

import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class    __UpperCAmelCase	(			lowerCamelCase__     ,    unittest.TestCase		):
   UpperCamelCase  	=	GPTNeoXJapaneseTokenizer
   UpperCamelCase  	=	False
   UpperCamelCase  	=	{"""do_clean_text""": False, """add_prefix_space""": False}



   def 			__magic_name__       (				self				:	Tuple		):
     super().setUp()

     UpperCAmelCase			:				Tuple										=					[
         '''こん''',
         '''こんに''',
         '''にちは''',
         '''ばんは''',
         '''世界,㔺界''',
         '''、''',
         '''。''',
         '''<BR>''',
         '''<SP>''',
         '''<TAB>''',
         '''<URL>''',
         '''<EMAIL>''',
         '''<TEL>''',
         '''<DATE>''',
         '''<PRICE>''',
         '''<BLOCK>''',
         '''<KIGOU>''',
         '''<U2000U2BFF>''',
         '''<|emoji1|>''',
         '''<unk>''',
         '''<|startoftext|>''',
         '''<|endoftext|>''',
     ]
     UpperCAmelCase			:				List[str]										=					{'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
     UpperCAmelCase			:				List[str]										=					{'''unk_token''': '''<unk>'''}

     UpperCAmelCase			:				int										=					os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES['''vocab_file''']		)
     UpperCAmelCase			:				int										=					os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES['''emoji_file''']		)
     with open(self.vocab_file,						'''w''',						encoding='''utf-8'''		) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)
     with open(self.emoji_file,						'''w'''		) as emoji_writer:
       emoji_writer.write(json.dumps(__A		)		)



   def 			__magic_name__       (				self				:	str,						**__A				:	Optional[int]		):
     kwargs.update(self.special_tokens_map		)
     return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname,						**__A		)



   def 			__magic_name__       (				self				:	Any,						__A				:	str		):
     UpperCAmelCase			:				List[str]										=					'''こんにちは、世界。 \nこんばんは、㔺界。😀'''
     UpperCAmelCase			:				Union[str, Any]										=					'''こんにちは、世界。 \nこんばんは、世界。😀'''
     return input_text, output_text



   def 			__magic_name__       (				self				:	List[Any],						__A				:	int		):
     UpperCAmelCase					,		UpperCAmelCase			:				int										=					self.get_input_output_texts(__A		)
     UpperCAmelCase			:				str										=					tokenizer.encode(__A,						add_special_tokens=__A		)
     UpperCAmelCase			:				List[Any]										=					tokenizer.decode(__A,						clean_up_tokenization_spaces=__A		)
     return text, ids



   def 			__magic_name__       (				self				:	List[Any]		):
     pass  # TODO add if relevant



   def 			__magic_name__       (				self				:	Optional[Any]		):
     pass  # TODO add if relevant



   def 			__magic_name__       (				self				:	Dict		):
     pass  # TODO add if relevant



   def 			__magic_name__       (				self				:	Any		):
     UpperCAmelCase			:				Union[str, Any]										=					self.get_tokenizer()

     # Testing tokenization
     UpperCAmelCase			:				Dict										=					'''こんにちは、世界。　こんばんは、㔺界。'''
     UpperCAmelCase			:				List[str]										=					['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
     UpperCAmelCase			:				List[str]										=					tokenizer.tokenize(__A		)
     self.assertListEqual(__A,						__A		)

     # Testing conversion to ids without special tokens
     UpperCAmelCase			:				Any										=					[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
     UpperCAmelCase			:				Union[str, Any]										=					tokenizer.convert_tokens_to_ids(__A		)
     self.assertListEqual(__A,						__A		)

     # Testing conversion to ids with special tokens
     UpperCAmelCase			:				str										=					tokens + [tokenizer.unk_token]
     UpperCAmelCase			:				List[str]										=					[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
     UpperCAmelCase			:				Tuple										=					tokenizer.convert_tokens_to_ids(__A		)
     self.assertListEqual(__A,						__A		)



