




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class     __lowerCAmelCase (       __magic_name__			,				unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       KandinskyVaaControlnetPipeline
			snake_case_   		=       ['''image_embeds''', '''negative_image_embeds''', '''hint''']
			snake_case_   		=       ['''image_embeds''', '''negative_image_embeds''', '''hint''']
			snake_case_   		=       [
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''latents''',
			    '''guidance_scale''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			snake_case_   		=       False


			@property
			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							return 32


			@property
			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							return 32


			@property
			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							return self.time_input_dim


			@property
			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							return self.time_input_dim * 4


			@property
			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							return 100


			@property
			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							torch.manual_seed(0							)

							__lowerCamelCase		       =    {
							    'in_channels': 8,
							    # Out channels is double in channels because predicts mean and variance
							    'out_channels': 8,
							    'addition_embed_type': 'image_hint',
							    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
							    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
							    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
							    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
							    'layers_per_block': 1,
							    'encoder_hid_dim': self.text_embedder_hidden_size,
							    'encoder_hid_dim_type': 'image_proj',
							    'cross_attention_dim': self.cross_attention_dim,
							    'attention_head_dim': 4,
							    'resnet_time_scale_shift': 'scale_shift',
							    'class_embed_type': None,
							}

							__lowerCamelCase		       =    UNetaDConditionModel(**lowerCamelCase__							)
							return model


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



							'''simple docstring'''
							return {
							    "block_out_channels": [32, 32, 64, 64],
							    "down_block_types": [
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "AttnDownEncoderBlock2D",
							    ],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
							    "vq_embed_dim": 4,
							}


			@property
			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							torch.manual_seed(0							)
							__lowerCamelCase		       =    VQModel(**self.dummy_movq_kwargs							)
							return model


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.dummy_unet
							__lowerCamelCase		       =    self.dummy_movq

							__lowerCamelCase		       =    DDIMScheduler(
							    num_train_timesteps=1_000			,						beta_schedule='linear'			,						beta_start=0.0_00_85			,						beta_end=0.0_12			,						clip_sample=lowerCamelCase__			,						set_alpha_to_one=lowerCamelCase__			,						steps_offset=1			,						prediction_type='epsilon'			,						thresholding=lowerCamelCase__			,						)

							__lowerCamelCase		       =    {
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}
							return components


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__=0							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    floats_tensor((1, self.text_embedder_hidden_size)			,						rng=random.Random(lowerCamelCase__							)							).to(lowerCamelCase__							)
							__lowerCamelCase		       =    floats_tensor((1, self.text_embedder_hidden_size)			,						rng=random.Random(seed + 1							)							).to(
							    lowerCamelCase__							)

							# create hint
							__lowerCamelCase		       =    floats_tensor((1, 3, 64, 64)			,						rng=random.Random(lowerCamelCase__							)							).to(lowerCamelCase__							)

							if str(lowerCamelCase__							).startswith('mps'							):
											__lowerCamelCase		       =    torch.manual_seed(lowerCamelCase__							)
							else:
											__lowerCamelCase		       =    torch.Generator(device=lowerCamelCase__							).manual_seed(lowerCamelCase__							)
							__lowerCamelCase		       =    {
							    'image_embeds': image_embeds,
							    'negative_image_embeds': negative_image_embeds,
							    'hint': hint,
							    'generator': generator,
							    'height': 64,
							    'width': 64,
							    'guidance_scale': 4.0,
							    'num_inference_steps': 2,
							    'output_type': 'np',
							}
							return inputs






			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    'cpu'

							__lowerCamelCase		       =    self.get_dummy_components()

							__lowerCamelCase		       =    self.pipeline_class(**lowerCamelCase__							)
							__lowerCamelCase		       =    pipe.to(lowerCamelCase__							)

							pipe.set_progress_bar_config(disable=lowerCamelCase__							)

							__lowerCamelCase		       =    pipe(**self.get_dummy_inputs(lowerCamelCase__							)							)
							__lowerCamelCase		       =    output.images

							__lowerCamelCase		       =    pipe(
							    **self.get_dummy_inputs(lowerCamelCase__							)			,						return_dict=lowerCamelCase__			,						)[0]

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

							assert image.shape == (1, 64, 64, 3)

							__lowerCamelCase		       =    np.array(
							    [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]							)

							assert (
							    np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
							), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2
							), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







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

			"""simple docstring"""







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



							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'							)

							__lowerCamelCase		       =    load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/hint_image_cat.png'							)
							__lowerCamelCase		       =    torch.from_numpy(np.array(lowerCamelCase__							)							).float() / 2_55.0
							__lowerCamelCase		       =    hint.permute(2			,						0			,						1							).unsqueeze(0							)

