




"""simple docstring"""




import argparse

import torch

from diffusers import UNetaDConditionModel
def     A_	(						snake_case_   :				str  ,snake_case_   :				List[str]  ,snake_case_   :				Tuple  ,snake_case_   :				int=None  ,snake_case_   :				Optional[int]=None  ,snake_case_   :				Optional[int]=None			):







    '''simple docstring'''
    assert isinstance(snake_case_  ,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():
            UpperCamelCase :      Optional[Any]      				=							old_checkpoint[path]
            UpperCamelCase :      List[str]      				=							old_tensor.shape[0] // 3

            UpperCamelCase :      Any      				=							(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

            UpperCamelCase :      Any      				=							old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

            UpperCamelCase :      List[str]      				=							query.reshape(snake_case_			)
            UpperCamelCase :      List[Any]      				=							key.reshape(snake_case_			)
            UpperCamelCase :      Any      				=							value.reshape(snake_case_			)

    for path in paths:
        UpperCamelCase :      Tuple      				=							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:
                UpperCamelCase :      List[str]      				=							new_path.replace(replacement["""old"""]  ,replacement["""new"""]			)

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







    '''simple docstring'''
    UpperCamelCase :      Tuple      				=							[]
    for old_item in old_list:
        UpperCamelCase :      Any      				=							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     A_	(						snake_case_   :				Dict  ,snake_case_   :				str=1			):







    '''simple docstring'''
    if n_shave_prefix_segments >= 0:
        return ".".join(path.split("""."""			)[n_shave_prefix_segments:]			)
    else:
        return ".".join(path.split("""."""			)[:n_shave_prefix_segments]			)
def     A_	(						snake_case_   :				List[str]  ,snake_case_   :				Optional[Any]=0			):







    '''simple docstring'''
    UpperCamelCase :      List[str]      				=							[]
    for old_item in old_list:
        mapping.append({"""old""": old_item, """new""": old_item}			)

    return mapping
def     A_	(						snake_case_   :				Optional[Any]  ,snake_case_   :				Dict=0			):







    '''simple docstring'''
    UpperCamelCase :      List[Any]      				=							[]
    for old_item in old_list:
        UpperCamelCase :      Optional[Any]      				=							old_item.replace("""in_layers.0"""  ,"""norm1"""			)
        UpperCamelCase :      Tuple      				=							new_item.replace("""in_layers.2"""  ,"""conv1"""			)

        UpperCamelCase :      Union[str, Any]      				=							new_item.replace("""out_layers.0"""  ,"""norm2"""			)
        UpperCamelCase :      Dict      				=							new_item.replace("""out_layers.3"""  ,"""conv2"""			)

        UpperCamelCase :      List[Any]      				=							new_item.replace("""emb_layers.1"""  ,"""time_emb_proj"""			)
        UpperCamelCase :      Optional[Any]      				=							new_item.replace("""skip_connection"""  ,"""conv_shortcut"""			)

        UpperCamelCase :      Optional[Any]      				=							shave_segments(snake_case_  ,n_shave_prefix_segments=snake_case_			)

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

    return mapping
def     A_	(						snake_case_   :				str  ,snake_case_   :				int  ,snake_case_   :				Union[str, Any]=None  ,snake_case_   :				List[str]=False			):







    '''simple docstring'''
    UpperCamelCase :      Tuple      				=							{}
    UpperCamelCase :      List[str]      				=							list(checkpoint.keys()			)

    UpperCamelCase :      List[Any]      				=							"""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_0_0 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"""			):
                UpperCamelCase :      Optional[Any]      				=							"""model_ema.""" + """""".join(key.split("""."""			)[1:]			)
                UpperCamelCase :      Optional[Any]      				=							checkpoint.pop(snake_case_			)
    else:
        if sum(k.startswith("""model_ema"""			) for k in keys			) > 1_0_0:
            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:
            UpperCamelCase :      Optional[Any]      				=							checkpoint.pop(snake_case_			)

    UpperCamelCase :      Optional[Any]      				=							{}

    UpperCamelCase :      List[str]      				=							unet_state_dict["""time_embed.0.weight"""]
    UpperCamelCase :      Tuple      				=							unet_state_dict["""time_embed.0.bias"""]
    UpperCamelCase :      Tuple      				=							unet_state_dict["""time_embed.2.weight"""]
    UpperCamelCase :      Optional[Any]      				=							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":
        UpperCamelCase :      Union[str, Any]      				=							unet_state_dict["""label_emb.0.0.weight"""]
        UpperCamelCase :      int      				=							unet_state_dict["""label_emb.0.0.bias"""]
        UpperCamelCase :      List[str]      				=							unet_state_dict["""label_emb.0.2.weight"""]
        UpperCamelCase :      Any      				=							unet_state_dict["""label_emb.0.2.bias"""]
    else:
        raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}'			)

