




'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import SwinaSRImageProcessor
				from transformers.image_transforms import get_image_size

class 			A    (		unittest.TestCase    ):







							def __init__(  self       :  Tuple							,			lowerCAmelCase_       :  Any							,			lowerCAmelCase_       :  Dict=7							,			lowerCAmelCase_       :  Tuple=3							,			lowerCAmelCase_       :  List[Any]=18							,			lowerCAmelCase_       :  Tuple=30							,			lowerCAmelCase_       :  int=4_00							,			lowerCAmelCase_       :  Any=True							,			lowerCAmelCase_       :  List[Any]=1 / 2_55							,			lowerCAmelCase_       :  Optional[int]=True							,			lowerCAmelCase_       :  Dict=8							,			)			->							Any:


														"""simple docstring"""


														_a	     =				parent
														_a	     =				batch_size
														_a	     =				num_channels
														_a	     =				image_size
														_a	     =				min_resolution
														_a	     =				max_resolution
														_a	     =				do_rescale
														_a	     =				rescale_factor
														_a	     =				do_pad
														_a	     =				pad_size







							def    __lowerCAmelCase			(  self       :  List[Any]					)			->							Union[str, Any]:


														"""simple docstring"""


														return {
														    "do_rescale": self.do_rescale,
														    "rescale_factor": self.rescale_factor,
														    "do_pad": self.do_pad,
														    "pad_size": self.pad_size,
														}







							def    __lowerCAmelCase			(  self       :  Optional[int]							,			lowerCAmelCase_       :  int=False							,			lowerCAmelCase_       :  int=False							,			lowerCAmelCase_       :  Optional[int]=False					)			->							Tuple:


														"""simple docstring"""


														assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

														if equal_resolution:
																					_a	     =				[]
																					for i in range(self.batch_size					):
																												image_inputs.append(
																												    np.random.randint(
																												        2_55							,			size=(self.num_channels, self.max_resolution, self.max_resolution)							,			dtype=np.uinta					)					)
														else:
																					_a	     =				[]
																					for i in range(self.batch_size					):
																												_a     ,							_a	     =				np.random.choice(np.arange(self.min_resolution							,			self.max_resolution					)							,			2					)
																												image_inputs.append(np.random.randint(2_55							,			size=(self.num_channels, width, height)							,			dtype=np.uinta					)					)

														if not numpify and not torchify:
																					# PIL expects the channel dimension as last dimension
																					_a	     =				[Image.fromarray(np.moveaxis(lowercase__							,			0							,			-1					)					) for x in image_inputs]

														if torchify:
																					_a	     =				[torch.from_numpy(lowercase__					) for x in image_inputs]

														return image_inputs



@require_torch
@require_vision
class 			A    (		_a     ,unittest.TestCase    ):
							lowercase_							=				SwinaSRImageProcessor if is_vision_available() else None







							def    __lowerCAmelCase			(  self       :  List[str]					)			->							Union[str, Any]:


														"""simple docstring"""


														_a	     =				SwinaSRImageProcessingTester(self					)







							@property
							def    __lowerCAmelCase			(  self       :  Optional[int]					)			->							Dict:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def    __lowerCAmelCase			(  self       :  Any					)			->							List[str]:


														"""simple docstring"""


														_a	     =				self.image_processing_class(**self.image_processor_dict					)
														self.assertTrue(hasattr(lowercase__							,			'''do_rescale'''					)					)
														self.assertTrue(hasattr(lowercase__							,			'''rescale_factor'''					)					)
														self.assertTrue(hasattr(lowercase__							,			'''do_pad'''					)					)
														self.assertTrue(hasattr(lowercase__							,			'''pad_size'''					)					)







							def    __lowerCAmelCase			(  self       :  str					)			->							Dict:


														"""simple docstring"""


														pass







							def    __lowerCAmelCase			(  self       :  Tuple							,			lowerCAmelCase_       :  Union[str, Any]					)			->							Optional[Any]:


														"""simple docstring"""


														_a     ,							_a	     =				get_image_size(lowercase__					)
														_a	     =				self.image_processor_tester.pad_size

														_a	     =				(old_height // size + 1) * size - old_height
														_a	     =				(old_width // size + 1) * size - old_width
														return old_height + pad_height, old_width + pad_width







							def    __lowerCAmelCase			(  self       :  Any					)			->							Tuple:


														"""simple docstring"""


														_a	     =				self.image_processing_class(**self.image_processor_dict					)
														# create random PIL images
														_a	     =				self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__					)
														for image in image_inputs:
																					self.assertIsInstance(lowercase__							,			Image.Image					)

