



'''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  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		={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_       		=v.replace(lowercase__       ,	lowercase__       )
          a_       		=v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    a_       		=v.replace(lowercase__       ,	lowercase__       )
               a_       		=v
     a_       		={v: vae_state_dict[k] for k, v in mapping.items()}
     a_       		=["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_       		=reshape_weight_for_sd(lowercase__       )
     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  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		={}
     a_       		={}
     a_       		={}
     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_       		=k[: -len(".q_proj.weight"       )]
               a_       		=k[-len("q_proj.weight"       )]
               if k_pre not in capture_qkv_weight:
                    a_       		=[None, None, None]
               a_       		=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_       		=k[: -len(".q_proj.bias"       )]
               a_       		=k[-len("q_proj.bias"       )]
               if k_pre not in capture_qkv_bias:
                    a_       		=[None, None, None]
               a_       		=v
               continue

          a_       		=textenc_pattern.sub(lambda lowercase__       : protected[re.escape(m.group(0       )       )]       ,	lowercase__       )
          a_       		=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_       		=textenc_pattern.sub(lambda lowercase__       : protected[re.escape(m.group(0       )       )]       ,	lowercase__       )
          a_       		=torch.cat(lowercase__       )

     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_       		=textenc_pattern.sub(lambda lowercase__       : protected[re.escape(m.group(0       )       )]       ,	lowercase__       )
          a_       		=torch.cat(lowercase__       )

     return new_state_dict






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     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'''


from __future__ import annotations

lowercase							       =			[]






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     for i in range(len(lowercase__       )       ):
          if board[row][i] == 1:
               return False
     for i in range(len(lowercase__       )       ):
          if board[i][column] == 1:
               return False
     for i, j in zip(range(lowercase__       ,	-1       ,	-1       )       ,	range(lowercase__       ,	-1       ,	-1       )       ):
          if board[i][j] == 1:
               return False
     for i, j in zip(range(lowercase__       ,	-1       ,	-1       )       ,	range(lowercase__       ,	len(lowercase__       )       )       ):
          if board[i][j] == 1:
               return False
     return True






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     if row >= len(lowercase__       ):
          solution.append(lowercase__       )
          printboard(lowercase__       )
          print()
          return True
     for i in range(len(lowercase__       )       ):
          if is_safe(lowercase__       ,	lowercase__       ,	lowercase__       ):
               a_       		=1
               solve(lowercase__       ,	row + 1       )
               a_       		=0
     return False






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     for i in range(len(lowercase__       )       ):
          for j in range(len(lowercase__       )       ):
               if board[i][j] == 1:
                    print("Q"       ,	end=" "       )
               else:
                    print("."       ,	end=" "       )
          print()


# n=int(input("The no. of queens"))
lowercase							       =			8
lowercase							       =			[[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print('''The total no. of solutions are :''', len(solution))


