






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_					:		Tuple           = {
    '''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   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple      ,     __magic_name__   :    str=""      ,     __magic_name__   :    List[Any]="/"   )						->     Any:






	"""simple docstring"""



	UpperCamelCase	:Any		=      []

	for k, v in params.items():
		UpperCamelCase	:Dict		=      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   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Optional[int]   )						->     int:






	"""simple docstring"""



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

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple      ,     __magic_name__   :    Dict   )						->     Optional[int]:






	"""simple docstring"""



	UpperCamelCase	:int		=      pt_attn_layer.in_proj_weight.chunk(3      ,     dim=0   )
	UpperCamelCase	:List[str]		=      pt_attn_layer.in_proj_bias.chunk(3      ,     dim=0   )

	UpperCamelCase	:Tuple		=      pt_attn_layer.out_proj.weight
	UpperCamelCase	:Dict		=      pt_attn_layer.out_proj.bias

	UpperCamelCase	:Optional[Any]		=      q_proj
	UpperCamelCase	:List[str]		=      q_proj_bias

	UpperCamelCase	:List[str]		=      k_proj
	UpperCamelCase	:int		=      k_proj_bias

	UpperCamelCase	:Tuple		=      v_proj
	UpperCamelCase	:Optional[Any]		=      v_proj_bias

	UpperCamelCase	:Tuple		=      out_proj_weights
	UpperCamelCase	:Tuple		=      out_proj_bias

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Union[str, Any]      ,     __magic_name__   :    int   )						->     Dict:






	"""simple docstring"""



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

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Dict      ,     __magic_name__   :    Optional[int]   )						->     Any:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      pt_linear.weight
	UpperCamelCase	:int		=      pt_linear.bias

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    List[str]      ,     __magic_name__   :    List[Any]   )						->     List[str]:






	"""simple docstring"""



	copy_linear(hf_layer.layer_norma      ,     pt_layer.ln_a   )
	copy_linear(hf_layer.layer_norma      ,     pt_layer.ln_a   )

	# copy MLP
	copy_mlp(hf_layer.mlp      ,     pt_layer.mlp   )

	# copy attn
	copy_attn_layer(hf_layer.self_attn      ,     pt_layer.attn   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple      ,     __magic_name__   :    Dict   )						->     Optional[int]:






	"""simple docstring"""



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

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Optional[Any]      ,     __magic_name__   :    Union[str, Any]   )						->     List[Any]:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      pt_model.token_embedding.weight
	UpperCamelCase	:List[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   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple      ,     __magic_name__   :    Any   )						->     Optional[int]:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      pt_model.text_projection.data.T

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

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Dict      ,     __magic_name__   :    Dict   )						->     Optional[Any]:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      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	:Tuple		=      pt_model.visual.conva.weight.data
	UpperCamelCase	:Dict		=      pt_model.visual.class_embedding
	UpperCamelCase	:Tuple		=      pt_model.visual.positional_embedding.data

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

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple      ,     __magic_name__   :    Tuple   )						->     List[str]:






	"""simple docstring"""



	UpperCamelCase	:int		=      flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]   )

	UpperCamelCase	:Tuple		=      torch.from_numpy(flax_class_token_params["""scale"""]   )
	UpperCamelCase	:Optional[int]		=      torch.from_numpy(flax_class_token_params["""bias"""]   )
	UpperCamelCase	:Union[str, Any]		=      nn.Parameter(UpperCAmelCase_   )
	UpperCamelCase	:Dict		=      nn.Parameter(UpperCAmelCase_   )

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Union[str, Any]      ,     __magic_name__   :    Union[str, Any]   )						->     Dict:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      hf_model.state_dict()
	UpperCamelCase	:Optional[int]		=      {}

	# Rename class prediction head flax params to pytorch HF
	UpperCamelCase	:Dict		=      flatten_nested_dict(flax_params["""class_head"""]   )

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

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

		UpperCamelCase	:Union[str, Any]		=      nn.Parameter(torch.from_numpy(UpperCAmelCase_   )   )

	# Rename box prediction box flax params to pytorch HF
	UpperCamelCase	:Optional[int]		=      flatten_nested_dict(flax_params["""obj_box_head"""]   )

	for flax_key, v in flax_box_params.items():
		UpperCamelCase	:Optional[Any]		=      flax_key.replace("""/"""      ,     """."""   )
		UpperCamelCase	:Union[str, Any]		=      torch_key.replace(""".kernel"""      ,     """.weight"""   )
		UpperCamelCase	:Tuple		=      torch_key.replace("""_"""      ,     """"""   ).lower()
		UpperCamelCase	:int		=      'box_head.' + torch_key

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

		UpperCamelCase	:Optional[Any]		=      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   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Tuple      ,     __magic_name__   :    Union[str, Any]   )						->     Union[str, Any]:






