





'''simple docstring'''


import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 				lowerCAmelCase_						(						_a  ,unittest.TestCase						):
 __lowerCamelCase  :							List[str]    					=   BertweetTokenizer
 __lowerCamelCase  :							Optional[Any]    					=   False




 def 							_snake_case				(		self						)      ->							List[Any]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     _lowerCAmelCase		     = ["I", "m", "V@@", "R@@", "r", "e@@"]
     _lowerCAmelCase		     = dict(zip(SCREAMING_SNAKE_CASE__     ,       range(len(SCREAMING_SNAKE_CASE__						)						)						)						)
     _lowerCAmelCase		     = ["#version: 0.2", "a m</w>"]
     _lowerCAmelCase		     = {"unk_token": "<unk>"}

     _lowerCAmelCase		     = os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES["vocab_file"]						)
     _lowerCAmelCase		     = os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES["merges_file"]						)
     with open(self.vocab_file     ,       "w"     ,       encoding="utf-8"						) as fp:
         for token in vocab_tokens:
             fp.write(f'''{token} {vocab_tokens[token]}\n'''						)
     with open(self.merges_file     ,       "w"     ,       encoding="utf-8"						) as fp:
         fp.write("\n".join(SCREAMING_SNAKE_CASE__						)						)




 def 							_snake_case				(		self     ,       **_lowerCAmelCase						)      ->							Optional[Any]:
     kwargs.update(self.special_tokens_map						)
     return BertweetTokenizer.from_pretrained(self.tmpdirname     ,       **SCREAMING_SNAKE_CASE__						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Optional[Any]:
     _lowerCAmelCase		     = "I am VinAI Research"
     _lowerCAmelCase		     = "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
     return input_text, output_text





 def 							_snake_case				(		self						)      ->							Optional[Any]:
     _lowerCAmelCase		     = BertweetTokenizer(self.vocab_file     ,       self.merges_file     ,       **self.special_tokens_map						)
     _lowerCAmelCase		     = "I am VinAI Research"
     _lowerCAmelCase		     = "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
     _lowerCAmelCase		     = tokenizer.tokenize(SCREAMING_SNAKE_CASE__						)
     self.assertListEqual(SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__						)

     _lowerCAmelCase		     = tokens + [tokenizer.unk_token]

     _lowerCAmelCase		     = [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__						)     ,       SCREAMING_SNAKE_CASE__						)









'''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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							Optional[int]    					=   ["pixel_values"]




 def __init__(		self     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = PIL.Image.NEAREST     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = 1 / 255     ,       _lowerCAmelCase = False     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = True     ,       **_lowerCAmelCase     ,       )      ->							None:
     super().__init__(**_lowerCAmelCase						)
     _lowerCAmelCase		     = size if size is not None else {"height": 346, "width": 346}
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     _lowerCAmelCase		     = crop_size if crop_size is not None else {"height": 289, "width": 289}
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase     ,       param_name="crop_size"						)

     _lowerCAmelCase		     = do_resize
     _lowerCAmelCase		     = size
     _lowerCAmelCase		     = resample
     _lowerCAmelCase		     = do_center_crop
     _lowerCAmelCase		     = crop_size
     _lowerCAmelCase		     = do_rescale
     _lowerCAmelCase		     = rescale_factor
     _lowerCAmelCase		     = rescale_offset
     _lowerCAmelCase		     = do_normalize
     _lowerCAmelCase		     = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     _lowerCAmelCase		     = image_std if image_std is not None else IMAGENET_STANDARD_STD
     _lowerCAmelCase		     = include_top




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = PIL.Image.NEAREST     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
     return resize(
         _lowerCAmelCase     ,       size=(size["height"], size["width"])     ,       resample=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
     return center_crop(_lowerCAmelCase     ,       size=(size["height"], size["width"])     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = True     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							List[str]:
     if offset:
         _lowerCAmelCase		     = (image - 127.5) * scale
         if data_format is not None:
             _lowerCAmelCase		     = to_channel_dimension_format(_lowerCAmelCase     ,       _lowerCAmelCase						)
         _lowerCAmelCase		     = rescaled_image.astype(np.floataa						)
     else:
         _lowerCAmelCase		     = rescale(_lowerCAmelCase     ,       scale=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)
     return rescaled_image




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       **_lowerCAmelCase     ,       )      ->							np.ndarray:
     return normalize(_lowerCAmelCase     ,       mean=_lowerCAmelCase     ,       std=_lowerCAmelCase     ,       data_format=_lowerCAmelCase     ,       **_lowerCAmelCase						)





 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = None     ,       _lowerCAmelCase = ChannelDimension.FIRST     ,       **_lowerCAmelCase     ,       )      ->							PIL.Image.Image:
     _lowerCAmelCase		     = do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase		     = resample if resample is not None else self.resample
     _lowerCAmelCase		     = do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowerCAmelCase		     = do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase		     = rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase		     = rescale_offset if rescale_offset is not None else self.rescale_offset
     _lowerCAmelCase		     = do_normalize if do_normalize is not None else self.do_normalize
     _lowerCAmelCase		     = image_mean if image_mean is not None else self.image_mean
     _lowerCAmelCase		     = image_std if image_std is not None else self.image_std
     _lowerCAmelCase		     = include_top if include_top is not None else self.include_top

     _lowerCAmelCase		     = size if size is not None else self.size
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase						)
     _lowerCAmelCase		     = crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase		     = get_size_dict(_lowerCAmelCase     ,       param_name="crop_size"						)

     _lowerCAmelCase		     = make_list_of_images(_lowerCAmelCase						)

     if not valid_images(_lowerCAmelCase						):
         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_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."						)

     # All transformations expect numpy arrays.
     _lowerCAmelCase		     = [to_numpy_array(_lowerCAmelCase						) for image in images]

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

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

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

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

     if include_top:
         _lowerCAmelCase		     = [self.normalize(image=_lowerCAmelCase     ,       mean=[0, 0, 0]     ,       std=_lowerCAmelCase						) for image in images]

     _lowerCAmelCase		     = [to_channel_dimension_format(_lowerCAmelCase     ,       _lowerCAmelCase						) for image in images]

     _lowerCAmelCase		     = {"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase     ,       tensor_type=_lowerCAmelCase						)


