       , UpperCAmelCase_				:   Optional[Union[str, TensorType]] = None       , UpperCAmelCase_				:   Union[str, ChannelDimension] = ChannelDimension.FIRST       , **UpperCAmelCase_				:   List[Any]       , )       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase__:      Dict			  =rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase__:      int			  =do_pad if do_pad is not None else self.do_pad
   lowerCamelCase__:      Dict			  =pad_size if pad_size is not None else self.pad_size

   lowerCamelCase__:      Any			  =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_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(UpperCAmelCase_) for image in images]

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

   if do_pad:
     lowerCamelCase__:      Tuple			  =[self.pad(UpperCAmelCase_       , size=UpperCAmelCase_) for image in images]

   lowerCamelCase__:      Tuple			  =[to_channel_dimension_format(UpperCAmelCase_       , UpperCAmelCase_) for image in images]

   lowerCamelCase__:      Union[str, Any]			  ={"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_)

