

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							IFPipeline
    lowerCamelCase			=							TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
    lowerCamelCase			=							TEXT_TO_IMAGE_BATCH_PARAMS
    lowerCamelCase			=							PipelineTesterMixin.required_optional_params - {'latents'}


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





        '''simple docstring'''







        return self._get_dummy_components()


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Tuple,lowercase_ :       List[str]=0		)->    List[str]:





        '''simple docstring'''







        if str(lowercase_		).startswith('mps'		):
            A__          =   torch.manual_seed(lowercase_		)
        else:
            A__          =   torch.Generator(device=lowercase_		).manual_seed(lowercase_		)

        A__          =   {
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'output_type': 'numpy',
        }

        return inputs


    def 						snake_case__     (      self :       List[str]		)->    Optional[int]:





        '''simple docstring'''







        self._test_save_load_optional_components()


    @unittest.skipIf(torch_device != 'cuda',reason='float16 requires CUDA'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    Union[str, Any]:





        '''simple docstring'''







        super().test_save_load_floataa(expected_max_diff=1E-1		)


    def 						snake_case__     (      self :       Optional[int]		)->    Optional[int]:





        '''simple docstring'''







        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)


    def 						snake_case__     (      self :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        self._test_save_load_local()


    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        self._test_inference_batch_single_identical(
            expected_max_diff=1E-2,)


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available(),reason='XFormers attention is only available with CUDA and `xformers` installed',)
    def 						snake_case__     (      self :       Any		)->    Any:





        '''simple docstring'''







        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)




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







    """simple docstring"""





    def 						snake_case__     (      self :       Any		)->    Optional[int]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Dict		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0',variant='fp16',torch_dtype=torch.floataa		)

        A__          =   IFSuperResolutionPipeline.from_pretrained(
            'DeepFloyd/IF-II-L-v1.0',variant='fp16',torch_dtype=torch.floataa,text_encoder=lowercase_,tokenizer=lowercase_		)

        # pre compute text embeddings and remove T5 to save memory

        pipe_a.text_encoder.to('cuda'		)

        A__      ,						A__          =   pipe_a.encode_prompt('anime turtle',device='cuda'		)

        del pipe_a.tokenizer
        del pipe_a.text_encoder
        gc.collect()

        A__          =   None
        A__          =   None

        pipe_a.enable_model_cpu_offload()
        pipe_a.enable_model_cpu_offload()

        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

        self._test_if(lowercase_,lowercase_,lowercase_,lowercase_		)

        pipe_a.remove_all_hooks()
        pipe_a.remove_all_hooks()

        # img2img

        A__          =   IFImgaImgPipeline(**pipe_a.components		)
        A__          =   IFImgaImgSuperResolutionPipeline(**pipe_a.components		)

        pipe_a.enable_model_cpu_offload()
        pipe_a.enable_model_cpu_offload()

        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

        self._test_if_imgaimg(lowercase_,lowercase_,lowercase_,lowercase_		)

        pipe_a.remove_all_hooks()
        pipe_a.remove_all_hooks()

        # inpainting

        A__          =   IFInpaintingPipeline(**pipe_a.components		)
        A__          =   IFInpaintingSuperResolutionPipeline(**pipe_a.components		)

        pipe_a.enable_model_cpu_offload()
        pipe_a.enable_model_cpu_offload()

        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
        pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

        self._test_if_inpainting(lowercase_,lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Tuple,lowercase_ :       List[Any],lowercase_ :       Optional[int],lowercase_ :       Any,lowercase_ :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        _start_torch_memory_measurement()

        A__          =   torch.Generator(device='cpu'		).manual_seed(0		)
        A__          =   pipe_a(
            prompt_embeds=lowercase_,negative_prompt_embeds=lowercase_,num_inference_steps=2,generator=lowercase_,output_type='np',)

        A__          =   output.images[0]

        assert image.shape == (6_4, 6_4, 3)

        A__          =   torch.cuda.max_memory_allocated()
        assert mem_bytes < 1_3 * 1_0**9

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'		)
        assert_mean_pixel_difference(lowercase_,lowercase_		)

        # pipeline 2

        _start_torch_memory_measurement()

        A__          =   torch.Generator(device='cpu'		).manual_seed(0		)

