

"""simple docstring"""



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,
)


SCREAMING_SNAKE_CASE_       :						List[Any]					=    {
    'vit_b32': {
        'embed_dim': 5_1_2,
        'image_resolution': 7_6_8,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 1_2,
        'vision_width': 7_6_8,
        'vision_patch_size': 3_2,
        'transformer_width': 5_1_2,
        'transformer_heads': 8,
        'transformer_layers': 1_2,
    },
    'vit_b16': {
        'embed_dim': 5_1_2,
        'image_resolution': 7_6_8,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 1_2,
        'vision_width': 7_6_8,
        'vision_patch_size': 1_6,
        'transformer_width': 5_1_2,
        'transformer_heads': 8,
        'transformer_layers': 1_2,
    },
    'vit_l14': {
        'embed_dim': 7_6_8,
        'image_resolution': 8_4_0,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 2_4,
        'vision_width': 1_0_2_4,
        'vision_patch_size': 1_4,
        'transformer_width': 7_6_8,
        'transformer_heads': 1_2,
        'transformer_layers': 1_2,
    },
}





def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       Union[str, Any]=""       , UpperCAmelCase_	:       Any="/"      ):
     A__				      =	[]

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

          if isinstance(UpperCAmelCase_       , collections.MutableMapping      ):
               items.extend(flatten_nested_dict(UpperCAmelCase_       , UpperCAmelCase_       , sep=UpperCAmelCase_      ).items()      )
          else:
               items.append((new_key, v)      )
     return dict(UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Union[str, Any]      ):
     return jax.tree_util.tree_map(lambda UpperCAmelCase_      : x.astype(jnp.floataa      ) if x.dtype == jnp.bfloataa else x       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Any       , UpperCAmelCase_	:       Union[str, Any]      ):
     A__      ,					A__      ,					A__				      =	pt_attn_layer.in_proj_weight.chunk(3       , dim=0      )
     A__      ,					A__      ,					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 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       List[Any]      ):
     copy_linear(hf_mlp.fca       , pt_mlp.c_fc      )
     copy_linear(hf_mlp.fca       , pt_mlp.c_proj      )





def 			_snake_case  (     UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       str      ):
     A__				      =	pt_linear.weight
     A__				      =	pt_linear.bias





def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       str      ):
     # 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 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]       , UpperCAmelCase_	:       Tuple      ):
     for hf_layer, pt_layer in zip(UpperCAmelCase_       , UpperCAmelCase_      ):
          copy_layer(UpperCAmelCase_       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       Dict      ):
     # 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 			_snake_case  (     UpperCAmelCase_	:       Optional[int]       , UpperCAmelCase_	:       List[Any]      ):
     # copy projection
     A__				      =	pt_model.text_projection.data.T

     # copy text encoder
     copy_encoder(hf_model.text_model       , UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]       , UpperCAmelCase_	:       List[Any]      ):
     # 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 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       List[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(UpperCAmelCase_      )
     A__				      =	nn.Parameter(UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Any      ):
     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(UpperCAmelCase_      )      )

     # 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(UpperCAmelCase_      )      )

     # Copy flax params to PyTorch params
     for name, param in new_params.items():
          if name in pt_params.keys():
               pt_params[name].copy_(UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       List[Any]       , UpperCAmelCase_	:       Optional[int]      ):
     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(UpperCAmelCase_      )

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

          # Copy flax CLIP attn params to HF PyTorch params
          A__				      =	torch.from_numpy(UpperCAmelCase_      )
          hf_backbone.state_dict()[torch_key].copy_(UpperCAmelCase_      )





def 			_snake_case  (     UpperCAmelCase_	:       Optional[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(UpperCAmelCase_      )
               A__				      =	params[base + """out.weight"""].shape[-1]
               A__				      =	params[base + """out.weight"""].reshape(UpperCAmelCase_       , UpperCAmelCase_      ).T
               A__				      =	params[base + """out.bias"""]
          else:
               A__				      =	v
     return new_params





def 			_snake_case  (     UpperCAmelCase_	:       int       , UpperCAmelCase_	:       Dict      ):
     A__				      =	CLIP(**UpperCAmelCase_      )
     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(UpperCAmelCase_      )
     new_torch_params.update(UpperCAmelCase_      )
     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_(UpperCAmelCase_      )
          else:
               A__				      =	param

     return torch_clip_params, torch_model, attn_params





@torch.no_grad()
def 			_snake_case  (     UpperCAmelCase_	:       Optional[Any]       , UpperCAmelCase_	:       Tuple       , UpperCAmelCase_	:       Union[str, Any]       , UpperCAmelCase_	:       List[str]       , UpperCAmelCase_	:       Dict=None      ):
     A__				      =	Repository(UpperCAmelCase_       , clone_from=F"""google/{pytorch_dump_folder_path}"""      )
     repo.git_pull()

     if config_path is not None:
          A__				      =	OwlViTConfig.from_pretrained(UpperCAmelCase_      )
     else:
          A__				      =	OwlViTConfig()