    UpperCamelCase :      Any      				=							unet_state_dict["""input_blocks.0.0.weight"""]
    UpperCamelCase :      str      				=							unet_state_dict["""input_blocks.0.0.bias"""]

    UpperCamelCase :      Optional[int]      				=							[v for v in unet_state_dict if v.startswith("""input_blocks.0.1"""			)]
    UpperCamelCase :      Any      				=							renew_attention_paths(snake_case_			)
    UpperCamelCase :      Any      				=							{"""old""": """input_blocks.0.1""", """new""": """transformer_in"""}
    assign_to_checkpoint(snake_case_  ,snake_case_  ,snake_case_  ,additional_replacements=[meta_path]  ,config=snake_case_			)

    UpperCamelCase :      Dict      				=							unet_state_dict["""out.0.weight"""]
    UpperCamelCase :      Optional[int]      				=							unet_state_dict["""out.0.bias"""]
    UpperCamelCase :      Any      				=							unet_state_dict["""out.2.weight"""]
    UpperCamelCase :      List[Any]      				=							unet_state_dict["""out.2.bias"""]

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

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

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

    for i in range(1  ,snake_case_			):
        UpperCamelCase :      Tuple      				=							(i - 1) // (config["""layers_per_block"""] + 1)
        UpperCamelCase :      Any      				=							(i - 1) % (config["""layers_per_block"""] + 1)

        UpperCamelCase :      Optional[int]      				=							[
            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
        ]
        UpperCamelCase :      int      				=							[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
        UpperCamelCase :      List[str]      				=							[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:
            UpperCamelCase :      List[Any]      				=							unet_state_dict.pop(
                f'input_blocks.{i}.op.weight'			)
            UpperCamelCase :      Optional[Any]      				=							unet_state_dict.pop(
                f'input_blocks.{i}.op.bias'			)

        UpperCamelCase :      Optional[int]      				=							renew_resnet_paths(snake_case_			)
        UpperCamelCase :      Dict      				=							{"""old""": f'input_blocks.{i}.0', """new""": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
        assign_to_checkpoint(
            snake_case_  ,snake_case_  ,snake_case_  ,additional_replacements=[meta_path]  ,config=snake_case_			)

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

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

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

    UpperCamelCase :      str      				=							middle_blocks[0]
    UpperCamelCase :      Optional[Any]      				=							[key for key in resnet_a if """temopral_conv""" in key]
    UpperCamelCase :      List[str]      				=							middle_blocks[1]
    UpperCamelCase :      Dict      				=							middle_blocks[2]
    UpperCamelCase :      Optional[int]      				=							middle_blocks[3]
    UpperCamelCase :      Tuple      				=							[key for key in resnet_a if """temopral_conv""" in key]

    UpperCamelCase :      Dict      				=							renew_resnet_paths(snake_case_			)
    UpperCamelCase :      List[Any]      				=							{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
    assign_to_checkpoint(
        snake_case_  ,snake_case_  ,snake_case_  ,config=snake_case_  ,additional_replacements=[meta_path]			)

    UpperCamelCase :      int      				=							renew_temp_conv_paths(snake_case_			)
    UpperCamelCase :      Union[str, Any]      				=							{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
    assign_to_checkpoint(
        snake_case_  ,snake_case_  ,snake_case_  ,config=snake_case_  ,additional_replacements=[meta_path]			)

    UpperCamelCase :      Tuple      				=							renew_resnet_paths(snake_case_			)
    UpperCamelCase :      Any      				=							{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
    assign_to_checkpoint(
        snake_case_  ,snake_case_  ,snake_case_  ,config=snake_case_  ,additional_replacements=[meta_path]			)

    UpperCamelCase :      Any      				=							renew_temp_conv_paths(snake_case_			)
    UpperCamelCase :      Tuple      				=							{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
    assign_to_checkpoint(
        snake_case_  ,snake_case_  ,snake_case_  ,config=snake_case_  ,additional_replacements=[meta_path]			)

    UpperCamelCase :      Union[str, Any]      				=							renew_attention_paths(snake_case_			)
    UpperCamelCase :      Optional[int]      				=							{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
    assign_to_checkpoint(
        snake_case_  ,snake_case_  ,snake_case_  ,additional_replacements=[meta_path]  ,config=snake_case_			)

