
'''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,
)


a__		: 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   ,	UpperCAmelCase=""   ,	UpperCAmelCase="/"						)->      int:







      """simple docstring"""
      __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						)->      Any:







      """simple docstring"""
      return jax.tree_util.tree_map(lambda UpperCAmelCase						: x.astype(jnp.floataa						) if x.dtype == jnp.bfloataa else x   ,	UpperCAmelCase_						)





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      str:







      """simple docstring"""
      __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   ,	UpperCAmelCase						)->      Optional[Any]:







      """simple docstring"""
      copy_linear(hf_mlp.fca   ,	pt_mlp.c_fc						)
      copy_linear(hf_mlp.fca   ,	pt_mlp.c_proj						)





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      Tuple:







      """simple docstring"""
      __A													=      pt_linear.weight
      __A													=      pt_linear.bias





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      Optional[Any]:







      """simple docstring"""
      # 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   ,	UpperCAmelCase						)->      List[Any]:







      """simple docstring"""
      for hf_layer, pt_layer in zip(UpperCAmelCase_   ,	UpperCAmelCase_						):
            copy_layer(UpperCAmelCase_   ,	UpperCAmelCase_						)





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # 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   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # copy projection
      __A													=      pt_model.text_projection.data.T

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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      int:







      """simple docstring"""
      # 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   ,	UpperCAmelCase						)->      Any:







      """simple docstring"""
      __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   ,	UpperCAmelCase						)->      str:







      """simple docstring"""
      __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   ,	UpperCAmelCase						)->      str:







      """simple docstring"""
      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						)->      str:







      """simple docstring"""
      __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   ,	UpperCAmelCase						)->      str:







      """simple docstring"""
      __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   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase=None						)->      Tuple:







      """simple docstring"""
      __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=1_6						)

      # 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__":
 a__		: 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."
 )
 a__		: Optional[Any]  							=							parser.parse_args()

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

 # Load from checkpoint and convert params to float-32
 a__		: Optional[int]  							=							checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
 a__		: 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
 a__		: 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))






