


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
 import transformers
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     random.seed(UpperCamelCase_    )
     np.random.seed(UpperCamelCase_    )
     torch.manual_seed(UpperCamelCase_    )
     torch.cuda.manual_seed_all(UpperCamelCase_    )
     # ^^ safe to call this function even if cuda is not available

class   A__ :







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case = 0.9999      , __snake_case = 0.0      , __snake_case = 0      , __snake_case = False      , __snake_case = 1.0      , __snake_case = 2 / 3      , __snake_case = None      , __snake_case = None      , **__snake_case      , ):

         if isinstance(__snake_case      , torch.nn.Module   ):
              snake_case					=    (
                  '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
                  '''Please pass the parameters of the module instead.'''
              )
              deprecate(
                  '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''      , '''1.0.0'''      , __snake_case      , standard_warn=__snake_case      , )
              snake_case					=    parameters.parameters()

              # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
              snake_case					=    True

         if kwargs.get('''max_value'''      , __snake_case   ) is not None:
              snake_case					=    '''The `max_value` argument is deprecated. Please use `decay` instead.'''
              deprecate('''max_value'''      , '''1.0.0'''      , __snake_case      , standard_warn=__snake_case   )
              snake_case					=    kwargs['''max_value''']

         if kwargs.get('''min_value'''      , __snake_case   ) is not None:
              snake_case					=    '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
              deprecate('''min_value'''      , '''1.0.0'''      , __snake_case      , standard_warn=__snake_case   )
              snake_case					=    kwargs['''min_value''']

         snake_case					=    list(__snake_case   )
         snake_case					=    [p.clone().detach() for p in parameters]

         if kwargs.get('''device'''      , __snake_case   ) is not None:
              snake_case					=    '''The `device` argument is deprecated. Please use `to` instead.'''
              deprecate('''device'''      , '''1.0.0'''      , __snake_case      , standard_warn=__snake_case   )
              self.to(device=kwargs['''device''']   )

         snake_case					=    None

         snake_case					=    decay
         snake_case					=    min_decay
         snake_case					=    update_after_step
         snake_case					=    use_ema_warmup
         snake_case					=    inv_gamma
         snake_case					=    power
         snake_case					=    0
         snake_case					=    None  # set in `step()`

         snake_case					=    model_cls
         snake_case					=    model_config
    @classmethod
    def 		a_      (	cls      , __snake_case      , __snake_case   ):
         snake_case			,  snake_case					=    model_cls.load_config(__snake_case      , return_unused_kwargs=__snake_case   )
         snake_case					=    model_cls.from_pretrained(__snake_case   )

         snake_case					=    cls(model.parameters()      , model_cls=__snake_case      , model_config=model.config   )

         ema_model.load_state_dict(__snake_case   )
         return ema_model
    def 		a_      (	self      , __snake_case   ):
         if self.model_cls is None:
              raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''   )

         if self.model_config is None:
              raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''   )

         snake_case					=    self.model_cls.from_config(self.model_config   )
         snake_case					=    self.state_dict()
         state_dict.pop('''shadow_params'''      , __snake_case   )

         model.register_to_config(**__snake_case   )
         self.copy_to(model.parameters()   )
         model.save_pretrained(__snake_case   )
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    max(0      , optimization_step - self.update_after_step - 1   )

         if step <= 0:
              return 0.0

         if self.use_ema_warmup:
              snake_case					=    1 - (1 + step / self.inv_gamma) ** -self.power
         else:
              snake_case					=    (1 + step) / (1_0 + step)

         snake_case					=    min(__snake_case      , self.decay   )
         # make sure decay is not smaller than min_decay
         snake_case					=    max(__snake_case      , self.min_decay   )
         return cur_decay_value
    @torch.no_grad()
    def 		a_      (	self      , __snake_case   ):
         if isinstance(__snake_case      , torch.nn.Module   ):
              snake_case					=    (
                  '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
                  '''Please pass the parameters of the module instead.'''
              )
              deprecate(
                  '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''      , '''1.0.0'''      , __snake_case      , standard_warn=__snake_case      , )
              snake_case					=    parameters.parameters()

         snake_case					=    list(__snake_case   )

         self.optimization_step += 1

         # Compute the decay factor for the exponential moving average.
         snake_case					=    self.get_decay(self.optimization_step   )
         snake_case					=    decay
         snake_case					=    1 - decay

         snake_case					=    contextlib.nullcontext
         if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
              import deepspeed

         for s_param, param in zip(self.shadow_params      , __snake_case   ):
              if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   snake_case					=    deepspeed.zero.GatheredParameters(__snake_case      , modifier_rank=__snake_case   )

