


'''simple docstring'''


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

__magic_name__			=      [
    # (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'),
]

__magic_name__			=      [
    # (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'),
]

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

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

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




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


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

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

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

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


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




def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]):
      # convert HF linear weights to SD conv2d weights
      return w.reshape(*w.shape							,						1							,						1)




def      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      A_						:  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_						:  str          =				v.replace(snake_case_							,						snake_case_)
            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_						:  List[Any]          =				v.replace(snake_case_							,						snake_case_)
                  A_						:  Tuple          =				v
      A_						:  List[Any]          =				{v: vae_state_dict[k] for k, v in mapping.items()}
      A_						:  int          =				['''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_						:  Tuple          =				reshape_weight_for_sd(snake_case_)
      return new_state_dict


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


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

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




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):
      A_						:  Optional[int]          =				{}
      A_						:  Optional[Any]          =				{}
      A_						:  List[Any]          =				{}
      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_						:  Optional[int]          =				k[: -len(""".q_proj.weight""")]
                  A_						:  Union[str, Any]          =				k[-len("""q_proj.weight""")]
                  if k_pre not in capture_qkv_weight:
                        A_						:  Optional[Any]          =				[None, None, None]
                  A_						:  List[Any]          =				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_						:  List[Any]          =				k[: -len(""".q_proj.bias""")]
                  A_						:  Any          =				k[-len("""q_proj.bias""")]
                  if k_pre not in capture_qkv_bias:
                        A_						:  Dict          =				[None, None, None]
                  A_						:  Optional[Any]          =				v
                  continue

            A_						:  Optional[Any]          =				textenc_pattern.sub(lambda lowerCamelCase: protected[re.escape(m.group(0))]							,						snake_case_)
            A_						:  Tuple          =				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_						:  str          =				textenc_pattern.sub(lambda lowerCamelCase: protected[re.escape(m.group(0))]							,						snake_case_)
            A_						:  Tuple          =				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""")
            A_						:  List[str]          =				textenc_pattern.sub(lambda lowerCamelCase: protected[re.escape(m.group(0))]							,						snake_case_)
            A_						:  List[Any]          =				torch.cat(snake_case_)

      return new_state_dict




def      lowerCamelCase					(      lowerCamelCase  :      str):
      return text_enc_dict


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

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

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

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

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

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

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

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




'''simple docstring'''


import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
     from ...processing_utils import ProcessorMixin
     from ...utils import TensorType

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


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit_text_model"""






  def __init__(							self :       Union[str, Any]	,_a :       Any=49408	,_a :       Any=512	,_a :       Tuple=2048	,_a :       Dict=12	,_a :       Optional[int]=8	,_a :       Tuple=16	,_a :       Tuple="quick_gelu"	,_a :       Optional[Any]=1e-5	,_a :       List[Any]=0.0	,_a :       Optional[int]=0.02	,_a :       Dict=1.0	,_a :       Dict=0	,_a :       Any=49406	,_a :       Tuple=49407	,**_a :       List[Any]	,):

        '''simple docstring'''




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

        A_						:  Tuple          =				vocab_size
        A_						:  int          =				hidden_size
        A_						:  Optional[int]          =				intermediate_size
        A_						:  Optional[int]          =				num_hidden_layers
        A_						:  Union[str, Any]          =				num_attention_heads
        A_						:  int          =				max_position_embeddings
        A_						:  str          =				hidden_act
        A_						:  Union[str, Any]          =				layer_norm_eps
        A_						:  Tuple          =				attention_dropout
        A_						:  Union[str, Any]          =				initializer_range
        A_						:  List[Any]          =				initializer_factor






  @classmethod
  def        _a							(							cls :       List[str]	,_a :       Union[str, os.PathLike]	,**_a :       str ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  int          =				cls.get_config_dict(_a	,**_a )

        # get the text config dict if we are loading from OwlViTConfig
        if config_dict.get("""model_type""" ) == "owlvit":
              A_						:  Union[str, Any]          =				config_dict["""text_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(_a	,**_a )







