


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()







class 		_snake_case  (				unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	StableDiffusionLDMaDPipeline
 SCREAMING_SNAKE_CASE__				=	TEXT_TO_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE__				=	TEXT_TO_IMAGE_BATCH_PARAMS
 SCREAMING_SNAKE_CASE__				=	TEXT_TO_IMAGE_IMAGE_PARAMS




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:Dict										=	UNetaDConditionModel(
           block_out_channels=(32, 64) ,  layers_per_block=2 ,  sample_size=32 ,  in_channels=4 ,  out_channels=4 ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,  cross_attention_dim=32 ,  )
       a						:Tuple										=	DDIMScheduler(
           beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  clip_sample=_lowerCamelCase ,  set_alpha_to_one=_lowerCamelCase ,  )
       torch.manual_seed(0   )
       a						:Union[str, Any]										=	AutoencoderKL(
           block_out_channels=[32, 64] ,  in_channels=6 ,  out_channels=6 ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,  latent_channels=4 ,  )
       torch.manual_seed(0   )
       a						:str										=	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 ,  )
       a						:str										=	CLIPTextModel(_lowerCamelCase   )
       a						:List[str]										=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       a						:Optional[Any]										=	{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=0   ):
       if str(_lowerCamelCase   ).startswith('''mps'''   ):
             a						:Tuple										=	torch.manual_seed(_lowerCamelCase   )
       else:
             a						:Union[str, Any]										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
       a						:int										=	{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       a						:Any										=	self.get_dummy_components()
       a						:Optional[Any]										=	StableDiffusionLDMaDPipeline(**_lowerCamelCase   )
       a						:List[Any]										=	ldmad_pipe.to(_lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Dict										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Optional[int]										=	ldmad_pipe(**_lowerCamelCase   )
       a						:Union[str, Any]										=	output.rgb, output.depth

       a						:Union[str, Any]										=	rgb[0, -3:, -3:, -1]
       a						:Any										=	depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       a						:List[Any]										=	np.array(
           [0.3733_8176, 0.7_0247, 0.7420_3193, 0.5164_3604, 0.5825_6793, 0.6093_2136, 0.418_1095, 0.4835_5877, 0.4653_5262]   )
       a						:Tuple										=	np.array([103.4_6727, 85.81_2004, 87.84_9236]   )

       assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb   ).max() < 1e-2
       assert np.abs(image_slice_depth.flatten() - expected_slice_depth   ).max() < 1e-2




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	self.get_dummy_components()
       a						:List[str]										=	StableDiffusionLDMaDPipeline(**_lowerCamelCase   )
       a						:int										=	ldmad_pipe.to(_lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Optional[Any]										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Optional[int]										=	3 * [inputs['''prompt''']]

       # forward
       a						:Tuple										=	ldmad_pipe(**_lowerCamelCase   )
       a						:str										=	output.rgb, output.depth
       a						:List[str]										=	rgb_slice_a[0, -3:, -3:, -1]
       a						:Tuple										=	depth_slice_a[0, -3:, -1]

       a						:Optional[Any]										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Tuple										=	3 * [inputs.pop('''prompt'''   )]

       a						:Dict										=	ldmad_pipe.tokenizer(
           _lowerCamelCase ,  padding='''max_length''' ,  max_length=ldmad_pipe.tokenizer.model_max_length ,  truncation=_lowerCamelCase ,  return_tensors='''pt''' ,  )
       a						:List[Any]										=	text_inputs['''input_ids'''].to(_lowerCamelCase   )

       a						:List[Any]										=	ldmad_pipe.text_encoder(_lowerCamelCase   )[0]

       a						:List[str]										=	prompt_embeds

       # forward
       a						:Optional[Any]										=	ldmad_pipe(**_lowerCamelCase   )
       a						:List[str]										=	output.rgb, output.depth
       a						:Union[str, Any]										=	rgb_slice_a[0, -3:, -3:, -1]
       a						:Optional[Any]										=	depth_slice_a[0, -3:, -1]

       assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()   ).max() < 1e-4
       assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()   ).max() < 1e-4




