


"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class      A(  unittest.TestCase		):

		"""simple docstring"""





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

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	UNetaDModel(
					    block_out_channels=(32, 64)					,				layers_per_block=2					,				sample_size=32					,				in_channels=3					,				out_channels=3					,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,				)
					return model





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					:List[Any]					     =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






@slow
@require_torch
class      A(  unittest.TestCase		):

		"""simple docstring"""





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

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					_UpperCamelCase					:Any					     =	np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2





"""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__   )

