





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

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

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

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

def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple				)	->				List[str]:
     '''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(_UpperCAmelCase  ,						_UpperCAmelCase				)
               A_          =       v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               A_          =       v.replace(_UpperCAmelCase  ,						_UpperCAmelCase				)
          A_          =       v
     A_          =       {v: unet_state_dict[k] for k, v in mapping.items()}
     return new_state_dict


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

a__      :     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):
            a__      :     Any         =		f'''encoder.down_blocks.{i}.resnets.{j}.'''
            a__      :     int         =		f'''encoder.down.{i}.block.{j}.'''
            vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

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


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:Tuple				)	->				List[Any]:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:str				)	->				List[str]:
     '''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(_UpperCAmelCase  ,						_UpperCAmelCase				)
          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(_UpperCAmelCase  ,						_UpperCAmelCase				)
               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(_UpperCAmelCase				)
     return new_state_dict


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


a__      :     Tuple         =		[
    # (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__      :     Optional[Any]         =		{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
a__      :     Optional[Any]         =		re.compile('|'.join(protected.keys()))

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

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[str]				)	->				Optional[Any]:
     '''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 _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]  ,						_UpperCAmelCase				)
          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 _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]  ,						_UpperCAmelCase				)
          A_          =       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'''				)
          A_          =       textenc_pattern.sub(lambda _UpperCAmelCase				: protected[re.escape(m.group(0				)				)]  ,						_UpperCAmelCase				)
          A_          =       torch.cat(_UpperCAmelCase				)

     return new_state_dict

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]				)	->				Tuple:
     '''simple docstring'''


     return text_enc_dict


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

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

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

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

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

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

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

      if args.use_safetensors:
            save_file(state_dict, args.checkpoint_path)
      else:
            a__      :     Union[str, Any]         =		{'state_dict': state_dict}
            torch.save(state_dict, args.checkpoint_path)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :     str         =		{
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[Any]         =		['RoFormerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[str]         =		[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     List[Any]         =		[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__      :     Dict         =		[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      a__      :     Union[str, Any]         =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



