




import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


UpperCAmelCase__            =	{
    'vit_b32': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 12,
        'vision_width': 768,
        'vision_patch_size': 32,
        'transformer_width': 512,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_b16': {
        'embed_dim': 512,
        'image_resolution': 768,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 12,
        'vision_width': 768,
        'vision_patch_size': 16,
        'transformer_width': 512,
        'transformer_heads': 8,
        'transformer_layers': 12,
    },
    'vit_l14': {
        'embed_dim': 768,
        'image_resolution': 840,
        'context_length': 16,
        'vocab_size': 49408,
        'vision_layers': 24,
        'vision_width': 1024,
        'vision_patch_size': 14,
        'transformer_width': 768,
        'transformer_heads': 12,
        'transformer_layers': 12,
    },
}
def 		_a					(   a				:Optional[Any]			,							a				:Dict=""			,							a				:int="/"				)							->    Any:
 a							=   []

 for k, v in params.items():
  a							=   parent_key + sep + k if parent_key else k

  if isinstance(_SCREAMING_SNAKE_CASE			,							collections.MutableMapping				):
   items.extend(flatten_nested_dict(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE			,							sep=_SCREAMING_SNAKE_CASE				).items()				)
  else:
   items.append((new_key, v)				)
 return dict(_SCREAMING_SNAKE_CASE				)
def 		_a					(   a				:Tuple				)							->    Optional[Any]:
 return jax.tree_util.tree_map(lambda a				: x.astype(jnp.floataa				) if x.dtype == jnp.bfloataa else x			,							_SCREAMING_SNAKE_CASE				)
def 		_a					(   a				:Any			,							a				:Optional[Any]				)							->    int:
 a							=   pt_attn_layer.in_proj_weight.chunk(3			,							dim=0				)
 a							=   pt_attn_layer.in_proj_bias.chunk(3			,							dim=0				)

 a							=   pt_attn_layer.out_proj.weight
 a							=   pt_attn_layer.out_proj.bias

 a							=   q_proj
 a							=   q_proj_bias

 a							=   k_proj
 a							=   k_proj_bias

 a							=   v_proj
 a							=   v_proj_bias

 a							=   out_proj_weights
 a							=   out_proj_bias
def 		_a					(   a				:Optional[int]			,							a				:Union[str, Any]				)							->    Tuple:
 copy_linear(hf_mlp.fca			,							pt_mlp.c_fc				)
 copy_linear(hf_mlp.fca			,							pt_mlp.c_proj				)
def 		_a					(   a				:List[str]			,							a				:Optional[int]				)							->    Union[str, Any]:
 a							=   pt_linear.weight
 a							=   pt_linear.bias
def 		_a					(   a				:Optional[Any]			,							a				:List[str]				)							->    Dict:
 # copy layer norms
 copy_linear(hf_layer.layer_norma			,							pt_layer.ln_a				)
 copy_linear(hf_layer.layer_norma			,							pt_layer.ln_a				)

 # copy MLP
 copy_mlp(hf_layer.mlp			,							pt_layer.mlp				)

 # copy attn
 copy_attn_layer(hf_layer.self_attn			,							pt_layer.attn				)
def 		_a					(   a				:Any			,							a				:Dict				)							->    Union[str, Any]:
 for hf_layer, pt_layer in zip(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE				):
  copy_layer(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE				)
def 		_a					(   a				:Dict			,							a				:Dict				)							->    Optional[Any]:
 # copy  embeds
 a							=   pt_model.token_embedding.weight
 a							=   pt_model.positional_embedding

 # copy layer norm
 copy_linear(hf_encoder.final_layer_norm			,							pt_model.ln_final				)

 # copy hidden layers
 copy_layers(hf_encoder.encoder.layers			,							pt_model.transformer.resblocks				)
def 		_a					(   a				:str			,							a				:Optional[Any]				)							->    Dict:
 # copy projection
 a							=   pt_model.text_projection.data.T

 # copy text encoder
 copy_encoder(hf_model.text_model			,							_SCREAMING_SNAKE_CASE				)
def 		_a					(   a				:List[str]			,							a				:Union[str, Any]				)							->    int:
 # copy projection
 a							=   pt_model.visual.proj.data.T

 # copy layer norms
 copy_linear(hf_model.vision_model.pre_layernorm			,							pt_model.visual.ln_pre				)
 copy_linear(hf_model.vision_model.post_layernorm			,							pt_model.visual.ln_post				)

 # copy embeds
 a							=   pt_model.visual.conva.weight.data
 a							=   pt_model.visual.class_embedding
 a							=   pt_model.visual.positional_embedding.data

