'''simple docstring'''





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

SCREAMING_SNAKE_CASE_:					List[Any]  =[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

SCREAMING_SNAKE_CASE_:					Optional[Any]  =[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

SCREAMING_SNAKE_CASE_:					Tuple  =[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
    # loop over downblocks/upblocks

    for j in range(2):
        # loop over resnets/attentions for downblocks
        SCREAMING_SNAKE_CASE_:					List[str]  =f"down_blocks.{i}.resnets.{j}."
        SCREAMING_SNAKE_CASE_:					Optional[Any]  =f"input_blocks.{3*i + j + 1}.0."
        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

        if i < 3:
            # no attention layers in down_blocks.3
            SCREAMING_SNAKE_CASE_:					List[str]  =f"down_blocks.{i}.attentions.{j}."
            SCREAMING_SNAKE_CASE_:					List[Any]  =f"input_blocks.{3*i + j + 1}.1."
            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

    for j in range(3):
        # loop over resnets/attentions for upblocks
        SCREAMING_SNAKE_CASE_:					Any  =f"up_blocks.{i}.resnets.{j}."
        SCREAMING_SNAKE_CASE_:					Dict  =f"output_blocks.{3*i + j}.0."
        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

        if i > 0:
            # no attention layers in up_blocks.0
            SCREAMING_SNAKE_CASE_:					Optional[Any]  =f"up_blocks.{i}.attentions.{j}."
            SCREAMING_SNAKE_CASE_:					Union[str, Any]  =f"output_blocks.{3*i + j}.1."
            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

    if i < 3:
        # no downsample in down_blocks.3
        SCREAMING_SNAKE_CASE_:					Optional[Any]  =f"down_blocks.{i}.downsamplers.0.conv."
        SCREAMING_SNAKE_CASE_:					Optional[Any]  =f"input_blocks.{3*(i+1)}.0.op."
        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

        # no upsample in up_blocks.3
        SCREAMING_SNAKE_CASE_:					List[str]  =f"up_blocks.{i}.upsamplers.0."
        SCREAMING_SNAKE_CASE_:					List[Any]  =f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

SCREAMING_SNAKE_CASE_:					List[Any]  ='mid_block.attentions.0.'
SCREAMING_SNAKE_CASE_:					int  ='middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =f"mid_block.resnets.{j}."
    SCREAMING_SNAKE_CASE_:					int  =f"middle_block.{2*j}."
    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def      lowerCAmelCase_    ( snake_case_	:			str      )   ->				List[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       {k: k for k in unet_state_dict.keys()}
     for sd_name, hf_name in unet_conversion_map:
          UpperCAmelCase_	   =       sd_name
     for k, v in mapping.items():
          if "resnets" in k:
               for sd_part, hf_part in unet_conversion_map_resnet:
                    UpperCAmelCase_	   =       v.replace(snake_case_ ,   snake_case_      )
               UpperCAmelCase_	   =       v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               UpperCAmelCase_	   =       v.replace(snake_case_ ,   snake_case_      )
          UpperCAmelCase_	   =       v
     UpperCAmelCase_	   =       {v: unet_state_dict[k] for k, v in mapping.items()}
     return new_state_dict


# ================#
# VAE Conversion #
# ================#

SCREAMING_SNAKE_CASE_:					Any  =[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
    # down_blocks have two resnets
    for j in range(2):
        SCREAMING_SNAKE_CASE_:					Any  =f"encoder.down_blocks.{i}.resnets.{j}."
        SCREAMING_SNAKE_CASE_:					str  =f"encoder.down.{i}.block.{j}."
        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

    if i < 3:
        SCREAMING_SNAKE_CASE_:					Any  =f"down_blocks.{i}.downsamplers.0."
        SCREAMING_SNAKE_CASE_:					List[Any]  =f"down.{i}.downsample."
        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

        SCREAMING_SNAKE_CASE_:					List[str]  =f"up_blocks.{i}.upsamplers.0."
        SCREAMING_SNAKE_CASE_:					Dict  =f"up.{3-i}.upsample."
        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

    # up_blocks have three resnets
    # also, up blocks in hf are numbered in reverse from sd
    for j in range(3):
        SCREAMING_SNAKE_CASE_:					Optional[int]  =f"decoder.up_blocks.{i}.resnets.{j}."
        SCREAMING_SNAKE_CASE_:					Tuple  =f"decoder.up.{3-i}.block.{j}."
        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
    SCREAMING_SNAKE_CASE_:					str  =f"mid_block.resnets.{i}."
    SCREAMING_SNAKE_CASE_:					Optional[Any]  =f"mid.block_{i+1}."
    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


