




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


UpperCAmelCase_		:					Dict          =  False




class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :str			):
										'''simple docstring'''

										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

						@property
						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										return 12

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return 12

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=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      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						str										=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
										return tokenizer

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Optional[int]										=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(__snake_case			)

						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[int]										=12
										__magic_name__						:						str										=12

										__magic_name__						:						Optional[int]										={
										    """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""",
										}

										__magic_name__						:						int										=TransformeraDModel(**__snake_case			)
										return model

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Any										="""cpu"""

										__magic_name__						:						List[str]										=self.dummy_vqvae
										__magic_name__						:						List[Any]										=self.dummy_text_encoder
										__magic_name__						:						List[Any]										=self.dummy_tokenizer
										__magic_name__						:						List[Any]										=self.dummy_transformer
										__magic_name__						:						Tuple										=VQDiffusionScheduler(self.num_embed			)
										__magic_name__						:						Optional[int]										=LearnedClassifierFreeSamplingEmbeddings(learnable=__snake_case			)

										__magic_name__						:						List[str]										=VQDiffusionPipeline(
										    vqvae=__snake_case     ,			text_encoder=__snake_case     ,			tokenizer=__snake_case     ,			transformer=__snake_case     ,			scheduler=__snake_case     ,			learned_classifier_free_sampling_embeddings=__snake_case     ,			)
										__magic_name__						:						int										=pipe.to(__snake_case			)
										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Any										="""teddy bear playing in the pool"""

										__magic_name__						:						List[str]										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						Optional[Any]										=pipe([prompt]     ,			generator=__snake_case     ,			num_inference_steps=2     ,			output_type="""np"""			)
										__magic_name__						:						Any										=output.images

										__magic_name__						:						List[str]										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						Dict										=pipe(
										    [prompt]     ,			generator=__snake_case     ,			output_type="""np"""     ,			return_dict=__snake_case     ,			num_inference_steps=2			)[0]

										__magic_name__						:						List[str]										=image[0, -3:, -3:, -1]
										__magic_name__						:						int										=image_from_tuple[0, -3:, -3:, -1]

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

										__magic_name__						:						Optional[int]										=np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]			)

										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      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										="""cpu"""

										__magic_name__						:						Union[str, Any]										=self.dummy_vqvae
										__magic_name__						:						str										=self.dummy_text_encoder
										__magic_name__						:						Dict										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_transformer
										__magic_name__						:						Tuple										=VQDiffusionScheduler(self.num_embed			)
										__magic_name__						:						int										=LearnedClassifierFreeSamplingEmbeddings(
										    learnable=__snake_case     ,			hidden_size=self.text_embedder_hidden_size     ,			length=tokenizer.model_max_length			)

										__magic_name__						:						int										=VQDiffusionPipeline(
										    vqvae=__snake_case     ,			text_encoder=__snake_case     ,			tokenizer=__snake_case     ,			transformer=__snake_case     ,			scheduler=__snake_case     ,			learned_classifier_free_sampling_embeddings=__snake_case     ,			)
										__magic_name__						:						Dict										=pipe.to(__snake_case			)
										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Optional[Any]										="""teddy bear playing in the pool"""

										__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						Tuple										=pipe([prompt]     ,			generator=__snake_case     ,			num_inference_steps=2     ,			output_type="""np"""			)
										__magic_name__						:						List[str]										=output.images

										__magic_name__						:						Optional[Any]										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						str										=pipe(
										    [prompt]     ,			generator=__snake_case     ,			output_type="""np"""     ,			return_dict=__snake_case     ,			num_inference_steps=2			)[0]

										__magic_name__						:						Any										=image[0, -3:, -3:, -1]
										__magic_name__						:						Tuple										=image_from_tuple[0, -3:, -3:, -1]

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

										__magic_name__						:						Union[str, Any]										=np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]			)

										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 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""			)

										__magic_name__						:						int										=VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""			)
										__magic_name__						:						Tuple										=pipeline.to(__snake_case			)
										pipeline.set_progress_bar_config(disable=__snake_case			)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										__magic_name__						:						int										=torch.Generator(device=__snake_case			).manual_seed(0			)
										__magic_name__						:						Optional[Any]										=pipeline(
										    """teddy bear playing in the pool"""     ,			num_images_per_prompt=1     ,			generator=__snake_case     ,			output_type="""np"""     ,			)