	"""simple docstring"""



	for k, v in flax_attn_params.items():
		if k.startswith("""transformer"""   ):
			UpperCamelCase	:Any		=      k.replace("""transformer.resblocks"""      ,     """text_model.encoder.layers"""   )
		else:
			UpperCamelCase	:Dict		=      k.replace("""visual.transformer.resblocks"""      ,     """vision_model.encoder.layers"""   )

		UpperCamelCase	:List[str]		=      torch_key.replace("""attn"""      ,     """self_attn"""   )
		UpperCamelCase	:Dict		=      torch_key.replace("""key"""      ,     """k_proj"""   )
		UpperCamelCase	:Any		=      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	:Any		=      v.shape[0] * v.shape[1]
			UpperCamelCase	:Tuple		=      v.reshape(UpperCAmelCase_   )

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

		if "weight" in torch_key and "out" not in torch_key:
			UpperCamelCase	:Optional[Any]		=      (v.shape[0], v.shape[1] * v.shape[2])
			UpperCamelCase	:Tuple		=      v.reshape(UpperCAmelCase_   ).T

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

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Dict   )						->     List[Any]:






	"""simple docstring"""



	UpperCamelCase	:List[str]		=      {}
	UpperCamelCase	:List[str]		=      []

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

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

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str      ,     __magic_name__   :    str   )						->     Tuple:






	"""simple docstring"""



	UpperCamelCase	:Union[str, Any]		=      CLIP(**UpperCAmelCase_   )
	torch_model.eval()
	UpperCamelCase	:str		=      torch_model.state_dict()

	UpperCamelCase	:str		=      flatten_nested_dict(flax_params["""backbone"""]["""clip"""]   )
	UpperCamelCase	:Optional[int]		=      {}

	for flax_key, v in flax_clip_params.items():
		UpperCamelCase	:Any		=      flax_key.replace("""/"""      ,     """."""   )
		UpperCamelCase	:int		=      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	:Optional[int]		=      torch_key[5:]

		UpperCamelCase	:Optional[int]		=      torch_key.replace("""text_projection.kernel"""      ,     """text_projection"""   )
		UpperCamelCase	:Optional[int]		=      torch_key.replace("""visual.proj.kernel"""      ,     """visual.proj"""   )
		UpperCamelCase	:Dict		=      torch_key.replace(""".scale"""      ,     """.weight"""   )
		UpperCamelCase	:List[str]		=      torch_key.replace(""".kernel"""      ,     """.weight"""   )

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

		UpperCamelCase	:Dict		=      v

	UpperCamelCase	:int		=      _convert_attn_layers(UpperCAmelCase_   )
	new_torch_params.update(UpperCAmelCase_   )
	UpperCamelCase	:List[Any]		=      {}

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

	return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Dict      ,     __magic_name__   :    Optional[Any]      ,     __magic_name__   :    Union[str, Any]      ,     __magic_name__   :    Dict      ,     __magic_name__   :    Optional[int]=None   )						->     str:






	"""simple docstring"""



	UpperCamelCase	:Optional[Any]		=      Repository(UpperCAmelCase_      ,     clone_from=f"""google/{pytorch_dump_folder_path}"""   )
	repo.git_pull()

	if config_path is not None:
		UpperCamelCase	:int		=      OwlViTConfig.from_pretrained(UpperCAmelCase_   )
	else:
		UpperCamelCase	:Optional[Any]		=      OwlViTConfig()

	UpperCamelCase	:List[str]		=      OwlViTModel(UpperCAmelCase_   ).eval()
	UpperCamelCase	:List[Any]		=      OwlViTForObjectDetection(UpperCAmelCase_   ).eval()

	copy_text_model_and_projection(UpperCAmelCase_      ,     UpperCAmelCase_   )
	copy_vision_model_and_projection(UpperCAmelCase_      ,     UpperCAmelCase_   )
	UpperCamelCase	:int		=      pt_backbone.logit_scale
	copy_flax_attn_params(UpperCAmelCase_      ,     UpperCAmelCase_   )

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

	# Initialize processor
	UpperCamelCase	:List[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__":
	UpperCAmelCase_					:		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.'''
	)
	UpperCAmelCase_					:		Optional[int]           = parser.parse_args()

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

	# Load from checkpoint and convert params to float-32
	UpperCAmelCase_					:		str           = 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[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)



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCAmelCase__             =		{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """mask_emb""": """masked_spec_embed""",
}






def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[Any]						)    ->					Any:




	'''simple docstring'''




	for attribute in key.split('.'						):
		_UpperCamelCase :		Any								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						)

	if weight_type is not None:
		_UpperCamelCase :		List[str]								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						).shape
	else:
		_UpperCamelCase :		Optional[int]								=    hf_pointer.shape

	assert hf_shape == value.shape, (
	    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
	    F''' {value.shape} for {full_name}'''
	)

	if weight_type == "weight":
		_UpperCamelCase :		Tuple								=    value
	elif weight_type == "weight_g":
		_UpperCamelCase :		Optional[Any]								=    value
	elif weight_type == "weight_v":
		_UpperCamelCase :		Tuple								=    value
	elif weight_type == "bias":
		_UpperCamelCase :		Any								=    value
	else:
		_UpperCamelCase :		Tuple								=    value

	logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    []
	_UpperCamelCase :		List[Any]								=    fairseq_model.state_dict()