SCREAMING_SNAKE_CASE_:					Optional[int]  =[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]





def      lowerCAmelCase_    ( snake_case_	:			Tuple      )   ->				Optional[int]:


     '''simple docstring'''




     return w.reshape(*w.shape ,   1 ,   1      )





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any]      )   ->				List[str]:


     '''simple docstring'''




     UpperCAmelCase_	   =       {k: k for k in vae_state_dict.keys()}
     for k, v in mapping.items():
          for sd_part, hf_part in vae_conversion_map:
               UpperCAmelCase_	   =       v.replace(snake_case_ ,   snake_case_      )
          UpperCAmelCase_	   =       v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    UpperCAmelCase_	   =       v.replace(snake_case_ ,   snake_case_      )
               UpperCAmelCase_	   =       v
     UpperCAmelCase_	   =       {v: vae_state_dict[k] for k, v in mapping.items()}
     UpperCAmelCase_	   =       ["q", "k", "v", "proj_out"]
     for k, v in new_state_dict.items():
          for weight_name in weights_to_convert:
               if f"""mid.attn_1.{weight_name}.weight""" in k:
                    print(f"""Reshaping {k} for SD format"""      )
                    UpperCAmelCase_	   =       reshape_weight_for_sd(snake_case_      )
     return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


SCREAMING_SNAKE_CASE_:					Tuple  =[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
SCREAMING_SNAKE_CASE_:					Optional[int]  ={re.escape(x[1]): x[0] for x in textenc_conversion_lst}
SCREAMING_SNAKE_CASE_:					List[str]  =re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
SCREAMING_SNAKE_CASE_:					Union[str, Any]  ={'q': 0, 'k': 1, 'v': 2}





def      lowerCAmelCase_    ( snake_case_	:			Any      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       {}
     UpperCAmelCase_	   =       {}
     UpperCAmelCase_	   =       {}
     for k, v in text_enc_dict.items():
          if (
              k.endswith(".self_attn.q_proj.weight"      )
              or k.endswith(".self_attn.k_proj.weight"      )
              or k.endswith(".self_attn.v_proj.weight"      )
          ):
               UpperCAmelCase_	   =       k[: -len(".q_proj.weight"      )]
               UpperCAmelCase_	   =       k[-len("q_proj.weight"      )]
               if k_pre not in capture_qkv_weight:
                    UpperCAmelCase_	   =       [None, None, None]
               UpperCAmelCase_	   =       v
               continue

          if (
              k.endswith(".self_attn.q_proj.bias"      )
              or k.endswith(".self_attn.k_proj.bias"      )
              or k.endswith(".self_attn.v_proj.bias"      )
          ):
               UpperCAmelCase_	   =       k[: -len(".q_proj.bias"      )]
               UpperCAmelCase_	   =       k[-len("q_proj.bias"      )]
               if k_pre not in capture_qkv_bias:
                    UpperCAmelCase_	   =       [None, None, None]
               UpperCAmelCase_	   =       v
               continue

          UpperCAmelCase_	   =       textenc_pattern.sub(lambda snake_case_      : protected[re.escape(m.group(0      )      )] ,   snake_case_      )
          UpperCAmelCase_	   =       v

     for k_pre, tensors in capture_qkv_weight.items():
          if None in tensors:
               raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"      )
          UpperCAmelCase_	   =       textenc_pattern.sub(lambda snake_case_      : protected[re.escape(m.group(0      )      )] ,   snake_case_      )
          UpperCAmelCase_	   =       torch.cat(snake_case_      )

     for k_pre, tensors in capture_qkv_bias.items():
          if None in tensors:
               raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"      )
          UpperCAmelCase_	   =       textenc_pattern.sub(lambda snake_case_      : protected[re.escape(m.group(0      )      )] ,   snake_case_      )
          UpperCAmelCase_	   =       torch.cat(snake_case_      )

     return new_state_dict





def      lowerCAmelCase_    ( snake_case_	:			Tuple      )   ->				Optional[int]:


     '''simple docstring'''




     return text_enc_dict


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =argparse.ArgumentParser()

    parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
    parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
    parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
    parser.add_argument(
        '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
    )