              with context_manager():
                   if param.requires_grad:
                        s_param.sub_(one_minus_decay * (s_param - param)   )
                   else:
                        s_param.copy_(__snake_case   )
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    list(__snake_case   )
         for s_param, param in zip(self.shadow_params      , __snake_case   ):
              param.data.copy_(s_param.to(param.device   ).data   )
    def 		a_      (	self      , __snake_case=None      , __snake_case=None   ):
         snake_case					=    [
             p.to(device=__snake_case      , dtype=__snake_case   ) if p.is_floating_point() else p.to(device=__snake_case   )
             for p in self.shadow_params
         ]
    def 		a_      (	self   ):
         return {
             "decay": self.decay,
             "min_decay": self.min_decay,
             "optimization_step": self.optimization_step,
             "update_after_step": self.update_after_step,
             "use_ema_warmup": self.use_ema_warmup,
             "inv_gamma": self.inv_gamma,
             "power": self.power,
             "shadow_params": self.shadow_params,
         }
    def 		a_      (	self      , __snake_case   ):
         snake_case					=    [param.detach().cpu().clone() for param in parameters]
    def 		a_      (	self      , __snake_case   ):
         if self.temp_stored_params is None:
              raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''   )
         for c_param, param in zip(self.temp_stored_params      , __snake_case   ):
              param.data.copy_(c_param.data   )

         # Better memory-wise.
         snake_case					=    None




    def 		a_      (	self      , __snake_case   ):
         snake_case					=    copy.deepcopy(__snake_case   )

         snake_case					=    state_dict.get('''decay'''      , self.decay   )
         if self.decay < 0.0 or self.decay > 1.0:
              raise ValueError('''Decay must be between 0 and 1'''   )

         snake_case					=    state_dict.get('''min_decay'''      , self.min_decay   )
         if not isinstance(self.min_decay      , __snake_case   ):
              raise ValueError('''Invalid min_decay'''   )

         snake_case					=    state_dict.get('''optimization_step'''      , self.optimization_step   )
         if not isinstance(self.optimization_step      , __snake_case   ):
              raise ValueError('''Invalid optimization_step'''   )

         snake_case					=    state_dict.get('''update_after_step'''      , self.update_after_step   )
         if not isinstance(self.update_after_step      , __snake_case   ):
              raise ValueError('''Invalid update_after_step'''   )

         snake_case					=    state_dict.get('''use_ema_warmup'''      , self.use_ema_warmup   )
         if not isinstance(self.use_ema_warmup      , __snake_case   ):
              raise ValueError('''Invalid use_ema_warmup'''   )

         snake_case					=    state_dict.get('''inv_gamma'''      , self.inv_gamma   )
         if not isinstance(self.inv_gamma      , (float, int)   ):
              raise ValueError('''Invalid inv_gamma'''   )

         snake_case					=    state_dict.get('''power'''      , self.power   )
         if not isinstance(self.power      , (float, int)   ):
              raise ValueError('''Invalid power'''   )

         snake_case					=    state_dict.get('''shadow_params'''      , __snake_case   )
         if shadow_params is not None:
              snake_case					=    shadow_params
              if not isinstance(self.shadow_params      , __snake_case   ):
                   raise ValueError('''shadow_params must be a list'''   )
              if not all(isinstance(__snake_case      , torch.Tensor   ) for p in self.shadow_params   ):
                   raise ValueError('''shadow_params must all be Tensors'''   )








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__ (   snake_case__	, unittest.TestCase     ):







    """simple docstring"""

    __magic_name__        =		UnCLIPImageVariationPipeline
    __magic_name__        =		IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
    __magic_name__        =		IMAGE_VARIATION_BATCH_PARAMS

    __magic_name__        =		[
        'generator',
        'return_dict',
        'decoder_num_inference_steps',
        'super_res_num_inference_steps',
    ]
    __magic_name__        =		False
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         return self.time_input_dim
    @property
    def 		a_      (	self   ):
         return self.time_input_dim * 4
    @property
    def 		a_      (	self   ):
         return 1_0_0
    @property
    def 		a_      (	self   ):
         snake_case					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
         return tokenizer
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    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=3_7      , layer_norm_eps=1E-05      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1_0_0_0      , )
         return CLIPTextModelWithProjection(__snake_case   )
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    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=3_2      , intermediate_size=3_7      , patch_size=1      , )
         return CLIPVisionModelWithProjection(__snake_case   )
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