	_UpperCamelCase :		int								=    hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_UpperCamelCase :		Dict								=    False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							hf_model.config.feat_extract_norm == 'group'				,							)
			_UpperCamelCase :		Union[str, Any]								=    True
		else:
			for key, mapped_key in MAPPING.items():
				_UpperCamelCase :		Tuple								=    mapped_key

				if key in name:
					_UpperCamelCase :		List[Any]								=    True
					if "*" in mapped_key:
						_UpperCamelCase :		List[Any]								=    name.split(UpperCAmelCase_						)[0].split('.'						)[-2]
						_UpperCamelCase :		Optional[Any]								=    mapped_key.replace('*'				,							UpperCAmelCase_						)
					if "weight_g" in name:
						_UpperCamelCase :		List[Any]								=    'weight_g'
					elif "weight_v" in name:
						_UpperCamelCase :		Tuple								=    'weight_v'
					elif "weight" in name:
						_UpperCamelCase :		Optional[Any]								=    'weight'
					elif "bias" in name:
						_UpperCamelCase :		List[Any]								=    'bias'
					else:
						_UpperCamelCase :		str								=    None
					set_recursively(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						)
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_						)

	logger.warning(F'''Unused weights: {unused_weights}'''						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Union[str, Any]				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					Dict:




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    full_name.split('conv_layers.'						)[-1]
	_UpperCamelCase :		str								=    name.split('.'						)
	_UpperCamelCase :		List[Any]								=    int(items[0]						)
	_UpperCamelCase :		Tuple								=    int(items[1]						)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
			)
			_UpperCamelCase :		Any								=    value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
			)
			_UpperCamelCase :		List[str]								=    value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
			    " found."
			)
			_UpperCamelCase :		str								=    value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'''{full_name} has size {value.shape}, but'''
			    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
			)
			_UpperCamelCase :		Dict								=    value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
	else:
		unused_weights.append(UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple						)    ->					List[str]:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    HubertConfig()
	_UpperCamelCase :		List[str]								=    model.config

	_UpperCamelCase :		Any								=    fs_config.activation_dropout
	_UpperCamelCase :		Tuple								=    False
	_UpperCamelCase :		Tuple								=    fs_config.attention_dropout
	_UpperCamelCase :		Dict								=    False
	_UpperCamelCase :		Optional[Any]								=    eval(fs_config.extractor_conv_feature_layers						)
	_UpperCamelCase :		Dict								=    [x[0] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[1] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[2] for x in conv_layers]
	_UpperCamelCase :		Dict								=    'gelu'
	_UpperCamelCase :		List[Any]								=    'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_UpperCamelCase :		List[Any]								=    False
	_UpperCamelCase :		List[Any]								=    0.0
	_UpperCamelCase :		Optional[int]								=    0.0
	_UpperCamelCase :		Optional[int]								=    fs_config.activation_fn
	_UpperCamelCase :		Optional[int]								=    fs_config.dropout
	_UpperCamelCase :		Union[str, Any]								=    fs_config.encoder_embed_dim
	_UpperCamelCase :		Tuple								=    0.0_2
	_UpperCamelCase :		int								=    fs_config.encoder_ffn_embed_dim
	_UpperCamelCase :		int								=    1e-5
	_UpperCamelCase :		Optional[Any]								=    0.0
	_UpperCamelCase :		List[str]								=    fs_config.encoder_attention_heads
	_UpperCamelCase :		List[str]								=    fs_config.conv_pos_groups
	_UpperCamelCase :		Union[str, Any]								=    fs_config.conv_pos
	_UpperCamelCase :		int								=    len(UpperCAmelCase_						)
	_UpperCamelCase :		Tuple								=    fs_config.encoder_layers

	return config






@torch.no_grad()
def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict=None						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    distilhubert().model.model

	if config_path is not None:
		_UpperCamelCase :		Optional[Any]								=    HubertConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Optional[int]								=    convert_config(UpperCAmelCase_						)
	_UpperCamelCase :		List[Any]								=    model.eval()

	_UpperCamelCase :		List[Any]								=    WavaVecaFeatureExtractor(
	    feature_size=1				,							sampling_rate=1_6_0_0_0				,							padding_value=0				,							do_normalize=UpperCAmelCase_				,							return_attention_mask=UpperCAmelCase_				,							)
	_UpperCamelCase :		Optional[Any]								=    HubertModel(UpperCAmelCase_						)

	recursively_load_weights(UpperCAmelCase_				,							UpperCAmelCase_						)

	feature_extractor.save_pretrained(UpperCAmelCase_						)
	hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
							lowerCAmelCase__             =		argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							lowerCAmelCase__             =		parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)




