

'''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


lowerCAmelCase__											=	logging.get_logger(__name__)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			['pixel_values']






  def __init__(      self   :     Optional[int]					,lowercase__   :     bool = True					,lowercase__   :     Optional[Dict[str, int]] = None					,lowercase__   :     PILImageResampling = PILImageResampling.BICUBIC					,lowercase__   :     bool = True					,lowercase__   :     bool = True					,lowercase__   :     Union[int, float] = 1 / 2_5_5					,lowercase__   :     Dict[str, int] = None					,lowercase__   :     bool = True					,lowercase__   :     Optional[Union[float, List[float]]] = None					,lowercase__   :     Optional[Union[float, List[float]]] = None					,**lowercase__   :     Tuple					,):
   super().__init__(**lowercase__			)
   __lowercase							  = size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
   __lowercase							  = get_size_dict(lowercase__			)
   __lowercase							  = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
   __lowercase							  = get_size_dict(lowercase__					,default_to_square=lowercase__					,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 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     np.ndarray					,lowercase__   :     Dict[str, int]					,lowercase__   :     PILImageResampling = PILImageResampling.BILINEAR					,lowercase__   :     Optional[Union[str, ChannelDimension]] = None					,**lowercase__   :     Optional[Any]					,):
   __lowercase							  = get_size_dict(lowercase__			)

   if "shortest_edge" in size:
    __lowercase							  = get_resize_output_image_size(lowercase__					,size=size['''shortest_edge''']					,default_to_square=lowercase__			)
    # 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(lowercase__					,size=lowercase__					,resample=lowercase__					,data_format=lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     np.ndarray					,lowercase__   :     Dict[str, int]					,lowercase__   :     Optional[Union[str, ChannelDimension]] = None					,**lowercase__   :     int					,):
   __lowercase							  = get_size_dict(lowercase__			)
   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(lowercase__					,size=(size['''height'''], size['''width'''])					,data_format=lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     np.ndarray					,lowercase__   :     float					,lowercase__   :     Optional[Union[str, ChannelDimension]] = None					,**lowercase__   :     Any			):
   return rescale(lowercase__					,scale=lowercase__					,data_format=lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     np.ndarray					,lowercase__   :     Union[float, List[float]]					,lowercase__   :     Union[float, List[float]]					,lowercase__   :     Optional[Union[str, ChannelDimension]] = None					,**lowercase__   :     List[str]					,):
   return normalize(lowercase__					,mean=lowercase__					,std=lowercase__					,data_format=lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     ImageInput					,lowercase__   :     Optional[bool] = None					,lowercase__   :     Dict[str, int] = None					,lowercase__   :     PILImageResampling = None					,lowercase__   :     bool = None					,lowercase__   :     int = None					,lowercase__   :     Optional[bool] = None					,lowercase__   :     Optional[float] = None					,lowercase__   :     Optional[bool] = None					,lowercase__   :     Optional[Union[float, List[float]]] = None					,lowercase__   :     Optional[Union[float, List[float]]] = None					,lowercase__   :     Optional[Union[str, TensorType]] = None					,lowercase__   :     Union[str, ChannelDimension] = ChannelDimension.FIRST					,**lowercase__   :     Tuple					,):
   __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(lowercase__					,param_name='''crop_size'''					,default_to_square=lowercase__			)
   __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(lowercase__			)

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

   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:
    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(lowercase__			) for image in images]

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

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

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

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

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

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






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


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



class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		BartphoTokenizer
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		True







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														_lowerCAmelCase     :							str    =    ["▁This", "▁is", "▁a", "▁t", "est"]
														_lowerCAmelCase     :							List[str]    =    dict(zip(__a,			range(len(__a))))
														_lowerCAmelCase     :							Optional[Any]    =    {"unk_token": "<unk>"}

														_lowerCAmelCase     :							Optional[int]    =    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     :							Optional[Any]    =    BartphoTokenizer(__a,			self.monolingual_vocab_file,			**self.special_tokens_map)
														tokenizer.save_pretrained(self.tmpdirname)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self):

														'''simple docstring'''

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

														_lowerCAmelCase     :							Tuple    =    tokens + [tokenizer.unk_token]
														_lowerCAmelCase     :							List[str]    =    [4, 5, 6, 3, 3, 7, 8, 3]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__a),			__a)
