
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": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 32,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 16,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 7_68,
        "image_resolution": 8_40,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 24,
        "vision_width": 10_24,
        "vision_patch_size": 14,
        "transformer_width": 7_68,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}


def  A__ (       __lowerCamelCase,  __lowerCamelCase="",  __lowerCamelCase="/"						):
   SCREAMING_SNAKE_CASE_	=      []

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

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


def  A__ (       __lowerCamelCase						):
   return jax.tree_util.tree_map(lambda __lowerCamelCase						: x.astype(jnp.floataa						) if x.dtype == jnp.bfloataa else x,  __lowerCamelCase						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      pt_attn_layer.in_proj_weight.chunk(3,  dim=0						)
   SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      pt_attn_layer.in_proj_bias.chunk(3,  dim=0						)

   SCREAMING_SNAKE_CASE_	=      pt_attn_layer.out_proj.weight
   SCREAMING_SNAKE_CASE_	=      pt_attn_layer.out_proj.bias

   SCREAMING_SNAKE_CASE_	=      q_proj
   SCREAMING_SNAKE_CASE_	=      q_proj_bias

   SCREAMING_SNAKE_CASE_	=      k_proj
   SCREAMING_SNAKE_CASE_	=      k_proj_bias

   SCREAMING_SNAKE_CASE_	=      v_proj
   SCREAMING_SNAKE_CASE_	=      v_proj_bias

   SCREAMING_SNAKE_CASE_	=      out_proj_weights
   SCREAMING_SNAKE_CASE_	=      out_proj_bias


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   copy_linear(hf_mlp.fca,  pt_mlp.c_fc						)
   copy_linear(hf_mlp.fca,  pt_mlp.c_proj						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      pt_linear.weight
   SCREAMING_SNAKE_CASE_	=      pt_linear.bias


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   # 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__ (       __lowerCamelCase,  __lowerCamelCase						):
   for hf_layer, pt_layer in zip(__lowerCamelCase,  __lowerCamelCase						):
      copy_layer(__lowerCamelCase,  __lowerCamelCase						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   # copy  embeds
   SCREAMING_SNAKE_CASE_	=      pt_model.token_embedding.weight
   SCREAMING_SNAKE_CASE_	=      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__ (       __lowerCamelCase,  __lowerCamelCase						):
   # copy projection
   SCREAMING_SNAKE_CASE_	=      pt_model.text_projection.data.T

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


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   # copy projection
   SCREAMING_SNAKE_CASE_	=      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
   SCREAMING_SNAKE_CASE_	=      pt_model.visual.conva.weight.data
   SCREAMING_SNAKE_CASE_	=      pt_model.visual.class_embedding
   SCREAMING_SNAKE_CASE_	=      pt_model.visual.positional_embedding.data

   # copy encoder
   copy_layers(hf_model.vision_model.encoder.layers,  pt_model.visual.transformer.resblocks						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']						)

   SCREAMING_SNAKE_CASE_	=      torch.from_numpy(flax_class_token_params['''scale''']						)
   SCREAMING_SNAKE_CASE_	=      torch.from_numpy(flax_class_token_params['''bias''']						)
   SCREAMING_SNAKE_CASE_	=      nn.Parameter(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      nn.Parameter(__lowerCamelCase						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      hf_model.state_dict()
   SCREAMING_SNAKE_CASE_	=      {}

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

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

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

      SCREAMING_SNAKE_CASE_	=      nn.Parameter(torch.from_numpy(__lowerCamelCase						)						)

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

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

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

      SCREAMING_SNAKE_CASE_	=      nn.Parameter(torch.from_numpy(__lowerCamelCase						)						)

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


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   for k, v in flax_attn_params.items():
      if k.startswith('''transformer'''						):
         SCREAMING_SNAKE_CASE_	=      k.replace('''transformer.resblocks''',  '''text_model.encoder.layers'''						)
      else:
         SCREAMING_SNAKE_CASE_	=      k.replace('''visual.transformer.resblocks''',  '''vision_model.encoder.layers'''						)

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

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

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

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

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


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      {}
   SCREAMING_SNAKE_CASE_	=      []

