





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   :							int     =	{
    """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 				__UpperCamelCase  (		snake_case    ,	snake_case=""    ,	snake_case="/"     )  ->						List[Any]:


   '''simple docstring'''







   __A				 =       []

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

      if isinstance(snake_case    ,	collections.MutableMapping     ):
         items.extend(flatten_nested_dict(snake_case    ,	snake_case    ,	sep=snake_case     ).items()     )
      else:
         items.append((new_key, v)     )
   return dict(snake_case     )
def 				__UpperCamelCase  (		snake_case     )  ->						Dict:


   '''simple docstring'''







   return jax.tree_util.tree_map(lambda snake_case     : x.astype(jnp.floataa     ) if x.dtype == jnp.bfloataa else x    ,	snake_case     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						Optional[int]:


   '''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 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						Any:


   '''simple docstring'''







   copy_linear(hf_mlp.fca    ,	pt_mlp.c_fc     )
   copy_linear(hf_mlp.fca    ,	pt_mlp.c_proj     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						List[str]:


   '''simple docstring'''







   __A				 =       pt_linear.weight
   __A				 =       pt_linear.bias
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						Dict:


   '''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 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						Optional[Any]:


   '''simple docstring'''







   for hf_layer, pt_layer in zip(snake_case    ,	snake_case     ):
      copy_layer(snake_case    ,	snake_case     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						Tuple:


   '''simple docstring'''







   __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 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						List[str]:


   '''simple docstring'''







   __A				 =       pt_model.text_projection.data.T

   # copy text encoder
   copy_encoder(hf_model.text_model    ,	snake_case     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						Optional[int]:


   '''simple docstring'''







   __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 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						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(snake_case     )
   __A				 =       nn.Parameter(snake_case     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						Tuple:


   '''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(snake_case     )     )

   # 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(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_(snake_case     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						Optional[int]:


   '''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(snake_case     )

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

      # Copy flax CLIP attn params to HF PyTorch params
      __A				 =       torch.from_numpy(snake_case     )
      hf_backbone.state_dict()[torch_key].copy_(snake_case     )
def 				__UpperCamelCase  (		snake_case     )  ->						List[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(snake_case     )
         __A				 =       params[base + '''out.weight'''].shape[-1]
         __A				 =       params[base + '''out.weight'''].reshape(snake_case    ,	snake_case     ).T
         __A				 =       params[base + '''out.bias''']
      else:
         __A				 =       v
   return new_params
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						int:


   '''simple docstring'''







   __A				 =       CLIP(**snake_case     )
   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(snake_case     )
   new_torch_params.update(snake_case     )
   __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_(snake_case     )
      else:
         __A				 =       param

   return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case    ,	snake_case    ,	snake_case=None     )  ->						List[Any]:


   '''simple docstring'''







   __A				 =       Repository(snake_case    ,	clone_from=F"google/{pytorch_dump_folder_path}"     )
   repo.git_pull()

   if config_path is not None:
      __A				 =       OwlViTConfig.from_pretrained(snake_case     )
   else:
      __A				 =       OwlViTConfig()

   __A				 =       OwlViTModel(snake_case     ).eval()
   __A				 =       OwlViTForObjectDetection(snake_case     ).eval()

   copy_text_model_and_projection(snake_case    ,	snake_case     )
   copy_vision_model_and_projection(snake_case    ,	snake_case     )
   __A				 =       pt_backbone.logit_scale
   copy_flax_attn_params(snake_case    ,	snake_case     )

   __A				 =       hf_backbone
   copy_class_merge_token(snake_case    ,	snake_case     )
   copy_class_box_heads(snake_case    ,	snake_case     )

   # 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=snake_case    ,	tokenizer=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   :							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."""
    )
    _UpperCamelCase   :							Optional[int]     =	parser.parse_args()

