




'''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_  (a__  ):







						"""simple docstring"""


						def __init__(  self   :							int				,			_lowerCamelCase   :							Tuple=None				,			_lowerCamelCase   :							Optional[int]=None				,			_lowerCamelCase   :							List[str]=False				,			_lowerCamelCase   :							List[Any]=True				,			_lowerCamelCase   :							str=False				,			_lowerCamelCase   :							Tuple="<s>"				,			_lowerCamelCase   :							Tuple="</s>"				,			_lowerCamelCase   :							Tuple="<unk>"				,			_lowerCamelCase   :							Any="<sep>"				,			_lowerCamelCase   :							List[Any]="<pad>"				,			_lowerCamelCase   :							str="<cls>"				,			_lowerCamelCase   :							Optional[Any]="<mask>"				,			_lowerCamelCase   :							Tuple=["<eop>", "<eod>"]				,			**_lowerCamelCase   :							int				,			):





											"""simple docstring"""







											A_			:  Optional[int]			=				AddedToken(_lowerCamelCase				,			lstrip=_lowerCamelCase				,			rstrip=_lowerCamelCase     ) if isinstance(_lowerCamelCase				,			_lowerCamelCase     ) else mask_token

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

											A_			:  Tuple			=				3
											A_			:  List[Any]			=				do_lower_case
											A_			:  List[str]			=				remove_space
											A_			:  Union[str, Any]			=				keep_accents
											A_			:  List[Any]			=				vocab_file
											A_			:  Optional[Any]			=				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.'''     )
											A_			:  int			=				jieba
											A_			:  Optional[int]			=				str.maketrans(''' \n'''				,			'''\u2582\u2583'''     )







						def    _a     (  self   :							Optional[Any]				,			_lowerCamelCase   :							List[int]				,			_lowerCamelCase   :							Optional[List[int]] = None     ):





											"""simple docstring"""







											A_			:  str			=				[self.sep_token_id]
											A_			:  Tuple			=				[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   :							Optional[int]				,			_lowerCamelCase   :							List[int]				,			_lowerCamelCase   :							Optional[List[int]] = None     ):





											"""simple docstring"""







											A_			:  Tuple			=				[self.sep_token_id]
											A_			:  Any			=				[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   :							Dict				,			_lowerCamelCase   :							str				,			_lowerCamelCase   :							Optional[str] = None     ):





											"""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(_lowerCamelCase     ):
																logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
																return
											A_			:  str			=				os.path.join(
											    _lowerCamelCase				,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

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

											return (out_vocab_file,)







						def    _a     (  self   :							List[str]				,			_lowerCamelCase   :							Dict				,			*_lowerCamelCase   :							int				,			**_lowerCamelCase   :							int     ):





											"""simple docstring"""







											A_			:  List[Any]			=				[
											    ''' '''.join([x.translate(self.translator     ) for x in self.jieba.cut(_lowerCamelCase				,			cut_all=_lowerCamelCase     )]     )
											    for text in batch_text_or_text_pairs
											]
											return super()._batch_encode_plus(_lowerCamelCase				,			*_lowerCamelCase				,			**_lowerCamelCase     )







						def    _a     (  self   :							str				,			*_lowerCamelCase   :							int				,			**_lowerCamelCase   :							Optional[Any]     ):





											"""simple docstring"""







											A_			:  int			=				super()._decode(*_lowerCamelCase				,			**_lowerCamelCase     )
											A_			:  Optional[int]			=				text.replace(''' '''				,			''''''     ).replace('''\u2582'''				,			''' '''     ).replace('''\u2583'''				,			'''\n'''     )
											return text









'''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      UpperCamelCase_  (unittest.TestCase  ):