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

         processed_attn_layers.append(__lowerCamelCase						)
         SCREAMING_SNAKE_CASE_	=      params[base + '''out.weight'''].shape[-1]
         SCREAMING_SNAKE_CASE_	=      params[base + '''out.weight'''].reshape(__lowerCamelCase,  __lowerCamelCase						).T
         SCREAMING_SNAKE_CASE_	=      params[base + '''out.bias''']
      else:
         SCREAMING_SNAKE_CASE_	=      v
   return new_params


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      CLIP(**__lowerCamelCase						)
   torch_model.eval()
   SCREAMING_SNAKE_CASE_	=      torch_model.state_dict()

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

   for flax_key, v in flax_clip_params.items():
      SCREAMING_SNAKE_CASE_	=      flax_key.replace('''/''',  '''.'''						)
      SCREAMING_SNAKE_CASE_	=      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'''						)
      ):
         SCREAMING_SNAKE_CASE_	=      torch_key[5:]

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

      if "conv" in torch_key or "downsample.0.weight" in torch_key:
         SCREAMING_SNAKE_CASE_	=      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
         SCREAMING_SNAKE_CASE_	=      v.T

      SCREAMING_SNAKE_CASE_	=      v

   SCREAMING_SNAKE_CASE_	=      _convert_attn_layers(__lowerCamelCase						)
   new_torch_params.update(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      {}

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

   return torch_clip_params, torch_model, attn_params


@torch.no_grad()
def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase=None						):
   SCREAMING_SNAKE_CASE_	=      Repository(__lowerCamelCase,  clone_from=F'''google/{pytorch_dump_folder_path}'''						)
   repo.git_pull()

   if config_path is not None:
      SCREAMING_SNAKE_CASE_	=      OwlViTConfig.from_pretrained(__lowerCamelCase						)
   else:
      SCREAMING_SNAKE_CASE_	=      OwlViTConfig()

   SCREAMING_SNAKE_CASE_	=      OwlViTModel(__lowerCamelCase						).eval()
   SCREAMING_SNAKE_CASE_	=      OwlViTForObjectDetection(__lowerCamelCase						).eval()

   copy_text_model_and_projection(__lowerCamelCase,  __lowerCamelCase						)
   copy_vision_model_and_projection(__lowerCamelCase,  __lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      pt_backbone.logit_scale
   copy_flax_attn_params(__lowerCamelCase,  __lowerCamelCase						)

   SCREAMING_SNAKE_CASE_	=      hf_backbone
   copy_class_merge_token(__lowerCamelCase,  __lowerCamelCase						)
   copy_class_box_heads(__lowerCamelCase,  __lowerCamelCase						)

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

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

   # Initialize processor
   SCREAMING_SNAKE_CASE_	=      OwlViTProcessor(image_processor=__lowerCamelCase,  tokenizer=__lowerCamelCase						)
   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						,					__UpperCAmelCase						,					__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)




import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      CLIPSegTextConfig()
   SCREAMING_SNAKE_CASE_	=      CLIPSegVisionConfig(patch_size=16						)

   SCREAMING_SNAKE_CASE_	=      True if '''refined''' in model_name else False
   SCREAMING_SNAKE_CASE_	=      16 if '''rd16''' in model_name else 64

   SCREAMING_SNAKE_CASE_	=      CLIPSegConfig.from_text_vision_configs(
       __lowerCamelCase,  __lowerCamelCase,  use_complex_transposed_convolution=__lowerCamelCase,  reduce_dim=__lowerCamelCase,  )
   return config


