




'''simple docstring'''


import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation

class     A					(	unittest.TestCase					):







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							str:


														"""simple docstring"""


														_a			  =       get_activation('''swish'''      )

														self.assertIsInstance(lowerCAmelCase_							,    nn.SiLU      )

														self.assertEqual(act(torch.tensor(-1_00							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertNotEqual(act(torch.tensor(-1							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertEqual(act(torch.tensor(0							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertEqual(act(torch.tensor(20							,    dtype=torch.floataa      )      ).item()							,    20      )







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


														"""simple docstring"""


														_a			  =       get_activation('''silu'''      )

														self.assertIsInstance(lowerCAmelCase_							,    nn.SiLU      )

														self.assertEqual(act(torch.tensor(-1_00							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertNotEqual(act(torch.tensor(-1							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertEqual(act(torch.tensor(0							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertEqual(act(torch.tensor(20							,    dtype=torch.floataa      )      ).item()							,    20      )







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       get_activation('''mish'''      )

														self.assertIsInstance(lowerCAmelCase_							,    nn.Mish      )

														self.assertEqual(act(torch.tensor(-2_00							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertNotEqual(act(torch.tensor(-1							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertEqual(act(torch.tensor(0							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertEqual(act(torch.tensor(20							,    dtype=torch.floataa      )      ).item()							,    20      )







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							int:


														"""simple docstring"""


														_a			  =       get_activation('''gelu'''      )

														self.assertIsInstance(lowerCAmelCase_							,    nn.GELU      )

														self.assertEqual(act(torch.tensor(-1_00							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertNotEqual(act(torch.tensor(-1							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertEqual(act(torch.tensor(0							,    dtype=torch.floataa      )      ).item()							,    0      )
														self.assertEqual(act(torch.tensor(20							,    dtype=torch.floataa      )      ).item()							,    20      )







'''simple docstring'''


import gc
import random
import unittest

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

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       UnCLIPImageVariationPipeline
							lowercase_           =       IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
							lowercase_           =       IMAGE_VARIATION_BATCH_PARAMS

							lowercase_           =       [
							    'generator',
							    'return_dict',
							    'decoder_num_inference_steps',
							    'super_res_num_inference_steps',
							]
							lowercase_           =       False







							@property
							def      __lowerCAmelCase    (						self :						int      )    ->							str:


														"""simple docstring"""


														return 32







							@property
							def      __lowerCAmelCase    (						self :						Any      )    ->							str:


														"""simple docstring"""


														return 32







							@property
							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Optional[int]:


														"""simple docstring"""


														return self.time_input_dim







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


														"""simple docstring"""


														return self.time_input_dim * 4







							@property
							def      __lowerCAmelCase    (						self :						int      )    ->							Optional[Any]:


														"""simple docstring"""


														return 1_00







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


														"""simple docstring"""


														_a			  =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )
														return tokenizer







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


														"""simple docstring"""


														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=10_00							,    )
														return CLIPTextModelWithProjection(lowerCAmelCase_      )







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


														"""simple docstring"""


														torch.manual_seed(0      )
														_a			  =       CLIPVisionConfig(
														    hidden_size=self.text_embedder_hidden_size							,    projection_dim=self.text_embedder_hidden_size							,    num_hidden_layers=5							,    num_attention_heads=4							,    image_size=32							,    intermediate_size=37							,    patch_size=1							,    )
														return CLIPVisionModelWithProjection(lowerCAmelCase_      )







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


														"""simple docstring"""


														torch.manual_seed(0      )

														_a			  =       {
														    '''clip_embeddings_dim''': self.text_embedder_hidden_size,
														    '''time_embed_dim''': self.time_embed_dim,
														    '''cross_attention_dim''': self.cross_attention_dim,
														}

														_a			  =       UnCLIPTextProjModel(**lowerCAmelCase_      )
														return model







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


														"""simple docstring"""


														torch.manual_seed(0      )

														_a			  =       {
														    '''sample_size''': 32,
														    # RGB in channels
														    '''in_channels''': 3,
														    # Out channels is double in channels because predicts mean and variance
														    '''out_channels''': 6,
														    '''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,
														    '''cross_attention_dim''': self.cross_attention_dim,
														    '''attention_head_dim''': 4,
														    '''resnet_time_scale_shift''': '''scale_shift''',
														    '''class_embed_type''': '''identity''',
														}

														_a			  =       UNetaDConditionModel(**lowerCAmelCase_      )
														return model







							@property
							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							str:


														"""simple docstring"""


														return {
														    "sample_size": 64,
														    "layers_per_block": 1,
														    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
														    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
														    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
														    "in_channels": 6,
														    "out_channels": 3,
														}







							@property
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							str:


														"""simple docstring"""


														torch.manual_seed(0      )

														_a			  =       UNetaDModel(**self.dummy_super_res_kwargs      )
														return model







							@property
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							int:


														"""simple docstring"""


														torch.manual_seed(1      )

