'''simple docstring'''





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

SCREAMING_SNAKE_CASE_:	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'),
]

SCREAMING_SNAKE_CASE_:	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'),
]

SCREAMING_SNAKE_CASE_:	Union[str, 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
        SCREAMING_SNAKE_CASE_:	Any    =f"down_blocks.{i}.resnets.{j}."
        SCREAMING_SNAKE_CASE_:	Tuple    =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
            SCREAMING_SNAKE_CASE_:	Optional[Any]    =f"down_blocks.{i}.attentions.{j}."
            SCREAMING_SNAKE_CASE_:	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
        SCREAMING_SNAKE_CASE_:	Union[str, Any]    =f"up_blocks.{i}.resnets.{j}."
        SCREAMING_SNAKE_CASE_:	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
            SCREAMING_SNAKE_CASE_:	int    =f"up_blocks.{i}.attentions.{j}."
            SCREAMING_SNAKE_CASE_:	Optional[int]    =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
        SCREAMING_SNAKE_CASE_:	Union[str, Any]    =f"down_blocks.{i}.downsamplers.0.conv."
        SCREAMING_SNAKE_CASE_:	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
        SCREAMING_SNAKE_CASE_:	int    =f"up_blocks.{i}.upsamplers.0."
        SCREAMING_SNAKE_CASE_:	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))

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

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





def       lowerCAmelCase_							(   snake_case_			:					Optional[Any]     )       -> List[str]:


     '''simple docstring'''




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


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

SCREAMING_SNAKE_CASE_:	int    =[
    # (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):
        SCREAMING_SNAKE_CASE_:	Tuple    =f"encoder.down_blocks.{i}.resnets.{j}."
        SCREAMING_SNAKE_CASE_:	int    =f"encoder.down.{i}.block.{j}."
        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

    if i < 3:
        SCREAMING_SNAKE_CASE_:	int    =f"down_blocks.{i}.downsamplers.0."
        SCREAMING_SNAKE_CASE_:	str    =f"down.{i}.downsample."
        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

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


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





def       lowerCAmelCase_							(   snake_case_			:					Tuple     )       -> Tuple:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					Optional[Any]     )       -> Optional[Any]:


     '''simple docstring'''




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


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


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

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





def       lowerCAmelCase_							(   snake_case_			:					Union[str, Any]     )       -> Tuple:


     '''simple docstring'''




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

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

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

     return new_state_dict





def       lowerCAmelCase_							(   snake_case_			:					List[Any]     )       -> Union[str, Any]:


     '''simple docstring'''




     return text_enc_dict


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	str    =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.'
    )

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

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

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

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

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

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

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











'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      List[Any]						      ={
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip_2_vision_model"""
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=14_08		,			lowercase_ :int=61_44		,			lowercase_ :List[str]=39		,			lowercase_ :List[Any]=16		,			lowercase_ :Union[str, Any]=2_24		,			lowercase_ :List[str]=14		,			lowercase_ :Dict="gelu"		,			lowercase_ :Tuple=0.0_0_0_0_1		,			lowercase_ :Dict=0.0		,			lowercase_ :Optional[int]=1E-10		,			lowercase_ :Optional[Any]=True		,			**lowercase_ :List[str]		,			)->				int:
     super().__init__(**lowercase_				)

     A__							=     hidden_size
     A__							=     intermediate_size
     A__							=     num_hidden_layers
     A__							=     num_attention_heads
     A__							=     patch_size
     A__							=     image_size
     A__							=     initializer_range
     A__							=     attention_dropout
     A__							=     layer_norm_eps
     A__							=     hidden_act
     A__							=     qkv_bias



 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[Any]		,			lowercase_ :Union[str, os.PathLike]		,			**lowercase_ :Optional[Any]				)->				"PretrainedConfig":
     cls._set_token_in_kwargs(lowercase_				)

     A__,      A__							=     cls.get_config_dict(lowercase_		,			**lowercase_				)

