






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

import torch
from safetensors.torch import load_file, save_file


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

a_					= [
    # (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'),
]

a_					= [
    # (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'),
]

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

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

for j in range(2):
  a_					= F'''mid_block.resnets.{j}.'''
  a_					= F'''middle_block.{2*j}.'''
  unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def  _a(  UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[Any]									={k: k for k in unet_state_dict.keys()}
      for sd_name, hf_name in unet_conversion_map:
            SCREAMING_SNAKE_CASE__     :				Tuple									=sd_name
      for k, v in mapping.items():
            if "resnets" in k:
                  for sd_part, hf_part in unet_conversion_map_resnet:
                        SCREAMING_SNAKE_CASE__     :				Optional[Any]									=v.replace(UpperCamelCase__,	UpperCamelCase__							)
                  SCREAMING_SNAKE_CASE__     :				str									=v
      for k, v in mapping.items():
            for sd_part, hf_part in unet_conversion_map_layer:
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=v.replace(UpperCamelCase__,	UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=v
      SCREAMING_SNAKE_CASE__     :				Dict									={v: unet_state_dict[k] for k, v in mapping.items()}
      return new_state_dict


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

a_					= [
    # (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):
    a_					= F'''encoder.down_blocks.{i}.resnets.{j}.'''
    a_					= F'''encoder.down.{i}.block.{j}.'''
    vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

  if i < 3:
    a_					= F'''down_blocks.{i}.downsamplers.0.'''
    a_					= F'''down.{i}.downsample.'''
    vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

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


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





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






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





def  _a(  UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






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


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


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

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





def  _a(  UpperCamelCase__				:						List[str]							):




      '''simple docstring'''






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

            SCREAMING_SNAKE_CASE__     :				List[Any]									=textenc_pattern.sub(lambda UpperCamelCase__							: protected[re.escape(m.group(0							)							)],	UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				int									=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'''							)
            SCREAMING_SNAKE_CASE__     :				int									=textenc_pattern.sub(lambda UpperCamelCase__							: protected[re.escape(m.group(0							)							)],	UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.cat(UpperCamelCase__							)

      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'''							)
            SCREAMING_SNAKE_CASE__     :				Any									=textenc_pattern.sub(lambda UpperCamelCase__							: protected[re.escape(m.group(0							)							)],	UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				Any									=torch.cat(UpperCamelCase__							)

      return new_state_dict





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      return text_enc_dict


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

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

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

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

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

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

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

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







'''simple docstring'''
from math import isqrt





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=[True] * max_number
      for i in range(2,	isqrt(max_number - 1							) + 1							):
            if is_prime[i]:
                  for j in range(i**2,	UpperCamelCase__,	UpperCamelCase__							):
                        SCREAMING_SNAKE_CASE__     :				Any									=False

      return [i for i in range(2,	UpperCamelCase__							) if is_prime[i]]





def  _a(  UpperCamelCase__				:						int = 1_0**8							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=calculate_prime_numbers(max_number // 2							)

      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							) - 1
      while left <= right:
            while prime_numbers[left] * prime_numbers[right] >= max_number:
                  right -= 1
            semiprimes_count += right - left + 1
            left += 1

      return semiprimes_count


if __name__ == "__main__":
  print(F'''{solution() = }''')