


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""": 4_9408,
        """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""": 4_9408,
        """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""": 4_9408,
        """vision_layers""": 24,
        """vision_width""": 1024,
        """vision_patch_size""": 14,
        """transformer_width""": 768,
        """transformer_heads""": 12,
        """transformer_layers""": 12,
    },
}






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE=""					,					SCREAMING_SNAKE_CASE="/"					)->  Dict:




      """simple docstring"""

      snake_case_       =      []

      for k, v in params.items():
            snake_case_       =      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        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

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






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  List[Any]:




      """simple docstring"""

      snake_case_       ,    snake_case_       ,    snake_case_       =      pt_attn_layer.in_proj_weight.chunk(3					,					dim=0					)
      snake_case_       ,    snake_case_       ,    snake_case_       =      pt_attn_layer.in_proj_bias.chunk(3					,					dim=0					)

      snake_case_       =      pt_attn_layer.out_proj.weight
      snake_case_       =      pt_attn_layer.out_proj.bias

      snake_case_       =      q_proj
      snake_case_       =      q_proj_bias

      snake_case_       =      k_proj
      snake_case_       =      k_proj_bias

      snake_case_       =      v_proj
      snake_case_       =      v_proj_bias

      snake_case_       =      out_proj_weights
      snake_case_       =      out_proj_bias






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  List[str]:




      """simple docstring"""

      copy_linear(hf_mlp.fca					,					pt_mlp.c_fc					)
      copy_linear(hf_mlp.fca					,					pt_mlp.c_proj					)






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  Optional[Any]:




      """simple docstring"""

      snake_case_       =      pt_linear.weight
      snake_case_       =      pt_linear.bias






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  str:




      """simple docstring"""

      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        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  str:




      """simple docstring"""

      for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					):
            copy_layer(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  Dict:




      """simple docstring"""

      snake_case_       =      pt_model.token_embedding.weight
      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        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

      snake_case_       =      pt_model.text_projection.data.T

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






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  Tuple:




      """simple docstring"""

      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
      snake_case_       =      pt_model.visual.conva.weight.data
      snake_case_       =      pt_model.visual.class_embedding
      snake_case_       =      pt_model.visual.positional_embedding.data

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






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  Optional[int]:




      """simple docstring"""

      snake_case_       =      flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']					)

      snake_case_       =      torch.from_numpy(flax_class_token_params['''scale''']					)
      snake_case_       =      torch.from_numpy(flax_class_token_params['''bias''']					)
      snake_case_       =      nn.Parameter(SCREAMING_SNAKE_CASE					)
      snake_case_       =      nn.Parameter(SCREAMING_SNAKE_CASE					)






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  Any:




      """simple docstring"""

      snake_case_       =      hf_model.state_dict()
      snake_case_       =      {}

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

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

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

            snake_case_       =      nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE					)					)

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

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

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

            snake_case_       =      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        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  int:




      """simple docstring"""

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

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

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

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

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

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






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Union[str, Any]:




      """simple docstring"""

      snake_case_       =      {}
      snake_case_       =      []

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

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






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)->  Dict:




      """simple docstring"""

      snake_case_       =      CLIP(**SCREAMING_SNAKE_CASE					)
      torch_model.eval()
      snake_case_       =      torch_model.state_dict()

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

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

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

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

            snake_case_       =      v

      snake_case_       =      _convert_attn_layers(SCREAMING_SNAKE_CASE					)
      new_torch_params.update(SCREAMING_SNAKE_CASE					)
      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():
                  snake_case_       =      torch.from_numpy(new_torch_params[name]					)
                  torch_clip_params[name].copy_(SCREAMING_SNAKE_CASE					)
            else:
                  snake_case_       =      param

      return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE=None					)->  str:




      """simple docstring"""

      snake_case_       =      Repository(SCREAMING_SNAKE_CASE					,					clone_from=f'''google/{pytorch_dump_folder_path}'''					)
      repo.git_pull()

      if config_path is not None:
            snake_case_       =      OwlViTConfig.from_pretrained(SCREAMING_SNAKE_CASE					)
      else:
            snake_case_       =      OwlViTConfig()

      snake_case_       =      OwlViTModel(SCREAMING_SNAKE_CASE					).eval()
      snake_case_       =      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					)
      snake_case_       =      pt_backbone.logit_scale
      copy_flax_attn_params(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)

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

      # Initialize processor
      snake_case_       =      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,							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 json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase			=    logging.get_logger(__name__)






def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Any:




