
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case :		int							      =  logging.get_logger(__name__)

snake_case :		Any							      =  {
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
snake_case :		Optional[Any]							      =  [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  List[str]      ,   _snake_case      :  Dict      ,   _snake_case      :  Optional[Any]   )				->		Tuple:







	'''simple docstring'''


	for attribute in key.split("."   ):
		__magic_name__		:		Tuple	   =     getattr(A__      ,   A__   )

	if weight_type is not None:
		__magic_name__		:		Optional[Any]	   =     getattr(A__      ,   A__   ).shape
	else:
		__magic_name__		:		int	   =     hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    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":
		__magic_name__		:		Any	   =     value
	elif weight_type == "weight_g":
		__magic_name__		:		Tuple	   =     value
	elif weight_type == "weight_v":
		__magic_name__		:		List[Any]	   =     value
	elif weight_type == "bias":
		__magic_name__		:		Optional[int]	   =     value
	else:
		__magic_name__		:		Dict	   =     value

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






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  int   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     []
	__magic_name__		:		str	   =     fairseq_model.state_dict()

	__magic_name__		:		Dict	   =     hf_model.unispeech_sat.feature_extractor

	for name, value in fairseq_dict.items():
		__magic_name__		:		Tuple	   =     False
		if "conv_layers" in name:
			load_conv_layer(
			    A__      ,   A__      ,   A__      ,   A__      ,   hf_model.config.feat_extract_norm == "group"      ,   )
			__magic_name__		:		List[str]	   =     True
		else:
			for key, mapped_key in MAPPING.items():
				__magic_name__		:		List[str]	   =     "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split("w2v_model."   )[-1] == name.split("."   )[0]:
					if "layer_norm_for_extract" in name and (".".join(name.split("."   )[:-1]   ) != key):
						# special case since naming is very similar
						continue
					__magic_name__		:		Tuple	   =     True
					if "*" in mapped_key:
						__magic_name__		:		List[str]	   =     name.split(A__   )[0].split("."   )[-2]
						__magic_name__		:		Optional[Any]	   =     mapped_key.replace("*"      ,   A__   )
					if "weight_g" in name:
						__magic_name__		:		int	   =     "weight_g"
					elif "weight_v" in name:
						__magic_name__		:		int	   =     "weight_v"
					elif "bias" in name:
						__magic_name__		:		List[Any]	   =     "bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						__magic_name__		:		Tuple	   =     "weight"
					else:
						__magic_name__		:		Optional[Any]	   =     None
					set_recursively(A__      ,   A__      ,   A__      ,   A__      ,   A__   )
				continue
		if not is_used:
			unused_weights.append(A__   )

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






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  Tuple      ,   _snake_case      :  Optional[int]      ,   _snake_case      :  List[str]      ,   _snake_case      :  List[Any]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     full_name.split("conv_layers."   )[-1]
	__magic_name__		:		Any	   =     name.split("."   )
	__magic_name__		:		Union[str, Any]	   =     int(items[0]   )
	__magic_name__		:		Dict	   =     int(items[1]   )

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''   )
			__magic_name__		:		List[str]	   =     value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''   )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''   )
			__magic_name__		:		Optional[int]	   =     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:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''   )
			__magic_name__		:		str	   =     value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''   )
			__magic_name__		:		Optional[int]	   =     value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
	else:
		unused_weights.append(A__   )






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Tuple      ,   _snake_case      :  str      ,   _snake_case      :  int=None      ,   _snake_case      :  Optional[int]=None      ,   _snake_case      :  Optional[int]=True   )				->		Any:







	'''simple docstring'''


	if config_path is not None:
		__magic_name__		:		List[Any]	   =     UniSpeechSatConfig.from_pretrained(A__   )
	else:
		__magic_name__		:		str	   =     UniSpeechSatConfig()

	__magic_name__		:		List[Any]	   =     ""

	if is_finetuned:
		__magic_name__		:		Dict	   =     UniSpeechSatForCTC(A__   )
	else:
		__magic_name__		:		Tuple	   =     UniSpeechSatForPreTraining(A__   )

	__magic_name__   ,    __magic_name__   ,    __magic_name__		:		Optional[int]	   =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]      ,   arg_overrides={"data": "/".join(dict_path.split("/"   )[:-1]   )}   )
	__magic_name__		:		str	   =     model[0].eval()

	recursively_load_weights(A__      ,   A__   )

	hf_wavavec.save_pretrained(A__   )


if __name__ == "__main__":
				snake_case :		Tuple							      =  argparse.ArgumentParser()
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
				parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				parser.add_argument(
				    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
				)
				snake_case :		List[Any]							      =  parser.parse_args()
				convert_unispeech_sat_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)



'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}






class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[str]							=			'convnextv2'






  def __init__(      self   :     Tuple					,lowercase__   :     Dict=3					,lowercase__   :     int=4					,lowercase__   :     Optional[int]=4					,lowercase__   :     Optional[Any]=None					,lowercase__   :     List[str]=None					,lowercase__   :     Tuple="gelu"					,lowercase__   :     Dict=0.0_2					,lowercase__   :     List[Any]=1e-1_2					,lowercase__   :     Optional[int]=0.0					,lowercase__   :     int=2_2_4					,lowercase__   :     int=None					,lowercase__   :     Tuple=None					,**lowercase__   :     List[str]					,):
   super().__init__(**lowercase__			)

   __lowercase							  = num_channels
   __lowercase							  = patch_size
   __lowercase							  = num_stages
   __lowercase							  = [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
   __lowercase							  = [3, 3, 9, 3] if depths is None else depths
   __lowercase							  = hidden_act
   __lowercase							  = initializer_range
   __lowercase							  = layer_norm_eps
   __lowercase							  = drop_path_rate
   __lowercase							  = image_size
   __lowercase							  = ['''stem'''] + [F"stage{idx}" for idx in range(1					,len(self.depths			) + 1			)]
   __lowercase  ,	__lowercase							  = get_aligned_output_features_output_indices(
       out_features=lowercase__					,out_indices=lowercase__					,stage_names=self.stage_names			)

