




import argparse

import torch

from diffusers import UNetaDConditionModel

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			Dict    ,    __UpperCAmelCase		:			Any    ,    __UpperCAmelCase		:			Tuple=None    ,    __UpperCAmelCase		:			Any=None    ,    __UpperCAmelCase		:			List[Any]=None							)      ->				Any:
      assert isinstance(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE							), "Paths should be a list of dicts containing 'old' and 'new' keys."

      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
            for path, path_map in attention_paths_to_split.items():
                  SCREAMING_SNAKE_CASE_					=    old_checkpoint[path]
                  SCREAMING_SNAKE_CASE_					=    old_tensor.shape[0] // 3

                  SCREAMING_SNAKE_CASE_					=    (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

                  SCREAMING_SNAKE_CASE_					=    old_tensor.shape[0] // config['num_head_channels'] // 3

                  SCREAMING_SNAKE_CASE_					=    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
                  SCREAMING_SNAKE_CASE_					=    old_tensor.split(channels // num_heads    ,    dim=1							)

                  SCREAMING_SNAKE_CASE_					=    query.reshape(_SCREAMING_SNAKE_CASE							)
                  SCREAMING_SNAKE_CASE_					=    key.reshape(_SCREAMING_SNAKE_CASE							)
                  SCREAMING_SNAKE_CASE_					=    value.reshape(_SCREAMING_SNAKE_CASE							)

      for path in paths:
            SCREAMING_SNAKE_CASE_					=    path['new']

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        SCREAMING_SNAKE_CASE_					=    new_path.replace(replacement['old']    ,    replacement['new']							)

        # proj_attn.weight has to be converted from conv 1D to linear
            SCREAMING_SNAKE_CASE_					=    old_checkpoint[path['old']]
            SCREAMING_SNAKE_CASE_					=    ['proj_attn.weight']
            SCREAMING_SNAKE_CASE_					=    ['proj_out.weight', 'proj_in.weight']
            if any(k in new_path for k in names							):
                  SCREAMING_SNAKE_CASE_					=    weight[:, :, 0]
            elif any(k in new_path for k in names_a							) and len(weight.shape							) > 2 and ".attentions." not in new_path:
                  SCREAMING_SNAKE_CASE_					=    weight[:, :, 0]
            else:
                  SCREAMING_SNAKE_CASE_					=    weight

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[int]    ,    __UpperCAmelCase		:			str=0							)      ->				Any:
      SCREAMING_SNAKE_CASE_					=    []
      for old_item in old_list:
            SCREAMING_SNAKE_CASE_					=    old_item

            #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
            #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

            #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
            #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

            #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

            mapping.append({'old': old_item, 'new': new_item}							)

      return mapping

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			Optional[int]=1							)      ->				int:
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split('.'							)[n_shave_prefix_segments:]							)
      else:
            return ".".join(path.split('.'							)[:n_shave_prefix_segments]							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			str=0							)      ->				List[Any]:
      SCREAMING_SNAKE_CASE_					=    []
      for old_item in old_list:
            mapping.append({'old': old_item, 'new': old_item}							)

      return mapping

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Any    ,    __UpperCAmelCase		:			Union[str, Any]=0							)      ->				Tuple:
      SCREAMING_SNAKE_CASE_					=    []
      for old_item in old_list:
            SCREAMING_SNAKE_CASE_					=    old_item.replace('in_layers.0'    ,    'norm1'							)
            SCREAMING_SNAKE_CASE_					=    new_item.replace('in_layers.2'    ,    'conv1'							)

            SCREAMING_SNAKE_CASE_					=    new_item.replace('out_layers.0'    ,    'norm2'							)
            SCREAMING_SNAKE_CASE_					=    new_item.replace('out_layers.3'    ,    'conv2'							)

            SCREAMING_SNAKE_CASE_					=    new_item.replace('emb_layers.1'    ,    'time_emb_proj'							)
            SCREAMING_SNAKE_CASE_					=    new_item.replace('skip_connection'    ,    'conv_shortcut'							)