						"""simple docstring"""


						def __init__(  self   :							Tuple				,			_lowerCamelCase   :							str				,			_lowerCamelCase   :							str=7				,			_lowerCamelCase   :							Tuple=3				,			_lowerCamelCase   :							Any=18				,			_lowerCamelCase   :							Optional[int]=30				,			_lowerCamelCase   :							List[Any]=400				,			_lowerCamelCase   :							int=True				,			_lowerCamelCase   :							Dict=1 / 255				,			_lowerCamelCase   :							List[str]=True				,			_lowerCamelCase   :							Optional[int]=8				,			):





											"""simple docstring"""







											A_			:  Tuple			=				parent
											A_			:  str			=				batch_size
											A_			:  Optional[Any]			=				num_channels
											A_			:  Union[str, Any]			=				image_size
											A_			:  Optional[int]			=				min_resolution
											A_			:  List[Any]			=				max_resolution
											A_			:  int			=				do_rescale
											A_			:  Optional[Any]			=				rescale_factor
											A_			:  Any			=				do_pad
											A_			:  Tuple			=				pad_size







						def    _a     (  self   :							List[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    _a     (  self   :							Tuple				,			_lowerCamelCase   :							List[str]=False				,			_lowerCamelCase   :							Tuple=False				,			_lowerCamelCase   :							str=False     ):





											"""simple docstring"""







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

											if equal_resolution:
																A_			:  List[Any]			=				[]
																for i in range(self.batch_size     ):
																					image_inputs.append(
																					    np.random.randint(
																					        255				,			size=(self.num_channels, self.max_resolution, self.max_resolution)				,			dtype=np.uinta     )     )
											else:
																A_			:  List[Any]			=				[]
																for i in range(self.batch_size     ):
																					A_			,A_			:  List[str]			=				np.random.choice(np.arange(self.min_resolution				,			self.max_resolution     )				,			2     )
																					image_inputs.append(np.random.randint(255				,			size=(self.num_channels, width, height)				,			dtype=np.uinta     )     )

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

											if torchify:
																A_			:  str			=				[torch.from_numpy(_lowerCamelCase     ) for x in image_inputs]

											return image_inputs






@require_torch
@require_vision
class      UpperCamelCase_  (a__,   unittest.TestCase  ):







						"""simple docstring"""


						_lowerCAmelCase         =       SwinaSRImageProcessor if is_vision_available() else None







						def    _a     (  self   :							List[str]     ):





											"""simple docstring"""







											A_			:  int			=				SwinaSRImageProcessingTester(self     )







						@property
						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											return self.image_processor_tester.prepare_image_processor_dict()







						def    _a     (  self   :							Any     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''do_rescale'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''rescale_factor'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''do_pad'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''pad_size'''     )     )







						def    _a     (  self   :							Tuple     ):





											"""simple docstring"""







											pass







						def    _a     (  self   :							Optional[int]				,			_lowerCamelCase   :							Optional[int]     ):





											"""simple docstring"""







											A_			,A_			:  Any			=				get_image_size(_lowerCamelCase     )
											A_			:  List[Any]			=				self.image_processor_tester.pad_size

											A_			:  Dict			=				(old_height // size + 1) * size - old_height
											A_			:  Optional[Any]			=				(old_width // size + 1) * size - old_width
											return old_height + pad_height, old_width + pad_width







						def    _a     (  self   :							int     ):





											"""simple docstring"""







											A_			:  Dict			=				self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											A_			:  Optional[int]			=				self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase     )
											for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase				,			Image.Image     )

											# Test not batched input
											A_			:  Any			=				image_processor(image_inputs[0]				,			return_tensors='''pt'''     ).pixel_values
											A_			,A_			:  List[Any]			=				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    _a     (  self   :							Optional[Any]     ):





											"""simple docstring"""







											A_			:  Optional[Any]			=				self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											A_			:  Dict			=				self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase				,			numpify=_lowerCamelCase     )
											for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase				,			np.ndarray     )

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







						def    _a     (  self   :							int     ):





											"""simple docstring"""







											A_			:  Union[str, Any]			=				self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											A_			:  List[Any]			=				self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCamelCase				,			torchify=_lowerCamelCase     )
											for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase				,			torch.Tensor     )

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



