





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       (UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		str			 =	UnCLIPImageVariationPipeline
   _UpperCamelCase   :		List[Any]			 =	IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
   _UpperCamelCase   :		Dict			 =	IMAGE_VARIATION_BATCH_PARAMS

   _UpperCamelCase   :		List[Any]			 =	[
       'generator',
       'return_dict',
       'decoder_num_inference_steps',
       'super_res_num_inference_steps',
   ]
   _UpperCamelCase   :		Optional[int]			 =	False


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Dict:





     """simple docstring"""







     return 32


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





     """simple docstring"""







     return 32


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Tuple:





     """simple docstring"""







     return self.time_input_dim


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





     """simple docstring"""







     return self.time_input_dim * 4


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Any:





     """simple docstring"""







     return 100


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





     """simple docstring"""







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


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





     """simple docstring"""







     torch.manual_seed(0	)
     lowercase__           =  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-0_5      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1_000      ,					)
     return CLIPTextModelWithProjection(a	)


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





     """simple docstring"""







     torch.manual_seed(0	)
     lowercase__           =  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(a	)


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





     """simple docstring"""







     torch.manual_seed(0	)

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

     lowercase__           =  UnCLIPTextProjModel(**a	)
     return model


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





     """simple docstring"""







     torch.manual_seed(0	)

     lowercase__           =  {
         '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',
     }

     lowercase__           =  UNetaDConditionModel(**a	)
     return model


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





     """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 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       str:





     """simple docstring"""







     torch.manual_seed(0	)

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


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





     """simple docstring"""







     torch.manual_seed(1	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Any:





     """simple docstring"""







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

     lowercase__           =  UnCLIPScheduler(
         variance_type='learned_range'      ,					prediction_type='epsilon'      ,					num_train_timesteps=1_000      ,					)

     lowercase__           =  UnCLIPScheduler(
         variance_type='fixed_small_log'      ,					prediction_type='epsilon'      ,					num_train_timesteps=1_000      ,					)

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

     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    Tuple      ,					a      :    int=0      ,					a      :    Dict=True	)->       Any:





     """simple docstring"""







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

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

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  'cpu'

     lowercase__           =  self.get_dummy_components()

     lowercase__           =  self.pipeline_class(**a	)
     lowercase__           =  pipe.to(a	)

     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs(a      ,					pil_image=a	)

     lowercase__           =  pipe(**a	)
     lowercase__           =  output.images

     lowercase__           =  self.get_dummy_inputs(a      ,					pil_image=a	)

     lowercase__           =  pipe(
         **a      ,					return_dict=a      ,					)[0]

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

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

     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       List[str]:





     """simple docstring"""







     lowercase__           =  'cpu'

     lowercase__           =  self.get_dummy_components()

     lowercase__           =  self.pipeline_class(**a	)
     lowercase__           =  pipe.to(a	)

     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs(a      ,					pil_image=a	)

     lowercase__           =  pipe(**a	)
     lowercase__           =  output.images

     lowercase__           =  self.get_dummy_inputs(a      ,					pil_image=a	)

     lowercase__           =  pipe(
         **a      ,					return_dict=a      ,					)[0]

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

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

     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Union[str, Any]:





     """simple docstring"""







     lowercase__           =  'cpu'

     lowercase__           =  self.get_dummy_components()

     lowercase__           =  self.pipeline_class(**a	)
     lowercase__           =  pipe.to(a	)

     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  self.get_dummy_inputs(a      ,					pil_image=a	)
     lowercase__           =  [
         pipeline_inputs['image'],
         pipeline_inputs['image'],
     ]

     lowercase__           =  pipe(**a	)
     lowercase__           =  output.images

     lowercase__           =  self.get_dummy_inputs(a      ,					pil_image=a	)
     lowercase__           =  [
         tuple_pipeline_inputs['image'],
         tuple_pipeline_inputs['image'],
     ]

     lowercase__           =  pipe(
         **a      ,					return_dict=a      ,					)[0]

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

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

     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  torch.device('cpu'	)
     class     SCREAMING_SNAKE_CASE       :
        _UpperCamelCase   :		Union[str, Any]			 =	1






     lowercase__           =  self.get_dummy_components()

     lowercase__           =  self.pipeline_class(**a	)
     lowercase__           =  pipe.to(a	)

     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  torch.Generator(device=a	).manual_seed(0	)
     lowercase__           =  pipe.decoder.dtype
     lowercase__           =  1

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

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

     lowercase__           =  self.get_dummy_inputs(a      ,					pil_image=a	)

     lowercase__           =  pipe(
         **a      ,					decoder_latents=a      ,					super_res_latents=a	).images

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

     lowercase__           =  pipe(
         **a      ,					decoder_latents=a      ,					super_res_latents=a      ,					image_embeddings=a      ,					).images

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


   @skip_mps
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  torch_device == 'cpu'

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

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


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





     """simple docstring"""







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

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       List[str]:





     """simple docstring"""







     lowercase__           =  [
         'decoder_num_inference_steps',
         'super_res_num_inference_steps',
     ]

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


   @skip_mps
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       int:





     """simple docstring"""







     return super().test_dict_tuple_outputs_equivalent()


   @skip_mps
   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       Optional[int]:





     """simple docstring"""







     return super().test_save_load_local()


   @skip_mps
   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Union[str, Any]:





     """simple docstring"""







     return super().test_save_load_optional_components()






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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Any:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       Dict:





     """simple docstring"""







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

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

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

     lowercase__           =  output.images[0]

     assert image.shape == (256, 256, 3)

     assert_mean_pixel_difference(a      ,					a      ,					15	)









from typing import TYPE_CHECKING

from ..utils import _LazyModule


lowercase_          =		{
    """config""": [
        """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
        """OnnxConfig""",
        """OnnxConfigWithPast""",
        """OnnxSeq2SeqConfigWithPast""",
        """PatchingSpec""",
    ],
    """convert""": ["""export""", """validate_model_outputs"""],
    """features""": ["""FeaturesManager"""],
    """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""],
}


if TYPE_CHECKING:
      from .config import (
          EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
          OnnxConfig,
          OnnxConfigWithPast,
          OnnxSeqaSeqConfigWithPast,
          PatchingSpec,
      )
      from .convert import export, validate_model_outputs
      from .features import FeaturesManager
      from .utils import ParameterFormat, compute_serialized_parameters_size

else:
      import sys

      lowercase_          =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


