





"""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 							snake_case							(			__UpperCAmelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       int         =       UnCLIPImageVariationPipeline
      _A     :       List[Any]         =       IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
      _A     :       int         =       IMAGE_VARIATION_BATCH_PARAMS

      _A     :       Tuple         =       [
          'generator',
          'return_dict',
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]
      _A     :       Optional[int]         =       False

      @property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        return 32

      @property
      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        return 32

      @property
      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        return self.time_input_dim

      @property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        return self.time_input_dim * 4

      @property
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        return 100

      @property
      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
        return tokenizer

      @property
      def       A_					(							self       :      str ):


        '''simple docstring'''




        torch.manual_seed(0 )
        __UpperCAmelCase    :						List[str]       =						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=1_000						,				)
        return CLIPTextModelWithProjection(__lowercase )

      @property
      def       A_					(							self       :      str ):


        '''simple docstring'''




        torch.manual_seed(0 )
        __UpperCAmelCase    :						List[Any]       =						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(__lowercase )

      @property
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        torch.manual_seed(0 )

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

        __UpperCAmelCase    :						Union[str, Any]       =						UnCLIPTextProjModel(**__lowercase )
        return model

      @property
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        torch.manual_seed(0 )

        __UpperCAmelCase    :						Tuple       =						{
            '''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''',
        }

        __UpperCAmelCase    :						Dict       =						UNetaDConditionModel(**__lowercase )
        return model

      @property
      def       A_					(							self       :      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       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        torch.manual_seed(0 )

        __UpperCAmelCase    :						Tuple       =						UNetaDModel(**self.dummy_super_res_kwargs )
        return model

      @property
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        torch.manual_seed(1 )

        __UpperCAmelCase    :						Any       =						UNetaDModel(**self.dummy_super_res_kwargs )
        return model

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.dummy_decoder
        __UpperCAmelCase    :						List[str]       =						self.dummy_text_proj
        __UpperCAmelCase    :						Optional[int]       =						self.dummy_text_encoder
        __UpperCAmelCase    :						str       =						self.dummy_tokenizer
        __UpperCAmelCase    :						str       =						self.dummy_super_res_first
        __UpperCAmelCase    :						Any       =						self.dummy_super_res_last

        __UpperCAmelCase    :						List[str]       =						UnCLIPScheduler(
            variance_type='''learned_range'''						,				prediction_type='''epsilon'''						,				num_train_timesteps=1_000						,				)

        __UpperCAmelCase    :						List[Any]       =						UnCLIPScheduler(
            variance_type='''fixed_small_log'''						,				prediction_type='''epsilon'''						,				num_train_timesteps=1_000						,				)

        __UpperCAmelCase    :						Optional[Any]       =						CLIPImageProcessor(crop_size=32						,				size=32 )

        __UpperCAmelCase    :						Dict       =						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       :      Optional[Any]						,				__lowercase       :      int						,				__lowercase       :      Union[str, Any]=0						,				__lowercase       :      Optional[Any]=True ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						floats_tensor((1, 3, 32, 32)						,				rng=random.Random(__lowercase ) ).to(__lowercase )
        if str(__lowercase ).startswith('''mps''' ):
          __UpperCAmelCase    :						Tuple       =						torch.manual_seed(__lowercase )
        else:
          __UpperCAmelCase    :						Optional[int]       =						torch.Generator(device=__lowercase ).manual_seed(__lowercase )

        if pil_image:
          __UpperCAmelCase    :						List[Any]       =						input_image * 0.5 + 0.5
          __UpperCAmelCase    :						List[Any]       =						input_image.clamp(0						,				1 )
          __UpperCAmelCase    :						int       =						input_image.cpu().permute(0						,				2						,				3						,				1 ).float().numpy()
          __UpperCAmelCase    :						Dict       =						DiffusionPipeline.numpy_to_pil(__lowercase )[0]

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

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						'''cpu'''

        __UpperCAmelCase    :						List[Any]       =						self.get_dummy_components()

        __UpperCAmelCase    :						Any       =						self.pipeline_class(**__lowercase )
        __UpperCAmelCase    :						List[Any]       =						pipe.to(__lowercase )

        pipe.set_progress_bar_config(disable=__lowercase )

        __UpperCAmelCase    :						str       =						self.get_dummy_inputs(__lowercase						,				pil_image=__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						pipe(**__lowercase )
        __UpperCAmelCase    :						int       =						output.images