   @slow
   def 			__magic_name__       (				self				:	Dict		):
     UpperCAmelCase			:				Optional[int]										=					self.tokenizer_class.from_pretrained('''abeja/gpt-neox-japanese-2.7b'''		)

     UpperCAmelCase			:				Optional[Any]										=					tokenizer.encode('''ありがとう。''',						add_special_tokens=__A		)
     UpperCAmelCase			:				Any										=					tokenizer.encode('''どういたしまして。''',						add_special_tokens=__A		)

     UpperCAmelCase			:				Dict										=					tokenizer.build_inputs_with_special_tokens(__A		)
     UpperCAmelCase			:				str										=					tokenizer.build_inputs_with_special_tokens(__A,						__A		)

     assert encoded_sentence == ids_a
     assert encoded_pair == ids_a + ids_a



   def 			__magic_name__       (				self				:	Union[str, Any]		):
     # Intentionally convert some words to accommodate character fluctuations unique to Japanese
     pass





   def 			__magic_name__       (				self				:	Optional[int]		):
     # tokenizer has no padding token
     pass








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,
)


_lowerCamelCase							:  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      a__	(						UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     Dict=""							,					UpperCAmelCase				:     Optional[Any]="/"     )    ->		Optional[int]:
  UpperCAmelCase			:				str										=					[]

  for k, v in params.items():
    UpperCAmelCase			:				int										=					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      a__	(						UpperCAmelCase				:     Optional[int]     )    ->		Tuple:
  return jax.tree_util.tree_map(lambda UpperCAmelCase     : x.astype(jnp.floataa     ) if x.dtype == jnp.bfloataa else x							,					UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     Union[str, Any]							,					UpperCAmelCase				:     List[Any]     )    ->		str:
  UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				List[Any]										=					pt_attn_layer.in_proj_weight.chunk(3							,					dim=0     )
  UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				Tuple										=					pt_attn_layer.in_proj_bias.chunk(3							,					dim=0     )

  UpperCAmelCase			:				Optional[Any]										=					pt_attn_layer.out_proj.weight
  UpperCAmelCase			:				Tuple										=					pt_attn_layer.out_proj.bias

  UpperCAmelCase			:				List[str]										=					q_proj
  UpperCAmelCase			:				Optional[int]										=					q_proj_bias

  UpperCAmelCase			:				Any										=					k_proj
  UpperCAmelCase			:				Optional[Any]										=					k_proj_bias

  UpperCAmelCase			:				Optional[Any]										=					v_proj
  UpperCAmelCase			:				Optional[Any]										=					v_proj_bias

  UpperCAmelCase			:				str										=					out_proj_weights
  UpperCAmelCase			:				List[str]										=					out_proj_bias

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict     )    ->		Optional[int]:
  copy_linear(hf_mlp.fca							,					pt_mlp.c_fc     )
  copy_linear(hf_mlp.fca							,					pt_mlp.c_proj     )

def      a__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     str     )    ->		Dict:
  UpperCAmelCase			:				Dict										=					pt_linear.weight
  UpperCAmelCase			:				List[Any]										=					pt_linear.bias

def      a__	(						UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     int     )    ->		Tuple:
  # 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__	(						UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     Union[str, Any]     )    ->		int:
  for hf_layer, pt_layer in zip(UpperCAmelCase							,					UpperCAmelCase     ):
    copy_layer(UpperCAmelCase							,					UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     Dict     )    ->		Union[str, Any]:
  # copy  embeds
  UpperCAmelCase			:				List[Any]										=					pt_model.token_embedding.weight
  UpperCAmelCase			:				Any										=					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__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     Tuple     )    ->		str:
  # copy projection
  UpperCAmelCase			:				int										=					pt_model.text_projection.data.T

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

def      a__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[str]     )    ->		List[Any]:
  # copy projection
  UpperCAmelCase			:				List[Any]										=					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
  UpperCAmelCase			:				Dict										=					pt_model.visual.conva.weight.data
  UpperCAmelCase			:				Any										=					pt_model.visual.class_embedding
  UpperCAmelCase			:				str										=					pt_model.visual.positional_embedding.data

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

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Any     )    ->		Union[str, Any]:
  UpperCAmelCase			:				str										=					flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']     )

