




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

snake_case__      :	Tuple       	=      [
    # (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"""),
]

snake_case__      :	Dict       	=      [
    # (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"""),
]

snake_case__      :	Any       	=      []
# 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
  snake_case__      :	List[str]       	=      f'down_blocks.{i}.resnets.{j}.'
  snake_case__      :	List[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
   snake_case__      :	str       	=      f'down_blocks.{i}.attentions.{j}.'
   snake_case__      :	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
  snake_case__      :	int       	=      f'up_blocks.{i}.resnets.{j}.'
  snake_case__      :	Tuple       	=      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
   snake_case__      :	int       	=      f'up_blocks.{i}.attentions.{j}.'
   snake_case__      :	int       	=      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
  snake_case__      :	Dict       	=      f'down_blocks.{i}.downsamplers.0.conv.'
  snake_case__      :	List[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
  snake_case__      :	List[str]       	=      f'up_blocks.{i}.upsamplers.0.'
  snake_case__      :	str       	=      f'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

snake_case__      :	List[str]       	=      """mid_block.attentions.0."""
snake_case__      :	int       	=      """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 snake_case__      :	Optional[int]       	=      f'mid_block.resnets.{j}.'
 snake_case__      :	Optional[Any]       	=      f'middle_block.{2*j}.'
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def     _snake_case    (__lowercase):
   # 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.
   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(__lowercase    ,      __lowercase)
         UpperCamelCase_									=     v
   for k, v in mapping.items():
      for sd_part, hf_part in unet_conversion_map_layer:
         UpperCamelCase_									=     v.replace(__lowercase    ,      __lowercase)
      UpperCamelCase_									=     v
   UpperCamelCase_									=     {v: unet_state_dict[k] for k, v in mapping.items()}
   return new_state_dict


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

snake_case__      :	str       	=      [
    # (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):
  snake_case__      :	Optional[Any]       	=      f'encoder.down_blocks.{i}.resnets.{j}.'
  snake_case__      :	Dict       	=      f'encoder.down.{i}.block.{j}.'
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

 if i < 3:
  snake_case__      :	Tuple       	=      f'down_blocks.{i}.downsamplers.0.'
  snake_case__      :	Any       	=      f'down.{i}.downsample.'
  vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

  snake_case__      :	Dict       	=      f'up_blocks.{i}.upsamplers.0.'
  snake_case__      :	Optional[int]       	=      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):
  snake_case__      :	Any       	=      f'decoder.up_blocks.{i}.resnets.{j}.'
  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):
 snake_case__      :	Any       	=      f'mid_block.resnets.{i}.'
 snake_case__      :	Any       	=      f'mid.block_{i+1}.'
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


snake_case__      :	List[str]       	=      [
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]






def     _snake_case    (__lowercase):
   # convert HF linear weights to SD conv2d weights
   return w.reshape(*w.shape    ,      1    ,      1)






def     _snake_case    (__lowercase):
   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(__lowercase    ,      __lowercase)
      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(__lowercase    ,      __lowercase)
         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(__lowercase)
   return new_state_dict


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


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

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
snake_case__      :	Optional[int]       	=      {"""q""": 0, """k""": 1, """v""": 2}






def     _snake_case    (__lowercase):
   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 __lowercase: protected[re.escape(m.group(0))]    ,      __lowercase)
      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 __lowercase: protected[re.escape(m.group(0))]    ,      __lowercase)
      UpperCamelCase_									=     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')
      UpperCamelCase_									=     textenc_pattern.sub(lambda __lowercase: protected[re.escape(m.group(0))]    ,      __lowercase)
      UpperCamelCase_									=     torch.cat(__lowercase)

   return new_state_dict






def     _snake_case    (__lowercase):
   return text_enc_dict


if __name__ == "__main__":
 snake_case__      :	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."""
 )

 snake_case__      :	List[Any]       	=      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
 snake_case__      :	str       	=      osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
 snake_case__      :	Optional[Any]       	=      osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
 snake_case__      :	Dict       	=      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):
  snake_case__      :	Optional[Any]       	=      load_file(unet_path, device="""cpu""")
 else:
  snake_case__      :	List[str]       	=      osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
  snake_case__      :	str       	=      torch.load(unet_path, map_location="""cpu""")

 if osp.exists(vae_path):
  snake_case__      :	Any       	=      load_file(vae_path, device="""cpu""")
 else:
  snake_case__      :	Dict       	=      osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
  snake_case__      :	str       	=      torch.load(vae_path, map_location="""cpu""")

 if osp.exists(text_enc_path):
  snake_case__      :	Dict       	=      load_file(text_enc_path, device="""cpu""")
 else:
  snake_case__      :	Optional[int]       	=      osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
  snake_case__      :	Union[str, Any]       	=      torch.load(text_enc_path, map_location="""cpu""")

 # Convert the UNet model
 snake_case__      :	str       	=      convert_unet_state_dict(unet_state_dict)
 snake_case__      :	Union[str, Any]       	=      {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 snake_case__      :	Optional[Any]       	=      convert_vae_state_dict(vae_state_dict)
 snake_case__      :	int       	=      {"""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
 snake_case__      :	Dict       	=      """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
  snake_case__      :	Optional[int]       	=      {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
  snake_case__      :	Union[str, Any]       	=      convert_text_enc_state_dict_vaa(text_enc_dict)
  snake_case__      :	Dict       	=      {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
 else:
  snake_case__      :	List[Any]       	=      convert_text_enc_state_dict(text_enc_dict)
  snake_case__      :	str       	=      {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

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

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  snake_case__      :	str       	=      {"""state_dict""": state_dict}
  torch.save(state_dict, args.checkpoint_path)













import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


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


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def __init__(     self		,		*_UpperCAmelCase		,		**_UpperCAmelCase )			-> None:
     warnings.warn(
         'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
         ' Please use VideoMAEImageProcessor instead.'		,		_UpperCAmelCase		,		)
     super().__init__(*_UpperCAmelCase		,		**_UpperCAmelCase )





