"""simple docstring"""
import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowercase__ 	=     [
    # (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'),
]

lowercase__ 	=     [
    # (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'),
]

lowercase__ 	=     []
# 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
              lowercase__ 	=     f"down_blocks.{i}.resnets.{j}."
              lowercase__ 	=     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
                     lowercase__ 	=     f"down_blocks.{i}.attentions.{j}."
                     lowercase__ 	=     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
              lowercase__ 	=     f"up_blocks.{i}.resnets.{j}."
              lowercase__ 	=     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
                     lowercase__ 	=     f"up_blocks.{i}.attentions.{j}."
                     lowercase__ 	=     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
              lowercase__ 	=     f"down_blocks.{i}.downsamplers.0.conv."
              lowercase__ 	=     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
              lowercase__ 	=     f"up_blocks.{i}.upsamplers.0."
              lowercase__ 	=     f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
              unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowercase__ 	=     'mid_block.attentions.0.'
lowercase__ 	=     'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
       lowercase__ 	=     f"mid_block.resnets.{j}."
       lowercase__ 	=     f"middle_block.{2*j}."
       unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  # buyer beware: this is a *brittle* function,
  # and correct output requires that all of these pieces interact in
  # the exact order in which I have arranged them.
  a__:					Optional[Any]      							=     {k: k for k in unet_state_dict.keys()}
  for sd_name, hf_name in unet_conversion_map:
    a__:					Tuple      							=     sd_name
  for k, v in mapping.items():
    if "resnets" in k:
      for sd_part, hf_part in unet_conversion_map_resnet:
        a__:					Optional[int]      							=     v.replace(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
      a__:					Union[str, Any]      							=     v
  for k, v in mapping.items():
    for sd_part, hf_part in unet_conversion_map_layer:
      a__:					int      							=     v.replace(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
    a__:					Dict      							=     v
  a__:					Optional[int]      							=     {v: unet_state_dict[k] for k, v in mapping.items()}
  return new_state_dict


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

lowercase__ 	=     [
    # (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):
              lowercase__ 	=     f"encoder.down_blocks.{i}.resnets.{j}."
              lowercase__ 	=     f"encoder.down.{i}.block.{j}."
              vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

       if i < 3:
              lowercase__ 	=     f"down_blocks.{i}.downsamplers.0."
              lowercase__ 	=     f"down.{i}.downsample."
              vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

              lowercase__ 	=     f"up_blocks.{i}.upsamplers.0."
              lowercase__ 	=     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):
              lowercase__ 	=     f"decoder.up_blocks.{i}.resnets.{j}."
              lowercase__ 	=     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):
       lowercase__ 	=     f"mid_block.resnets.{i}."
       lowercase__ 	=     f"mid.block_{i+1}."
       vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->List[str]:
  # convert HF linear weights to SD conv2d weights
  return w.reshape(*w.shape		,					1		,					1       )







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Tuple:
  a__:					Tuple      							=     {k: k for k in vae_state_dict.keys()}
  for k, v in mapping.items():
    for sd_part, hf_part in vae_conversion_map:
      a__:					List[str]      							=     v.replace(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
    a__:					Any      							=     v
  for k, v in mapping.items():
    if "attentions" in k:
      for sd_part, hf_part in vae_conversion_map_attn:
        a__:					str      							=     v.replace(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
      a__:					Optional[Any]      							=     v
  a__:					Optional[int]      							=     {v: vae_state_dict[k] for k, v in mapping.items()}
  a__:					Dict      							=     ['''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'       )
        a__:					Union[str, Any]      							=     reshape_weight_for_sd(_SCREAMING_SNAKE_CASE       )
  return new_state_dict


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


lowercase__ 	=     [
    # (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'),
]
lowercase__ 	=     {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowercase__ 	=     re.compile('|'.join(protected.keys()))