  UpperCAmelCase			:				str										=					torch.from_numpy(flax_class_token_params['''scale''']     )
  UpperCAmelCase			:				Union[str, Any]										=					torch.from_numpy(flax_class_token_params['''bias''']     )
  UpperCAmelCase			:				Union[str, Any]										=					nn.Parameter(UpperCAmelCase     )
  UpperCAmelCase			:				int										=					nn.Parameter(UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Optional[Any]     )    ->		List[Any]:
  UpperCAmelCase			:				int										=					hf_model.state_dict()
  UpperCAmelCase			:				Any										=					{}

  # Rename class prediction head flax params to pytorch HF
  UpperCAmelCase			:				Optional[Any]										=					flatten_nested_dict(flax_params['''class_head''']     )

  for flax_key, v in flax_class_params.items():
    UpperCAmelCase			:				List[str]										=					flax_key.replace('''/'''							,					'''.'''     )
    UpperCAmelCase			:				Dict										=					torch_key.replace('''.kernel'''							,					'''.weight'''     )
    UpperCAmelCase			:				Optional[int]										=					torch_key.replace('''Dense_0'''							,					'''dense0'''     )
    UpperCAmelCase			:				List[Any]										=					'''class_head.''' + torch_key

    if "weight" in torch_key and v.ndim == 2:
      UpperCAmelCase			:				Union[str, Any]										=					v.T

    UpperCAmelCase			:				List[str]										=					nn.Parameter(torch.from_numpy(UpperCAmelCase     )     )

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

  for flax_key, v in flax_box_params.items():
    UpperCAmelCase			:				Any										=					flax_key.replace('''/'''							,					'''.'''     )
    UpperCAmelCase			:				Any										=					torch_key.replace('''.kernel'''							,					'''.weight'''     )
    UpperCAmelCase			:				List[Any]										=					torch_key.replace('''_'''							,					''''''     ).lower()
    UpperCAmelCase			:				int										=					'''box_head.''' + torch_key

    if "weight" in torch_key and v.ndim == 2:
      UpperCAmelCase			:				List[str]										=					v.T

    UpperCAmelCase			:				int										=					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      a__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[Any]     )    ->		List[Any]:
  for k, v in flax_attn_params.items():
    if k.startswith('''transformer'''     ):
      UpperCAmelCase			:				Optional[int]										=					k.replace('''transformer.resblocks'''							,					'''text_model.encoder.layers'''     )
    else:
      UpperCAmelCase			:				Tuple										=					k.replace('''visual.transformer.resblocks'''							,					'''vision_model.encoder.layers'''     )

    UpperCAmelCase			:				List[Any]										=					torch_key.replace('''attn'''							,					'''self_attn'''     )
    UpperCAmelCase			:				Union[str, Any]										=					torch_key.replace('''key'''							,					'''k_proj'''     )
    UpperCAmelCase			:				int										=					torch_key.replace('''value'''							,					'''v_proj'''     )
    UpperCAmelCase			:				Dict										=					torch_key.replace('''query'''							,					'''q_proj'''     )
    UpperCAmelCase			:				int										=					torch_key.replace('''out'''							,					'''out_proj'''     )

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

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

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

    # Copy flax CLIP attn params to HF PyTorch params
    UpperCAmelCase			:				List[str]										=					torch.from_numpy(UpperCAmelCase     )
    hf_backbone.state_dict()[torch_key].copy_(UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     Union[str, Any]     )    ->		List[str]:
  UpperCAmelCase			:				Optional[Any]										=					{}
  UpperCAmelCase			:				Dict										=					[]

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

      processed_attn_layers.append(UpperCAmelCase     )
      UpperCAmelCase			:				List[str]										=					params[base + '''out.weight'''].shape[-1]
      UpperCAmelCase			:				Tuple										=					params[base + '''out.weight'''].reshape(UpperCAmelCase							,					UpperCAmelCase     ).T
      UpperCAmelCase			:				Tuple										=					params[base + '''out.bias''']
    else:
      UpperCAmelCase			:				Optional[int]										=					v
  return new_params

def      a__	(						UpperCAmelCase				:     Tuple							,					UpperCAmelCase				:     Optional[Any]     )    ->		int:
  UpperCAmelCase			:				Optional[int]										=					CLIP(**UpperCAmelCase     )
  torch_model.eval()
  UpperCAmelCase			:				Optional[Any]										=					torch_model.state_dict()

