'''simple docstring'''




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__lowercase          =  {
    '''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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}






def 				snake_case__							(			_A:					Optional[int]      ,   _A:					Optional[int]      ,   _A:					Dict      ,   _A:					Optional[int]      ,   _A:					List[Any] )    ->	Tuple:

		'''simple docstring'''


		for attribute in key.split(""".""" ):
				lowerCAmelCase   					=  getattr(_A      ,   _A )

		if weight_type is not None:
				lowerCAmelCase   					=  getattr(_A      ,   _A ).shape
		else:
				lowerCAmelCase   					=  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":
				lowerCAmelCase   					=  value
		elif weight_type == "weight_g":
				lowerCAmelCase   					=  value
		elif weight_type == "weight_v":
				lowerCAmelCase   					=  value
		elif weight_type == "bias":
				lowerCAmelCase   					=  value
		else:
				lowerCAmelCase   					=  value

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






def 				snake_case__							(			_A:					List[Any]      ,   _A:					Union[str, Any]      ,   _A:					List[str] )    ->	List[Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  []
		lowerCAmelCase   					=  fairseq_model.state_dict()

		lowerCAmelCase   					=  hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				lowerCAmelCase   					=  False
				if "conv_layers" in name:
						load_conv_layer(
						    _A      ,   _A      ,   _A      ,   _A      ,   hf_model.config.feat_extract_norm == """group"""      ,   )
						lowerCAmelCase   					=  True
				else:
						for key, mapped_key in MAPPING.items():
								lowerCAmelCase   					=  """hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

								if key in name or (key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0] and not is_finetuned):
										lowerCAmelCase   					=  True
										if "*" in mapped_key:
												lowerCAmelCase   					=  name.split(_A )[0].split(""".""" )[-2]
												lowerCAmelCase   					=  mapped_key.replace("""*"""      ,   _A )
										if "weight_g" in name:
												lowerCAmelCase   					=  """weight_g"""
										elif "weight_v" in name:
												lowerCAmelCase   					=  """weight_v"""
										elif "weight" in name:
												lowerCAmelCase   					=  """weight"""
										elif "bias" in name:
												lowerCAmelCase   					=  """bias"""
										else:
												lowerCAmelCase   					=  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 				snake_case__							(			_A:					List[str]      ,   _A:					List[str]      ,   _A:					List[Any]      ,   _A:					Tuple      ,   _A:					List[Any] )    ->	Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  full_name.split("""conv_layers.""" )[-1]
		lowerCAmelCase   					=  name.split(""".""" )
		lowerCAmelCase   					=  int(items[0] )
		lowerCAmelCase   					=  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."
						)
						lowerCAmelCase   					=  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."
						)
						lowerCAmelCase   					=  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."
						)
						lowerCAmelCase   					=  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."
						)
						lowerCAmelCase   					=  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 				snake_case__							(			_A:					Any      ,   _A:					Union[str, Any]      ,   _A:					Optional[Any]=None      ,   _A:					Optional[int]=None      ,   _A:					List[str]=True )    ->	Any:

		'''simple docstring'''


		if config_path is not None:
				lowerCAmelCase   					=  HubertConfig.from_pretrained(_A )
		else:
				lowerCAmelCase   					=  HubertConfig()

		if is_finetuned:
				if dict_path:
						lowerCAmelCase   					=  Dictionary.load(_A )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						lowerCAmelCase   					=  target_dict.pad_index
						lowerCAmelCase   					=  target_dict.bos_index
						lowerCAmelCase   					=  target_dict.eos_index
						lowerCAmelCase   					=  len(target_dict.symbols )
						lowerCAmelCase   					=  os.path.join(_A      ,   """vocab.json""" )
						if not os.path.isdir(_A ):
								logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_A ) )
								return
						os.makedirs(_A      ,   exist_ok=_A )
						with open(_A      ,   """w"""      ,   encoding="""utf-8""" ) as vocab_handle:
								json.dump(target_dict.indices      ,   _A )
						lowerCAmelCase   					=  WavaVecaCTCTokenizer(
						    _A      ,   unk_token=target_dict.unk_word      ,   pad_token=target_dict.pad_word      ,   bos_token=target_dict.bos_word      ,   eos_token=target_dict.eos_word      ,   word_delimiter_token="""|"""      ,   do_lower_case=_A      ,   )
						lowerCAmelCase   					=  True if config.feat_extract_norm == """layer""" else False
						lowerCAmelCase   					=  WavaVecaFeatureExtractor(
						    feature_size=1      ,   sampling_rate=16000      ,   padding_value=0      ,   do_normalize=_A      ,   return_attention_mask=_A      ,   )
						lowerCAmelCase   					=  WavaVecaProcessor(feature_extractor=_A      ,   tokenizer=_A )
						processor.save_pretrained(_A )

				lowerCAmelCase   					=  HubertForCTC(_A )
		else:
				lowerCAmelCase   					=  HubertModel(_A )

		if is_finetuned:
				lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]      ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
		else:
				lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

		lowerCAmelCase   					=  model[0].eval()

		recursively_load_weights(_A      ,   _A      ,   _A )

		hf_wavavec.save_pretrained(_A )


if __name__ == "__main__":
						__lowercase          =  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'''
						)
						__lowercase          =  parser.parse_args()
						convert_hubert_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)





'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					warnings.warn(
					    """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
					    """ Please use DeformableDetrImageProcessor instead."""       ,    __lowerCAmelCase       ,    )
					super().__init__(*__lowerCAmelCase       ,    **__lowerCAmelCase)