														_a			  =       UNetaDModel(**self.dummy_super_res_kwargs      )
														return model







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.dummy_decoder
														_a			  =       self.dummy_text_proj
														_a			  =       self.dummy_text_encoder
														_a			  =       self.dummy_tokenizer
														_a			  =       self.dummy_super_res_first
														_a			  =       self.dummy_super_res_last

														_a			  =       UnCLIPScheduler(
														    variance_type='''learned_range'''							,    prediction_type='''epsilon'''							,    num_train_timesteps=10_00							,    )

														_a			  =       UnCLIPScheduler(
														    variance_type='''fixed_small_log'''							,    prediction_type='''epsilon'''							,    num_train_timesteps=10_00							,    )

														_a			  =       CLIPImageProcessor(crop_size=32							,    size=32      )

														_a			  =       self.dummy_image_encoder

														return {
														    "decoder": decoder,
														    "text_encoder": text_encoder,
														    "tokenizer": tokenizer,
														    "text_proj": text_proj,
														    "feature_extractor": feature_extractor,
														    "image_encoder": image_encoder,
														    "super_res_first": super_res_first,
														    "super_res_last": super_res_last,
														    "decoder_scheduler": decoder_scheduler,
														    "super_res_scheduler": super_res_scheduler,
														}







							def      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						List[str]							,    lowerCAmelCase_ :						Optional[Any]=0							,    lowerCAmelCase_ :						Dict=True      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       floats_tensor((1, 3, 32, 32)							,    rng=random.Random(lowerCAmelCase_      )      ).to(lowerCAmelCase_      )
														if str(lowerCAmelCase_      ).startswith('''mps'''      ):
																					_a			  =       torch.manual_seed(lowerCAmelCase_      )
														else:
																					_a			  =       torch.Generator(device=lowerCAmelCase_      ).manual_seed(lowerCAmelCase_      )

														if pil_image:
																					_a			  =       input_image * 0.5 + 0.5
																					_a			  =       input_image.clamp(0							,    1      )
																					_a			  =       input_image.cpu().permute(0							,    2							,    3							,    1      ).float().numpy()
																					_a			  =       DiffusionPipeline.numpy_to_pil(lowerCAmelCase_      )[0]

														return {
														    "image": input_image,
														    "generator": generator,
														    "decoder_num_inference_steps": 2,
														    "super_res_num_inference_steps": 2,
														    "output_type": "np",
														}







							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       '''cpu'''

														_a			  =       self.get_dummy_components()

														_a			  =       self.pipeline_class(**lowerCAmelCase_      )
														_a			  =       pipe.to(lowerCAmelCase_      )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )

														_a			  =       pipe(**lowerCAmelCase_      )
														_a			  =       output.images

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )

														_a			  =       pipe(
														    **lowerCAmelCase_							,    return_dict=lowerCAmelCase_							,    )[0]

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

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

														_a			  =       np.array(
														    [
														        0.9_9_9_7,
														        0.0_0_0_2,
														        0.9_9_9_7,
														        0.9_9_9_7,
														        0.9_9_6_9,
														        0.0_0_2_3,
														        0.9_9_9_7,
														        0.9_9_6_9,
														        0.9_9_7_0,
														    ]      )

														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      __lowerCAmelCase    (						self :						int      )    ->							Dict:


														"""simple docstring"""


														_a			  =       '''cpu'''

														_a			  =       self.get_dummy_components()

														_a			  =       self.pipeline_class(**lowerCAmelCase_      )
														_a			  =       pipe.to(lowerCAmelCase_      )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )

														_a			  =       pipe(**lowerCAmelCase_      )
														_a			  =       output.images

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )

														_a			  =       pipe(
														    **lowerCAmelCase_							,    return_dict=lowerCAmelCase_							,    )[0]

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

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

														_a			  =       np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]      )

														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      __lowerCAmelCase    (						self :						Optional[int]      )    ->							str:


														"""simple docstring"""


														_a			  =       '''cpu'''

														_a			  =       self.get_dummy_components()

														_a			  =       self.pipeline_class(**lowerCAmelCase_      )
														_a			  =       pipe.to(lowerCAmelCase_      )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )
														_a			  =       [
														    pipeline_inputs['''image'''],
														    pipeline_inputs['''image'''],
														]

														_a			  =       pipe(**lowerCAmelCase_      )
														_a			  =       output.images

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )
														_a			  =       [
														    tuple_pipeline_inputs['''image'''],
														    tuple_pipeline_inputs['''image'''],
														]

														_a			  =       pipe(
														    **lowerCAmelCase_							,    return_dict=lowerCAmelCase_							,    )[0]

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

														assert image.shape == (2, 64, 64, 3)

														_a			  =       np.array(
														    [
														        0.9_9_9_7,
														        0.9_9_8_9,
														        0.0_0_0_8,
														        0.0_0_2_1,
														        0.9_9_6_0,
														        0.0_0_1_8,
														        0.0_0_1_4,
														        0.0_0_0_2,
														        0.9_9_3_3,
														    ]      )