            SCREAMING_SNAKE_CASE_					=    shave_segments(_SCREAMING_SNAKE_CASE    ,    n_shave_prefix_segments=_SCREAMING_SNAKE_CASE							)

            if "temopral_conv" not in old_item:
                  mapping.append({'old': old_item, 'new': new_item}							)

      return mapping

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			Union[str, Any]    ,    __UpperCAmelCase		:			Any=None    ,    __UpperCAmelCase		:			Optional[Any]=False							)      ->				Any:
      SCREAMING_SNAKE_CASE_					=    {}
      SCREAMING_SNAKE_CASE_					=    list(checkpoint.keys()							)

      SCREAMING_SNAKE_CASE_					=    'model.diffusion_model.'

      # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
      if sum(k.startswith('model_ema'							) for k in keys							) > 1_00 and extract_ema:
            print(f"Checkpoint {path} has both EMA and non-EMA weights."							)
            print(
                'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
                ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'							)
            for key in keys:
                  if key.startswith('model.diffusion_model'							):
                        SCREAMING_SNAKE_CASE_					=    'model_ema.' + ''.join(key.split('.'							)[1:]							)
                        SCREAMING_SNAKE_CASE_					=    checkpoint.pop(_SCREAMING_SNAKE_CASE							)
      else:
            if sum(k.startswith('model_ema'							) for k in keys							) > 1_00:
                  print(
                      'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
                      ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'							)

            for key in keys:
                  SCREAMING_SNAKE_CASE_					=    checkpoint.pop(_SCREAMING_SNAKE_CASE							)

      SCREAMING_SNAKE_CASE_					=    {}

      SCREAMING_SNAKE_CASE_					=    unet_state_dict['time_embed.0.weight']
      SCREAMING_SNAKE_CASE_					=    unet_state_dict['time_embed.0.bias']
      SCREAMING_SNAKE_CASE_					=    unet_state_dict['time_embed.2.weight']
      SCREAMING_SNAKE_CASE_					=    unet_state_dict['time_embed.2.bias']

      if config["class_embed_type"] is None:
            # No parameters to port
            ...
      elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
            SCREAMING_SNAKE_CASE_					=    unet_state_dict['label_emb.0.0.weight']
            SCREAMING_SNAKE_CASE_					=    unet_state_dict['label_emb.0.0.bias']
            SCREAMING_SNAKE_CASE_					=    unet_state_dict['label_emb.0.2.weight']
            SCREAMING_SNAKE_CASE_					=    unet_state_dict['label_emb.0.2.bias']
      else:
            raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}"							)

      SCREAMING_SNAKE_CASE_					=    unet_state_dict['input_blocks.0.0.weight']
      SCREAMING_SNAKE_CASE_					=    unet_state_dict['input_blocks.0.0.bias']

      SCREAMING_SNAKE_CASE_					=    [v for v in unet_state_dict if v.startswith('input_blocks.0.1'							)]
      SCREAMING_SNAKE_CASE_					=    renew_attention_paths(_SCREAMING_SNAKE_CASE							)
      SCREAMING_SNAKE_CASE_					=    {'old': 'input_blocks.0.1', 'new': 'transformer_in'}
      assign_to_checkpoint(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)

      SCREAMING_SNAKE_CASE_					=    unet_state_dict['out.0.weight']
      SCREAMING_SNAKE_CASE_					=    unet_state_dict['out.0.bias']
      SCREAMING_SNAKE_CASE_					=    unet_state_dict['out.2.weight']
      SCREAMING_SNAKE_CASE_					=    unet_state_dict['out.2.bias']

      # Retrieves the keys for the input blocks only
      SCREAMING_SNAKE_CASE_					=    len({'.'.join(layer.split('.'							)[:2]							) for layer in unet_state_dict if 'input_blocks' in layer}							)
      SCREAMING_SNAKE_CASE_					=    {
          layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
          for layer_id in range(_SCREAMING_SNAKE_CASE							)
      }