 # copy encoder
 copy_layers(hf_model.vision_model.encoder.layers			,							pt_model.visual.transformer.resblocks				)
def 		_a					(   a				:List[str]			,							a				:List[Any]				)							->    Optional[Any]:
 a							=   flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']				)

 a							=   torch.from_numpy(flax_class_token_params['''scale''']				)
 a							=   torch.from_numpy(flax_class_token_params['''bias''']				)
 a							=   nn.Parameter(_SCREAMING_SNAKE_CASE				)
 a							=   nn.Parameter(_SCREAMING_SNAKE_CASE				)
def 		_a					(   a				:Optional[int]			,							a				:List[Any]				)							->    Optional[int]:
 a							=   hf_model.state_dict()
 a							=   {}

 # Rename class prediction head flax params to pytorch HF
 a							=   flatten_nested_dict(flax_params['''class_head''']				)

 for flax_key, v in flax_class_params.items():
  a							=   flax_key.replace('''/'''			,							'''.'''				)
  a							=   torch_key.replace('''.kernel'''			,							'''.weight'''				)
  a							=   torch_key.replace('''Dense_0'''			,							'''dense0'''				)
  a							=   'class_head.' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   a							=   v.T

  a							=   nn.Parameter(torch.from_numpy(_SCREAMING_SNAKE_CASE				)				)

 # Rename box prediction box flax params to pytorch HF
 a							=   flatten_nested_dict(flax_params['''obj_box_head''']				)

 for flax_key, v in flax_box_params.items():
  a							=   flax_key.replace('''/'''			,							'''.'''				)
  a							=   torch_key.replace('''.kernel'''			,							'''.weight'''				)
  a							=   torch_key.replace('''_'''			,							''''''				).lower()
  a							=   'box_head.' + torch_key

  if "weight" in torch_key and v.ndim == 2:
   a							=   v.T

  a							=   nn.Parameter(torch.from_numpy(_SCREAMING_SNAKE_CASE				)				)

 # Copy flax params to PyTorch params
 for name, param in new_params.items():
  if name in pt_params.keys():
   pt_params[name].copy_(_SCREAMING_SNAKE_CASE				)
def 		_a					(   a				:Any			,							a				:Dict				)							->    List[str]:
 for k, v in flax_attn_params.items():
  if k.startswith('''transformer'''				):
   a							=   k.replace('''transformer.resblocks'''			,							'''text_model.encoder.layers'''				)
  else:
   a							=   k.replace('''visual.transformer.resblocks'''			,							'''vision_model.encoder.layers'''				)

  a							=   torch_key.replace('''attn'''			,							'''self_attn'''				)
  a							=   torch_key.replace('''key'''			,							'''k_proj'''				)
  a							=   torch_key.replace('''value'''			,							'''v_proj'''				)
  a							=   torch_key.replace('''query'''			,							'''q_proj'''				)
  a							=   torch_key.replace('''out'''			,							'''out_proj'''				)

  if "bias" in torch_key and v.ndim == 2:
   a							=   v.shape[0] * v.shape[1]
   a							=   v.reshape(_SCREAMING_SNAKE_CASE				)

  if "weight" in torch_key and "out" in torch_key:
   a							=   (v.shape[0] * v.shape[1], v.shape[2])
   a							=   v.reshape(_SCREAMING_SNAKE_CASE				).T

  if "weight" in torch_key and "out" not in torch_key:
   a							=   (v.shape[0], v.shape[1] * v.shape[2])
   a							=   v.reshape(_SCREAMING_SNAKE_CASE				).T

  # Copy flax CLIP attn params to HF PyTorch params
  a							=   torch.from_numpy(_SCREAMING_SNAKE_CASE				)
  hf_backbone.state_dict()[torch_key].copy_(_SCREAMING_SNAKE_CASE				)
def 		_a					(   a				:Optional[Any]				)							->    int:
 a							=   {}
 a							=   []

 for k, v in params.items():
  if "attn." in k:
   a							=   k[: k.rindex('''attn.'''				) + 5]
   if base in processed_attn_layers:
    continue

   processed_attn_layers.append(_SCREAMING_SNAKE_CASE				)
   a							=   params[base + 'out.weight'].shape[-1]
   a							=   params[base + 'out.weight'].reshape(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE				).T
   a							=   params[base + 'out.bias']
  else:
   a							=   v
 return new_params
def 		_a					(   a				:Dict			,							a				:Union[str, Any]				)							->    int:
 a							=   CLIP(**_SCREAMING_SNAKE_CASE				)
 torch_model.eval()
 a							=   torch_model.state_dict()

