





"""simple docstring"""
from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case     :		Dict       						= logging.get_logger(__name__)






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							['''pixel_values''']


					def __init__(					self			:Any     ,       __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 / 2_55     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN     ,       __UpperCamelCase			:Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD     ,       **__UpperCamelCase			:Dict     ,       ):
												super().__init__(**__UpperCamelCase		)
												A		=  size if size is not None else {"shortest_edge": 2_24}
												A		=  get_size_dict(__UpperCamelCase     ,       default_to_square=__UpperCamelCase		)
												A		=  crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
												A		=  get_size_dict(__UpperCamelCase     ,       param_name="crop_size"		)

												A		=  do_resize
												A		=  size
												A		=  resample
												A		=  do_center_crop
												A		=  crop_size
												A		=  do_rescale
												A		=  rescale_factor
												A		=  do_normalize
												A		=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												A		=  image_std if image_std is not None else IMAGENET_DEFAULT_STD


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:np.ndarray     ,       __UpperCamelCase			:Dict[str, int]     ,       __UpperCamelCase			:PILImageResampling = PILImageResampling.BICUBIC     ,       __UpperCamelCase			:Optional[Union[str, ChannelDimension]] = None     ,       **__UpperCamelCase			:int     ,       ):
												A		=  get_size_dict(__UpperCamelCase     ,       default_to_square=__UpperCamelCase		)
												# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
												if "shortest_edge" in size:
																			A		=  int((2_56 / 2_24) * size["shortest_edge"]		)
																			A		=  get_resize_output_image_size(__UpperCamelCase     ,       size=__UpperCamelCase     ,       default_to_square=__UpperCamelCase		)
																			A		=  {"height": output_size[0], "width": output_size[1]}
												if "height" not in size_dict or "width" not in size_dict:
																			raise ValueError(
																			    f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"		)
												return resize(
												    __UpperCamelCase     ,       size=(size_dict["height"], size_dict["width"])     ,       resample=__UpperCamelCase     ,       data_format=__UpperCamelCase     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:np.ndarray     ,       __UpperCamelCase			:Dict[str, int]     ,       __UpperCamelCase			:Optional[Union[str, ChannelDimension]] = None     ,       **__UpperCamelCase			:Dict     ,       ):
												A		=  get_size_dict(__UpperCamelCase		)
												if "height" not in size or "width" not in size:
																			raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"		)
												return center_crop(__UpperCamelCase     ,       size=(size["height"], size["width"])     ,       data_format=__UpperCamelCase     ,       **__UpperCamelCase		)


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


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


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

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

												A		=  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."		)

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

												if do_resize:
																			A		=  [self.resize(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		) for image in images]

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

												if do_rescale:
																			A		=  [self.rescale(__UpperCamelCase     ,       __UpperCamelCase		) for image in images]

												if do_normalize:
																			A		=  [self.normalize(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		) for image in images]

												A		=  [to_channel_dimension_format(__UpperCamelCase     ,       __UpperCamelCase		) for image in images]

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









"""simple docstring"""
import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							RoFormerTokenizer
					UpperCamelCase      =							RoFormerTokenizerFast
					UpperCamelCase      =							True
					UpperCamelCase      =							True


					def 	lowerCamelCase       (					self			:List[str]		):
												super().setUp()


					def 	lowerCamelCase       (					self			:int     ,       **__UpperCamelCase			:List[Any]		):
												return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Tuple     ,       **__UpperCamelCase			:Optional[int]		):
												return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												A		=  "永和服装饰品有限公司,今天天气非常好"
												A		=  "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
												return input_text, output_text


					def 	lowerCamelCase       (					self			:int		):
												A		=  self.get_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)

												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)

												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												A		=  self.get_rust_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)
												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												pass


					def 	lowerCamelCase       (					self			:Tuple		):
												pass


					def 	lowerCamelCase       (					self			:List[str]		):
												pass