      # Retrieves the keys for the middle blocks only
      SCREAMING_SNAKE_CASE_					=    len({'.'.join(layer.split('.'							)[:2]							) for layer in unet_state_dict if 'middle_block' in layer}							)
      SCREAMING_SNAKE_CASE_					=    {
          layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
          for layer_id in range(_SCREAMING_SNAKE_CASE							)
      }

      # Retrieves the keys for the output blocks only
      SCREAMING_SNAKE_CASE_					=    len({'.'.join(layer.split('.'							)[:2]							) for layer in unet_state_dict if 'output_blocks' in layer}							)
      SCREAMING_SNAKE_CASE_					=    {
          layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
          for layer_id in range(_SCREAMING_SNAKE_CASE							)
      }

      for i in range(1    ,    _SCREAMING_SNAKE_CASE							):
            SCREAMING_SNAKE_CASE_					=    (i - 1) // (config['layers_per_block'] + 1)
            SCREAMING_SNAKE_CASE_					=    (i - 1) % (config['layers_per_block'] + 1)

            SCREAMING_SNAKE_CASE_					=    [
                key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
            ]
            SCREAMING_SNAKE_CASE_					=    [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
            SCREAMING_SNAKE_CASE_					=    [key for key in input_blocks[i] if f"input_blocks.{i}.2" in key]

            if f"input_blocks.{i}.op.weight" in unet_state_dict:
                  SCREAMING_SNAKE_CASE_					=    unet_state_dict.pop(
                      f"input_blocks.{i}.op.weight"							)
                  SCREAMING_SNAKE_CASE_					=    unet_state_dict.pop(
                      f"input_blocks.{i}.op.bias"							)

            SCREAMING_SNAKE_CASE_					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE							)
            SCREAMING_SNAKE_CASE_					=    {'old': f"input_blocks.{i}.0", 'new': f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
            assign_to_checkpoint(
                _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)

            SCREAMING_SNAKE_CASE_					=    [key for key in resnets if 'temopral_conv' in key]
            SCREAMING_SNAKE_CASE_					=    renew_temp_conv_paths(_SCREAMING_SNAKE_CASE							)
            SCREAMING_SNAKE_CASE_					=    {
                'old': f"input_blocks.{i}.0.temopral_conv",
                'new': f"down_blocks.{block_id}.temp_convs.{layer_in_block_id}",
            }
            assign_to_checkpoint(
                _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)

            if len(_SCREAMING_SNAKE_CASE							):
                  SCREAMING_SNAKE_CASE_					=    renew_attention_paths(_SCREAMING_SNAKE_CASE							)
                  SCREAMING_SNAKE_CASE_					=    {'old': f"input_blocks.{i}.1", 'new': f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
                  assign_to_checkpoint(
                      _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)

            if len(_SCREAMING_SNAKE_CASE							):
                  SCREAMING_SNAKE_CASE_					=    renew_attention_paths(_SCREAMING_SNAKE_CASE							)
                  SCREAMING_SNAKE_CASE_					=    {
                      'old': f"input_blocks.{i}.2",
                      'new': f"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
                  }
                  assign_to_checkpoint(
                      _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)

      SCREAMING_SNAKE_CASE_					=    middle_blocks[0]
      SCREAMING_SNAKE_CASE_					=    [key for key in resnet_a if 'temopral_conv' in key]
      SCREAMING_SNAKE_CASE_					=    middle_blocks[1]
      SCREAMING_SNAKE_CASE_					=    middle_blocks[2]
      SCREAMING_SNAKE_CASE_					=    middle_blocks[3]
      SCREAMING_SNAKE_CASE_					=    [key for key in resnet_a if 'temopral_conv' in key]