def  A__ (       __lowerCamelCase						):
   # update prefixes
   if "clip_model" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''clip_model''',  '''clip'''						)
   if "transformer" in name:
      if "visual" in name:
         SCREAMING_SNAKE_CASE_	=      name.replace('''visual.transformer''',  '''vision_model'''						)
      else:
         SCREAMING_SNAKE_CASE_	=      name.replace('''transformer''',  '''text_model'''						)
   if "resblocks" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''resblocks''',  '''encoder.layers'''						)
   if "ln_1" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''ln_1''',  '''layer_norm1'''						)
   if "ln_2" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''ln_2''',  '''layer_norm2'''						)
   if "c_fc" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''c_fc''',  '''fc1'''						)
   if "c_proj" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''c_proj''',  '''fc2'''						)
   if "attn" in name and "self" not in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''attn''',  '''self_attn'''						)
   # text encoder
   if "token_embedding" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''token_embedding''',  '''text_model.embeddings.token_embedding'''						)
   if "positional_embedding" in name and "visual" not in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''positional_embedding''',  '''text_model.embeddings.position_embedding.weight'''						)
   if "ln_final" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''ln_final''',  '''text_model.final_layer_norm'''						)
   # vision encoder
   if "visual.class_embedding" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.class_embedding''',  '''vision_model.embeddings.class_embedding'''						)
   if "visual.conv1" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.conv1''',  '''vision_model.embeddings.patch_embedding'''						)
   if "visual.positional_embedding" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.positional_embedding''',  '''vision_model.embeddings.position_embedding.weight'''						)
   if "visual.ln_pre" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.ln_pre''',  '''vision_model.pre_layrnorm'''						)
   if "visual.ln_post" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.ln_post''',  '''vision_model.post_layernorm'''						)
   # projection layers
   if "visual.proj" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''visual.proj''',  '''visual_projection.weight'''						)
   if "text_projection" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''text_projection''',  '''text_projection.weight'''						)
   # decoder
   if "trans_conv" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''trans_conv''',  '''transposed_convolution'''						)
   if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
      SCREAMING_SNAKE_CASE_	=      '''decoder.''' + name
   if "blocks" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''blocks''',  '''decoder.layers'''						)
   if "linear1" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''linear1''',  '''mlp.fc1'''						)
   if "linear2" in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''linear2''',  '''mlp.fc2'''						)
   if "norm1" in name and "layer_" not in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''norm1''',  '''layer_norm1'''						)
   if "norm2" in name and "layer_" not in name:
      SCREAMING_SNAKE_CASE_	=      name.replace('''norm2''',  '''layer_norm2'''						)

   return name


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   for key in orig_state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_	=      orig_state_dict.pop(__lowerCamelCase						)

      if key.startswith('''clip_model'''						) and "attn.in_proj" in key:
         SCREAMING_SNAKE_CASE_	=      key.split('''.'''						)
         if "visual" in key:
            SCREAMING_SNAKE_CASE_	=      int(key_split[4]						)
            SCREAMING_SNAKE_CASE_	=      config.vision_config.hidden_size
            SCREAMING_SNAKE_CASE_	=      '''vision_model'''
         else:
            SCREAMING_SNAKE_CASE_	=      int(key_split[3]						)
            SCREAMING_SNAKE_CASE_	=      config.text_config.hidden_size
            SCREAMING_SNAKE_CASE_	=      '''text_model'''

         if "weight" in key:
            SCREAMING_SNAKE_CASE_	=      val[:dim, :]
            SCREAMING_SNAKE_CASE_	=      val[
                dim : dim * 2, :
            ]
            SCREAMING_SNAKE_CASE_	=      val[-dim:, :]
         else:
            SCREAMING_SNAKE_CASE_	=      val[:dim]
            SCREAMING_SNAKE_CASE_	=      val[dim : dim * 2]
            SCREAMING_SNAKE_CASE_	=      val[-dim:]
      elif "self_attn" in key and "out_proj" not in key:
         SCREAMING_SNAKE_CASE_	=      key.split('''.'''						)
         SCREAMING_SNAKE_CASE_	=      int(key_split[1]						)
         SCREAMING_SNAKE_CASE_	=      config.reduce_dim
         if "weight" in key:
            SCREAMING_SNAKE_CASE_	=      val[:dim, :]
            SCREAMING_SNAKE_CASE_	=      val[dim : dim * 2, :]
            SCREAMING_SNAKE_CASE_	=      val[-dim:, :]
         else:
            SCREAMING_SNAKE_CASE_	=      val[:dim]
            SCREAMING_SNAKE_CASE_	=      val[dim : dim * 2]
            SCREAMING_SNAKE_CASE_	=      val[-dim:]
      else:
         SCREAMING_SNAKE_CASE_	=      rename_key(__lowerCamelCase						)
         if "visual_projection" in new_name or "text_projection" in new_name:
            SCREAMING_SNAKE_CASE_	=      val.T
         SCREAMING_SNAKE_CASE_	=      val

   return orig_state_dict


def  A__ (       ):
   SCREAMING_SNAKE_CASE_	=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   SCREAMING_SNAKE_CASE_	=      Image.open(requests.get(__lowerCamelCase,  stream=__lowerCamelCase						).raw						)
   return image