     # get the vision config dict if we are loading from Blip2Config
     if config_dict.get("model_type"				) == "blip-2":
         A__							=     config_dict["vision_config"]

     if "model_type" in config_dict and hasattr(cls		,			"model_type"				) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

     return cls.from_dict(lowercase_		,			**lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip_2_qformer"""
 def __init__(							self :Dict		,			lowercase_ :int=3_05_22		,			lowercase_ :Union[str, Any]=7_68		,			lowercase_ :Tuple=12		,			lowercase_ :Union[str, Any]=12		,			lowercase_ :str=30_72		,			lowercase_ :Any="gelu"		,			lowercase_ :Optional[int]=0.1		,			lowercase_ :List[str]=0.1		,			lowercase_ :Dict=5_12		,			lowercase_ :Optional[int]=0.0_2		,			lowercase_ :Dict=1E-12		,			lowercase_ :List[Any]=0		,			lowercase_ :str="absolute"		,			lowercase_ :List[str]=2		,			lowercase_ :Tuple=14_08		,			**lowercase_ :Tuple		,			)->				List[Any]:
     super().__init__(pad_token_id=lowercase_		,			**lowercase_				)

     A__							=     vocab_size
     A__							=     hidden_size
     A__							=     num_hidden_layers
     A__							=     num_attention_heads
     A__							=     hidden_act
     A__							=     intermediate_size
     A__							=     hidden_dropout_prob
     A__							=     attention_probs_dropout_prob
     A__							=     max_position_embeddings
     A__							=     initializer_range
     A__							=     layer_norm_eps
     A__							=     position_embedding_type
     A__							=     cross_attention_frequency
     A__							=     encoder_hidden_size



 @classmethod
 def 	UpperCAmelCase_				(							cls :int		,			lowercase_ :Union[str, os.PathLike]		,			**lowercase_ :str				)->				"PretrainedConfig":
     cls._set_token_in_kwargs(lowercase_				)

     A__,      A__							=     cls.get_config_dict(lowercase_		,			**lowercase_				)

     # get the qformer config dict if we are loading from Blip2Config
     if config_dict.get("model_type"				) == "blip-2":
         A__							=     config_dict["qformer_config"]

     if "model_type" in config_dict and hasattr(cls		,			"model_type"				) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

     return cls.from_dict(lowercase_		,			**lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip-2"""
 __lowercase  						=							True
 def __init__(							self :int		,			lowercase_ :List[Any]=None		,			lowercase_ :Dict=None		,			lowercase_ :Tuple=None		,			lowercase_ :List[str]=32		,			**lowercase_ :Any				)->				Union[str, Any]:
     super().__init__(**lowercase_				)

     if vision_config is None:
         A__							=     {}
         logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."				)

     if qformer_config is None:
         A__							=     {}
         logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."				)

     if text_config is None:
         A__							=     {}
         logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."				)

     A__							=     BlipaVisionConfig(**lowercase_				)
     A__							=     BlipaQFormerConfig(**lowercase_				)
     A__							=     text_config["model_type"] if "model_type" in text_config else "opt"
     A__							=     CONFIG_MAPPING[text_model_type](**lowercase_				)

     A__							=     self.text_config.tie_word_embeddings
     A__							=     self.text_config.is_encoder_decoder

     A__							=     num_query_tokens
     A__							=     self.vision_config.hidden_size
     A__							=     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
     A__							=     1.0
     A__							=     0.0_2
 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[Any]		,			lowercase_ :BlipaVisionConfig		,			lowercase_ :BlipaQFormerConfig		,			lowercase_ :PretrainedConfig		,			**lowercase_ :Any		,			)->				Optional[int]:
     return cls(
         vision_config=vision_config.to_dict()		,			qformer_config=qformer_config.to_dict()		,			text_config=text_config.to_dict()		,			**lowercase_		,			)



 def 	UpperCAmelCase_				(							self :Dict				)->				Any:
     A__							=     copy.deepcopy(self.__dict__				)
     A__							=     self.vision_config.to_dict()
     A__							=     self.qformer_config.to_dict()
     A__							=     self.text_config.to_dict()
     A__							=     self.__class__.model_type
     return output



