






import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_A			:    Union[str, Any]					  =logging.get_logger(__name__)

_A			:    List[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''',
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}
_A			:    List[str]					  =[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Dict:
							for attribute in key.split("""."""   ):
														lowerCamelCase__      :			List[Any] 						= getattr(UpperCamelCase       ,    UpperCamelCase   )

							if weight_type is not None:
														lowerCamelCase__      :			Tuple 						= getattr(UpperCamelCase       ,    UpperCamelCase   ).shape
							else:
														lowerCamelCase__      :			List[str] 						= 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__      :			List[str] 						= value
							elif weight_type == "weight_g":
														lowerCamelCase__      :			List[Any] 						= value
							elif weight_type == "weight_v":
														lowerCamelCase__      :			List[str] 						= value
							elif weight_type == "bias":
														lowerCamelCase__      :			Optional[Any] 						= value
							else:
														lowerCamelCase__      :			Optional[Any] 						= value

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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			List[str] 						= []
							lowerCamelCase__      :			List[str] 						= fairseq_model.state_dict()

							lowerCamelCase__      :			Tuple 						= hf_model.feature_extractor
							lowerCamelCase__      :			Dict 						= hf_model.adapter

							for name, value in fairseq_dict.items():
														lowerCamelCase__      :			Optional[int] 						= False
														if "conv_layers" in name:
																					load_conv_layer(
																					    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    hf_model.config.feat_extract_norm == """group"""       ,    )
																					lowerCamelCase__      :			List[str] 						= True
														elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]   ):
																					load_adapter(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
																					lowerCamelCase__      :			str 						= True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("""w2v_model."""   )[-1] == name.split("""."""   )[0]:
																																			lowerCamelCase__      :			Dict 						= True
																																			if "*" in mapped_key:
																																										lowerCamelCase__      :			Dict 						= name.split(UpperCamelCase   )[0].split("""."""   )[-2]
																																										lowerCamelCase__      :			Union[str, Any] 						= mapped_key.replace("""*"""       ,    UpperCamelCase   )
																																			if "weight_g" in name:
																																										lowerCamelCase__      :			List[Any] 						= """weight_g"""
																																			elif "weight_v" in name:
																																										lowerCamelCase__      :			int 						= """weight_v"""
																																			elif "bias" in name:
																																										lowerCamelCase__      :			Tuple 						= """bias"""
																																			elif "weight" in name:
																																										lowerCamelCase__      :			Any 						= """weight"""
																																			else:
																																										lowerCamelCase__      :			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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> str:
							lowerCamelCase__      :			Optional[int] 						= full_name.split("""conv_layers."""   )[-1]
							lowerCamelCase__      :			Optional[Any] 						= name.split("""."""   )
							lowerCamelCase__      :			Dict 						= int(items[0]   )
							lowerCamelCase__      :			Dict 						= 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__      :			Union[str, 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.'''
																					)
																					lowerCamelCase__      :			Optional[Any] 						= 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__      :			Any 						= 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__      :			Tuple 						= value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
							else:
														unused_weights.append(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Optional[int]:
							lowerCamelCase__      :			Any 						= full_name.split("""adaptor."""   )[-1]
							lowerCamelCase__      :			Union[str, Any] 						= name.split("""."""   )

							if items[1].isdigit():
														lowerCamelCase__      :			Union[str, Any] 						= int(items[1]   )
							else:
														lowerCamelCase__      :			Optional[Any] 						= None

							if "adaptor" not in full_name:
														if "proj_ln" in full_name:
																					# has to be layer norm
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.bias.data.shape
																												), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
																												lowerCamelCase__      :			Any 						= value
																												logger.info(f'''Adapter proj layer norm bias was initialized from {full_name}.'''   )
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj_layer_norm.weight.data.shape
																												), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
																												lowerCamelCase__      :			str 						= value
														else:
																					# has to be projection layer
																					if "bias" in name:
																												assert (
																												    value.shape == adapter.proj.bias.data.shape
																												), f'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
																												lowerCamelCase__      :			List[Any] 						= value
																												logger.info(f'''Adapter proj layer bias was initialized from {full_name}.'''   )
																					if "weight" in name:
																												assert (
																												    value.shape == adapter.proj.weight.data.shape
																												), f'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
																												lowerCamelCase__      :			str 						= value
																												logger.info(f'''Adapter proj layer weight was initialized from {full_name}.'''   )
							elif isinstance(UpperCamelCase       ,    UpperCamelCase   ):
														if "bias" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																					), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
																					lowerCamelCase__      :			Union[str, Any] 						= value
																					logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''   )
														elif "weight" in name:
																					assert (
																					    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																					), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
																					lowerCamelCase__      :			List[Any] 						= value
																					logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.'''   )
							else:
														unused_weights.append(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[Any]:
							lowerCamelCase__      :			List[str] 						= emb.weight.shape
							lowerCamelCase__      :			Optional[Any] 						= nn.Linear(UpperCamelCase       ,    UpperCamelCase       ,    bias=UpperCamelCase   )
							lowerCamelCase__      :			List[Any] 						= emb.weight.data
							return lin_layer