							__lowerCamelCase		       =    KandinskyVaaPriorPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-prior'			,						torch_dtype=torch.floataa							)
							pipe_prior.to(lowerCamelCase__							)

							__lowerCamelCase		       =    KandinskyVaaControlnetPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-controlnet-depth'			,						torch_dtype=torch.floataa							)
							__lowerCamelCase		       =    pipeline.to(lowerCamelCase__							)
							pipeline.set_progress_bar_config(disable=lowerCamelCase__							)

							__lowerCamelCase		       =    'A robot, 4k photo'

							__lowerCamelCase		       =    torch.Generator(device='cuda'							).manual_seed(0							)
							__lowerCamelCase       ,						__lowerCamelCase		       =    pipe_prior(
							    lowerCamelCase__			,						generator=lowerCamelCase__			,						num_inference_steps=5			,						negative_prompt=''			,						).to_tuple()

							__lowerCamelCase		       =    torch.Generator(device='cuda'							).manual_seed(0							)
							__lowerCamelCase		       =    pipeline(
							    image_embeds=lowerCamelCase__			,						negative_image_embeds=lowerCamelCase__			,						hint=lowerCamelCase__			,						generator=lowerCamelCase__			,						num_inference_steps=100			,						output_type='np'			,						)

							__lowerCamelCase		       =    output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(lowerCamelCase__			,						lowerCamelCase__							)










"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list






@require_torchaudio
@require_sentencepiece
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       List[str]:
   _a  :       Any             =	"""laion/clap-htsat-unfused"""
   _a  :       Union[str, Any]             =	tempfile.mkdtemp()


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						Any							)					->       Dict:
   return RobertaTokenizer.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						List[str]							)					->       int:
   return ClapFeatureExtractor.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       Tuple:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						List[str]							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_tokenizer()
   _a  :       Any             =	self.get_feature_extractor()

   _a  :       Optional[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   processor.save_pretrained(self.tmpdirname							)
   _a  :       List[str]             =	ClapProcessor.from_pretrained(self.tmpdirname							)

   self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer.get_vocab()							)
   self.assertIsInstance(processor.tokenizer							,						UpperCAmelCase__							)

   self.assertEqual(processor.feature_extractor.to_json_string()							,						feature_extractor.to_json_string()							)
   self.assertIsInstance(processor.feature_extractor							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						Dict							)					->       Optional[int]:
   _a  :       Tuple             =	ClapProcessor(tokenizer=self.get_tokenizer()							,						feature_extractor=self.get_feature_extractor()							)
   processor.save_pretrained(self.tmpdirname							)

   _a  :       Dict             =	self.get_tokenizer(bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							)
   _a  :       Union[str, Any]             =	self.get_feature_extractor(do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   _a  :       Union[str, Any]             =	ClapProcessor.from_pretrained(
       self.tmpdirname							,						bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							,						do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer_add_kwargs.get_vocab()							)
   self.assertIsInstance(processor.tokenizer							,						UpperCAmelCase__							)

   self.assertEqual(processor.feature_extractor.to_json_string()							,						feature_extractor_add_kwargs.to_json_string()							)
   self.assertIsInstance(processor.feature_extractor							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   _a  :       Optional[int]             =	self.get_feature_extractor()
   _a  :       Tuple             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	floats_list((3, 1000)							)

   _a  :       List[Any]             =	feature_extractor(UpperCAmelCase__							,						return_tensors="""np"""							)
   _a  :       List[str]             =	processor(audios=UpperCAmelCase__							,						return_tensors="""np"""							)

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()							,						input_processor[key].sum()							,						delta=1E-2							)


 def _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_feature_extractor()
   _a  :       Any             =	self.get_tokenizer()

   _a  :       Any             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Optional[int]             =	"""This is a test string"""

   _a  :       Tuple             =	processor(text=UpperCAmelCase__							)

   _a  :       int             =	tokenizer(UpperCAmelCase__							)

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]							,						encoded_processor[key]							)


 def _lowercase	(     self		:						List[Any]							)					->       Any:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       List[str]             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   _a  :       Dict             =	processor.batch_decode(UpperCAmelCase__							)
   _a  :       Any             =	tokenizer.batch_decode(UpperCAmelCase__							)

   self.assertListEqual(UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						Dict							)					->       List[str]:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       Optional[Any]             =	self.get_tokenizer()

   _a  :       Union[str, Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   self.assertListEqual(
       processor.model_input_names[2:]							,						feature_extractor.model_input_names							,						msg="""`processor` and `feature_extractor` model input names do not match"""							,						)