  UpperCAmelCase			:				List[str]										=					flatten_nested_dict(flax_params['''backbone''']['''clip''']     )
  UpperCAmelCase			:				Any										=					{}

  for flax_key, v in flax_clip_params.items():
    UpperCAmelCase			:				Optional[int]										=					flax_key.replace('''/'''							,					'''.'''     )
    UpperCAmelCase			:				Dict										=					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'''     )
    ):
      UpperCAmelCase			:				List[str]										=					torch_key[5:]

    UpperCAmelCase			:				Dict										=					torch_key.replace('''text_projection.kernel'''							,					'''text_projection'''     )
    UpperCAmelCase			:				Tuple										=					torch_key.replace('''visual.proj.kernel'''							,					'''visual.proj'''     )
    UpperCAmelCase			:				Dict										=					torch_key.replace('''.scale'''							,					'''.weight'''     )
    UpperCAmelCase			:				str										=					torch_key.replace('''.kernel'''							,					'''.weight'''     )

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

    UpperCAmelCase			:				Union[str, Any]										=					v

  UpperCAmelCase			:				Dict										=					_convert_attn_layers(UpperCAmelCase     )
  new_torch_params.update(UpperCAmelCase     )
  UpperCAmelCase			:				int										=					{}

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

  return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def      a__	(						UpperCAmelCase				:     str							,					UpperCAmelCase				:     Union[str, Any]							,					UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     Optional[Any]=None     )    ->		Optional[int]:
  UpperCAmelCase			:				Optional[int]										=					Repository(UpperCAmelCase							,					clone_from=f'''google/{pytorch_dump_folder_path}'''     )
  repo.git_pull()

  if config_path is not None:
    UpperCAmelCase			:				str										=					OwlViTConfig.from_pretrained(UpperCAmelCase     )
  else:
    UpperCAmelCase			:				List[str]										=					OwlViTConfig()

  UpperCAmelCase			:				List[str]										=					OwlViTModel(UpperCAmelCase     ).eval()
  UpperCAmelCase			:				Optional[int]										=					OwlViTForObjectDetection(UpperCAmelCase     ).eval()

  copy_text_model_and_projection(UpperCAmelCase							,					UpperCAmelCase     )
  copy_vision_model_and_projection(UpperCAmelCase							,					UpperCAmelCase     )
  UpperCAmelCase			:				Tuple										=					pt_backbone.logit_scale
  copy_flax_attn_params(UpperCAmelCase							,					UpperCAmelCase     )

  UpperCAmelCase			:				int										=					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
  UpperCAmelCase			:				List[str]										=					OwlViTImageProcessor(
      size=config.vision_config.image_size							,					crop_size=config.vision_config.image_size     )
  # Initialize tokenizer
  UpperCAmelCase			:				Union[str, Any]										=					CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''							,					pad_token='''!'''							,					model_max_length=16     )

  # Initialize processor
  UpperCAmelCase			:				str										=					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__":
     _lowerCamelCase							:  List[str]     =				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."
     )
     _lowerCamelCase							:  List[Any]     =				parser.parse_args()

     # Initialize PyToch clip model
     _lowerCamelCase							:  Any     =				args.owlvit_version
     if model_name == "clip_b16":
          _lowerCamelCase							:  Optional[int]     =				CONFIGS["vit_b16"]
     elif model_name == "clip_b32":
          _lowerCamelCase							:  Dict     =				CONFIGS["vit_b32"]
     elif model_name == "clip_l14":
          _lowerCamelCase							:  Optional[Any]     =				CONFIGS["vit_l14"]

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

     # Convert CLIP backbone
     _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase							:  Optional[Any]     =				convert_clip_backbone(flax_params, torch_config)

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