        A__          =   floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(0		)		).to(lowercase_		)

        A__          =   pipe_a(
            prompt_embeds=lowercase_,negative_prompt_embeds=lowercase_,image=lowercase_,generator=lowercase_,num_inference_steps=2,output_type='np',)

        A__          =   output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)

        A__          =   torch.cuda.max_memory_allocated()
        assert mem_bytes < 4 * 1_0**9

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'		)
        assert_mean_pixel_difference(lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       List[Any],lowercase_ :       Optional[int],lowercase_ :       List[str],lowercase_ :       Any		)->    Optional[int]:





        '''simple docstring'''







        _start_torch_memory_measurement()

        A__          =   floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(0		)		).to(lowercase_		)

        A__          =   torch.Generator(device='cpu'		).manual_seed(0		)

        A__          =   pipe_a(
            prompt_embeds=lowercase_,negative_prompt_embeds=lowercase_,image=lowercase_,num_inference_steps=2,generator=lowercase_,output_type='np',)

        A__          =   output.images[0]

        assert image.shape == (6_4, 6_4, 3)

        A__          =   torch.cuda.max_memory_allocated()
        assert mem_bytes < 1_0 * 1_0**9

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'		)
        assert_mean_pixel_difference(lowercase_,lowercase_		)

        # pipeline 2

        _start_torch_memory_measurement()

        A__          =   torch.Generator(device='cpu'		).manual_seed(0		)

        A__          =   floats_tensor((1, 3, 2_5_6, 2_5_6),rng=random.Random(0		)		).to(lowercase_		)
        A__          =   floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(0		)		).to(lowercase_		)

        A__          =   pipe_a(
            prompt_embeds=lowercase_,negative_prompt_embeds=lowercase_,image=lowercase_,original_image=lowercase_,generator=lowercase_,num_inference_steps=2,output_type='np',)

        A__          =   output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)

        A__          =   torch.cuda.max_memory_allocated()
        assert mem_bytes < 4 * 1_0**9

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'		)
        assert_mean_pixel_difference(lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       str,lowercase_ :       int,lowercase_ :       List[str],lowercase_ :       int		)->    Tuple:





        '''simple docstring'''







        _start_torch_memory_measurement()

        A__          =   floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(0		)		).to(lowercase_		)
        A__          =   floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(1		)		).to(lowercase_		)

        A__          =   torch.Generator(device='cpu'		).manual_seed(0		)
        A__          =   pipe_a(
            prompt_embeds=lowercase_,negative_prompt_embeds=lowercase_,image=lowercase_,mask_image=lowercase_,num_inference_steps=2,generator=lowercase_,output_type='np',)

        A__          =   output.images[0]

        assert image.shape == (6_4, 6_4, 3)

        A__          =   torch.cuda.max_memory_allocated()
        assert mem_bytes < 1_0 * 1_0**9

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'		)
        assert_mean_pixel_difference(lowercase_,lowercase_		)

        # pipeline 2

        _start_torch_memory_measurement()

        A__          =   torch.Generator(device='cpu'		).manual_seed(0		)

        A__          =   floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(0		)		).to(lowercase_		)
        A__          =   floats_tensor((1, 3, 2_5_6, 2_5_6),rng=random.Random(0		)		).to(lowercase_		)
        A__          =   floats_tensor((1, 3, 2_5_6, 2_5_6),rng=random.Random(1		)		).to(lowercase_		)

        A__          =   pipe_a(
            prompt_embeds=lowercase_,negative_prompt_embeds=lowercase_,image=lowercase_,mask_image=lowercase_,original_image=lowercase_,generator=lowercase_,num_inference_steps=2,output_type='np',)

        A__          =   output.images[0]

        assert image.shape == (2_5_6, 2_5_6, 3)

        A__          =   torch.cuda.max_memory_allocated()
        assert mem_bytes < 4 * 1_0**9

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'		)
        assert_mean_pixel_difference(lowercase_,lowercase_		)




def 						_snake_case(    )							->      Dict:





    '''simple docstring'''






    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()