    UpperCamelCase :      Optional[Any]      				=							renew_attention_paths(snake_case_			)
    UpperCamelCase :      Tuple      				=							{"""old""": """middle_block.2""", """new""": """mid_block.temp_attentions.0"""}
    assign_to_checkpoint(
        snake_case_  ,snake_case_  ,snake_case_  ,additional_replacements=[meta_path]  ,config=snake_case_			)

    for i in range(snake_case_			):
        UpperCamelCase :      Tuple      				=							i // (config["""layers_per_block"""] + 1)
        UpperCamelCase :      Union[str, Any]      				=							i % (config["""layers_per_block"""] + 1)
        UpperCamelCase :      str      				=							[shave_segments(snake_case_  ,2			) for name in output_blocks[i]]
        UpperCamelCase :      Optional[Any]      				=							{}

        for layer in output_block_layers:
            UpperCamelCase				,							UpperCamelCase :      Union[str, Any]      				=							layer.split("""."""			)[0], shave_segments(snake_case_  ,1			)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(snake_case_			)
            else:
                UpperCamelCase :      str      				=							[layer_name]

        if len(snake_case_			) > 1:
            UpperCamelCase :      str      				=							[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
            UpperCamelCase :      str      				=							[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
            UpperCamelCase :      Union[str, Any]      				=							[key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]

            UpperCamelCase :      Optional[Any]      				=							renew_resnet_paths(snake_case_			)
            UpperCamelCase :      Optional[int]      				=							renew_resnet_paths(snake_case_			)

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

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

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

                # Clear attentions as they have been attributed above.
                if len(snake_case_			) == 2:
                    UpperCamelCase :      Dict      				=							[]

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

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

            UpperCamelCase :      Optional[Any]      				=							[l for l in output_block_layers if """temopral_conv""" in l]
            for path in temopral_conv_paths:
                UpperCamelCase :      List[Any]      				=							path.split("""temopral_conv."""			)[-1]
                UpperCamelCase :      Tuple      				=							""".""".join(["""output_blocks""", str(snake_case_			), str(snake_case_			), """temopral_conv""", pruned_path]			)
                UpperCamelCase :      str      				=							""".""".join(["""up_blocks""", str(snake_case_			), """temp_convs""", str(snake_case_			), pruned_path]			)
                UpperCamelCase :      List[Any]      				=							unet_state_dict[old_path]

    return new_checkpoint


if __name__ == "__main__":
  __A       :						List[str]												=			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.''')
  __A       :						List[Any]												=			parser.parse_args()

  __A       :						Any												=			torch.load(args.checkpoint_path, map_location='''cpu''')
  __A       :						Any												=			UNetaDConditionModel()

  __A       :						Optional[int]												=			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

  __A       :						Any												=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  __A       :						Any												=			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 --












"""simple docstring"""







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)





class      UpperCamelCase_       (				a_     ):



  def __init__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 0.93       ,						snake_case__ = True       ,						)	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	mixing_coeff
         UpperCAmelCase					=	leapfrog_steps

         super().__init__()
         self.register_modules(
             vae=snake_case__       ,						text_encoder=snake_case__       ,						tokenizer=snake_case__       ,						unet=snake_case__       ,						scheduler=snake_case__       ,						)

         UpperCAmelCase					=	2 ** (len(self.vae.config.block_out_channels ) - 1)
         UpperCAmelCase					=	VaeImageProcessor(vae_scale_factor=self.vae_scale_factor )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None       ,						snake_case__ = False )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	self.tokenizer(
             snake_case__       ,						padding="""max_length"""       ,						max_length=self.tokenizer.model_max_length       ,						truncation=snake_case__       ,						return_tensors="""pt"""       ,						)

         UpperCAmelCase					=	self.text_encoder(text_inputs.input_ids.to(self.device ) ).last_hidden_state

         UpperCAmelCase					=	prompt_embeds.to(dtype=self.text_encoder.dtype       ,						device=self.device )

         if do_classifier_free_guidance:
                UpperCAmelCase					=	"""""" if negative_prompt is None else negative_prompt

                UpperCAmelCase					=	self.tokenizer(
                    snake_case__       ,						padding="""max_length"""       ,						max_length=self.tokenizer.model_max_length       ,						truncation=snake_case__       ,						return_tensors="""pt"""       ,						)

                UpperCAmelCase					=	self.text_encoder(uncond_input.input_ids.to(self.device ) ).last_hidden_state

