

"""simple docstring"""




import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin



@require_jieba
class   __lowercase    (			lowerCAmelCase__		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Union[str, Any]      							=					CpmAntTokenizer
			_UpperCAmelCase       :							Any      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									super().setUp()

									SCREAMING_SNAKE_CASE_:			List[str]  		=	[
									    "<d>",
									    "</d>",
									    "<s>",
									    "</s>",
									    "</_>",
									    "<unk>",
									    "<pad>",
									    "</n>",
									    "我",
									    "是",
									    "C",
									    "P",
									    "M",
									    "A",
									    "n",
									    "t",
									]
									SCREAMING_SNAKE_CASE_:			Dict  		=	os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES["vocab_file"])
									with open(self.vocab_file       ,						"w"       ,						encoding="utf-8") as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))




			@tooslow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			int  		=	CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"今天天气真好！"
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	["今天", "天气", "真", "好", "！"]
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.tokenize(a__)
									self.assertListEqual(a__       ,						a__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"今天天气真好！"
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[tokenizer.bos_token] + tokens

									SCREAMING_SNAKE_CASE_:			Dict  		=	[6, 9802, 1_4962, 2082, 831, 244]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(a__)       ,						a__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer.decode(a__)
									self.assertEqual(a__       ,						a__)









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 convert_to_rgb, 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


if is_vision_available():
						import PIL


lowerCAmelCase							:  Optional[Any]            =						logging.get_logger(__name__)



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					['''pixel_values''']



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Dict[str, int] = None       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Union[int, float] = 1 / 255       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					bool = True       ,						**lowerCAmelCase__  :					Tuple       ,						):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	size if size is not None else {"height": 384, "width": 384}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Dict[str, int]       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									if "height" not in size or "width" not in size:
															raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
									SCREAMING_SNAKE_CASE_:			int  		=	(size["height"], size["width"])
									return resize(lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



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



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




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					ImageInput       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Dict[str, int]] = None       ,						lowerCAmelCase__  :					PILImageResampling = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[float] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[str, TensorType]] = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					ChannelDimension = ChannelDimension.FIRST       ,						**lowerCAmelCase__  :					Dict       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_:			Any  		=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE_:			str  		=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	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_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:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	[convert_to_rgb(lowerCAmelCase__) for image in images]

									# All transformations expect numpy arrays.
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[to_numpy_array(lowerCAmelCase__) for image in images]

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

									if do_rescale:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[self.rescale(image=lowerCAmelCase__       ,						scale=lowerCAmelCase__) for image in images]

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

									SCREAMING_SNAKE_CASE_:			str  		=	[to_channel_dimension_format(lowerCAmelCase__       ,						lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	BatchFeature(data={"pixel_values": images}       ,						tensor_type=lowerCAmelCase__)

									return encoded_outputs