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

    # Load from checkpoint and convert params to float-32
    _UpperCamelCase   :							Optional[Any]     =	checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["""optimizer"""]["""target"""]
    _UpperCamelCase   :							List[Any]     =	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   :							List[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)








import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
_UpperCamelCase   :							str     =	"""stas/tiny-wmt19-en-ru"""







class 						_lowerCAmelCase(						_a		,   unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__      					=   FSMTTokenizer
  lowerCamelCase__      					=   False

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     __A				 =       [
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''w</w>''',
         '''r</w>''',
         '''t</w>''',
         '''lo''',
         '''low''',
         '''er</w>''',
         '''low</w>''',
         '''lowest</w>''',
         '''newer</w>''',
         '''wider</w>''',
         '''<unk>''',
     ]
     __A				 =       dict(zip(UpperCAmelCase  ,    range(len(UpperCAmelCase     )     )     )     )
     __A				 =       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

     __A				 =       ['''en''', '''ru''']
     __A				 =       {
         '''langs''': self.langs,
         '''src_vocab_size''': 10,
         '''tgt_vocab_size''': 20,
     }

     __A				 =       os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''src_vocab_file''']     )
     __A				 =       os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''tgt_vocab_file''']     )
     __A				 =       os.path.join(self.tmpdirname  ,    '''tokenizer_config.json'''     )
     __A				 =       os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''merges_file''']     )
     with open(self.src_vocab_file  ,    '''w'''     ) as fp:
        fp.write(json.dumps(UpperCAmelCase     )     )
     with open(self.tgt_vocab_file  ,    '''w'''     ) as fp:
        fp.write(json.dumps(UpperCAmelCase     )     )
     with open(self.merges_file  ,    '''w'''     ) as fp:
        fp.write('''\n'''.join(UpperCAmelCase     )     )
     with open(UpperCAmelCase  ,    '''w'''     ) as fp:
        fp.write(json.dumps(UpperCAmelCase     )     )

  @cached_property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''     )

  @cached_property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       FSMTTokenizer.from_pretrained(UpperCAmelCase     )
     self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]  ,    ['''en''', '''ru''']     )
     self.assertEqual(tokenizer.src_vocab_size  ,    21     )
     self.assertEqual(tokenizer.tgt_vocab_size  ,    21     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     __A				 =       FSMTTokenizer(self.langs  ,    self.src_vocab_file  ,    self.tgt_vocab_file  ,    self.merges_file     )

     __A				 =       '''lower'''
     __A				 =       ['''low''', '''er</w>''']
     __A				 =       tokenizer.tokenize(UpperCAmelCase     )
     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

     __A				 =       tokens + ['''<unk>''']
     __A				 =       [14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase     )  ,    UpperCAmelCase     )

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       self.tokenizer_ru_en

     __A				 =       tokenizer.encode('''sequence builders'''  ,    add_special_tokens=UpperCAmelCase     )
     __A				 =       tokenizer.encode('''multi-sequence build'''  ,    add_special_tokens=UpperCAmelCase     )

     __A				 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase     )
     __A				 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase  ,    UpperCAmelCase     )

     assert encoded_sentence == text + [2]
     assert encoded_pair == text + [2] + text_a + [2]

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       self.tokenizer_en_ru
     __A				 =       self.tokenizer_ru_en

     __A				 =       [
         [
             '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
             [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
         ],
         ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
     ]

     # if data needs to be recreated or added, run:
     # import torch
     # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
     # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

     for src_text, tgt_input_ids in targets:
        __A				 =       tokenizer_enc.encode(UpperCAmelCase  ,    return_tensors=UpperCAmelCase     )
        self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

        # and decode backward, using the reversed languages model
        __A				 =       tokenizer_dec.decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )
        self.assertEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     __A				 =       FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''  ,    do_lower_case=UpperCAmelCase     )
     __A				 =       tokenizer.tokenize('''USA is United States of America'''     )
     __A				 =       ['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''     )
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     pass





  @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''     )
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     pass

