





'''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,
)


_SCREAMING_SNAKE_CASE    =			{
    """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       _lowerCAmelCase  ( lowerCamelCase_	:     Dict						,				lowerCamelCase_	:     Any=""						,				lowerCamelCase_	:     List[str]="/" ):
			__lowercase   =							[]

			for k, v in params.items():
						__lowercase   =							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       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple ):
			return jax.tree_util.tree_map(lambda lowerCamelCase_ : x.astype(jnp.floataa ) if x.dtype == jnp.bfloataa else x						,				UpperCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     Union[str, Any] ):
			__lowercase  ,						__lowercase  ,						__lowercase   =							pt_attn_layer.in_proj_weight.chunk(3						,				dim=0 )
			__lowercase  ,						__lowercase  ,						__lowercase   =							pt_attn_layer.in_proj_bias.chunk(3						,				dim=0 )

			__lowercase   =							pt_attn_layer.out_proj.weight
			__lowercase   =							pt_attn_layer.out_proj.bias

			__lowercase   =							q_proj
			__lowercase   =							q_proj_bias

			__lowercase   =							k_proj
			__lowercase   =							k_proj_bias

			__lowercase   =							v_proj
			__lowercase   =							v_proj_bias

			__lowercase   =							out_proj_weights
			__lowercase   =							out_proj_bias




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     Union[str, Any] ):
			copy_linear(hf_mlp.fca						,				pt_mlp.c_fc )
			copy_linear(hf_mlp.fca						,				pt_mlp.c_proj )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Union[str, Any]						,				lowerCamelCase_	:     Tuple ):
			__lowercase   =							pt_linear.weight
			__lowercase   =							pt_linear.bias




def       _lowerCAmelCase  ( lowerCamelCase_	:     Any						,				lowerCamelCase_	:     Optional[Any] ):
			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  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     List[str] ):



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




def       _lowerCAmelCase  ( lowerCamelCase_	:     Dict						,				lowerCamelCase_	:     Union[str, Any] ):
			__lowercase   =							pt_model.token_embedding.weight
			__lowercase   =							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  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     Dict ):
			__lowercase   =							pt_model.text_projection.data.T

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




def       _lowerCAmelCase  ( lowerCamelCase_	:     Any						,				lowerCamelCase_	:     str ):
			__lowercase   =							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
			__lowercase   =							pt_model.visual.conva.weight.data
			__lowercase   =							pt_model.visual.class_embedding
			__lowercase   =							pt_model.visual.positional_embedding.data

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




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     Any ):
			__lowercase   =							flatten_nested_dict(flax_params['''backbone''']['''merged_class_token'''] )

			__lowercase   =							torch.from_numpy(flax_class_token_params['''scale'''] )
			__lowercase   =							torch.from_numpy(flax_class_token_params['''bias'''] )
			__lowercase   =							nn.Parameter(UpperCamelCase_ )
			__lowercase   =							nn.Parameter(UpperCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     Union[str, Any] ):
			__lowercase   =							hf_model.state_dict()
			__lowercase   =							{}

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

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

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

						__lowercase   =							nn.Parameter(torch.from_numpy(UpperCamelCase_ ) )

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

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

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

						__lowercase   =							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       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     List[str] ):



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

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

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

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

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

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




def       _lowerCAmelCase  ( lowerCamelCase_	:     Any ):
			__lowercase   =							{}
			__lowercase   =							[]

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

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




def       _lowerCAmelCase  ( lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     int ):
			__lowercase   =							CLIP(**UpperCamelCase_ )
			torch_model.eval()
			__lowercase   =							torch_model.state_dict()

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

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

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

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

						__lowercase   =							v

			__lowercase   =							_convert_attn_layers(UpperCamelCase_ )
			new_torch_params.update(UpperCamelCase_ )
			__lowercase   =							{}

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

			return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def       _lowerCAmelCase  ( lowerCamelCase_	:     Any						,				lowerCamelCase_	:     Union[str, Any]						,				lowerCamelCase_	:     Dict						,				lowerCamelCase_	:     List[Any]						,				lowerCamelCase_	:     List[Any]=None ):
			__lowercase   =							Repository(UpperCamelCase_						,				clone_from=f"google/{pytorch_dump_folder_path}" )
			repo.git_pull()

			if config_path is not None:
						__lowercase   =							OwlViTConfig.from_pretrained(UpperCamelCase_ )
			else:
						__lowercase   =							OwlViTConfig()

			__lowercase   =							OwlViTModel(UpperCamelCase_ ).eval()
			__lowercase   =							OwlViTForObjectDetection(UpperCamelCase_ ).eval()

			copy_text_model_and_projection(UpperCamelCase_						,				UpperCamelCase_ )
			copy_vision_model_and_projection(UpperCamelCase_						,				UpperCamelCase_ )
			__lowercase   =							pt_backbone.logit_scale
			copy_flax_attn_params(UpperCamelCase_						,				UpperCamelCase_ )