      SCREAMING_SNAKE_CASE_					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE							)
      SCREAMING_SNAKE_CASE_					=    {'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    config=_SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]							)

      SCREAMING_SNAKE_CASE_					=    renew_temp_conv_paths(_SCREAMING_SNAKE_CASE							)
      SCREAMING_SNAKE_CASE_					=    {'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    config=_SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]							)

      SCREAMING_SNAKE_CASE_					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE							)
      SCREAMING_SNAKE_CASE_					=    {'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    config=_SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]							)

      SCREAMING_SNAKE_CASE_					=    renew_temp_conv_paths(_SCREAMING_SNAKE_CASE							)
      SCREAMING_SNAKE_CASE_					=    {'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    config=_SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]							)

      SCREAMING_SNAKE_CASE_					=    renew_attention_paths(_SCREAMING_SNAKE_CASE							)
      SCREAMING_SNAKE_CASE_					=    {'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)

      SCREAMING_SNAKE_CASE_					=    renew_attention_paths(_SCREAMING_SNAKE_CASE							)
      SCREAMING_SNAKE_CASE_					=    {'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
      assign_to_checkpoint(
          _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)

      for i in range(_SCREAMING_SNAKE_CASE							):
            SCREAMING_SNAKE_CASE_					=    i // (config['layers_per_block'] + 1)
            SCREAMING_SNAKE_CASE_					=    i % (config['layers_per_block'] + 1)
            SCREAMING_SNAKE_CASE_					=    [shave_segments(_SCREAMING_SNAKE_CASE    ,    2							) for name in output_blocks[i]]
            SCREAMING_SNAKE_CASE_					=    {}

            for layer in output_block_layers:
                  SCREAMING_SNAKE_CASE_					=    layer.split('.'							)[0], shave_segments(_SCREAMING_SNAKE_CASE    ,    1							)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE							)
                  else:
                        SCREAMING_SNAKE_CASE_					=    [layer_name]

            if len(_SCREAMING_SNAKE_CASE							) > 1:
                  SCREAMING_SNAKE_CASE_					=    [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
                  SCREAMING_SNAKE_CASE_					=    [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
                  SCREAMING_SNAKE_CASE_					=    [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key]

                  SCREAMING_SNAKE_CASE_					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE							)
                  SCREAMING_SNAKE_CASE_					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE							)

                  SCREAMING_SNAKE_CASE_					=    {'old': f"output_blocks.{i}.0", 'new': f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
                  assign_to_checkpoint(
                      _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)

                  SCREAMING_SNAKE_CASE_					=    [key for key in resnets if 'temopral_conv' in key]
                  SCREAMING_SNAKE_CASE_					=    renew_temp_conv_paths(_SCREAMING_SNAKE_CASE							)
                  SCREAMING_SNAKE_CASE_					=    {
                      'old': f"output_blocks.{i}.0.temopral_conv",
                      'new': f"up_blocks.{block_id}.temp_convs.{layer_in_block_id}",
                  }
                  assign_to_checkpoint(
                      _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)

                  SCREAMING_SNAKE_CASE_					=    {k: sorted(_SCREAMING_SNAKE_CASE							) for k, v in output_block_list.items()}
                  if ["conv.bias", "conv.weight"] in output_block_list.values():
                        SCREAMING_SNAKE_CASE_					=    list(output_block_list.values()							).index(['conv.bias', 'conv.weight']							)
                        SCREAMING_SNAKE_CASE_					=    unet_state_dict[
                            f"output_blocks.{i}.{index}.conv.weight"
                        ]
                        SCREAMING_SNAKE_CASE_					=    unet_state_dict[
                            f"output_blocks.{i}.{index}.conv.bias"
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(_SCREAMING_SNAKE_CASE							) == 2:
                              SCREAMING_SNAKE_CASE_					=    []

