






"""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,
    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


_snake_case					   =     logging.get_logger(__name__)


if is_vision_available():
 import PIL






class  UpperCamelCase    (			snake_case_   ):
 UpperCamelCase      : Dict    =      ['''pixel_values''']


 def __init__(  self	:		Tuple							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Dict[str, int] = None							,		UpperCAmelCase__	:		PILImageResampling = PILImageResampling.BICUBIC							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Dict[str, int] = None							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Union[int, float] = 1 / 255							,		UpperCAmelCase__	:		bool = True							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		bool = True							,		**UpperCAmelCase__	:		Dict							,		)      -> None:
   super().__init__(**UpperCAmelCase__							)
   _a						: Dict    =			size if size is not None else {"""shortest_edge""": 224}
   _a						: Optional[Any]    =			get_size_dict(UpperCAmelCase__							,		default_to_square=UpperCAmelCase__							)
   _a						: List[str]    =			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
   _a						: List[str]    =			get_size_dict(UpperCAmelCase__							)

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


 def _lowercase			(  self	:		Union[str, Any]							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Dict[str, int]							,		UpperCAmelCase__	:		PILImageResampling = PILImageResampling.BICUBIC							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		Union[str, Any]							,		)      -> np.ndarray:
   _a						: Dict    =			get_size_dict(UpperCAmelCase__							,		default_to_square=UpperCAmelCase__							)
   _a						: Union[str, Any]    =			get_resize_output_image_size(
       UpperCAmelCase__							,		size=(size["""height"""], size["""width"""])							,		default_to_square=UpperCAmelCase__							)
   return resize(UpperCAmelCase__							,		size=UpperCAmelCase__							,		resample=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		str							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Dict[str, int]							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		Optional[int]							,		)      -> np.ndarray:
   _a						: Tuple    =			get_size_dict(UpperCAmelCase__							)
   return center_crop(UpperCAmelCase__							,		size=(size["""height"""], size["""width"""])							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Union[int, float]							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		str							,		)      -> List[str]:
   return rescale(UpperCAmelCase__							,		scale=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		List[str]							,		UpperCAmelCase__	:		np.ndarray							,		UpperCAmelCase__	:		Union[float, List[float]]							,		UpperCAmelCase__	:		Union[float, List[float]]							,		UpperCAmelCase__	:		Optional[Union[str, ChannelDimension]] = None							,		**UpperCAmelCase__	:		Optional[Any]							,		)      -> np.ndarray:
   return normalize(UpperCAmelCase__							,		mean=UpperCAmelCase__							,		std=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__							)







 def _lowercase			(  self	:		Any							,		UpperCAmelCase__	:		ImageInput							,		UpperCAmelCase__	:		bool = None							,		UpperCAmelCase__	:		Dict[str, int] = None							,		UpperCAmelCase__	:		PILImageResampling = None							,		UpperCAmelCase__	:		bool = None							,		UpperCAmelCase__	:		int = None							,		UpperCAmelCase__	:		bool = None							,		UpperCAmelCase__	:		float = None							,		UpperCAmelCase__	:		bool = None							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		Optional[Union[float, List[float]]] = None							,		UpperCAmelCase__	:		bool = None							,		UpperCAmelCase__	:		Optional[Union[str, TensorType]] = None							,		UpperCAmelCase__	:		Optional[ChannelDimension] = ChannelDimension.FIRST							,		**UpperCAmelCase__	:		Tuple							,		)      -> PIL.Image.Image:
   _a						: Tuple    =			do_resize if do_resize is not None else self.do_resize
   _a						: Dict    =			size if size is not None else self.size
   _a						: Optional[int]    =			get_size_dict(UpperCAmelCase__							,		default_to_square=UpperCAmelCase__							)
   _a						: List[Any]    =			resample if resample is not None else self.resample
   _a						: Optional[int]    =			do_center_crop if do_center_crop is not None else self.do_center_crop
   _a						: Tuple    =			crop_size if crop_size is not None else self.crop_size
   _a						: List[str]    =			get_size_dict(UpperCAmelCase__							)
   _a						: Union[str, Any]    =			do_rescale if do_rescale is not None else self.do_rescale
   _a						: Optional[int]    =			rescale_factor if rescale_factor is not None else self.rescale_factor
   _a						: str    =			do_normalize if do_normalize is not None else self.do_normalize
   _a						: Dict    =			image_mean if image_mean is not None else self.image_mean
   _a						: List[str]    =			image_std if image_std is not None else self.image_std
   _a						: int    =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   _a						: Union[str, 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_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:
     _a						: List[str]    =			[convert_to_rgb(UpperCAmelCase__							) for image in images]

   # All transformations expect numpy arrays.
   _a						: Dict    =			[to_numpy_array(UpperCAmelCase__							) for image in images]

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

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

   if do_rescale:
     _a						: List[str]    =			[self.rescale(image=UpperCAmelCase__							,		scale=UpperCAmelCase__							) for image in images]

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

   _a						: Dict    =			[to_channel_dimension_format(UpperCAmelCase__							,		UpperCAmelCase__							) for image in images]

   _a						: List[Any]    =			{"""pixel_values""": images}
   return BatchFeature(data=UpperCAmelCase__							,		tensor_type=UpperCAmelCase__							)









"""simple docstring"""




import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin






class  UpperCamelCase    (			snake_case_	,  unittest.TestCase   ):
 UpperCamelCase      : Union[str, Any]    =      BertweetTokenizer
 UpperCamelCase      : Optional[Any]    =      False


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

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

   _a						: Union[str, Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""]							)
   _a						: List[Any]    =			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(UpperCAmelCase__							)							)


 def _lowercase			(  self	:		Any							,		**UpperCAmelCase__	:		Optional[Any]							)      -> Dict:
   kwargs.update(self.special_tokens_map							)
   return BertweetTokenizer.from_pretrained(self.tmpdirname							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		Optional[int]							)      -> Dict:
   _a						: List[str]    =			"""I am VinAI Research"""
   _a						: Optional[int]    =			"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
   return input_text, output_text







 def _lowercase			(  self	:		Tuple							)      -> Optional[int]:
   _a						: Optional[int]    =			BertweetTokenizer(self.vocab_file							,		self.merges_file							,		**self.special_tokens_map							)
   _a						: Tuple    =			"""I am VinAI Research"""
   _a						: Dict    =			"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
   _a						: str    =			tokenizer.tokenize(UpperCAmelCase__							)
   self.assertListEqual(UpperCAmelCase__							,		UpperCAmelCase__							)

   _a						: Tuple    =			tokens + [tokenizer.unk_token]

   _a						: Tuple    =			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__							)							,		UpperCAmelCase__							)

