





"""simple docstring"""


import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


a__					:     Optional[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 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase=""	,     _UpperCAmelCase="/"	)    ->  Any:

 '''simple docstring'''






 snake_case__    							=				[]

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

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





def 							a__			(  _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Optional[int]:

 '''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 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Tuple:

 '''simple docstring'''






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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Tuple:

 '''simple docstring'''






 snake_case__    							=				pt_linear.weight
 snake_case__    							=				pt_linear.bias





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Any:

 '''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 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  List[str]:

 '''simple docstring'''






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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Union[str, Any]:

 '''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 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






 snake_case__    							=				pt_model.text_projection.data.T

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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Any:

 '''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 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  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(lowercase__	)
 snake_case__    							=				nn.Parameter(lowercase__	)





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Tuple:

 '''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(lowercase__	)	)

 # 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(lowercase__	)	)

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





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Tuple:

 '''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(lowercase__	)

  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(lowercase__	).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(lowercase__	).T

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





def 							a__			(  _UpperCAmelCase	)    ->  Optional[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(lowercase__	)
   snake_case__    							=				params[base + "out.weight"].shape[-1]
   snake_case__    							=				params[base + "out.weight"].reshape(lowercase__	,     lowercase__	).T
   snake_case__    							=				params[base + "out.bias"]
  else:
   snake_case__    							=				v
 return new_params





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  str:

 '''simple docstring'''






 snake_case__    							=				CLIP(**lowercase__	)
 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(lowercase__	)
 new_torch_params.update(lowercase__	)
 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_(lowercase__	)
  else:
   snake_case__    							=				param

 return torch_clip_params, torch_model, attn_params





@torch.no_grad()
def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase=None	)    ->  Union[str, Any]:

 '''simple docstring'''






 snake_case__    							=				Repository(lowercase__	,     clone_from=f"google/{pytorch_dump_folder_path}"	)
 repo.git_pull()

 if config_path is not None:
  snake_case__    							=				OwlViTConfig.from_pretrained(lowercase__	)
 else:
  snake_case__    							=				OwlViTConfig()

 snake_case__    							=				OwlViTModel(lowercase__	).eval()
 snake_case__    							=				OwlViTForObjectDetection(lowercase__	).eval()

 copy_text_model_and_projection(lowercase__	,     lowercase__	)
 copy_vision_model_and_projection(lowercase__	,     lowercase__	)
 snake_case__    							=				pt_backbone.logit_scale
 copy_flax_attn_params(lowercase__	,     lowercase__	)

 snake_case__    							=				hf_backbone
 copy_class_merge_token(lowercase__	,     lowercase__	)
 copy_class_box_heads(lowercase__	,     lowercase__	)

 # 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=1_6	)

 # Initialize processor
 snake_case__    							=				OwlViTProcessor(image_processor=lowercase__	,     tokenizer=lowercase__	)
 image_processor.save_pretrained(repo.local_dir	)
 processor.save_pretrained(repo.local_dir	)

 repo.git_add()
 repo.git_commit("Upload model and processor"	)
 repo.git_push()


if __name__ == "__main__":
   a__					:     Any							=				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--owlvit_version""",
       default=None,
       type=str,
       required=True,
       help="""OWL-ViT model name [clip_b16, clip_b32, clip_l14].""",
   )
   parser.add_argument(
       """--owlvit_checkpoint""", default=None, type=str, required=True, help="""Path to flax model checkpoint."""
   )
   parser.add_argument("""--hf_config""", default=None, type=str, required=True, help="""Path to HF model config.""")
   parser.add_argument(
       """--pytorch_dump_folder_path""", default="""hf_model""", type=str, help="""Path to the output PyTorch model."""
   )
   a__					:     str							=				parser.parse_args()

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

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

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








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__					:     List[Any]							=				{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				["""BartTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[Any]							=				[
       """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BartForCausalLM""",
       """BartForConditionalGeneration""",
       """BartForQuestionAnswering""",
       """BartForSequenceClassification""",
       """BartModel""",
       """BartPretrainedModel""",
       """PretrainedBartModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[str]							=				[
       """TFBartForConditionalGeneration""",
       """TFBartForSequenceClassification""",
       """TFBartModel""",
       """TFBartPretrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				[
       """FlaxBartDecoderPreTrainedModel""",
       """FlaxBartForCausalLM""",
       """FlaxBartForConditionalGeneration""",
       """FlaxBartForQuestionAnswering""",
       """FlaxBartForSequenceClassification""",
       """FlaxBartModel""",
       """FlaxBartPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

else:
   import sys

   a__					:     int							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

