




"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class     lowercase_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					int				  =     ShapEImgaImgPipeline
 UpperCAmelCase   :					Dict				  =     ['''image''']
 UpperCAmelCase   :					str				  =     ['''image''']
 UpperCAmelCase   :					List[Any]				  =     [
     '''num_images_per_prompt''',
     '''num_inference_steps''',
     '''generator''',
     '''latents''',
     '''guidance_scale''',
     '''frame_size''',
     '''output_type''',
     '''return_dict''',
 ]
 UpperCAmelCase   :					int				  =     False

 @property
 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       return 32

 @property
 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       return 32

 @property
 def       lowerCAmelCase_							(			self	:	Dict		):
       return self.time_input_dim * 4

 @property
 def       lowerCAmelCase_							(			self	:	List[Any]		):
       return 8

 @property
 def       lowerCAmelCase_							(			self	:	Any		):
       torch.manual_seed(0		)
       _A        =       CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size   ,							image_size=64   ,							projection_dim=self.text_embedder_hidden_size   ,							intermediate_size=37   ,							num_attention_heads=4   ,							num_channels=3   ,							num_hidden_layers=5   ,							patch_size=1   ,							)

       _A        =       CLIPVisionModel(_UpperCAmelCase		)
       return model

 @property
 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       CLIPImageProcessor(
           crop_size=224   ,							do_center_crop=_UpperCAmelCase   ,							do_normalize=_UpperCAmelCase   ,							do_resize=_UpperCAmelCase   ,							image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]   ,							image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]   ,							resample=3   ,							size=224   ,							)

       return image_processor

 @property
 def       lowerCAmelCase_							(			self	:	str		):
       torch.manual_seed(0		)

       _A        =       {
           'num_attention_heads': 2,
           'attention_head_dim': 16,
           'embedding_dim': self.time_input_dim,
           'num_embeddings': 32,
           'embedding_proj_dim': self.text_embedder_hidden_size,
           'time_embed_dim': self.time_embed_dim,
           'num_layers': 1,
           'clip_embed_dim': self.time_input_dim * 2,
           'additional_embeddings': 0,
           'time_embed_act_fn': 'gelu',
           'norm_in_type': 'layer',
           'embedding_proj_norm_type': 'layer',
           'encoder_hid_proj_type': None,
           'added_emb_type': None,
       }

       _A        =       PriorTransformer(**_UpperCAmelCase		)
       return model

 @property
 def       lowerCAmelCase_							(			self	:	Any		):
       torch.manual_seed(0		)

       _A        =       {
           'param_shapes': (
               (self.renderer_dim, 93),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
           ),
           'd_latent': self.time_input_dim,
           'd_hidden': self.renderer_dim,
           'n_output': 12,
           'background': (
               0.1,
               0.1,
               0.1,
           ),
       }
       _A        =       ShapERenderer(**_UpperCAmelCase		)
       return model

 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       self.dummy_prior
       _A        =       self.dummy_image_encoder
       _A        =       self.dummy_image_processor
       _A        =       self.dummy_renderer

       _A        =       HeunDiscreteScheduler(
           beta_schedule='exp'   ,							num_train_timesteps=1_024   ,							prediction_type='sample'   ,							use_karras_sigmas=_UpperCAmelCase   ,							clip_sample=_UpperCAmelCase   ,							clip_sample_range=1.0   ,							)
       _A        =       {
           'prior': prior,
           'image_encoder': image_encoder,
           'image_processor': image_processor,
           'renderer': renderer,
           'scheduler': scheduler,
       }

       return components

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	Optional[int]=0		):
       _A        =       floats_tensor((1, 3, 64, 64)   ,							rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)

       if str(_UpperCAmelCase		).startswith('mps'		):
             _A        =       torch.manual_seed(_UpperCAmelCase		)
       else:
             _A        =       torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
       _A        =       {
           'image': input_image,
           'generator': generator,
           'num_inference_steps': 1,
           'frame_size': 32,
           'output_type': 'np',
       }
       return inputs

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       'cpu'

       _A        =       self.get_dummy_components()