'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  Optional[Any]							      =					old_item.replace("""in_layers.0"""							,    """norm1"""			)
        lowerCAmelCase__ :  Optional[int]							      =					new_item.replace("""in_layers.2"""							,    """conv1"""			)

        lowerCAmelCase__ :  Dict							      =					new_item.replace("""out_layers.0"""							,    """norm2"""			)
        lowerCAmelCase__ :  str							      =					new_item.replace("""out_layers.3"""							,    """conv2"""			)

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

        lowerCAmelCase__ :  Union[str, Any]							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

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

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  List[str]							      =					old_item

        lowerCAmelCase__ :  int							      =					new_item.replace("""norm.weight"""							,    """group_norm.weight"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""norm.bias"""							,    """group_norm.bias"""			)

        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""proj_out.weight"""							,    """proj_attn.weight"""			)
        lowerCAmelCase__ :  int							      =					new_item.replace("""proj_out.bias"""							,    """proj_attn.bias"""			)

        lowerCAmelCase__ :  str							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

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

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase=None							,    UpperCamelCase=None			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "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():
            lowerCAmelCase__ :  Any							      =					old_checkpoint[path]
            lowerCAmelCase__ :  int							      =					old_tensor.shape[0] // 3

            lowerCAmelCase__ :  int							      =					(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

            lowerCAmelCase__ :  Tuple							      =					old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

            lowerCAmelCase__ :  int							      =					query.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Union[str, Any]							      =					key.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Optional[int]							      =					value.reshape(UpperCamelCase			)

    for path in paths:
        lowerCAmelCase__ :  Any							      =					path["""new"""]

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

        # Global renaming happens here
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.0"""							,    """mid_block.resnets.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.1"""							,    """mid_block.attentions.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.2"""							,    """mid_block.resnets.1"""			)

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

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            lowerCAmelCase__ :  List[Any]							      =					old_checkpoint[path["""old"""]][:, :, 0]
        else:
            lowerCAmelCase__ :  Dict							      =					old_checkpoint[path["""old"""]]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					{}

    lowerCAmelCase__ :  str							      =					checkpoint["""time_embed.0.weight"""]
    lowerCAmelCase__ :  List[Any]							      =					checkpoint["""time_embed.0.bias"""]
    lowerCAmelCase__ :  int							      =					checkpoint["""time_embed.2.weight"""]
    lowerCAmelCase__ :  List[str]							      =					checkpoint["""time_embed.2.bias"""]

    lowerCAmelCase__ :  str							      =					checkpoint["""input_blocks.0.0.weight"""]
    lowerCAmelCase__ :  Any							      =					checkpoint["""input_blocks.0.0.bias"""]

    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.weight"""]
    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.bias"""]
    lowerCAmelCase__ :  str							      =					checkpoint["""out.2.weight"""]
    lowerCAmelCase__ :  Tuple							      =					checkpoint["""out.2.bias"""]

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

    # Retrieves the keys for the middle blocks only
    lowerCAmelCase__ :  Union[str, Any]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """middle_block""" in layer}			)
    lowerCAmelCase__ :  Union[str, Any]							      =					{
        layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase			)
    }

    # Retrieves the keys for the output blocks only
    lowerCAmelCase__ :  List[str]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """output_blocks""" in layer}			)
    lowerCAmelCase__ :  List[Any]							      =					{
        layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase			)
    }

    for i in range(1							,    UpperCamelCase			):
        lowerCAmelCase__ :  Dict							      =					(i - 1) // (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  Tuple							      =					(i - 1) % (config["""num_res_blocks"""] + 1)

        lowerCAmelCase__ :  Optional[int]							      =					[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
        lowerCAmelCase__ :  Optional[Any]							      =					[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

        if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
            lowerCAmelCase__ :  Optional[int]							      =					checkpoint[
                f"""input_blocks.{i}.0.op.weight"""
            ]
            lowerCAmelCase__ :  Tuple							      =					checkpoint[
                f"""input_blocks.{i}.0.op.bias"""
            ]
            continue

        lowerCAmelCase__ :  Optional[Any]							      =					renew_resnet_paths(UpperCamelCase			)
        lowerCAmelCase__ :  Dict							      =					{"""old""": f"""input_blocks.{i}.0""", """new""": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
        lowerCAmelCase__ :  Optional[Any]							      =					{"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
        assign_to_checkpoint(
            UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path, resnet_op]							,    config=UpperCamelCase			)