														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      __lowerCAmelCase    (						self :						Any      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       torch.device('''cpu'''      )

														class     A					:
																					lowercase_           =       1



														_a			  =       self.get_dummy_components()

														_a			  =       self.pipeline_class(**lowerCAmelCase_      )
														_a			  =       pipe.to(lowerCAmelCase_      )

														pipe.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       torch.Generator(device=lowerCAmelCase_      ).manual_seed(0      )
														_a			  =       pipe.decoder.dtype
														_a			  =       1

														_a			  =       (
														    batch_size,
														    pipe.decoder.config.in_channels,
														    pipe.decoder.config.sample_size,
														    pipe.decoder.config.sample_size,
														)
														_a			  =       pipe.prepare_latents(
														    lowerCAmelCase_							,    dtype=lowerCAmelCase_							,    device=lowerCAmelCase_							,    generator=lowerCAmelCase_							,    latents=lowerCAmelCase_							,    scheduler=DummyScheduler()      )

														_a			  =       (
														    batch_size,
														    pipe.super_res_first.config.in_channels // 2,
														    pipe.super_res_first.config.sample_size,
														    pipe.super_res_first.config.sample_size,
														)
														_a			  =       pipe.prepare_latents(
														    lowerCAmelCase_							,    dtype=lowerCAmelCase_							,    device=lowerCAmelCase_							,    generator=lowerCAmelCase_							,    latents=lowerCAmelCase_							,    scheduler=DummyScheduler()      )

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )

														_a			  =       pipe(
														    **lowerCAmelCase_							,    decoder_latents=lowerCAmelCase_							,    super_res_latents=lowerCAmelCase_      ).images

														_a			  =       self.get_dummy_inputs(lowerCAmelCase_							,    pil_image=lowerCAmelCase_      )
														# Don't pass image, instead pass embedding
														_a			  =       pipeline_inputs.pop('''image'''      )
														_a			  =       pipe.image_encoder(lowerCAmelCase_      ).image_embeds

														_a			  =       pipe(
														    **lowerCAmelCase_							,    decoder_latents=lowerCAmelCase_							,    super_res_latents=lowerCAmelCase_							,    image_embeddings=lowerCAmelCase_							,    ).images

														# make sure passing text embeddings manually is identical
														assert np.abs(img_out_a - img_out_a      ).max() < 1e-4







							@skip_mps
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Dict:


														"""simple docstring"""


														_a			  =       torch_device == '''cpu'''

														# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
														_a			  =       1e-2

														self._test_attention_slicing_forward_pass(
														    test_max_difference=lowerCAmelCase_							,    expected_max_diff=lowerCAmelCase_      )







							@skip_mps
							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       torch_device == '''cpu'''
														_a			  =       True
														_a			  =       [
														    '''decoder_num_inference_steps''',
														    '''super_res_num_inference_steps''',
														]

														self._test_inference_batch_single_identical(
														    test_max_difference=lowerCAmelCase_							,    relax_max_difference=lowerCAmelCase_							,    additional_params_copy_to_batched_inputs=lowerCAmelCase_							,    )







							def      __lowerCAmelCase    (						self :						Dict      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       [
														    '''decoder_num_inference_steps''',
														    '''super_res_num_inference_steps''',
														]

														if torch_device == "mps":
																					# TODO: MPS errors with larger batch sizes
																					_a			  =       [2, 3]
																					self._test_inference_batch_consistent(
																					    batch_sizes=lowerCAmelCase_							,    additional_params_copy_to_batched_inputs=lowerCAmelCase_							,    )
														else:
																					self._test_inference_batch_consistent(
																					    additional_params_copy_to_batched_inputs=lowerCAmelCase_      )







							@skip_mps
							def      __lowerCAmelCase    (						self :						str      )    ->							Any:


														"""simple docstring"""


														return super().test_dict_tuple_outputs_equivalent()







							@skip_mps
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Optional[int]:


														"""simple docstring"""


														return super().test_save_load_local()







							@skip_mps
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							List[str]:


														"""simple docstring"""


														return super().test_save_load_optional_components()



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







							def      __lowerCAmelCase    (						self :						Any      )    ->							List[str]:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						str      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''      )
														_a			  =       load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''      )

														_a			  =       UnCLIPImageVariationPipeline.from_pretrained(
														    '''kakaobrain/karlo-v1-alpha-image-variations'''							,    torch_dtype=torch.floataa      )
														_a			  =       pipeline.to(lowerCAmelCase_      )
														pipeline.set_progress_bar_config(disable=lowerCAmelCase_      )

														_a			  =       torch.Generator(device='''cpu'''      ).manual_seed(0      )
														_a			  =       pipeline(
														    lowerCAmelCase_							,    generator=lowerCAmelCase_							,    output_type='''np'''							,    )

														_a			  =       output.images[0]

														assert image.shape == (2_56, 2_56, 3)

														assert_mean_pixel_difference(lowerCAmelCase_							,    lowerCAmelCase_							,    15      )

