import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

_lowercase							:      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'''),
]

_lowercase							:      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'''),
]

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

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

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



def A__  (			lowercase:     str					)				->		str:
       # 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.
       A						:     Optional[int]         ={k: k for k in unet_state_dict.keys()}
       for sd_name, hf_name in unet_conversion_map:
              A						:     Optional[Any]         =sd_name
       for k, v in mapping.items():
              if "resnets" in k:
                     for sd_part, hf_part in unet_conversion_map_resnet:
                            A						:     str         =v.replace(lowercase,		lowercase					)
                     A						:     Union[str, Any]         =v
       for k, v in mapping.items():
              for sd_part, hf_part in unet_conversion_map_layer:
                     A						:     int         =v.replace(lowercase,		lowercase					)
              A						:     Tuple         =v
       A						:     List[Any]         ={v: unet_state_dict[k] for k, v in mapping.items()}
       return new_state_dict


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

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

    if i < 3:
        _lowercase							:      Dict      							=f'''down_blocks.{i}.downsamplers.0.'''
        _lowercase							:      str      							=f'''down.{i}.downsample.'''
        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

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


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



def A__  (			lowercase:     Optional[int]					)				->		Tuple:
       # convert HF linear weights to SD conv2d weights
       return w.reshape(*w.shape,		1,		1					)



def A__  (			lowercase:     List[Any]					)				->		List[str]:
       A						:     Optional[int]         ={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						:     int         =v.replace(lowercase,		lowercase					)
              A						:     List[Any]         =v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            A						:     Union[str, Any]         =v.replace(lowercase,		lowercase					)
                     A						:     Dict         =v
       A						:     Union[str, Any]         ={v: vae_state_dict[k] for k, v in mapping.items()}
       A						:     Dict         =['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						:     Optional[Any]         =reshape_weight_for_sd(lowercase					)
       return new_state_dict


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


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

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



def A__  (			lowercase:     Tuple					)				->		Any:
       A						:     Any         ={}
       A						:     Dict         ={}
       A						:     Optional[int]         ={}
       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						:     Tuple         =k[: -len('.q_proj.weight'					)]
                     A						:     Optional[int]         =k[-len('q_proj.weight'					)]
                     if k_pre not in capture_qkv_weight:
                            A						:     Optional[Any]         =[None, None, None]
                     A						:     Tuple         =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						:     int         =k[: -len('.q_proj.bias'					)]
                     A						:     List[Any]         =k[-len('q_proj.bias'					)]
                     if k_pre not in capture_qkv_bias:
                            A						:     Optional[int]         =[None, None, None]
                     A						:     Optional[Any]         =v
                     continue

              A						:     List[Any]         =textenc_pattern.sub(lambda lowercase					: protected[re.escape(m.group(0					)					)],		lowercase					)
              A						:     Optional[int]         =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						:     Optional[int]         =textenc_pattern.sub(lambda lowercase					: protected[re.escape(m.group(0					)					)],		lowercase					)
              A						:     List[Any]         =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						:     int         =textenc_pattern.sub(lambda lowercase					: protected[re.escape(m.group(0					)					)],		lowercase					)
              A						:     Tuple         =torch.cat(lowercase					)

       return new_state_dict



def A__  (			lowercase:     int					)				->		Union[str, Any]:
       return text_enc_dict


if __name__ == "__main__":
    _lowercase							:      Tuple      							=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							:      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
    _lowercase							:      Optional[Any]      							=osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
    _lowercase							:      List[Any]      							=osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
    _lowercase							:      Union[str, 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):
        _lowercase							:      Optional[Any]      							=load_file(unet_path, device='''cpu''')
    else:
        _lowercase							:      Any      							=osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
        _lowercase							:      Optional[int]      							=torch.load(unet_path, map_location='''cpu''')

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

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

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

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

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

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







_lowercase							:      Dict      							='''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
    from .utils import rich