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       a						:Optional[Any]										=	self.get_dummy_components()
       a						:List[Any]										=	PNDMScheduler(skip_prk_steps=_lowerCamelCase   )
       a						:Tuple										=	StableDiffusionLDMaDPipeline(**_lowerCamelCase   )
       a						:str										=	ldmad_pipe.to(_lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:str										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:List[str]										=	'''french fries'''
       a						:Any										=	ldmad_pipe(**_lowerCamelCase ,  negative_prompt=_lowerCamelCase   )

       a						:Optional[Any]										=	output.rgb, output.depth
       a						:str										=	rgb[0, -3:, -3:, -1]
       a						:List[str]										=	depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       a						:Any										=	np.array(
           [0.3_7044, 0.7181_1503, 0.722_3251, 0.4860_3675, 0.563_8391, 0.636_4948, 0.4283_3704, 0.490_1315, 0.4792_6217]   )
       a						:Union[str, Any]										=	np.array([107.8_4738, 84.6_2802, 89.96_2135]   )
       assert np.abs(rgb_slice.flatten() - expected_slice_rgb   ).max() < 1e-2
       assert np.abs(depth_slice.flatten() - expected_slice_depth   ).max() < 1e-2







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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase="cpu" ,  _lowerCamelCase=torch.floataa ,  _lowerCamelCase=0   ):
       a						:List[Any]										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
       a						:Optional[Any]										=	np.random.RandomState(_lowerCamelCase   ).standard_normal((1, 4, 64, 64)   )
       a						:Dict										=	torch.from_numpy(_lowerCamelCase   ).to(device=_lowerCamelCase ,  dtype=_lowerCamelCase   )
       a						:Tuple										=	{
           '''prompt''': '''a photograph of an astronaut riding a horse''',
           '''latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''   )
       a						:int										=	ldmad_pipe.to(_lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:List[str]										=	self.get_inputs(_lowerCamelCase   )
       a						:Union[str, Any]										=	ldmad_pipe(**_lowerCamelCase   )
       a						:List[Any]										=	output.rgb, output.depth
       a						:Dict										=	rgb[0, -3:, -3:, -1].flatten()
       a						:Dict										=	rgb[0, -3:, -1].flatten()

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512)

       a						:int										=	np.array(
           [0.5380_5465, 0.5670_7305, 0.548_6515, 0.5701_2236, 0.581_4511, 0.5625_3487, 0.5484_3014, 0.5509_2263, 0.645_9706]   )
       a						:Any										=	np.array(
           [0.926_3781, 0.667_8672, 0.548_6515, 0.9220_2145, 0.6783_1135, 0.5625_3487, 0.924_1694, 0.755_1478, 0.645_9706]   )
       assert np.abs(rgb_slice - expected_slice_rgb   ).max() < 3e-3
       assert np.abs(depth_slice - expected_slice_depth   ).max() < 3e-3







@nightly
@require_torch_gpu
class 		_snake_case  (				unittest.TestCase							):




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase="cpu" ,  _lowerCamelCase=torch.floataa ,  _lowerCamelCase=0   ):
       a						:Union[str, Any]										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
       a						:Dict										=	np.random.RandomState(_lowerCamelCase   ).standard_normal((1, 4, 64, 64)   )
       a						:Tuple										=	torch.from_numpy(_lowerCamelCase   ).to(device=_lowerCamelCase ,  dtype=_lowerCamelCase   )
       a						:Dict										=	{
           '''prompt''': '''a photograph of an astronaut riding a horse''',
           '''latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 50,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''   ).to(_lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Union[str, Any]										=	self.get_inputs(_lowerCamelCase   )
       a						:str										=	ldmad_pipe(**_lowerCamelCase   )
       a						:str										=	output.rgb, output.depth

       a						:List[Any]										=	0.49_5586
       a						:Dict										=	0.3379_5515
       a						:int										=	112.4_8518
       a						:List[Any]										=	98.48_9746
       assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()   ) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()   ) < 1e-3
       assert np.abs(expected_depth_std - depth.std()   ) < 1e-3




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''   ).to(_lowerCamelCase   )
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Optional[int]										=	self.get_inputs(_lowerCamelCase   )
       a						:str										=	ldmad_pipe(**_lowerCamelCase   )
       a						:List[Any]										=	output.rgb, output.depth

       a						:Any										=	0.419_4127
       a						:List[Any]										=	0.3537_5586
       a						:int										=	0.563_8502
       a						:Optional[int]										=	0.3468_6103

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512, 1)
       assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()   ) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()   ) < 1e-3
       assert np.abs(expected_depth_std - depth.std()   ) < 1e-3









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   )