       _A        =       self.pipeline_class(**_UpperCAmelCase		)
       _A        =       pipe.to(_UpperCAmelCase		)

       pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

       _A        =       pipe(**self.get_dummy_inputs(_UpperCAmelCase		)		)
       _A        =       output.images[0]
       _A        =       image[0, -3:, -3:, -1]

       assert image.shape == (20, 32, 32, 3)

       _A        =       np.array(
           [
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
               0.0003_9216,
           ]		)

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

 def       lowerCAmelCase_							(			self	:	Dict		):
       # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
       self._test_inference_batch_consistent(batch_sizes=[1, 2]		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       torch_device == 'cpu'
       _A        =       True
       self._test_inference_batch_single_identical(
           batch_size=2   ,							test_max_difference=_UpperCAmelCase   ,							relax_max_difference=_UpperCAmelCase   ,							)



 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       self.get_dummy_components()
       _A        =       self.pipeline_class(**_UpperCAmelCase		)
       _A        =       pipe.to(_UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

       _A        =       1
       _A        =       2

       _A        =       self.get_dummy_inputs(_UpperCAmelCase		)

       for key in inputs.keys():
             if key in self.batch_params:
                   _A        =       batch_size * [inputs[key]]

       _A        =       pipe(**_UpperCAmelCase   ,							num_images_per_prompt=_UpperCAmelCase		)[0]

       assert images.shape[0] == batch_size * num_images_per_prompt





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



 '''simple docstring'''

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



 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'		)
       _A        =       load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/shap_e/test_shap_e_img2img_out.npy'		)
       _A        =       ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'		)
       _A        =       pipe.to(_UpperCAmelCase		)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

       _A        =       torch.Generator(device=_UpperCAmelCase		).manual_seed(0		)

       _A        =       pipe(
           _UpperCAmelCase   ,							generator=_UpperCAmelCase   ,							guidance_scale=3.0   ,							num_inference_steps=64   ,							frame_size=64   ,							output_type='np'   ,							).images[0]

       assert images.shape == (20, 64, 64, 3)

       assert_mean_pixel_difference(_UpperCAmelCase   ,							_UpperCAmelCase		)





"""simple docstring"""

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a         =      {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

a         =      {
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

a         =      {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}






def 						_snake_case					( _snake_case							: Dict							)   -> int:






      '''simple docstring'''


      if isinstance(_snake_case    ,  _snake_case							):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise argparse.ArgumentTypeError('boolean value expected'							)






def 						_snake_case					( _snake_case							: Optional[int]    ,  _snake_case							: Tuple    ,  _snake_case							: Dict    ,  _snake_case							: Dict    ,  _snake_case							: Optional[Any]=False							)   -> List[str]:






      '''simple docstring'''


      _A        =       checkpoint[F'''{old_prefix}.in_layers.0.weight''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.0.bias''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.2.weight''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.2.bias''']
      _A        =       checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
      _A        =       checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.0.weight''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.0.bias''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.3.weight''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.3.bias''']

      if has_skip:
            _A        =       checkpoint[F'''{old_prefix}.skip_connection.weight''']
            _A        =       checkpoint[F'''{old_prefix}.skip_connection.bias''']

      return new_checkpoint






def 						_snake_case					( _snake_case							: List[Any]    ,  _snake_case							: int    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: int=None							)   -> Optional[int]:






      '''simple docstring'''


      _A       ,   _A       ,   _A        =       checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3    ,  dim=0							)
      _A       ,   _A       ,   _A        =       checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3    ,  dim=0							)

      _A        =       checkpoint[F'''{old_prefix}.norm.weight''']
      _A        =       checkpoint[F'''{old_prefix}.norm.bias''']

      _A        =       weight_q.squeeze(-1							).squeeze(-1							)
      _A        =       bias_q.squeeze(-1							).squeeze(-1							)
      _A        =       weight_k.squeeze(-1							).squeeze(-1							)
      _A        =       bias_k.squeeze(-1							).squeeze(-1							)
      _A        =       weight_v.squeeze(-1							).squeeze(-1							)
      _A        =       bias_v.squeeze(-1							).squeeze(-1							)