                UpperCAmelCase					=	torch.cat([negative_prompt_embeds, prompt_embeds] )

         return prompt_embeds



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         UpperCAmelCase					=	self.mixing_coeff * y + (1 - self.mixing_coeff) * x

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         UpperCAmelCase					=	(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	int(snake_case__ )

         UpperCAmelCase					=	self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

         return alpha_prod, 1 - alpha_prod



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	int:



         """simple docstring"""





         UpperCAmelCase					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         UpperCAmelCase					=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         UpperCAmelCase					=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

         UpperCAmelCase					=	(base - b_t * model_output) / a_t

         return model_input, next_model_input.to(base.dtype )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         UpperCAmelCase					=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         UpperCAmelCase					=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
         UpperCAmelCase					=	a_t * base + b_t * model_output

         return model_input, next_model_input.to(base.dtype )



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	str:



         """simple docstring"""





         UpperCAmelCase					=	1 / self.vae.config.scaling_factor * latents
         UpperCAmelCase					=	self.vae.decode(snake_case__ ).sample
         UpperCAmelCase					=	(image / 2 + 0.5).clamp(0       ,						1 )
         return image



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						)	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	image.to(device=self.device       ,						dtype=text_embeds.dtype )
         UpperCAmelCase					=	self.vae.encode(snake_case__ ).latent_dist.sample(snake_case__ )

         UpperCAmelCase					=	self.vae.config.scaling_factor * latent

         UpperCAmelCase					=	[latent.clone(), latent.clone()]

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                UpperCAmelCase					=	self.noise_mixing_layer(x=coupled_latents[0]       ,						y=coupled_latents[1] )

                # j - model_input index, k - base index
                for j in range(2 ):
                       UpperCAmelCase					=	j ^ 1

                       if self.leapfrog_steps:
                              if i % 2 == 0:
                                     UpperCAmelCase			,				UpperCAmelCase					=	j, k

                       UpperCAmelCase					=	coupled_latents[j]
                       UpperCAmelCase					=	coupled_latents[k]

                       UpperCAmelCase					=	torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                       UpperCAmelCase					=	self.unet(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).sample

                       if do_classifier_free_guidance:
                              UpperCAmelCase			,				UpperCAmelCase					=	noise_pred.chunk(2 )
                              UpperCAmelCase					=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                       UpperCAmelCase			,				UpperCAmelCase					=	self.noise_step(
                           base=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

         return coupled_latents



  @torch.no_grad()
  def __call__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 3.0       ,						snake_case__ = 50       ,						snake_case__ = 0.8       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = "pil"       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	self.image_processor.preprocess(snake_case__ )

         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )
         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )

         self.scheduler.set_timesteps(snake_case__       ,						self.device )

         UpperCAmelCase					=	num_inference_steps - int(num_inference_steps * strength )
         UpperCAmelCase					=	self.scheduler.timesteps[t_limit:]
         UpperCAmelCase					=	fwd_timesteps.flip(0 )

         UpperCAmelCase					=	self.prepare_latents(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                # j - model_input index, k - base index
                for k in range(2 ):
                       UpperCAmelCase					=	k ^ 1

                       if self.leapfrog_steps:
                              if i % 2 == 1:
                                     UpperCAmelCase			,				UpperCAmelCase					=	j, k

                       UpperCAmelCase					=	coupled_latents[j]
                       UpperCAmelCase					=	coupled_latents[k]

                       UpperCAmelCase					=	torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                       UpperCAmelCase					=	self.unet(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).sample

                       if do_classifier_free_guidance:
                              UpperCAmelCase			,				UpperCAmelCase					=	noise_pred.chunk(2 )
                              UpperCAmelCase					=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                       UpperCAmelCase			,				UpperCAmelCase					=	self.denoise_step(
                           base=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

                UpperCAmelCase					=	self.denoise_mixing_layer(x=coupled_latents[0]       ,						y=coupled_latents[1] )

         # either one is fine
         UpperCAmelCase					=	coupled_latents[0]

         if output_type not in ["latent", "pt", "np", "pil"]:
                UpperCAmelCase					=	(
                    f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
                    """`pil`, `np`, `pt`, `latent`"""
                )
                deprecate("""Unsupported output_type"""       ,						"""1.0.0"""       ,						snake_case__       ,						standard_warn=snake_case__ )
                UpperCAmelCase					=	"""np"""

         if output_type == "latent":
                UpperCAmelCase					=	final_latent
         else:
                UpperCAmelCase					=	self.decode_latents(snake_case__ )
                UpperCAmelCase					=	self.image_processor.postprocess(snake_case__       ,						output_type=snake_case__ )

         return image