        if len(UpperCamelCase			):
            lowerCAmelCase__ :  Optional[Any]							      =					renew_attention_paths(UpperCamelCase			)
            lowerCAmelCase__ :  Tuple							      =					{
                """old""": f"""input_blocks.{i}.1""",
                """new""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            lowerCAmelCase__ :  List[str]							      =					{
                f"""input_blocks.{i}.1.qkv.bias""": {
                    """key""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                    """query""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                    """value""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                },
                f"""input_blocks.{i}.1.qkv.weight""": {
                    """key""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                    """query""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                    """value""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                },
            }
            assign_to_checkpoint(
                UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    attention_paths_to_split=UpperCamelCase							,    config=UpperCamelCase							,    )

    lowerCAmelCase__ :  Dict							      =					middle_blocks[0]
    lowerCAmelCase__ :  Union[str, Any]							      =					middle_blocks[1]
    lowerCAmelCase__ :  Dict							      =					middle_blocks[2]

    lowerCAmelCase__ :  Any							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Dict							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Optional[int]							      =					renew_attention_paths(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[int]							      =					{
        """middle_block.1.qkv.bias""": {
            """key""": """mid_block.attentions.0.key.bias""",
            """query""": """mid_block.attentions.0.query.bias""",
            """value""": """mid_block.attentions.0.value.bias""",
        },
        """middle_block.1.qkv.weight""": {
            """key""": """mid_block.attentions.0.key.weight""",
            """query""": """mid_block.attentions.0.query.weight""",
            """value""": """mid_block.attentions.0.value.weight""",
        },
    }
    assign_to_checkpoint(
        UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    attention_paths_to_split=UpperCamelCase							,    config=UpperCamelCase			)

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

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

        if len(UpperCamelCase			) > 1:
            lowerCAmelCase__ :  str							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
            lowerCAmelCase__ :  Dict							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

            lowerCAmelCase__ :  Optional[int]							      =					renew_resnet_paths(UpperCamelCase			)
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase			)

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

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                lowerCAmelCase__ :  List[Any]							      =					list(output_block_list.values()			).index(["""conv.weight""", """conv.bias"""]			)
                lowerCAmelCase__ :  int							      =					checkpoint[
                    f"""output_blocks.{i}.{index}.conv.weight"""
                ]
                lowerCAmelCase__ :  int							      =					checkpoint[
                    f"""output_blocks.{i}.{index}.conv.bias"""
                ]

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

            if len(UpperCamelCase			):
                lowerCAmelCase__ :  Dict							      =					renew_attention_paths(UpperCamelCase			)
                lowerCAmelCase__ :  Tuple							      =					{
                    """old""": f"""output_blocks.{i}.1""",
                    """new""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                }
                lowerCAmelCase__ :  Tuple							      =					{
                    f"""output_blocks.{i}.1.qkv.bias""": {
                        """key""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                        """query""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                        """value""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                    },
                    f"""output_blocks.{i}.1.qkv.weight""": {
                        """key""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                        """query""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                        """value""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                    },
                }
                assign_to_checkpoint(
                    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions			) else None							,    config=UpperCamelCase							,    )
        else:
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase							,    n_shave_prefix_segments=1			)
            for path in resnet_0_paths:
                lowerCAmelCase__ :  Tuple							      =					""".""".join(["""output_blocks""", str(UpperCamelCase			), path["""old"""]]			)
                lowerCAmelCase__ :  List[Any]							      =					""".""".join(["""up_blocks""", str(UpperCamelCase			), """resnets""", str(UpperCamelCase			), path["""new"""]]			)

                lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint[old_path]

    return new_checkpoint


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

 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
 )

 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 _lowerCAmelCase      						=	parser.parse_args()

 _lowerCAmelCase      						=	torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  _lowerCAmelCase      						=	json.loads(f.read())

 _lowerCAmelCase      						=	convert_ldm_checkpoint(checkpoint, config)

 if "ldm" in config:
  del config["ldm"]

 _lowerCAmelCase      						=	UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _lowerCAmelCase      						=	DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  _lowerCAmelCase      						=	VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  _lowerCAmelCase      						=	LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)