                  if len(_SCREAMING_SNAKE_CASE							):
                        SCREAMING_SNAKE_CASE_					=    renew_attention_paths(_SCREAMING_SNAKE_CASE							)
                        SCREAMING_SNAKE_CASE_					=    {
                            'old': f"output_blocks.{i}.1",
                            'new': f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                        }
                        assign_to_checkpoint(
                            _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)

                  if len(_SCREAMING_SNAKE_CASE							):
                        SCREAMING_SNAKE_CASE_					=    renew_attention_paths(_SCREAMING_SNAKE_CASE							)
                        SCREAMING_SNAKE_CASE_					=    {
                            'old': f"output_blocks.{i}.2",
                            'new': f"up_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
                        }
                        assign_to_checkpoint(
                            _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    additional_replacements=[meta_path]    ,    config=_SCREAMING_SNAKE_CASE							)
            else:
                  SCREAMING_SNAKE_CASE_					=    renew_resnet_paths(_SCREAMING_SNAKE_CASE    ,    n_shave_prefix_segments=1							)
                  for path in resnet_0_paths:
                        SCREAMING_SNAKE_CASE_					=    '.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE							), path['old']]							)
                        SCREAMING_SNAKE_CASE_					=    '.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE							), 'resnets', str(_SCREAMING_SNAKE_CASE							), path['new']]							)
                        SCREAMING_SNAKE_CASE_					=    unet_state_dict[old_path]

                  SCREAMING_SNAKE_CASE_					=    [l for l in output_block_layers if 'temopral_conv' in l]
                  for path in temopral_conv_paths:
                        SCREAMING_SNAKE_CASE_					=    path.split('temopral_conv.'							)[-1]
                        SCREAMING_SNAKE_CASE_					=    '.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE							), str(_SCREAMING_SNAKE_CASE							), 'temopral_conv', pruned_path]							)
                        SCREAMING_SNAKE_CASE_					=    '.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE							), 'temp_convs', str(_SCREAMING_SNAKE_CASE							), pruned_path]							)
                        SCREAMING_SNAKE_CASE_					=    unet_state_dict[old_path]

      return new_checkpoint


if __name__ == "__main__":
   lowerCamelCase__				:     Optional[int]			       =							argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
   )
   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
   lowerCamelCase__				:     str			       =							parser.parse_args()

   lowerCamelCase__				:     Dict			       =							torch.load(args.checkpoint_path, map_location='cpu')
   lowerCamelCase__				:     Union[str, Any]			       =							UNetaDConditionModel()

   lowerCamelCase__				:     Optional[Any]			       =							convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

   lowerCamelCase__				:     Optional[int]			       =							set(unet.state_dict().keys()) - set(converted_ckpt.keys())
   lowerCamelCase__				:     List[str]			       =							set(converted_ckpt.keys()) - set(unet.state_dict().keys())

   assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

   # load state_dict
   unet.load_state_dict(converted_ckpt)

   unet.save_pretrained(args.dump_path)

   # -- finish converting the unet --








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase__				:     int			       =							False





@nightly
@require_torch_gpu
class  lowerCamelCase_							(   unittest.TestCase      ):


 '''simple docstring'''







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



 def    lowerCAmelCase_						(							self						:	Optional[int]		):
       SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'		)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'		)

       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt='first prompt'       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='numpy'       ,       ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_lowerCAmelCase		)

       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    generator.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt='first prompt'       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=2       ,       output_type='numpy'       ,       ).images

       assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"




 def    lowerCAmelCase_						(							self						:	Any		):
       SCREAMING_SNAKE_CASE_					=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'		)
       pipe.remove_unused_weights()
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    'cyberpunk 2077'
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'		)
       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    pipe(
           prompt=_lowerCAmelCase       ,       image=_lowerCAmelCase       ,       text_to_image_strength=0.75       ,       generator=_lowerCAmelCase       ,       guidance_scale=7.5       ,       num_inference_steps=50       ,       output_type='numpy'       ,       ).images

       SCREAMING_SNAKE_CASE_					=    image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE_					=    np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]		)

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