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit_vision_model"""






  def __init__(							self :       List[Any]	,_a :       Optional[Any]=768	,_a :       Tuple=3072	,_a :       Dict=12	,_a :       int=12	,_a :       Dict=3	,_a :       Tuple=768	,_a :       int=32	,_a :       int="quick_gelu"	,_a :       List[Any]=1e-5	,_a :       Tuple=0.0	,_a :       List[Any]=0.02	,_a :       str=1.0	,**_a :       int	,):

        '''simple docstring'''




        super().__init__(**_a )

        A_						:  List[str]          =				hidden_size
        A_						:  Union[str, Any]          =				intermediate_size
        A_						:  Union[str, Any]          =				num_hidden_layers
        A_						:  Optional[Any]          =				num_attention_heads
        A_						:  int          =				num_channels
        A_						:  str          =				image_size
        A_						:  List[Any]          =				patch_size
        A_						:  int          =				hidden_act
        A_						:  List[Any]          =				layer_norm_eps
        A_						:  List[str]          =				attention_dropout
        A_						:  str          =				initializer_range
        A_						:  str          =				initializer_factor






  @classmethod
  def        _a							(							cls :       List[Any]	,_a :       Union[str, os.PathLike]	,**_a :       str ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  Optional[int]          =				cls.get_config_dict(_a	,**_a )

        # get the vision config dict if we are loading from OwlViTConfig
        if config_dict.get("""model_type""" ) == "owlvit":
              A_						:  List[str]          =				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(_a	,**_a )







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""owlvit"""
  a_  					=							True






  def __init__(							self :       Union[str, Any]	,_a :       List[str]=None	,_a :       List[str]=None	,_a :       Dict=512	,_a :       List[Any]=2.6592	,_a :       Optional[Any]=True	,**_a :       Optional[int]	,):

        '''simple docstring'''




        super().__init__(**_a )

        if text_config is None:
              A_						:  List[Any]          =				{}
              logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )

        if vision_config is None:
              A_						:  Tuple          =				{}
              logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )

        A_						:  Dict          =				OwlViTTextConfig(**_a )
        A_						:  Dict          =				OwlViTVisionConfig(**_a )

        A_						:  Any          =				projection_dim
        A_						:  Optional[int]          =				logit_scale_init_value
        A_						:  Optional[int]          =				return_dict
        A_						:  Dict          =				1.0






  @classmethod
  def        _a							(							cls :       Union[str, Any]	,_a :       Union[str, os.PathLike]	,**_a :       Optional[int] ):

        '''simple docstring'''




        cls._set_token_in_kwargs(_a )

        A_   ,					A_						:  List[Any]          =				cls.get_config_dict(_a	,**_a )

        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(_a	,**_a )






  @classmethod
  def        _a							(							cls :       int	,_a :       Dict	,_a :       Dict	,**_a :       List[str] ):

        '''simple docstring'''




        A_						:  str          =				{}
        A_						:  int          =				text_config
        A_						:  Union[str, Any]          =				vision_config

        return cls.from_dict(_a	,**_a )






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        A_						:  Dict          =				copy.deepcopy(self.__dict__ )
        A_						:  str          =				self.text_config.to_dict()
        A_						:  Optional[int]          =				self.vision_config.to_dict()
        A_						:  List[Any]          =				self.__class__.model_type
        return output







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       int ):

        '''simple docstring'''




        return OrderedDict(
            [
                ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
            ] )






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        return OrderedDict(
            [
                ("""logits_per_image""", {0: """batch"""}),
                ("""logits_per_text""", {0: """batch"""}),
                ("""text_embeds""", {0: """batch"""}),
                ("""image_embeds""", {0: """batch"""}),
            ] )






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 1e-4






  def        _a							(							self :       int	,_a :       "ProcessorMixin"	,_a :       int = -1	,_a :       int = -1	,_a :       Optional["TensorType"] = None	,):

        '''simple docstring'''




        A_						:  Any          =				super().generate_dummy_inputs(
            processor.tokenizer	,batch_size=_a	,seq_length=_a	,framework=_a )
        A_						:  Any          =				super().generate_dummy_inputs(
            processor.image_processor	,batch_size=_a	,framework=_a )
        return {**text_input_dict, **image_input_dict}






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return 14