     A__				      =	OwlViTModel(UpperCAmelCase_      ).eval()
     A__				      =	OwlViTForObjectDetection(UpperCAmelCase_      ).eval()

     copy_text_model_and_projection(UpperCAmelCase_       , UpperCAmelCase_      )
     copy_vision_model_and_projection(UpperCAmelCase_       , UpperCAmelCase_      )
     A__				      =	pt_backbone.logit_scale
     copy_flax_attn_params(UpperCAmelCase_       , UpperCAmelCase_      )

     A__				      =	hf_backbone
     copy_class_merge_token(UpperCAmelCase_       , UpperCAmelCase_      )
     copy_class_box_heads(UpperCAmelCase_       , UpperCAmelCase_      )

     # 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=UpperCAmelCase_       , tokenizer=UpperCAmelCase_      )
     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__":
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    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.'
 )
 SCREAMING_SNAKE_CASE_       :						Optional[Any]					=    parser.parse_args()

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

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

 # Convert CLIP backbone
 SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_       :						str					=    convert_clip_backbone(flax_params, torch_config)

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









"""simple docstring"""



def 			_snake_case  (     UpperCAmelCase_	:       str       , UpperCAmelCase_	:       str       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int       , ):
     A__				      =	list(UpperCAmelCase_      )
     A__				      =	list(UpperCAmelCase_      )
     A__				      =	len(UpperCAmelCase_      )
     A__				      =	len(UpperCAmelCase_      )

     A__				      =	[
         [0 for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]
     A__				      =	[
         ["""0""" for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]

     for i in range(1       , len_source_seq + 1      ):
          A__				      =	i * delete_cost
          A__				      =	F"""D{source_seq[i - 1]:c}"""

     for i in range(1       , len_destination_seq + 1      ):
          A__				      =	i * insert_cost
          A__				      =	F"""I{destination_seq[i - 1]:c}"""

     for i in range(1       , len_source_seq + 1      ):
          for j in range(1       , len_destination_seq + 1      ):
               if source_seq[i - 1] == destination_seq[j - 1]:
                    A__				      =	costs[i - 1][j - 1] + copy_cost
                    A__				      =	F"""C{source_seq[i - 1]:c}"""
               else:
                    A__				      =	costs[i - 1][j - 1] + replace_cost
                    A__				      =	F"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]      )

               if costs[i - 1][j] + delete_cost < costs[i][j]:
                    A__				      =	costs[i - 1][j] + delete_cost
                    A__				      =	F"""D{source_seq[i - 1]:c}"""

               if costs[i][j - 1] + insert_cost < costs[i][j]:
                    A__				      =	costs[i][j - 1] + insert_cost
                    A__				      =	F"""I{destination_seq[j - 1]:c}"""

     return costs, ops





def 			_snake_case  (     UpperCAmelCase_	:       list[list[str]]       , UpperCAmelCase_	:       int       , UpperCAmelCase_	:       int      ):
     if i == 0 and j == 0:
          return []
     else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
               A__				      =	assemble_transformation(UpperCAmelCase_       , i - 1       , j - 1      )
               seq.append(ops[i][j]      )
               return seq
          elif ops[i][j][0] == "D":
               A__				      =	assemble_transformation(UpperCAmelCase_       , i - 1       , UpperCAmelCase_      )
               seq.append(ops[i][j]      )
               return seq
          else:
               A__				      =	assemble_transformation(UpperCAmelCase_       , UpperCAmelCase_       , j - 1      )
               seq.append(ops[i][j]      )
               return seq


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_       :						List[Any]					=    compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

 SCREAMING_SNAKE_CASE_       :						Any					=    len(operations)
 SCREAMING_SNAKE_CASE_       :						Dict					=    len(operations[0])
 SCREAMING_SNAKE_CASE_       :						Any					=    assemble_transformation(operations, m - 1, n - 1)

 SCREAMING_SNAKE_CASE_       :						List[str]					=    list('Python')
 SCREAMING_SNAKE_CASE_       :						Dict					=    0
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    0

 with open('min_cost.txt', 'w') as file:
  for op in sequence:
   print(''.join(string))

   if op[0] == "C":
    file.write('%-16s' % 'Copy %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost -= 1
   elif op[0] == "R":
    SCREAMING_SNAKE_CASE_       :						int					=    op[2]

    file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
    file.write('\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 1
   elif op[0] == "D":
    string.pop(i)

    file.write('%-16s' % 'Delete %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 2
   else:
    string.insert(i, op[1])

    file.write('%-16s' % 'Insert %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 2

   i += 1

  print(''.join(string))
  print('Cost: ', cost)

  file.write('\r\nMinimum cost: ' + str(cost))






