


"""simple docstring"""






import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DPTImageProcessor






class      A(  unittest.TestCase		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=7					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=18					,				SCREAMING_SNAKE_CASE__=30					,				SCREAMING_SNAKE_CASE__=4_00					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]					,				SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]					,				)      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	size if size is not None else {'''height''': 18, '''width''': 18}
					_UpperCamelCase					:Optional[int]					     =	parent
					_UpperCamelCase					:Optional[Any]					     =	batch_size
					_UpperCamelCase					:List[str]					     =	num_channels
					_UpperCamelCase					:List[Any]					     =	image_size
					_UpperCamelCase					:List[Any]					     =	min_resolution
					_UpperCamelCase					:List[str]					     =	max_resolution
					_UpperCamelCase					:Tuple					     =	do_resize
					_UpperCamelCase					:Any					     =	size
					_UpperCamelCase					:Optional[Any]					     =	do_normalize
					_UpperCamelCase					:Union[str, Any]					     =	image_mean
					_UpperCamelCase					:str					     =	image_std





		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					}






@require_torch
@require_vision
class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							DPTImageProcessor if is_vision_available() else None
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	DPTImageProcessingTester(self   )
		@property
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					return self.image_processor_tester.prepare_image_processor_dict()
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	self.image_processing_class(**self.image_processor_dict   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''image_mean'''   )   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''image_std'''   )   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''do_normalize'''   )   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''do_resize'''   )   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''size'''   )   )
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	self.image_processing_class.from_dict(self.image_processor_dict   )
					self.assertEqual(image_processor.size					,				{'''height''': 18, '''width''': 18}   )

					_UpperCamelCase					:Dict					     =	self.image_processing_class.from_dict(self.image_processor_dict					,				size=42   )
					self.assertEqual(image_processor.size					,				{'''height''': 42, '''width''': 42}   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.image_processing_class(**self.image_processor_dict   )
					# create random PIL images
					_UpperCamelCase					:List[Any]					     =	prepare_image_inputs(self.image_processor_tester					,				equal_resolution=SCREAMING_SNAKE_CASE__   )
					for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE__					,				Image.Image   )

					# Test not batched input
					_UpperCamelCase					:Optional[Any]					     =	image_processing(image_inputs[0]					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )					,				)

					# Test batched
					_UpperCamelCase					:Dict					     =	image_processing(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )					,				)
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.image_processing_class(**self.image_processor_dict   )
					# create random numpy tensors
					_UpperCamelCase					:Tuple					     =	prepare_image_inputs(self.image_processor_tester					,				equal_resolution=SCREAMING_SNAKE_CASE__					,				numpify=SCREAMING_SNAKE_CASE__   )
					for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE__					,				np.ndarray   )

					# Test not batched input
					_UpperCamelCase					:Optional[int]					     =	image_processing(image_inputs[0]					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )					,				)

					# Test batched
					_UpperCamelCase					:Tuple					     =	image_processing(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )					,				)





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

					"""simple docstring"""



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

					# Test not batched input
					_UpperCamelCase					:List[str]					     =	image_processing(image_inputs[0]					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )					,				)

					# Test batched
					_UpperCamelCase					:Union[str, Any]					     =	image_processing(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['''height'''],
					        self.image_processor_tester.size['''width'''],
					    )					,				)





"""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      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							CpmAntTokenizer
		A    =							False
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					super().setUp()

					_UpperCamelCase					:str					     =	[
					    '''<d>''',
					    '''</d>''',
					    '''<s>''',
					    '''</s>''',
					    '''</_>''',
					    '''<unk>''',
					    '''<pad>''',
					    '''</n>''',
					    '''我''',
					    '''是''',
					    '''C''',
					    '''P''',
					    '''M''',
					    '''A''',
					    '''n''',
					    '''t''',
					]
					_UpperCamelCase					:List[str]					     =	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   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b'''   )
					_UpperCamelCase					:List[str]					     =	'''今天天气真好！'''
					_UpperCamelCase					:Tuple					     =	['''今天''', '''天气''', '''真''', '''好''', '''！''']
					_UpperCamelCase					:Tuple					     =	tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	'''今天天气真好！'''
					_UpperCamelCase					:Optional[int]					     =	[tokenizer.bos_token] + tokens

					_UpperCamelCase					:List[str]					     =	[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:str					     =	tokenizer.decode(SCREAMING_SNAKE_CASE__   )
					self.assertEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

