"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowerCamelCase											=  False
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				List[Any]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						@property
						def 				snake_case__	(       self     )					->				List[Any]:
													return 12





						@property
						def 				snake_case__	(       self     )					->				List[Any]:
													return 12





						@property
						def 				snake_case__	(       self     )					->				Optional[Any]:
													return 32





						@property
						def 				snake_case__	(       self     )					->				Dict:
													torch.manual_seed(0     )
													A__	     =							VQModel(
													    block_out_channels=[32, 64]		,in_channels=3		,out_channels=3		,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,latent_channels=3		,num_vq_embeddings=self.num_embed		,vq_embed_dim=3		,)
													return model





						@property
						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
													return tokenizer





						@property
						def 				snake_case__	(       self     )					->				Any:
													torch.manual_seed(0     )
													A__	     =							CLIPTextConfig(
													    bos_token_id=0		,eos_token_id=2		,hidden_size=self.text_embedder_hidden_size		,intermediate_size=37		,layer_norm_eps=1e-05		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=10_00		,)
													return CLIPTextModel(__UpperCAmelCase     )





						@property
						def 				snake_case__	(       self     )					->				List[str]:
													torch.manual_seed(0     )

													A__	     =							12
													A__	     =							12

													A__	     =							{
													    'attention_bias': True,
													    'cross_attention_dim': 32,
													    'attention_head_dim': height * width,
													    'num_attention_heads': 1,
													    'num_vector_embeds': self.num_embed,
													    'num_embeds_ada_norm': self.num_embeds_ada_norm,
													    'norm_num_groups': 32,
													    'sample_size': width,
													    'activation_fn': 'geglu-approximate',
													}

													A__	     =							TransformeraDModel(**__UpperCAmelCase     )
													return model





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							'cpu'

													A__	     =							self.dummy_vqvae
													A__	     =							self.dummy_text_encoder
													A__	     =							self.dummy_tokenizer
													A__	     =							self.dummy_transformer
													A__	     =							VQDiffusionScheduler(self.num_embed     )
													A__	     =							LearnedClassifierFreeSamplingEmbeddings(learnable=__UpperCAmelCase     )

													A__	     =							VQDiffusionPipeline(
													    vqvae=__UpperCAmelCase		,text_encoder=__UpperCAmelCase		,tokenizer=__UpperCAmelCase		,transformer=__UpperCAmelCase		,scheduler=__UpperCAmelCase		,learned_classifier_free_sampling_embeddings=__UpperCAmelCase		,)
													A__	     =							pipe.to(__UpperCAmelCase     )
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							'teddy bear playing in the pool'

													A__	     =							torch.Generator(device=__UpperCAmelCase     ).manual_seed(0     )
													A__	     =							pipe([prompt]		,generator=__UpperCAmelCase		,num_inference_steps=2		,output_type='np'     )
													A__	     =							output.images

													A__	     =							torch.Generator(device=__UpperCAmelCase     ).manual_seed(0     )
													A__	     =							pipe(
													    [prompt]		,generator=__UpperCAmelCase		,output_type='np'		,return_dict=__UpperCAmelCase		,num_inference_steps=2     )[0]

													A__	     =							image[0, -3:, -3:, -1]
													A__	     =							image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 24, 24, 3)

													A__	     =							np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2]     )

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





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							'cpu'

													A__	     =							self.dummy_vqvae
													A__	     =							self.dummy_text_encoder
													A__	     =							self.dummy_tokenizer
													A__	     =							self.dummy_transformer
													A__	     =							VQDiffusionScheduler(self.num_embed     )
													A__	     =							LearnedClassifierFreeSamplingEmbeddings(
													    learnable=__UpperCAmelCase		,hidden_size=self.text_embedder_hidden_size		,length=tokenizer.model_max_length     )

													A__	     =							VQDiffusionPipeline(
													    vqvae=__UpperCAmelCase		,text_encoder=__UpperCAmelCase		,tokenizer=__UpperCAmelCase		,transformer=__UpperCAmelCase		,scheduler=__UpperCAmelCase		,learned_classifier_free_sampling_embeddings=__UpperCAmelCase		,)
													A__	     =							pipe.to(__UpperCAmelCase     )
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							'teddy bear playing in the pool'

													A__	     =							torch.Generator(device=__UpperCAmelCase     ).manual_seed(0     )
													A__	     =							pipe([prompt]		,generator=__UpperCAmelCase		,num_inference_steps=2		,output_type='np'     )
													A__	     =							output.images

													A__	     =							torch.Generator(device=__UpperCAmelCase     ).manual_seed(0     )
													A__	     =							pipe(
													    [prompt]		,generator=__UpperCAmelCase		,output_type='np'		,return_dict=__UpperCAmelCase		,num_inference_steps=2     )[0]

													A__	     =							image[0, -3:, -3:, -1]
													A__	     =							image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 24, 24, 3)

													A__	     =							np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8]     )

													assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2.0
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2






@slow
@require_torch_gpu
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				Optional[int]:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'     )

													A__	     =							VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'     )
													A__	     =							pipeline.to(__UpperCAmelCase     )
													pipeline.set_progress_bar_config(disable=__UpperCAmelCase     )

													# requires GPU generator for gumbel softmax
													# don't use GPU generator in tests though
													A__	     =							torch.Generator(device=__UpperCAmelCase     ).manual_seed(0     )
													A__	     =							pipeline(
													    'teddy bear playing in the pool'		,num_images_per_prompt=1		,generator=__UpperCAmelCase		,output_type='np'		,)

													A__	     =							output.images[0]

													assert image.shape == (2_56, 2_56, 3)
													assert np.abs(expected_image - image     ).max() < 2.0






"""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     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Optional[int]													=		CpmAntTokenizer
						lowerCAmelCase__  :						List[str]													=		False





						def 				snake_case__	(       self     )					->				Dict:
													super().setUp()

													A__	     =							[
													    '<d>',
													    '</d>',
													    '<s>',
													    '</s>',
													    '</_>',
													    '<unk>',
													    '<pad>',
													    '</n>',
													    '我',
													    '是',
													    'C',
													    'P',
													    'M',
													    'A',
													    'n',
													    't',
													]
													A__	     =							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 				snake_case__	(       self     )					->				List[Any]:
													A__	     =							CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b'     )
													A__	     =							'今天天气真好！'
													A__	     =							['今天', '天气', '真', '好', '！']
													A__	     =							tokenizer.tokenize(__UpperCAmelCase     )
													self.assertListEqual(__UpperCAmelCase		,__UpperCAmelCase     )
													A__	     =							'今天天气真好！'
													A__	     =							[tokenizer.bos_token] + tokens

													A__	     =							[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase     )		,__UpperCAmelCase     )

													A__	     =							tokenizer.decode(__UpperCAmelCase     )
													self.assertEqual(__UpperCAmelCase		,__UpperCAmelCase     )