														# Test not batched input
														_a	     =				image_processor(image_inputs[0]							,			return_tensors='''pt'''					).pixel_values
														_a     ,							_a	     =				self.calculate_expected_size(np.array(image_inputs[0]					)					)
														self.assertEqual(
														    encoded_images.shape							,			(
														        1,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )							,			)







							def    __lowerCAmelCase			(  self       :  Union[str, Any]					)			->							List[str]:


														"""simple docstring"""


														_a	     =				self.image_processing_class(**self.image_processor_dict					)
														# create random numpy tensors
														_a	     =				self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__							,			numpify=lowercase__					)
														for image in image_inputs:
																					self.assertIsInstance(lowercase__							,			np.ndarray					)

														# Test not batched input
														_a	     =				image_processor(image_inputs[0]							,			return_tensors='''pt'''					).pixel_values
														_a     ,							_a	     =				self.calculate_expected_size(image_inputs[0]					)
														self.assertEqual(
														    encoded_images.shape							,			(
														        1,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )							,			)







							def    __lowerCAmelCase			(  self       :  List[Any]					)			->							int:


														"""simple docstring"""


														_a	     =				self.image_processing_class(**self.image_processor_dict					)
														# create random PyTorch tensors
														_a	     =				self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__							,			torchify=lowercase__					)
														for image in image_inputs:
																					self.assertIsInstance(lowercase__							,			torch.Tensor					)

														# Test not batched input
														_a	     =				image_processor(image_inputs[0]							,			return_tensors='''pt'''					).pixel_values
														_a     ,							_a	     =				self.calculate_expected_size(image_inputs[0]					)
														self.assertEqual(
														    encoded_images.shape							,			(
														        1,
														        self.image_processor_tester.num_channels,
														        expected_height,
														        expected_width,
														    )							,			)



"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case    =		logging.get_logger(__name__)

snake_case    =		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case    =		{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__=None		,	lowercase__=None		,	lowercase__=False		,	lowercase__=True		,	lowercase__=False		,	lowercase__="<s>"		,	lowercase__="</s>"		,	lowercase__="<unk>"		,	lowercase__="<sep>"		,	lowercase__="<pad>"		,	lowercase__="<cls>"		,	lowercase__="<mask>"		,	lowercase__=["<eop>", "<eod>"]		,	**lowercase__		,	)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__			) if isinstance(lowercase__		,	lowercase__			) else mask_token

									super().__init__(
									    vocab_file=lowercase__		,	tokenizer_file=lowercase__		,	do_lower_case=lowercase__		,	remove_space=lowercase__		,	keep_accents=lowercase__		,	bos_token=lowercase__		,	eos_token=lowercase__		,	unk_token=lowercase__		,	sep_token=lowercase__		,	pad_token=lowercase__		,	cls_token=lowercase__		,	mask_token=lowercase__		,	additional_special_tokens=lowercase__		,	**lowercase__		,	)

									SCREAMING_SNAKE_CASE				=	3
									SCREAMING_SNAKE_CASE				=	do_lower_case
									SCREAMING_SNAKE_CASE				=	remove_space
									SCREAMING_SNAKE_CASE				=	keep_accents
									SCREAMING_SNAKE_CASE				=	vocab_file
									SCREAMING_SNAKE_CASE				=	False if not self.vocab_file else True

									try:
														import jieba
									except ModuleNotFoundError as error:
														raise error.__class__(
														    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
														    'See https://pypi.org/project/jieba/ for installation.'			)
									SCREAMING_SNAKE_CASE				=	jieba
									SCREAMING_SNAKE_CASE				=	str.maketrans(' \n'		,	'\u2582\u2583'			)


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE				=	[self.cls_token_id]
									if token_ids_a is None:
														return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE				=	[2]

									if token_ids_a is None:
														return len(token_ids_a + sep			) * [0] + cls_segment_id
									return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    Tuple[str]:
									"""simple docstring"""







									if not self.can_save_slow_tokenizer:
														raise ValueError(
														    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
														    'tokenizer.'			)

									if not os.path.isdir(lowercase__			):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
														return
									SCREAMING_SNAKE_CASE				=	os.path.join(
									    lowercase__		,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

									if os.path.abspath(self.vocab_file			) != os.path.abspath(lowercase__			):
														copyfile(self.vocab_file		,	lowercase__			)

									return (out_vocab_file,)


				def 							A					(				self		,	lowercase__		,	*lowercase__		,	**lowercase__			)    ->    Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[
									    ' '.join([x.translate(self.translator			) for x in self.jieba.cut(lowercase__		,	cut_all=lowercase__			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(lowercase__		,	*lowercase__		,	**lowercase__			)




				def 							A					(				self		,	*lowercase__		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	super()._decode(*lowercase__		,	**lowercase__			)
									SCREAMING_SNAKE_CASE				=	text.replace(' '		,	''			).replace('\u2582'		,	' '			).replace('\u2583'		,	'\n'			)
									return text
