


import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 		_snake_case  (				unittest.TestCase							):




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:List[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




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:List[Any]										=	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 ,  )
       return model




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:int										=	CLIPTextConfig(
           bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=32 ,  intermediate_size=37 ,  layer_norm_eps=1e-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=1000 ,  )
       return CLIPTextModel(_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	self.dummy_uncond_unet
       a						:Optional[int]										=	DDIMScheduler()
       a						:Union[str, Any]										=	self.dummy_vq_model

       a						:int										=	LDMPipeline(unet=_lowerCamelCase ,  vqvae=_lowerCamelCase ,  scheduler=_lowerCamelCase   )
       ldm.to(_lowerCamelCase   )
       ldm.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Union[str, Any]										=	torch.manual_seed(0   )
       a						:Optional[Any]										=	ldm(generator=_lowerCamelCase ,  num_inference_steps=2 ,  output_type='''numpy'''   ).images

       a						:Optional[int]										=	torch.manual_seed(0   )
       a						:Any										=	ldm(generator=_lowerCamelCase ,  num_inference_steps=2 ,  output_type='''numpy''' ,  return_dict=_lowerCamelCase   )[0]

       a						:str										=	image[0, -3:, -3:, -1]
       a						:Optional[int]										=	image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       a						:int										=	np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]   )
       a						:Optional[Any]										=	1e-2 if torch_device != '''mps''' else 3e-2

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < tolerance
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < tolerance







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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''   )
       ldm.to(_lowerCamelCase   )
       ldm.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:str										=	torch.manual_seed(0   )
       a						:Dict										=	ldm(generator=_lowerCamelCase ,  num_inference_steps=5 ,  output_type='''numpy'''   ).images

       a						:Optional[Any]										=	image[0, -3:, -3:, -1]

       assert image.shape == (1, 256, 256, 3)
       a						:Any										=	np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]   )
       a						:Dict										=	1e-2 if torch_device != '''mps''' else 3e-2

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < tolerance









import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


snake_case    :   List[Any]	    =					get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')







class 		_snake_case  (				_snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	BartphoTokenizer
 SCREAMING_SNAKE_CASE__				=	False
 SCREAMING_SNAKE_CASE__				=	True




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       super().setUp()

       a						:Dict										=	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
       a						:Optional[Any]										=	dict(zip(_lowerCamelCase ,  range(len(_lowerCamelCase   )   )   )   )
       a						:Tuple										=	{'''unk_token''': '''<unk>'''}

       a						:Union[str, Any]										=	os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES['''monolingual_vocab_file''']   )
       with open(self.monolingual_vocab_file ,  '''w''' ,  encoding='''utf-8'''   ) as fp:
             for token in vocab_tokens:
                   fp.write(F'''{token} {vocab_tokens[token]}\n'''   )

       a						:Any										=	BartphoTokenizer(_lowerCamelCase ,  self.monolingual_vocab_file ,  **self.special_tokens_map   )
       tokenizer.save_pretrained(self.tmpdirname   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  **_lowerCamelCase   ):
       kwargs.update(self.special_tokens_map   )
       return BartphoTokenizer.from_pretrained(self.tmpdirname ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:int										=	'''This is a là test'''
       a						:str										=	'''This is a<unk><unk> test'''
       return input_text, output_text




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	BartphoTokenizer(_lowerCamelCase ,  self.monolingual_vocab_file ,  **self.special_tokens_map   )
       a						:Optional[Any]										=	'''This is a là test'''
       a						:Tuple										=	'''▁This ▁is ▁a ▁l à ▁t est'''.split()
       a						:int										=	tokenizer.tokenize(_lowerCamelCase   )
       self.assertListEqual(_lowerCamelCase ,  _lowerCamelCase   )

       a						:Union[str, Any]										=	tokens + [tokenizer.unk_token]
       a						:str										=	[4, 5, 6, 3, 3, 7, 8, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase   ) ,  _lowerCamelCase   )