    SCREAMING_SNAKE_CASE_:					Tuple  =parser.parse_args()

    assert args.model_path is not None, "Must provide a model path!"

    assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

    # Path for safetensors
    SCREAMING_SNAKE_CASE_:					Optional[Any]  =osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
    SCREAMING_SNAKE_CASE_:					List[Any]  =osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
    SCREAMING_SNAKE_CASE_:					Optional[int]  =osp.join(args.model_path, 'text_encoder', 'model.safetensors')

    # Load models from safetensors if it exists, if it doesn't pytorch
    if osp.exists(unet_path):
        SCREAMING_SNAKE_CASE_:					Optional[Any]  =load_file(unet_path, device='cpu')
    else:
        SCREAMING_SNAKE_CASE_:					Optional[int]  =osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
        SCREAMING_SNAKE_CASE_:					Optional[int]  =torch.load(unet_path, map_location='cpu')

    if osp.exists(vae_path):
        SCREAMING_SNAKE_CASE_:					Optional[int]  =load_file(vae_path, device='cpu')
    else:
        SCREAMING_SNAKE_CASE_:					Union[str, Any]  =osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
        SCREAMING_SNAKE_CASE_:					Union[str, Any]  =torch.load(vae_path, map_location='cpu')

    if osp.exists(text_enc_path):
        SCREAMING_SNAKE_CASE_:					List[str]  =load_file(text_enc_path, device='cpu')
    else:
        SCREAMING_SNAKE_CASE_:					Optional[Any]  =osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
        SCREAMING_SNAKE_CASE_:					List[Any]  =torch.load(text_enc_path, map_location='cpu')

    # Convert the UNet model
    SCREAMING_SNAKE_CASE_:					Tuple  =convert_unet_state_dict(unet_state_dict)
    SCREAMING_SNAKE_CASE_:					List[str]  ={'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

    # Convert the VAE model
    SCREAMING_SNAKE_CASE_:					Dict  =convert_vae_state_dict(vae_state_dict)
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  ={'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

    # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
    SCREAMING_SNAKE_CASE_:					Optional[int]  ='text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

    if is_vaa_model:
        # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
        SCREAMING_SNAKE_CASE_:					Tuple  ={'transformer.' + k: v for k, v in text_enc_dict.items()}
        SCREAMING_SNAKE_CASE_:					Any  =convert_text_enc_state_dict_vaa(text_enc_dict)
        SCREAMING_SNAKE_CASE_:					Optional[Any]  ={'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
    else:
        SCREAMING_SNAKE_CASE_:					Dict  =convert_text_enc_state_dict(text_enc_dict)
        SCREAMING_SNAKE_CASE_:					Any  ={'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

    # Put together new checkpoint
    SCREAMING_SNAKE_CASE_:					Any  ={**unet_state_dict, **vae_state_dict, **text_enc_dict}
    if args.half:
        SCREAMING_SNAKE_CASE_:					Any  ={k: v.half() for k, v in state_dict.items()}

    if args.use_safetensors:
        save_file(state_dict, args.checkpoint_path)
    else:
        SCREAMING_SNAKE_CASE_:					Any  ={'state_dict': state_dict}
        torch.save(state_dict, args.checkpoint_path)





'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ={
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}




class 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):
    a__	:						Optional[Any]	       =  """resnet"""
    a__	:						Tuple	       =  ["""basic""", """bottleneck"""]



    def __init__(self   :   List[Any]       ,      __a   :   Any=3       ,      __a   :   Dict=64       ,      __a   :   Union[str, Any]=[256, 512, 1024, 2048]       ,      __a   :   str=[3, 4, 6, 3]       ,      __a   :   Optional[Any]="bottleneck"       ,      __a   :   Tuple="relu"       ,      __a   :   int=False       ,      __a   :   Optional[int]=None       ,      __a   :   str=None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         if layer_type not in self.layer_types:
              raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types			)}"""			)
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embedding_size
         UpperCAmelCase_	   =       hidden_sizes
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       layer_type
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       downsample_in_first_stage
         UpperCAmelCase_	   =       ["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(__a			) + 1			)]
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_aligned_output_features_output_indices(
             out_features=__a       ,      out_indices=__a       ,      stage_names=self.stage_names			)




class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  version.parse("""1.11"""     )



    @property
    def  _lowercase						(self   :   Optional[int]			):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]			)






    @property
    def  _lowercase						(self   :   str			):
         return 1E-3