      """simple docstring"""

      snake_case_       =      MobileNetVaConfig(layer_norm_eps=0.0_0_1					)

      if "quant" in model_name:
            raise ValueError('''Quantized models are not supported.'''					)

      snake_case_       =      re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''					,					SCREAMING_SNAKE_CASE					)
      if matches:
            snake_case_       =      float(matches[1]					)
            snake_case_       =      int(matches[2]					)

      if model_name.startswith('''deeplabv3_'''					):
            snake_case_       =      8
            snake_case_       =      21
            snake_case_       =      '''pascal-voc-id2label.json'''
      else:
            # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
            # of the usual 1000. The first class (index 0) is "background".
            snake_case_       =      1001
            snake_case_       =      '''imagenet-1k-id2label.json'''

      snake_case_       =      '''huggingface/label-files'''
      snake_case_       =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					repo_type='''dataset'''					)					,					'''r'''					)					)

      if config.num_labels == 1001:
            snake_case_       =      {int(SCREAMING_SNAKE_CASE					) + 1: v for k, v in idalabel.items()}
            snake_case_       =      '''background'''
      else:
            snake_case_       =      {int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}

      snake_case_       =      idalabel
      snake_case_       =      {v: k for k, v in idalabel.items()}

      return config






def        __lowerCAmelCase    ()->  str:




      """simple docstring"""

      snake_case_       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      snake_case_       =      Image.open(requests.get(SCREAMING_SNAKE_CASE					,					stream=SCREAMING_SNAKE_CASE					).raw					)
      return im






@torch.no_grad()
def        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE=False					)->  str:




      """simple docstring"""

      snake_case_       =      get_mobilenet_va_config(SCREAMING_SNAKE_CASE					)

      # Load 🤗 model
      if model_name.startswith('''deeplabv3_'''					):
            snake_case_       =      MobileNetVaForSemanticSegmentation(SCREAMING_SNAKE_CASE					).eval()
      else:
            snake_case_       =      MobileNetVaForImageClassification(SCREAMING_SNAKE_CASE					).eval()

      # Load weights from TensorFlow checkpoint
      load_tf_weights_in_mobilenet_va(SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					)

      # Check outputs on an image, prepared by MobileNetV2ImageProcessor
      snake_case_       =      MobileNetVaImageProcessor(
          crop_size={'''width''': config.image_size, '''height''': config.image_size}					,					size={'''shortest_edge''': config.image_size + 32}					,					)
      snake_case_       =      image_processor(images=prepare_img()					,					return_tensors='''pt'''					)
      snake_case_       =      model(**SCREAMING_SNAKE_CASE					)
      snake_case_       =      outputs.logits

      if model_name.startswith('''deeplabv3_'''					):
            assert logits.shape == (1, 21, 65, 65)

            if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                  snake_case_       =      torch.tensor(
                      [
                          [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
                          [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
                          [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
                      ]					)

            else:
                  raise ValueError(f'''Unknown model name: {model_name}'''					)

            assert torch.allclose(logits[0, :3, :3, :3]					,					SCREAMING_SNAKE_CASE					,					atol=1E-4					)
      else:
            assert logits.shape == (1, 1001)

            if model_name == "mobilenet_v2_1.4_224":
                  snake_case_       =      torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8]					)
            elif model_name == "mobilenet_v2_1.0_224":
                  snake_case_       =      torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5]					)
            elif model_name == "mobilenet_v2_0.75_160":
                  snake_case_       =      torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9]					)
            elif model_name == "mobilenet_v2_0.35_96":
                  snake_case_       =      torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]					)
            else:
                  snake_case_       =      None

            if expected_logits is not None:
                  assert torch.allclose(logits[0, :3]					,					SCREAMING_SNAKE_CASE					,					atol=1E-4					)

      Path(SCREAMING_SNAKE_CASE					).mkdir(exist_ok=SCREAMING_SNAKE_CASE					)
      print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''					)
      model.save_pretrained(SCREAMING_SNAKE_CASE					)
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
      image_processor.save_pretrained(SCREAMING_SNAKE_CASE					)

      if push_to_hub:
            print('''Pushing to the hub...'''					)
            snake_case_       =      '''google/''' + model_name
            image_processor.push_to_hub(SCREAMING_SNAKE_CASE					)
            model.push_to_hub(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
   UpperCAmelCase			=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default="""mobilenet_v2_1.0_224""",
       type=str,
       help="""Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.""",
   )
   parser.add_argument(
       """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", required=True, 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_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )