






'''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 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   unittest.TestCase  ):
      __a	       =UnCLIPImageVariationPipeline
      __a	       =IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
      __a	       =IMAGE_VARIATION_BATCH_PARAMS

      __a	       =[
          "generator",
          "return_dict",
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]
      __a	       =False




      @property
      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         return 32




      @property
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         return 32




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



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




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



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




      @property
      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         return 100




      @property
      def 			__UpperCamelCase				(	self   )				->int:



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




      @property
      def 			__UpperCamelCase				(	self   )				->Dict:



         '''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=1000			, )
         return CLIPTextModelWithProjection(lowerCamelCase   )




      @property
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''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 			__UpperCamelCase				(	self   )				->Tuple:



         '''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 			__UpperCamelCase				(	self   )				->Dict:



         '''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 			__UpperCamelCase				(	self   )				->Tuple:



         '''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 			__UpperCamelCase				(	self   )				->Dict:



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

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




      @property
      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         # seeded differently to get different unet than `self.dummy_super_res_first`
         torch.manual_seed(1   )

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




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



         '''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=1000			, )

         __a					  =     UnCLIPScheduler(
             variance_type='fixed_small_log'			, prediction_type='epsilon'			, num_train_timesteps=1000			, )

         __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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase=0			, lowerCamelCase=True   )				->Any:



         '''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 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''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(
             [
                 0.9997,
                 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 			__UpperCamelCase				(	self   )				->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([0.9997, 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 			__UpperCamelCase				(	self   )				->Optional[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					  =     [
             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(
             [
                 0.9997,
                 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 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     torch.device('cpu'   )
         class 				__SCREAMING_SNAKE_CASE							:
               __a	       =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 			__UpperCamelCase				(	self   )				->List[str]:



         '''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 			__UpperCamelCase				(	self   )				->Union[str, 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 			__UpperCamelCase				(	self   )				->Any:



         '''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 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return super().test_dict_tuple_outputs_equivalent()




      @skip_mps
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return super().test_save_load_local()






      @skip_mps
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         return super().test_save_load_optional_components()







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




      def 			__UpperCamelCase				(	self   )				->Optional[Any]:



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






      def 			__UpperCamelCase				(	self   )				->int:



         '''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 == (256, 256, 3)

         assert_mean_pixel_difference(lowerCamelCase			, lowerCamelCase			, 15   )







'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase   :							Dict         =   logging.get_logger(__name__)
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =["pixel_values"]




      def __init__(	self			, lowerCamelCase = True			, lowerCamelCase = 32			, lowerCamelCase=PILImageResampling.BILINEAR			, lowerCamelCase = True			, **lowerCamelCase			, )				->None:



         '''simple docstring'''
         __a					  =     do_resize
         __a					  =     do_rescale
         __a					  =     size_divisor
         __a					  =     resample
         super().__init__(**lowerCamelCase   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = None			, **lowerCamelCase   )				->np.ndarray:



         '''simple docstring'''
         __a  ,      __a					  =     get_image_size(lowerCamelCase   )
         # Rounds the height and width down to the closest multiple of size_divisor
         __a					  =     height // size_divisor * size_divisor
         __a					  =     width // size_divisor * size_divisor
         __a					  =     resize(lowerCamelCase			, (new_h, new_w)			, resample=lowerCamelCase			, data_format=lowerCamelCase			, **lowerCamelCase   )
         return image




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = None			, **lowerCamelCase   )				->np.ndarray:



         '''simple docstring'''
         return rescale(image=lowerCamelCase			, scale=lowerCamelCase			, data_format=lowerCamelCase			, **lowerCamelCase   )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase=None			, lowerCamelCase = None			, lowerCamelCase = None			, lowerCamelCase = ChannelDimension.FIRST			, **lowerCamelCase			, )				->BatchFeature:



         '''simple docstring'''
         __a					  =     do_resize if do_resize is not None else self.do_resize
         __a					  =     do_rescale if do_rescale is not None else self.do_rescale
         __a					  =     size_divisor if size_divisor is not None else self.size_divisor
         __a					  =     resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
            raise ValueError('size_divisor is required for resizing'   )

         __a					  =     make_list_of_images(lowerCamelCase   )

         if not valid_images(lowerCamelCase   ):
            raise ValueError('Invalid image(s)'   )

         # All transformations expect numpy arrays.
         __a					  =     [to_numpy_array(lowerCamelCase   ) for img in images]

         if do_resize:
            __a					  =     [self.resize(lowerCamelCase			, size_divisor=lowerCamelCase			, resample=lowerCamelCase   ) for image in images]

         if do_rescale:
            __a					  =     [self.rescale(lowerCamelCase			, scale=1 / 255   ) for image in images]

         __a					  =     [to_channel_dimension_format(lowerCamelCase			, lowerCamelCase   ) for image in images]

         __a					  =     {'pixel_values': images}
         return BatchFeature(data=lowerCamelCase			, tensor_type=lowerCamelCase   )