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

         snake_case					=    UnCLIPTextProjModel(**__snake_case   )
         return model
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    {
             '''sample_size''': 3_2,
             # 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''',
         }

         snake_case					=    UNetaDConditionModel(**__snake_case   )
         return model
    @property
    def 		a_      (	self   ):
         return {
             "sample_size": 6_4,
             "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 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    UNetaDModel(**self.dummy_super_res_kwargs   )
         return model
    @property
    def 		a_      (	self   ):
         # seeded differently to get different unet than `self.dummy_super_res_first`
         torch.manual_seed(1   )

         snake_case					=    UNetaDModel(**self.dummy_super_res_kwargs   )
         return model
    def 		a_      (	self   ):
         snake_case					=    self.dummy_decoder
         snake_case					=    self.dummy_text_proj
         snake_case					=    self.dummy_text_encoder
         snake_case					=    self.dummy_tokenizer
         snake_case					=    self.dummy_super_res_first
         snake_case					=    self.dummy_super_res_last

         snake_case					=    UnCLIPScheduler(
             variance_type='''learned_range'''      , prediction_type='''epsilon'''      , num_train_timesteps=1_0_0_0      , )

         snake_case					=    UnCLIPScheduler(
             variance_type='''fixed_small_log'''      , prediction_type='''epsilon'''      , num_train_timesteps=1_0_0_0      , )

         snake_case					=    CLIPImageProcessor(crop_size=3_2      , size=3_2   )

         snake_case					=    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 		a_      (	self      , __snake_case      , __snake_case=0      , __snake_case=True   ):
         snake_case					=    floats_tensor((1, 3, 3_2, 3_2)      , rng=random.Random(__snake_case   )   ).to(__snake_case   )
         if str(__snake_case   ).startswith('''mps'''   ):
              snake_case					=    torch.manual_seed(__snake_case   )
         else:
              snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(__snake_case   )

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

         return {
             "image": input_image,
             "generator": generator,
             "decoder_num_inference_steps": 2,
             "super_res_num_inference_steps": 2,
             "output_type": "np",
         }
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

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

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=    np.array(
             [
                 0.9997,
                 0.0002,
                 0.9997,
                 0.9997,
                 0.9969,
                 0.0023,
                 0.9997,
                 0.9969,
                 0.9970,
             ]   )

         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   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

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

         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=    np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]   )

         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   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         snake_case					=    [
             pipeline_inputs['''image'''],
             pipeline_inputs['''image'''],
         ]

         snake_case					=    pipe(**__snake_case   )
         snake_case					=    output.images

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )
         snake_case					=    [
             tuple_pipeline_inputs['''image'''],
             tuple_pipeline_inputs['''image'''],
         ]

         snake_case					=    pipe(
             **__snake_case      , return_dict=__snake_case      , )[0]

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

         assert image.shape == (2, 6_4, 6_4, 3)

         snake_case					=    np.array(
             [
                 0.9997,
                 0.9989,
                 0.0008,
                 0.0021,
                 0.9960,
                 0.0018,
                 0.0014,
                 0.0002,
                 0.9933,
             ]   )

         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   ):
         snake_case					=    torch.device('''cpu'''   )

         class   A__ :







             """simple docstring"""

             __magic_name__        =		1






         snake_case					=    self.get_dummy_components()

         snake_case					=    self.pipeline_class(**__snake_case   )
         snake_case					=    pipe.to(__snake_case   )

         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(0   )
         snake_case					=    pipe.decoder.dtype
         snake_case					=    1

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

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

         snake_case					=    self.get_dummy_inputs(__snake_case      , pil_image=__snake_case   )

         snake_case					=    pipe(
             **__snake_case      , decoder_latents=__snake_case      , super_res_latents=__snake_case   ).images

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

         snake_case					=    pipe(
             **__snake_case      , decoder_latents=__snake_case      , super_res_latents=__snake_case      , image_embeddings=__snake_case      , ).images

         # make sure passing text embeddings manually is identical
         assert np.abs(img_out_a - img_out_a   ).max() < 1E-4
    @skip_mps
    def 		a_      (	self   ):
         snake_case					=    torch_device == '''cpu'''

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

         self._test_attention_slicing_forward_pass(
             test_max_difference=__snake_case      , expected_max_diff=__snake_case   )
    @skip_mps
    def 		a_      (	self   ):
         snake_case					=    torch_device == '''cpu'''
         snake_case					=    True
         snake_case					=    [
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=__snake_case      , relax_max_difference=__snake_case      , additional_params_copy_to_batched_inputs=__snake_case      , )
    def 		a_      (	self   ):
         snake_case					=    [
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         if torch_device == "mps":
              # TODO: MPS errors with larger batch sizes
              snake_case					=    [2, 3]
              self._test_inference_batch_consistent(
                  batch_sizes=__snake_case      , additional_params_copy_to_batched_inputs=__snake_case      , )
         else:
              self._test_inference_batch_consistent(
                  additional_params_copy_to_batched_inputs=__snake_case   )
    @skip_mps
    def 		a_      (	self   ):
         return super().test_dict_tuple_outputs_equivalent()
    @skip_mps
    def 		a_      (	self   ):
         return super().test_save_load_local()




    @skip_mps
    def 		a_      (	self   ):
         return super().test_save_load_optional_components()






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







    """simple docstring"""

    def 		a_      (	self   ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




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

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

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

         snake_case					=    output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         assert_mean_pixel_difference(__snake_case      , __snake_case      , 1_5   )




