import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__lowerCamelCase    :				str							     =	[
    # (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'''),
]

__lowerCamelCase    :				List[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'''),
]

__lowerCamelCase    :				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
      __lowerCamelCase    :				str							     =	F"""down_blocks.{i}.resnets.{j}."""
      __lowerCamelCase    :				Optional[int]							     =	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
         __lowerCamelCase    :				Optional[int]							     =	F"""down_blocks.{i}.attentions.{j}."""
         __lowerCamelCase    :				Union[str, 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
      __lowerCamelCase    :				List[Any]							     =	F"""up_blocks.{i}.resnets.{j}."""
      __lowerCamelCase    :				Optional[int]							     =	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
         __lowerCamelCase    :				str							     =	F"""up_blocks.{i}.attentions.{j}."""
         __lowerCamelCase    :				List[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
      __lowerCamelCase    :				Optional[Any]							     =	F"""down_blocks.{i}.downsamplers.0.conv."""
      __lowerCamelCase    :				Tuple							     =	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
      __lowerCamelCase    :				str							     =	F"""up_blocks.{i}.upsamplers.0."""
      __lowerCamelCase    :				str							     =	F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
      unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__lowerCamelCase    :				Optional[Any]							     =	'''mid_block.attentions.0.'''
__lowerCamelCase    :				Tuple							     =	'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]			)							->      Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   {k: k for k in unet_state_dict.keys()}
  for sd_name, hf_name in unet_conversion_map:
    SCREAMING_SNAKE_CASE__    							=   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__    							=   v.replace(__UpperCamelCase				,  __UpperCamelCase			)
      SCREAMING_SNAKE_CASE__    							=   v
  for k, v in mapping.items():
    for sd_part, hf_part in unet_conversion_map_layer:
      SCREAMING_SNAKE_CASE__    							=   v.replace(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   v
  SCREAMING_SNAKE_CASE__    							=   {v: unet_state_dict[k] for k, v in mapping.items()}
  return new_state_dict


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

__lowerCamelCase    :				Dict							     =	[
    # (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):
      __lowerCamelCase    :				List[str]							     =	F"""encoder.down_blocks.{i}.resnets.{j}."""
      __lowerCamelCase    :				Tuple							     =	F"""encoder.down.{i}.block.{j}."""
      vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

   if i < 3:
      __lowerCamelCase    :				Any							     =	F"""down_blocks.{i}.downsamplers.0."""
      __lowerCamelCase    :				Optional[int]							     =	F"""down.{i}.downsample."""
      vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

      __lowerCamelCase    :				Optional[Any]							     =	F"""up_blocks.{i}.upsamplers.0."""
      __lowerCamelCase    :				List[str]							     =	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):
      __lowerCamelCase    :				Dict							     =	F"""decoder.up_blocks.{i}.resnets.{j}."""
      __lowerCamelCase    :				Optional[Any]							     =	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):
   __lowerCamelCase    :				Union[str, Any]							     =	F"""mid_block.resnets.{i}."""
   __lowerCamelCase    :				Dict							     =	F"""mid.block_{i+1}."""
   vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      str:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str			)							->      List[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   {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__    							=   v.replace(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   v
  for k, v in mapping.items():
    if "attentions" in k:
      for sd_part, hf_part in vae_conversion_map_attn:
        SCREAMING_SNAKE_CASE__    							=   v.replace(__UpperCamelCase				,  __UpperCamelCase			)
      SCREAMING_SNAKE_CASE__    							=   v
  SCREAMING_SNAKE_CASE__    							=   {v: vae_state_dict[k] for k, v in mapping.items()}
  SCREAMING_SNAKE_CASE__    							=   ["""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__    							=   reshape_weight_for_sd(__UpperCamelCase			)
  return new_state_dict


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


__lowerCamelCase    :				Optional[Any]							     =	[
    # (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'''),
]
__lowerCamelCase    :				int							     =	{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__lowerCamelCase    :				List[str]							     =	re.compile('''|'''.join(protected.keys()))

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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any			)							->      Optional[Any]:







  """simple docstring"""





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

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

  return new_state_dict



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





  return text_enc_dict


if __name__ == "__main__":
   __lowerCamelCase    :				List[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.'''
   )

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

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

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

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

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

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

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

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase    :				List[Any]							     =	logging.get_logger(__name__)

__lowerCamelCase    :				str							     =	{
    '''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"timesformer"




   def __init__(					self    :       Any ,			_lowercase    :       Tuple=2_24 ,			_lowercase    :       Tuple=16 ,			_lowercase    :       List[Any]=3 ,			_lowercase    :       str=8 ,			_lowercase    :       Optional[Any]=7_68 ,			_lowercase    :       List[str]=12 ,			_lowercase    :       List[str]=12 ,			_lowercase    :       Dict=30_72 ,			_lowercase    :       List[str]="gelu" ,			_lowercase    :       List[str]=0.0 ,			_lowercase    :       List[str]=0.0 ,			_lowercase    :       Tuple=0.02 ,			_lowercase    :       List[Any]=1E-6 ,			_lowercase    :       List[str]=True ,			_lowercase    :       Dict="divided_space_time" ,			_lowercase    :       Union[str, Any]=0 ,			**_lowercase    :       Union[str, Any] ,			):



     """simple docstring"""






     super().__init__(**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   image_size
     SCREAMING_SNAKE_CASE__    							=   patch_size
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   num_frames

     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   num_hidden_layers
     SCREAMING_SNAKE_CASE__    							=   num_attention_heads
     SCREAMING_SNAKE_CASE__    							=   intermediate_size
     SCREAMING_SNAKE_CASE__    							=   hidden_act
     SCREAMING_SNAKE_CASE__    							=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   initializer_range
     SCREAMING_SNAKE_CASE__    							=   layer_norm_eps
     SCREAMING_SNAKE_CASE__    							=   qkv_bias

     SCREAMING_SNAKE_CASE__    							=   attention_type
     SCREAMING_SNAKE_CASE__    							=   drop_path_rate