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      get_clipseg_config(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      CLIPSegForImageSegmentation(__lowerCamelCase						)
   model.eval()

   SCREAMING_SNAKE_CASE_	=      torch.load(__lowerCamelCase,  map_location='''cpu'''						)

   # remove some keys
   for key in state_dict.copy().keys():
      if key.startswith('''model'''						):
         state_dict.pop(__lowerCamelCase,  __lowerCamelCase						)

    # rename some keys
   SCREAMING_SNAKE_CASE_	=      convert_state_dict(__lowerCamelCase,  __lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      model.load_state_dict(__lowerCamelCase,  strict=__lowerCamelCase						)

   if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
      raise ValueError('''Missing keys that are not expected: {}'''.format(__lowerCamelCase						)						)
   if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
      raise ValueError(F'''Unexpected keys: {unexpected_keys}'''						)

   SCREAMING_SNAKE_CASE_	=      ViTImageProcessor(size=3_52						)
   SCREAMING_SNAKE_CASE_	=      CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''						)
   SCREAMING_SNAKE_CASE_	=      CLIPSegProcessor(image_processor=__lowerCamelCase,  tokenizer=__lowerCamelCase						)

   SCREAMING_SNAKE_CASE_	=      prepare_img()
   SCREAMING_SNAKE_CASE_	=      ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

   SCREAMING_SNAKE_CASE_	=      processor(text=__lowerCamelCase,  images=[image] * len(__lowerCamelCase						),  padding='''max_length''',  return_tensors='''pt'''						)

   with torch.no_grad():
      SCREAMING_SNAKE_CASE_	=      model(**__lowerCamelCase						)

   # verify values
   SCREAMING_SNAKE_CASE_	=      torch.tensor([0.11_10, -0.18_82, 0.16_45]						)
   SCREAMING_SNAKE_CASE_	=      torch.tensor([0.26_92, -0.71_97, -0.13_28]						)
   if model_name == "clipseg-rd64-refined":
      SCREAMING_SNAKE_CASE_	=      torch.tensor(
          [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]						)
   elif model_name == "clipseg-rd64":
      SCREAMING_SNAKE_CASE_	=      torch.tensor(
          [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]						)
   elif model_name == "clipseg-rd16":
      SCREAMING_SNAKE_CASE_	=      torch.tensor(
          [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]						)
   else:
      raise ValueError(F'''Model name {model_name} not supported.'''						)

   assert torch.allclose(outputs.logits[0, :3, :3],  __lowerCamelCase,  atol=1E-3						)
   assert torch.allclose(outputs.conditional_embeddings[0, :3],  __lowerCamelCase,  atol=1E-3						)
   assert torch.allclose(outputs.pooled_output[0, :3],  __lowerCamelCase,  atol=1E-3						)
   print('''Looks ok!'''						)

   if pytorch_dump_folder_path is not None:
      print(F'''Saving model and processor to {pytorch_dump_folder_path}'''						)
      model.save_pretrained(__lowerCamelCase						)
      processor.save_pretrained(__lowerCamelCase						)

   if push_to_hub:
      print(F'''Pushing model and processor for {model_name} to the hub'''						)
      model.push_to_hub(F'''CIDAS/{model_name}'''						)
      processor.push_to_hub(F'''CIDAS/{model_name}'''						)


if __name__ == "__main__":
   __UpperCAmelCase   						=     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="clipseg-rd64",
       type=str,
       choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"],
       help=(
           "Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"
           " reduce dimension)"
       ),
   )
   parser.add_argument(
       "--checkpoint_path",
       default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth",
       type=str,
       help=(
           "Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"
           " the decoder weights."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
   )
   parser.add_argument(
       "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
   )

   __UpperCAmelCase   						=     parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