        __UpperCAmelCase    :						int       =						self.get_dummy_inputs(__lowercase						,				pil_image=__lowercase )

        __UpperCAmelCase    :						Any       =						pipe(
            **__lowercase						,				return_dict=__lowercase						,				)[0]

        __UpperCAmelCase    :						Any       =						image[0, -3:, -3:, -1]
        __UpperCAmelCase    :						List[Any]       =						image_from_tuple[0, -3:, -3:, -1]

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

        __UpperCAmelCase    :						str       =						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       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						'''cpu'''

        __UpperCAmelCase    :						List[str]       =						self.get_dummy_components()

        __UpperCAmelCase    :						Optional[Any]       =						self.pipeline_class(**__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						pipe.to(__lowercase )

        pipe.set_progress_bar_config(disable=__lowercase )

        __UpperCAmelCase    :						List[str]       =						self.get_dummy_inputs(__lowercase						,				pil_image=__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						pipe(**__lowercase )
        __UpperCAmelCase    :						List[Any]       =						output.images

        __UpperCAmelCase    :						Optional[int]       =						self.get_dummy_inputs(__lowercase						,				pil_image=__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						pipe(
            **__lowercase						,				return_dict=__lowercase						,				)[0]

        __UpperCAmelCase    :						int       =						image[0, -3:, -3:, -1]
        __UpperCAmelCase    :						Dict       =						image_from_tuple[0, -3:, -3:, -1]

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

        __UpperCAmelCase    :						Union[str, Any]       =						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       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						'''cpu'''

        __UpperCAmelCase    :						Tuple       =						self.get_dummy_components()

        __UpperCAmelCase    :						Any       =						self.pipeline_class(**__lowercase )
        __UpperCAmelCase    :						Optional[Any]       =						pipe.to(__lowercase )

        pipe.set_progress_bar_config(disable=__lowercase )

        __UpperCAmelCase    :						Any       =						self.get_dummy_inputs(__lowercase						,				pil_image=__lowercase )
        __UpperCAmelCase    :						int       =						[
            pipeline_inputs['''image'''],
            pipeline_inputs['''image'''],
        ]

        __UpperCAmelCase    :						List[str]       =						pipe(**__lowercase )
        __UpperCAmelCase    :						List[Any]       =						output.images

        __UpperCAmelCase    :						List[str]       =						self.get_dummy_inputs(__lowercase						,				pil_image=__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						[
            tuple_pipeline_inputs['''image'''],
            tuple_pipeline_inputs['''image'''],
        ]

        __UpperCAmelCase    :						Optional[Any]       =						pipe(
            **__lowercase						,				return_dict=__lowercase						,				)[0]

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

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

        __UpperCAmelCase    :						Any       =						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       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						torch.device('''cpu''' )




        class 							snake_case							:

              '''simple docstring'''



              _A     :       int         =       1


        __UpperCAmelCase    :						Tuple       =						self.get_dummy_components()

        __UpperCAmelCase    :						int       =						self.pipeline_class(**__lowercase )
        __UpperCAmelCase    :						str       =						pipe.to(__lowercase )

        pipe.set_progress_bar_config(disable=__lowercase )

        __UpperCAmelCase    :						List[Any]       =						torch.Generator(device=__lowercase ).manual_seed(0 )
        __UpperCAmelCase    :						Optional[Any]       =						pipe.decoder.dtype
        __UpperCAmelCase    :						str       =						1

        __UpperCAmelCase    :						Any       =						(
            batch_size,
            pipe.decoder.config.in_channels,
            pipe.decoder.config.sample_size,
            pipe.decoder.config.sample_size,
        )
        __UpperCAmelCase    :						Tuple       =						pipe.prepare_latents(
            __lowercase						,				dtype=__lowercase						,				device=__lowercase						,				generator=__lowercase						,				latents=__lowercase						,				scheduler=DummyScheduler() )

        __UpperCAmelCase    :						Dict       =						(
            batch_size,
            pipe.super_res_first.config.in_channels // 2,
            pipe.super_res_first.config.sample_size,
            pipe.super_res_first.config.sample_size,
        )
        __UpperCAmelCase    :						List[str]       =						pipe.prepare_latents(
            __lowercase						,				dtype=__lowercase						,				device=__lowercase						,				generator=__lowercase						,				latents=__lowercase						,				scheduler=DummyScheduler() )