										__magic_name__						:						Dict										=output.images[0]

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








import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =KandinskyVaaPriorPipeline
				snake_case	    =['prompt']
				snake_case	    =['prompt', 'negative_prompt']
				snake_case	    =[
				    'num_images_per_prompt',
				    'generator',
				    'num_inference_steps',
				    'latents',
				    'negative_prompt',
				    'guidance_scale',
				    'output_type',
				    'return_dict',
				]
				snake_case	    =False





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return 32





				@property
				def 						__UpperCamelCase    (				self)  -> Tuple:
											return 32





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return self.time_input_dim





				@property
				def 						__UpperCamelCase    (				self)  -> str:
											return self.time_input_dim * 4





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return 100





				@property
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
											return tokenizer





				@property
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											torch.manual_seed(0)
											a__      =CLIPTextConfig(
											    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=self.text_embedder_hidden_size       ,  projection_dim=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=1000       ,  )
											return CLIPTextModelWithProjection(lowercase_)





				@property
				def 						__UpperCamelCase    (				self)  -> Tuple:
											torch.manual_seed(0)

											a__      ={
											    'num_attention_heads': 2,
											    'attention_head_dim': 12,
											    'embedding_dim': self.text_embedder_hidden_size,
											    'num_layers': 1,
											}

											a__      =PriorTransformer(**lowercase_)
											# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
											a__      =nn.Parameter(torch.ones(model.clip_std.shape))
											return model





				@property
				def 						__UpperCamelCase    (				self)  -> Any:
											torch.manual_seed(0)
											a__      =CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size       ,  image_size=224       ,  projection_dim=self.text_embedder_hidden_size       ,  intermediate_size=37       ,  num_attention_heads=4       ,  num_channels=3       ,  num_hidden_layers=5       ,  patch_size=14       ,  )

											a__      =CLIPVisionModelWithProjection(lowercase_)
											return model





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =CLIPImageProcessor(
											    crop_size=224       ,  do_center_crop=lowercase_       ,  do_normalize=lowercase_       ,  do_resize=lowercase_       ,  image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,  image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,  resample=3       ,  size=224       ,  )

											return image_processor





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =self.dummy_prior
											a__      =self.dummy_image_encoder
											a__      =self.dummy_text_encoder
											a__      =self.dummy_tokenizer
											a__      =self.dummy_image_processor

											a__      =UnCLIPScheduler(
											    variance_type='fixed_small_log'       ,  prediction_type='sample'       ,  num_train_timesteps=1000       ,  clip_sample=lowercase_       ,  clip_sample_range=10.0       ,  )

											a__      ={
											    'prior': prior,
											    'image_encoder': image_encoder,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'scheduler': scheduler,
											    'image_processor': image_processor,
											}

											return components





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_=0)  -> Tuple:
											if str(lowercase_).startswith('mps'):
																		a__      =torch.manual_seed(lowercase_)
											else:
																		a__      =torch.Generator(device=lowercase_).manual_seed(lowercase_)
											a__      ={
											    'prompt': 'horse',
											    'generator': generator,
											    'guidance_scale': 4.0,
											    'num_inference_steps': 2,
											    'output_type': 'np',
											}
											return inputs





				def 						__UpperCamelCase    (				self)  -> int:
											a__      ='cpu'

											a__      =self.get_dummy_components()

											a__      =self.pipeline_class(**lowercase_)
											a__      =pipe.to(lowercase_)

											pipe.set_progress_bar_config(disable=lowercase_)

											a__      =pipe(**self.get_dummy_inputs(lowercase_))
											a__      =output.image_embeds

											a__      =pipe(
											    **self.get_dummy_inputs(lowercase_)       ,  return_dict=lowercase_       ,  )[0]

											a__      =image[0, -10:]
											a__      =image_from_tuple[0, -10:]

											assert image.shape == (1, 32)

											a__      =np.array(
											    [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56])

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





				@skip_mps
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =torch_device == 'cpu'
											a__      =True
											a__      =False

											self._test_inference_batch_single_identical(
											    test_max_difference=lowercase_       ,  relax_max_difference=lowercase_       ,  test_mean_pixel_difference=lowercase_       ,  )





				@skip_mps
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =torch_device == 'cpu'
											a__      =False

											self._test_attention_slicing_forward_pass(
											    test_max_difference=lowercase_       ,  test_mean_pixel_difference=lowercase_       ,  )

