




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				"yolos"


    def __init__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=768       ,     SCREAMING_SNAKE_CASE__					:					List[str]=12       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=12       ,     SCREAMING_SNAKE_CASE__					:					Tuple=3_072       ,     SCREAMING_SNAKE_CASE__					:					List[Any]="gelu"       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=0.0       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=0.0       ,     SCREAMING_SNAKE_CASE__					:					List[str]=0.02       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=1e-1_2       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=[512, 864]       ,     SCREAMING_SNAKE_CASE__					:					Tuple=16       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=3       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=True       ,     SCREAMING_SNAKE_CASE__					:					str=100       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=False       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=1       ,     SCREAMING_SNAKE_CASE__					:					Any=5       ,     SCREAMING_SNAKE_CASE__					:					List[str]=2       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=5       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=2       ,     SCREAMING_SNAKE_CASE__					:					Tuple=0.1       ,     **SCREAMING_SNAKE_CASE__					:					int       ,     )				->	List[Any]:
         super().__init__(**SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			hidden_size
         lowerCAmelCase__          =			num_hidden_layers
         lowerCAmelCase__          =			num_attention_heads
         lowerCAmelCase__          =			intermediate_size
         lowerCAmelCase__          =			hidden_act
         lowerCAmelCase__          =			hidden_dropout_prob
         lowerCAmelCase__          =			attention_probs_dropout_prob
         lowerCAmelCase__          =			initializer_range
         lowerCAmelCase__          =			layer_norm_eps
         lowerCAmelCase__          =			image_size
         lowerCAmelCase__          =			patch_size
         lowerCAmelCase__          =			num_channels
         lowerCAmelCase__          =			qkv_bias
         lowerCAmelCase__          =			num_detection_tokens
         lowerCAmelCase__          =			use_mid_position_embeddings
         lowerCAmelCase__          =			auxiliary_loss
         # Hungarian matcher
         lowerCAmelCase__          =			class_cost
         lowerCAmelCase__          =			bbox_cost
         lowerCAmelCase__          =			giou_cost
         # Loss coefficients
         lowerCAmelCase__          =			bbox_loss_coefficient
         lowerCAmelCase__          =			giou_loss_coefficient
         lowerCAmelCase__          =			eos_coefficient






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				version.parse("1.11"   )


    @property
    def        a				(	self					:					List[Any]       )				->	Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]       )


    @property
    def        a				(	self					:					List[str]       )				->	float:
         return 1e-4


    @property
    def        a				(	self					:					Tuple       )				->	int:
         return 12






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

UpperCamelCase								=					[
    # (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'),
]

UpperCamelCase								=					[
    # (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'),
]

UpperCamelCase								=					[]
# 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
          UpperCamelCase								=					F"""down_blocks.{i}.resnets.{j}."""
          UpperCamelCase								=					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
               UpperCamelCase								=					F"""down_blocks.{i}.attentions.{j}."""
               UpperCamelCase								=					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
          UpperCamelCase								=					F"""up_blocks.{i}.resnets.{j}."""
          UpperCamelCase								=					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
               UpperCamelCase								=					F"""up_blocks.{i}.attentions.{j}."""
               UpperCamelCase								=					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
          UpperCamelCase								=					F"""down_blocks.{i}.downsamplers.0.conv."""
          UpperCamelCase								=					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
          UpperCamelCase								=					F"""up_blocks.{i}.upsamplers.0."""
          UpperCamelCase								=					F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
          unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCamelCase								=					'mid_block.attentions.0.'
UpperCamelCase								=					'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
     UpperCamelCase								=					F"""mid_block.resnets.{j}."""
     UpperCamelCase								=					F"""middle_block.{2*j}."""
     unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def 							_A       (						lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			{k: k for k in unet_state_dict.keys()}
     for sd_name, hf_name in unet_conversion_map:
          lowerCAmelCase__          =			sd_name
     for k, v in mapping.items():
          if "resnets" in k:
               for sd_part, hf_part in unet_conversion_map_resnet:
                    lowerCAmelCase__          =			v.replace(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               lowerCAmelCase__          =			v.replace(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			v
     lowerCAmelCase__          =			{v: unet_state_dict[k] for k, v in mapping.items()}
     return new_state_dict


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

UpperCamelCase								=					[
    # (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):
          UpperCamelCase								=					F"""encoder.down_blocks.{i}.resnets.{j}."""
          UpperCamelCase								=					F"""encoder.down.{i}.block.{j}."""
          vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

     if i < 3:
          UpperCamelCase								=					F"""down_blocks.{i}.downsamplers.0."""
          UpperCamelCase								=					F"""down.{i}.downsample."""
          vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

          UpperCamelCase								=					F"""up_blocks.{i}.upsamplers.0."""
          UpperCamelCase								=					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):
          UpperCamelCase								=					F"""decoder.up_blocks.{i}.resnets.{j}."""
          UpperCamelCase								=					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):
     UpperCamelCase								=					F"""mid_block.resnets.{i}."""
     UpperCamelCase								=					F"""mid.block_{i+1}."""
     vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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



def 							_A       (						lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			{k: k for k in vae_state_dict.keys()}
     for k, v in mapping.items():
          for sd_part, hf_part in vae_conversion_map:
               lowerCAmelCase__          =			v.replace(lowerCAmelCase_       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    lowerCAmelCase__          =			v.replace(lowerCAmelCase_       ,    lowerCAmelCase_      )
               lowerCAmelCase__          =			v
     lowerCAmelCase__          =			{v: vae_state_dict[k] for k, v in mapping.items()}
     lowerCAmelCase__          =			["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'      )
                    lowerCAmelCase__          =			reshape_weight_for_sd(lowerCAmelCase_      )
     return new_state_dict


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


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

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



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

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

          lowerCAmelCase__          =			textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			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"      )
          lowerCAmelCase__          =			textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			torch.cat(lowerCAmelCase_      )

     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"      )
          lowerCAmelCase__          =			textenc_pattern.sub(lambda lowerCAmelCase_      : protected[re.escape(m.group(0      )      )]       ,    lowerCAmelCase_      )
          lowerCAmelCase__          =			torch.cat(lowerCAmelCase_      )

     return new_state_dict



def 							_A       (						lowerCAmelCase_					: Any      ):
     """simple docstring"""

     return text_enc_dict


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

     UpperCamelCase								=					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
     UpperCamelCase								=					osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
     UpperCamelCase								=					osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
     UpperCamelCase								=					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):
          UpperCamelCase								=					load_file(unet_path, device='cpu')
     else:
          UpperCamelCase								=					osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
          UpperCamelCase								=					torch.load(unet_path, map_location='cpu')

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

     if osp.exists(text_enc_path):
          UpperCamelCase								=					load_file(text_enc_path, device='cpu')
     else:
          UpperCamelCase								=					osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
          UpperCamelCase								=					torch.load(text_enc_path, map_location='cpu')

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

     # Convert the VAE model
     UpperCamelCase								=					convert_vae_state_dict(vae_state_dict)
     UpperCamelCase								=					{'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
     UpperCamelCase								=					'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
          UpperCamelCase								=					{'transformer.' + k: v for k, v in text_enc_dict.items()}
          UpperCamelCase								=					convert_text_enc_state_dict_vaa(text_enc_dict)
          UpperCamelCase								=					{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
     else:
          UpperCamelCase								=					convert_text_enc_state_dict(text_enc_dict)
          UpperCamelCase								=					{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

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

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