        __UpperCAmelCase    :						int       =						self.get_dummy_inputs(__lowercase						,				pil_image=__lowercase )

        __UpperCAmelCase    :						List[str]       =						pipe(
            **__lowercase						,				decoder_latents=__lowercase						,				super_res_latents=__lowercase ).images

        __UpperCAmelCase    :						List[Any]       =						self.get_dummy_inputs(__lowercase						,				pil_image=__lowercase )
        # Don't pass image, instead pass embedding
        __UpperCAmelCase    :						Tuple       =						pipeline_inputs.pop('''image''' )
        __UpperCAmelCase    :						List[str]       =						pipe.image_encoder(__lowercase ).image_embeds

        __UpperCAmelCase    :						int       =						pipe(
            **__lowercase						,				decoder_latents=__lowercase						,				super_res_latents=__lowercase						,				image_embeddings=__lowercase						,				).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       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						torch_device == '''cpu'''

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

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

      @skip_mps
      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						torch_device == '''cpu'''
        __UpperCAmelCase    :						Tuple       =						True
        __UpperCAmelCase    :						int       =						[
            '''decoder_num_inference_steps''',
            '''super_res_num_inference_steps''',
        ]

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

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						[
            '''decoder_num_inference_steps''',
            '''super_res_num_inference_steps''',
        ]

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

      @skip_mps
      def       A_					(							self       :      int ):


        '''simple docstring'''




        return super().test_dict_tuple_outputs_equivalent()

      @skip_mps
      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        return super().test_save_load_local()







      @skip_mps
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        return super().test_save_load_optional_components()




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

      '''simple docstring'''



      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




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







      def       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png''' )
        __UpperCAmelCase    :						List[Any]       =						load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy''' )

        __UpperCAmelCase    :						List[str]       =						UnCLIPImageVariationPipeline.from_pretrained(
            '''kakaobrain/karlo-v1-alpha-image-variations'''						,				torch_dtype=torch.floataa )
        __UpperCAmelCase    :						List[str]       =						pipeline.to(__lowercase )
        pipeline.set_progress_bar_config(disable=__lowercase )

        __UpperCAmelCase    :						str       =						torch.Generator(device='''cpu''' ).manual_seed(0 )
        __UpperCAmelCase    :						Union[str, Any]       =						pipeline(
            __lowercase						,				generator=__lowercase						,				output_type='''np'''						,				)

        __UpperCAmelCase    :						Optional[Any]       =						output.images[0]

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

        assert_mean_pixel_difference(__lowercase						,				__lowercase						,				15 )






"""simple docstring"""





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						OmegaConf.load(UpperCAmelCase_  )
  __UpperCAmelCase    :						Union[str, Any]       =						torch.load(UpperCAmelCase_  ,			map_location='''cpu'''  )['''model''']
  __UpperCAmelCase    :						List[Any]       =						list(state_dict.keys()  )

  # extract state_dict for VQVAE
  __UpperCAmelCase    :						Dict       =						{}
  __UpperCAmelCase    :						Tuple       =						'''first_stage_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						state_dict[key]

    # extract state_dict for UNetLDM
  __UpperCAmelCase    :						Union[str, Any]       =						{}
  __UpperCAmelCase    :						Any       =						'''model.diffusion_model.'''
  for key in keys:
    if key.startswith(UpperCAmelCase_  ):
      __UpperCAmelCase    :						int       =						state_dict[key]

  __UpperCAmelCase    :						Any       =						config.model.params.first_stage_config.params
  __UpperCAmelCase    :						Dict       =						config.model.params.unet_config.params

  __UpperCAmelCase    :						Tuple       =						VQModel(**UpperCAmelCase_  ).eval()
  vqvae.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Any       =						UNetLDMModel(**UpperCAmelCase_  ).eval()
  unet.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						DDIMScheduler(
      timesteps=config.model.params.timesteps  ,			beta_schedule='''scaled_linear'''  ,			beta_start=config.model.params.linear_start  ,			beta_end=config.model.params.linear_end  ,			clip_sample=UpperCAmelCase_  ,			)

  __UpperCAmelCase    :						Tuple       =						LDMPipeline(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  pipeline.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:str				   =       argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  lowercase__				:Dict				   =       parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)