




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

lowerCamelCase__					:  Dict      							=				'mid_block.attentions.0.'
lowerCamelCase__					:  Any      							=				'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

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

def    UpperCAmelCase_				( __UpperCAmelCase		:      Tuple				)   ->					int:
      # 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.
      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__					:  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):
      lowerCamelCase__					:  Union[str, Any]      							=				f'''encoder.down_blocks.{i}.resnets.{j}.'''
      lowerCamelCase__					:  Union[str, Any]      							=				f'''encoder.down.{i}.block.{j}.'''
      vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

   if i < 3:
      lowerCamelCase__					:  int      							=				f'''down_blocks.{i}.downsamplers.0.'''
      lowerCamelCase__					:  List[Any]      							=				f'''down.{i}.downsample.'''
      vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

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


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

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

def    UpperCAmelCase_				( __UpperCAmelCase		:      List[str]				)   ->					int:
      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__					:  str      							=				[
    # (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__					:  Tuple      							=				{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCamelCase__					:  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    UpperCAmelCase_				( __UpperCAmelCase		:      List[str]				)   ->					Union[str, Any]:
      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    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[int]				)   ->					Optional[Any]:
      return text_enc_dict


if __name__ == "__main__":
   lowerCamelCase__					:  int      							=				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__					:  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__					:  Union[str, Any]      							=				osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
   lowerCamelCase__					:  Dict      							=				osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
   lowerCamelCase__					:  List[str]      							=				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__					:  List[str]      							=				osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
      lowerCamelCase__					:  Any      							=				torch.load(unet_path, map_location='cpu')

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

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

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

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

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

   if args.use_safetensors:
      save_file(state_dict, args.checkpoint_path)
   else:
      lowerCamelCase__					:  Optional[int]      							=				{'state_dict': state_dict}
      torch.save(state_dict, args.checkpoint_path)



'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_			:  Optional[int]										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[str]										=   {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		List[Any]     				=    '''fnet'''


 def __init__(   self   ,   __lowercase=32_000   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu_new"   ,   __lowercase=0.1   ,   __lowercase=512   ,   __lowercase=4   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=False   ,   __lowercase=512   ,   __lowercase=3   ,   __lowercase=1   ,   __lowercase=2   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(pad_token_id=__lowercase   ,   bos_token_id=__lowercase   ,   eos_token_id=__lowercase   ,   **__lowercase    )

  __A	:		Union[str, Any]	  =      vocab_size
  __A	:		Dict	  =      max_position_embeddings
  __A	:		List[str]	  =      hidden_size
  __A	:		Tuple	  =      num_hidden_layers
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		Dict	  =      hidden_act
  __A	:		List[str]	  =      hidden_dropout_prob
  __A	:		str	  =      initializer_range
  __A	:		Dict	  =      type_vocab_size
  __A	:		int	  =      layer_norm_eps
  __A	:		Tuple	  =      use_tpu_fourier_optimizations
  __A	:		Optional[Any]	  =      tpu_short_seq_length






