



'''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


a							   =				logging.get_logger(__name__)






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		Optional[Any]        =				["""pixel_values"""]


							def __init__(				self   :						List[str]      ,						a_   :						bool = True      ,						a_   :						Dict[str, int] = None      ,						a_   :						PILImageResampling = PILImageResampling.BICUBIC      ,						a_   :						bool = True      ,						a_   :						Dict[str, int] = None      ,						a_   :						bool = True      ,						a_   :						Union[int, float] = 1 / 2_5_5      ,						a_   :						bool = True      ,						a_   :						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN      ,						a_   :						Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD      ,						**a_   :						Any      ,						)			->					None:
										super().__init__(**a_			)
										snake_case:    Any      							=size if size is not None else {'shortest_edge': 2_2_4}
										snake_case:    List[str]      							=get_size_dict(a_      ,						default_to_square=a_			)
										snake_case:    int      							=crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
										snake_case:    Union[str, Any]      							=get_size_dict(a_      ,						param_name='crop_size'			)

										snake_case:    List[str]      							=do_resize
										snake_case:    Any      							=size
										snake_case:    Dict      							=resample
										snake_case:    Dict      							=do_center_crop
										snake_case:    Union[str, Any]      							=crop_size
										snake_case:    Optional[Any]      							=do_rescale
										snake_case:    Any      							=rescale_factor
										snake_case:    Union[str, Any]      							=do_normalize
										snake_case:    Union[str, Any]      							=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
										snake_case:    Any      							=image_std if image_std is not None else IMAGENET_DEFAULT_STD


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						np.ndarray      ,						a_   :						Dict[str, int]      ,						a_   :						PILImageResampling = PILImageResampling.BICUBIC      ,						a_   :						Optional[Union[str, ChannelDimension]] = None      ,						**a_   :						Tuple      ,						)			->					np.ndarray:
										snake_case:    int      							=get_size_dict(a_      ,						default_to_square=a_			)
										# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
										if "shortest_edge" in size:
													snake_case:    Union[str, Any]      							=int((2_5_6 / 2_2_4) * size['shortest_edge']			)
													snake_case:    Union[str, Any]      							=get_resize_output_image_size(a_      ,						size=a_      ,						default_to_square=a_			)
													snake_case:    Optional[Any]      							={'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(
										    a_      ,						size=(size_dict['height'], size_dict['width'])      ,						resample=a_      ,						data_format=a_      ,						**a_			)


							def    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						np.ndarray      ,						a_   :						Dict[str, int]      ,						a_   :						Optional[Union[str, ChannelDimension]] = None      ,						**a_   :						Dict      ,						)			->					np.ndarray:
										snake_case:    Dict      							=get_size_dict(a_			)
										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(a_      ,						size=(size['height'], size['width'])      ,						data_format=a_      ,						**a_			)


							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						np.ndarray      ,						a_   :						Union[int, float]      ,						a_   :						Optional[Union[str, ChannelDimension]] = None      ,						**a_   :						Optional[Any]      ,						)			->					np.ndarray:
										return rescale(a_      ,						scale=a_      ,						data_format=a_      ,						**a_			)


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




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

										snake_case:    int      							=size if size is not None else self.size
										snake_case:    Union[str, Any]      							=get_size_dict(a_      ,						default_to_square=a_			)
										snake_case:    Optional[int]      							=crop_size if crop_size is not None else self.crop_size
										snake_case:    Any      							=get_size_dict(a_      ,						param_name='crop_size'			)

										snake_case:    str      							=make_list_of_images(a_			)

										if not valid_images(a_			):
													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.
										snake_case:    List[str]      							=[to_numpy_array(a_			) for image in images]

										if do_resize:
													snake_case:    Dict      							=[self.resize(a_      ,						a_      ,						a_			) for image in images]

										if do_center_crop:
													snake_case:    Union[str, Any]      							=[self.center_crop(a_      ,						a_			) for image in images]

										if do_rescale:
													snake_case:    List[str]      							=[self.rescale(a_      ,						a_			) for image in images]

										if do_normalize:
													snake_case:    Dict      							=[self.normalize(a_      ,						a_      ,						a_			) for image in images]

										snake_case:    Optional[Any]      							=[to_channel_dimension_format(a_      ,						a_			) for image in images]

										snake_case:    int      							={'pixel_values': images}
										return BatchFeature(data=a_      ,						tensor_type=a_			)









'''simple docstring'''

import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				FunnelTokenizer
							UpperCAmelCase			:		Tuple        =				FunnelTokenizerFast
							UpperCAmelCase			:		List[str]        =				True
							UpperCAmelCase			:		Tuple        =				True


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[Any]:
										super().setUp()

										snake_case:    Optional[Any]      							=[
										    '<unk>',
										    '<cls>',
										    '<sep>',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										snake_case:    Any      							=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]			)			)


							def    UpperCamelCase     (				self   :						Tuple      ,						**a_   :						str			)			->					Any:
										return FunnelTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						List[Any]      ,						**a_   :						int			)			->					str:
										return FunnelTokenizerFast.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Union[str, Any]			)			->					Union[str, Any]:
										snake_case:    List[str]      							='UNwant\u00E9d,running'
										snake_case:    Dict      							='unwanted, running'
										return input_text, output_text


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					List[Any]:
										snake_case:    Union[str, Any]      							=self.tokenizer_class(self.vocab_file			)

										snake_case:    Dict      							=tokenizer.tokenize('UNwant\u00E9d,running'			)
										self.assertListEqual(a_      ,						['un', '##want', '##ed', ',', 'runn', '##ing']			)
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)      ,						[7, 4, 5, 1_0, 8, 9]			)




							def    UpperCamelCase     (				self   :						List[str]			)			->					Dict:
										snake_case:    Optional[Any]      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													snake_case:    str      							=tokenizer('UNwant\u00E9d,running'			)
													snake_case:    Union[str, Any]      							=len(inputs['input_ids']			) - 1
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len			)

													snake_case:    List[str]      							=tokenizer('UNwant\u00E9d,running'      ,						'UNwant\u00E9d,running'			)
													self.assertListEqual(inputs['token_type_ids']      ,						[2] + [0] * sentence_len + [1] * sentence_len			)