      _A        =       (
          checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1							).squeeze(-1							)
      )
      _A        =       checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1							).squeeze(-1							)

      return new_checkpoint






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: Any							)   -> str:






      '''simple docstring'''


      _A        =       torch.load(_snake_case    ,  map_location='cpu'							)
      _A        =       {}

      _A        =       checkpoint['time_embed.0.weight']
      _A        =       checkpoint['time_embed.0.bias']
      _A        =       checkpoint['time_embed.2.weight']
      _A        =       checkpoint['time_embed.2.bias']

      if unet_config["num_class_embeds"] is not None:
            _A        =       checkpoint['label_emb.weight']

      _A        =       checkpoint['input_blocks.0.0.weight']
      _A        =       checkpoint['input_blocks.0.0.bias']

      _A        =       unet_config['down_block_types']
      _A        =       unet_config['layers_per_block']
      _A        =       unet_config['attention_head_dim']
      _A        =       unet_config['block_out_channels']
      _A        =       1
      _A        =       channels_list[0]

      for i, layer_type in enumerate(_snake_case							):
            _A        =       channels_list[i]
            _A        =       current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(_snake_case							):
                        _A        =       F'''down_blocks.{i}.resnets.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.0'''
                        _A        =       True if j == 0 and downsample_block_has_skip else False
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(_snake_case							):
                        _A        =       F'''down_blocks.{i}.resnets.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.0'''
                        _A        =       True if j == 0 and downsample_block_has_skip else False
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        _A        =       F'''down_blocks.{i}.attentions.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.1'''
                        _A        =       convert_attention(
                            _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                        current_layer += 1

            if i != len(_snake_case							) - 1:
                  _A        =       F'''down_blocks.{i}.downsamplers.0'''
                  _A        =       F'''input_blocks.{current_layer}.0'''
                  _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                  current_layer += 1

            _A        =       current_channels

      # hardcoded the mid-block for now
      _A        =       'mid_block.resnets.0'
      _A        =       'middle_block.0'
      _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
      _A        =       'mid_block.attentions.0'
      _A        =       'middle_block.1'
      _A        =       convert_attention(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
      _A        =       'mid_block.resnets.1'
      _A        =       'middle_block.2'
      _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)

      _A        =       0
      _A        =       unet_config['up_block_types']

      for i, layer_type in enumerate(_snake_case							):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1							):
                        _A        =       F'''up_blocks.{i}.resnets.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.0'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        current_layer += 1

                  if i != len(_snake_case							) - 1:
                        _A        =       F'''up_blocks.{i}.upsamplers.0'''
                        _A        =       F'''output_blocks.{current_layer-1}.1'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
            elif layer_type == "AttnUpBlock2D":
                  for j in range(layers_per_block + 1							):
                        _A        =       F'''up_blocks.{i}.resnets.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.0'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        _A        =       F'''up_blocks.{i}.attentions.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.1'''
                        _A        =       convert_attention(
                            _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                        current_layer += 1

                  if i != len(_snake_case							) - 1:
                        _A        =       F'''up_blocks.{i}.upsamplers.0'''
                        _A        =       F'''output_blocks.{current_layer-1}.2'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)

      _A        =       checkpoint['out.0.weight']
      _A        =       checkpoint['out.0.bias']
      _A        =       checkpoint['out.2.weight']
      _A        =       checkpoint['out.2.bias']

      return new_checkpoint


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()

       parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
       parser.add_argument(
           '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
       )
       parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

       a         =      parser.parse_args()
       a         =      strabool(args.class_cond)

       a         =      os.path.basename(args.unet_path)
       print(F'''Checkpoint: {ckpt_name}''')

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              a         =      IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              a         =      LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              a         =      TEST_UNET_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       if not args.class_cond:
              a         =      None

       a         =      con_pt_to_diffuser(args.unet_path, unet_config)

       a         =      UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              a         =      CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              a         =      CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              a         =      CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       a         =      CMStochasticIterativeScheduler(**scheduler_config)

       a         =      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)

