



"""simple docstring"""
import unittest

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

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 							_UpperCAmelCase							( a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=KandinskyVaaPriorPipeline
     a__									=['''prompt''']
     a__									=['''prompt''', '''negative_prompt''']
     a__									=[
         '''num_images_per_prompt''',
         '''generator''',
         '''num_inference_steps''',
         '''latents''',
         '''negative_prompt''',
         '''guidance_scale''',
         '''output_type''',
         '''return_dict''',
     ]
     a__									=False





     @property
     def  __lowerCAmelCase	(					self		)							->					List[str]:
         return 3_2





     @property
     def  __lowerCAmelCase	(					self		)							->					Tuple:
         return 3_2





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





     @property
     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         return self.time_input_dim * 4





     @property
     def  __lowerCAmelCase	(					self		)							->					Any:
         return 1_0_0





     @property
     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							:  List[str]											=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
         return tokenizer





     @property
     def  __lowerCAmelCase	(					self		)							->					List[str]:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Any											=    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(A		)





     @property
     def  __lowerCAmelCase	(					self		)							->					Any:
         torch.manual_seed(0		)

         _UpperCAmelCase							:  Any											=    {
             '''num_attention_heads''': 2,
             '''attention_head_dim''': 1_2,
             '''embedding_dim''': self.text_embedder_hidden_size,
             '''num_layers''': 1,
         }

         _UpperCAmelCase							:  Tuple											=    PriorTransformer(**A		)
         # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
         _UpperCAmelCase							:  Any											=    nn.Parameter(torch.ones(model.clip_std.shape		)		)
         return model





     @property
     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[int]											=    CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size       ,	image_size=2_2_4       ,	projection_dim=self.text_embedder_hidden_size       ,	intermediate_size=3_7       ,	num_attention_heads=4       ,	num_channels=3       ,	num_hidden_layers=5       ,	patch_size=1_4       ,	)

         _UpperCAmelCase							:  Optional[Any]											=    CLIPVisionModelWithProjection(A		)
         return model





     @property
     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  Tuple											=    CLIPImageProcessor(
             crop_size=2_2_4       ,	do_center_crop=A       ,	do_normalize=A       ,	do_resize=A       ,	image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]       ,	image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]       ,	resample=3       ,	size=2_2_4       ,	)

         return image_processor





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Optional[int]											=    self.dummy_prior
         _UpperCAmelCase							:  List[str]											=    self.dummy_image_encoder
         _UpperCAmelCase							:  Tuple											=    self.dummy_text_encoder
         _UpperCAmelCase							:  int											=    self.dummy_tokenizer
         _UpperCAmelCase							:  int											=    self.dummy_image_processor

         _UpperCAmelCase							:  Optional[int]											=    UnCLIPScheduler(
             variance_type='''fixed_small_log'''       ,	prediction_type='''sample'''       ,	num_train_timesteps=1_0_0_0       ,	clip_sample=A       ,	clip_sample_range=10.0       ,	)

         _UpperCAmelCase							:  List[Any]											=    {
             '''prior''': prior,
             '''image_encoder''': image_encoder,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''scheduler''': scheduler,
             '''image_processor''': image_processor,
         }

         return components





     def  __lowerCAmelCase	(					self       ,	A       ,	A=0		)							->					List[str]:
         if str(A		).startswith('''mps'''		):
             _UpperCAmelCase							:  Optional[Any]											=    torch.manual_seed(A		)
         else:
             _UpperCAmelCase							:  str											=    torch.Generator(device=A		).manual_seed(A		)
         _UpperCAmelCase							:  Dict											=    {
             '''prompt''': '''horse''',
             '''generator''': generator,
             '''guidance_scale''': 4.0,
             '''num_inference_steps''': 2,
             '''output_type''': '''np''',
         }
         return inputs





     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  int											=    '''cpu'''

         _UpperCAmelCase							:  Tuple											=    self.get_dummy_components()

         _UpperCAmelCase							:  int											=    self.pipeline_class(**A		)
         _UpperCAmelCase							:  Dict											=    pipe.to(A		)

         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  Union[str, Any]											=    pipe(**self.get_dummy_inputs(A		)		)
         _UpperCAmelCase							:  Dict											=    output.image_embeds

         _UpperCAmelCase							:  Optional[Any]											=    pipe(
             **self.get_dummy_inputs(A		)       ,	return_dict=A       ,	)[0]

         _UpperCAmelCase							:  Union[str, Any]											=    image[0, -1_0:]
         _UpperCAmelCase							:  Optional[int]											=    image_from_tuple[0, -1_0:]

         assert image.shape == (1, 3_2)

         _UpperCAmelCase							:  str											=    np.array(
             [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2





     @skip_mps
     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Optional[int]											=    torch_device == '''cpu'''
         _UpperCAmelCase							:  Tuple											=    True
         _UpperCAmelCase							:  List[str]											=    False

         self._test_inference_batch_single_identical(
             test_max_difference=A       ,	relax_max_difference=A       ,	test_mean_pixel_difference=A       ,	)





     @skip_mps
     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Optional[int]											=    torch_device == '''cpu'''
         _UpperCAmelCase							:  Union[str, Any]											=    False

         self._test_attention_slicing_forward_pass(
             test_max_difference=A       ,	test_mean_pixel_difference=A       ,	)







"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    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							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					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)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

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