 a							=   flatten_nested_dict(flax_params['''backbone''']['''clip''']				)
 a							=   {}

 for flax_key, v in flax_clip_params.items():
  a							=   flax_key.replace('''/'''			,							'''.'''				)
  a							=   torch_key.replace('''text.token_embedding.embedding'''			,							'''token_embedding.kernel'''				)

  if (
      torch_key.startswith('''text.transformer'''				)
      or torch_key.startswith('''text.text_projection'''				)
      or torch_key.startswith('''text.ln_final'''				)
      or torch_key.startswith('''text.positional_embedding'''				)
  ):
   a							=   torch_key[5:]

  a							=   torch_key.replace('''text_projection.kernel'''			,							'''text_projection'''				)
  a							=   torch_key.replace('''visual.proj.kernel'''			,							'''visual.proj'''				)
  a							=   torch_key.replace('''.scale'''			,							'''.weight'''				)
  a							=   torch_key.replace('''.kernel'''			,							'''.weight'''				)

  if "conv" in torch_key or "downsample.0.weight" in torch_key:
   a							=   v.transpose(3			,							2			,							0			,							1				)

  elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
   # Fully connected layers are transposed, embeddings are not
   a							=   v.T

  a							=   v

 a							=   _convert_attn_layers(_SCREAMING_SNAKE_CASE				)
 new_torch_params.update(_SCREAMING_SNAKE_CASE				)
 a							=   {}

 # Copy flax CLIP backbone params to PyTorch params
 for name, param in new_torch_params.items():
  if name in torch_clip_params.keys():
   a							=   torch.from_numpy(new_torch_params[name]				)
   torch_clip_params[name].copy_(_SCREAMING_SNAKE_CASE				)
  else:
   a							=   param

 return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def 		_a					(   a				:Any			,							a				:Optional[int]			,							a				:Optional[int]			,							a				:Tuple			,							a				:List[Any]=None				)							->    str:
 a							=   Repository(_SCREAMING_SNAKE_CASE			,							clone_from=F"""google/{pytorch_dump_folder_path}"""				)
 repo.git_pull()

 if config_path is not None:
  a							=   OwlViTConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
 else:
  a							=   OwlViTConfig()

 a							=   OwlViTModel(_SCREAMING_SNAKE_CASE				).eval()
 a							=   OwlViTForObjectDetection(_SCREAMING_SNAKE_CASE				).eval()

 copy_text_model_and_projection(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE				)
 copy_vision_model_and_projection(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE				)
 a							=   pt_backbone.logit_scale
 copy_flax_attn_params(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE				)

 a							=   hf_backbone
 copy_class_merge_token(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE				)
 copy_class_box_heads(_SCREAMING_SNAKE_CASE			,							_SCREAMING_SNAKE_CASE				)

 # Save HF model
 hf_model.save_pretrained(repo.local_dir				)

 # Initialize image processor
 a							=   OwlViTImageProcessor(
     size=config.vision_config.image_size			,							crop_size=config.vision_config.image_size				)
 # Initialize tokenizer
 a							=   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''			,							pad_token='''!'''			,							model_max_length=16				)

 # Initialize processor
 a							=   OwlViTProcessor(image_processor=_SCREAMING_SNAKE_CASE			,							tokenizer=_SCREAMING_SNAKE_CASE				)
 image_processor.save_pretrained(repo.local_dir				)
 processor.save_pretrained(repo.local_dir				)

 repo.git_add()
 repo.git_commit('''Upload model and processor'''				)
 repo.git_push()


if __name__ == "__main__":
     UpperCAmelCase__            =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--owlvit_version",
         default=None,
         type=str,
         required=True,
         help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
     )
     parser.add_argument(
         "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
     )
     parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
     parser.add_argument(
         "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
     )
     UpperCAmelCase__            =	parser.parse_args()

     # Initialize PyToch clip model
     UpperCAmelCase__            =	args.owlvit_version
     if model_name == "clip_b16":
          UpperCAmelCase__            =	CONFIGS['vit_b16']
     elif model_name == "clip_b32":
          UpperCAmelCase__            =	CONFIGS['vit_b32']
     elif model_name == "clip_l14":
          UpperCAmelCase__            =	CONFIGS['vit_l14']

     # Load from checkpoint and convert params to float-32
     UpperCAmelCase__            =	checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
     UpperCAmelCase__            =	jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
     del variables

     # Convert CLIP backbone
     UpperCAmelCase__            =	convert_clip_backbone(flax_params, torch_config)

     convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)










from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
     from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline