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







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  a__:					List[str]      							=     {}
  a__:					Optional[int]      							=     {}
  a__:					List[Any]      							=     {}
  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'       )
    ):
      a__:					Dict      							=     k[: -len('.q_proj.weight'       )]
      a__:					Dict      							=     k[-len('q_proj.weight'       )]
      if k_pre not in capture_qkv_weight:
        a__:					Dict      							=     [None, None, None]
      a__:					Tuple      							=     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'       )
    ):
      a__:					Optional[Any]      							=     k[: -len('.q_proj.bias'       )]
      a__:					int      							=     k[-len('q_proj.bias'       )]
      if k_pre not in capture_qkv_bias:
        a__:					int      							=     [None, None, None]
      a__:					Dict      							=     v
      continue

    a__:					Optional[int]      							=     textenc_pattern.sub(lambda _SCREAMING_SNAKE_CASE       : protected[re.escape(m.group(0       )       )]		,					_SCREAMING_SNAKE_CASE       )
    a__:					Union[str, Any]      							=     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'       )
    a__:					Tuple      							=     textenc_pattern.sub(lambda _SCREAMING_SNAKE_CASE       : protected[re.escape(m.group(0       )       )]		,					_SCREAMING_SNAKE_CASE       )
    a__:					Optional[int]      							=     torch.cat(_SCREAMING_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'       )
    a__:					List[str]      							=     textenc_pattern.sub(lambda _SCREAMING_SNAKE_CASE       : protected[re.escape(m.group(0       )       )]		,					_SCREAMING_SNAKE_CASE       )
    a__:					Tuple      							=     torch.cat(_SCREAMING_SNAKE_CASE       )

  return new_state_dict







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  return text_enc_dict


if __name__ == "__main__":
       lowercase__ 	=     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.'
       )

       lowercase__ 	=     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
       lowercase__ 	=     osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
       lowercase__ 	=     osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
       lowercase__ 	=     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):
              lowercase__ 	=     load_file(unet_path, device='cpu')
       else:
              lowercase__ 	=     osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
              lowercase__ 	=     torch.load(unet_path, map_location='cpu')

       if osp.exists(vae_path):
              lowercase__ 	=     load_file(vae_path, device='cpu')
       else:
              lowercase__ 	=     osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
              lowercase__ 	=     torch.load(vae_path, map_location='cpu')

       if osp.exists(text_enc_path):
              lowercase__ 	=     load_file(text_enc_path, device='cpu')
       else:
              lowercase__ 	=     osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
              lowercase__ 	=     torch.load(text_enc_path, map_location='cpu')

       # Convert the UNet model
       lowercase__ 	=     convert_unet_state_dict(unet_state_dict)
       lowercase__ 	=     {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

       # Convert the VAE model
       lowercase__ 	=     convert_vae_state_dict(vae_state_dict)
       lowercase__ 	=     {'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
       lowercase__ 	=     '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
              lowercase__ 	=     {'transformer.' + k: v for k, v in text_enc_dict.items()}
              lowercase__ 	=     convert_text_enc_state_dict_vaa(text_enc_dict)
              lowercase__ 	=     {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
       else:
              lowercase__ 	=     convert_text_enc_state_dict(text_enc_dict)
              lowercase__ 	=     {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

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

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



"""simple docstring"""
import math







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[int]:
  if 0 not in (x, y):
    # We use the relation x^y = y*log10(x), where 10 is the base.
    return y * math.logaa(_SCREAMING_SNAKE_CASE       )
  else:
    if x == 0:  # 0 raised to any number is 0
      return 0
    elif y == 0:
      return 1  # any number raised to 0 is 1
  raise AssertionError('This should never happen'       )


if __name__ == "__main__":  # Main function
 # Read two numbers from input and typecast them to int using map function.
 # Here x is the base and y is the power.
 lowercase__ 	=     'Enter the base and the power separated by a comma: '
 lowercase__	, lowercase__ 	=     map(int, input(prompt).split(','))
 lowercase__	, lowercase__ 	=     map(int, input(prompt).split(','))

 # We find the log of each number, using the function res(), which takes two
 # arguments.
 lowercase__ 	=     res(xa, ya)
 lowercase__ 	=     res(xa, ya)

 # We check for the largest number
 if resa > resa:
  print('Largest number is', xa, '^', ya)
 elif resa > resa:
  print('Largest number is', xa, '^', ya)
 else:
  print('Both are equal')