			__lowercase   =							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
			__lowercase   =							OwlViTImageProcessor(
			    size=config.vision_config.image_size						,				crop_size=config.vision_config.image_size )
			# Initialize tokenizer
			__lowercase   =							CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''						,				pad_token='''!'''						,				model_max_length=1_6 )

			# Initialize processor
			__lowercase   =							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__":
	_SCREAMING_SNAKE_CASE    =			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.'''
	)
	_SCREAMING_SNAKE_CASE    =			parser.parse_args()

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

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

	# Convert CLIP backbone
	_SCREAMING_SNAKE_CASE    =			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 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,
)


__snake_case			:			int			   =   {
    """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 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Any=""			,				UpperCamelCase_				:    List[str]="/"						)     ->      List[str]:

						"""simple docstring"""



						lowerCAmelCase__			  =  []

						for k, v in params.items():
												lowerCAmelCase__			  =  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 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple						)     ->      Optional[int]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Union[str, Any]						)     ->      List[Any]:

						"""simple docstring"""



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

						lowerCAmelCase__			  =  pt_attn_layer.out_proj.weight
						lowerCAmelCase__			  =  pt_attn_layer.out_proj.bias

						lowerCAmelCase__			  =  q_proj
						lowerCAmelCase__			  =  q_proj_bias

						lowerCAmelCase__			  =  k_proj
						lowerCAmelCase__			  =  k_proj_bias

						lowerCAmelCase__			  =  v_proj
						lowerCAmelCase__			  =  v_proj_bias

						lowerCAmelCase__			  =  out_proj_weights
						lowerCAmelCase__			  =  out_proj_bias







def 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Union[str, Any]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Tuple						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_linear.weight
						lowerCAmelCase__			  =  pt_linear.bias







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Optional[Any]						)     ->      Tuple:

						"""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      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    List[str]						)     ->      Optional[int]:

						"""simple docstring"""



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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_model.token_embedding.weight
						lowerCAmelCase__			  =  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      (			UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Dict						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  pt_model.text_projection.data.T

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    str						)     ->      Tuple:

						"""simple docstring"""



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

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    Any						)     ->      List[str]:

						"""simple docstring"""



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

						lowerCAmelCase__			  =  torch.from_numpy(flax_class_token_params['scale']						)
						lowerCAmelCase__			  =  torch.from_numpy(flax_class_token_params['bias']						)
						lowerCAmelCase__			  =  nn.Parameter(UpperCamelCase_						)
						lowerCAmelCase__			  =  nn.Parameter(UpperCamelCase_						)







def 		_UpperCamelCase      (			UpperCamelCase_				:    str			,				UpperCamelCase_				:    Union[str, Any]						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  hf_model.state_dict()
						lowerCAmelCase__			  =  {}

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

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

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

												lowerCAmelCase__			  =  nn.Parameter(torch.from_numpy(UpperCamelCase_						)						)

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

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

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

												lowerCAmelCase__			  =  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 		_UpperCamelCase      (			UpperCamelCase_				:    Tuple			,				UpperCamelCase_				:    List[str]						)     ->      str:

						"""simple docstring"""



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

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

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

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

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

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any						)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  {}
						lowerCAmelCase__			  =  []

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

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







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    int						)     ->      Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  CLIP(**UpperCamelCase_						)
						torch_model.eval()
						lowerCAmelCase__			  =  torch_model.state_dict()

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

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

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

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

												lowerCAmelCase__			  =  v

						lowerCAmelCase__			  =  _convert_attn_layers(UpperCamelCase_						)
						new_torch_params.update(UpperCamelCase_						)
						lowerCAmelCase__			  =  {}

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

						return torch_clip_params, torch_model, attn_params







@torch.no_grad()
def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    List[Any]=None						)     ->      Any:

						"""simple docstring"""



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

						if config_path is not None:
												lowerCAmelCase__			  =  OwlViTConfig.from_pretrained(UpperCamelCase_						)
						else:
												lowerCAmelCase__			  =  OwlViTConfig()

						lowerCAmelCase__			  =  OwlViTModel(UpperCamelCase_						).eval()
						lowerCAmelCase__			  =  OwlViTForObjectDetection(UpperCamelCase_						).eval()

						copy_text_model_and_projection(UpperCamelCase_			,				UpperCamelCase_						)
						copy_vision_model_and_projection(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  pt_backbone.logit_scale
						copy_flax_attn_params(UpperCamelCase_			,				UpperCamelCase_						)

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

						# Initialize processor
						lowerCAmelCase__			  =  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__":
					__snake_case			:			Union[str, 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."""
					)
					__snake_case			:			Any			   =   parser.parse_args()

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

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

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