@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    )   -> Any:
							lowerCamelCase__      :			Union[str, Any] 						= WavaVecaConfig.from_pretrained(
							    UpperCamelCase       ,    add_adapter=UpperCamelCase       ,    adapter_stride=UpperCamelCase       ,    adapter_kernel_size=UpperCamelCase       ,    use_auth_token=UpperCamelCase       ,    output_hidden_size=UpperCamelCase       ,    )
							lowerCamelCase__      :			Optional[Any] 						= MBartConfig.from_pretrained(UpperCamelCase   )

							# load model
							lowerCamelCase__      :			List[str] 						= fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]       ,    arg_overrides={
							        """config_yaml""": config_yaml_path,
							        """data""": """/""".join(dict_path.split("""/"""   )[:-1]   ),
							        """w2v_path""": checkpoint_path,
							        """load_pretrained_decoder_from""": None,
							    }       ,    )
							lowerCamelCase__      :			Optional[Any] 						= model[0].eval()

							# load feature extractor
							lowerCamelCase__      :			List[str] 						= WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase       ,    use_auth_token=UpperCamelCase   )

							# set weights for wav2vec2 encoder
							lowerCamelCase__      :			Any 						= WavaVecaModel(UpperCamelCase   )

							recursively_load_weights_wavaveca(model.encoder       ,    UpperCamelCase   )

							# load decoder weights
							lowerCamelCase__      :			Optional[Any] 						= MBartForCausalLM(UpperCamelCase   )
							lowerCamelCase__      :			int 						= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       ,    strict=UpperCamelCase   )
							logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''   )
							logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''   )

							lowerCamelCase__      :			Union[str, Any] 						= SpeechEncoderDecoderModel(encoder=UpperCamelCase       ,    decoder=UpperCamelCase   )
							lowerCamelCase__      :			Tuple 						= False

							lowerCamelCase__      :			int 						= MBartaaTokenizer(UpperCamelCase   )
							tokenizer.save_pretrained(UpperCamelCase   )

							lowerCamelCase__      :			Optional[Any] 						= hf_wavavec.config.to_dict()
							lowerCamelCase__      :			int 						= tokenizer.pad_token_id
							lowerCamelCase__      :			Union[str, Any] 						= tokenizer.bos_token_id
							lowerCamelCase__      :			Optional[int] 						= tokenizer.eos_token_id
							lowerCamelCase__      :			List[str] 						= """mbart50"""
							lowerCamelCase__      :			List[str] 						= """wav2vec2"""

							lowerCamelCase__      :			Optional[int] 						= tokenizer.eos_token_id
							lowerCamelCase__      :			Tuple 						= 250004
							lowerCamelCase__      :			List[Any] 						= tokenizer.eos_token_id

							lowerCamelCase__      :			Optional[Any] 						= SpeechEncoderDecoderConfig.from_dict(UpperCamelCase   )

							hf_wavavec.save_pretrained(UpperCamelCase   )
							feature_extractor.save_pretrained(UpperCamelCase   )


if __name__ == "__main__":
			_A			:    Optional[Any]					  =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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
			parser.add_argument(
			    '''--encoder_config_path''',
			    default='''facebook/wav2vec2-xls-r-1b''',
			    type=str,
			    help='''Path to hf encoder wav2vec2 checkpoint config''',
			)
			parser.add_argument(
			    '''--decoder_config_path''',
			    default='''facebook/mbart-large-50-one-to-many-mmt''',
			    type=str,
			    help='''Path to hf decoder checkpoint config''',
			)
			parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
			parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
			parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
			parser.add_argument('''--encoder_output_dim''', default=1_024, type=int, help='''encoder output dim''')
			parser.add_argument('''--start_token_id''', default=250_004, type=int, help='''`decoder_start_token_id` of model config''')

			_A			:    List[Any]					  =parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    args.config_yaml_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    add_adapter=args.add_adapter,
			    adapter_kernel_size=args.adapter_kernel_size,
			    adapter_stride=args.adapter_stride,
			    decoder_start_token_id=args.start_token_id,
			    encoder_output_dim=args.encoder_output_dim,
			)









'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:    str					  =logging.get_logger(__name__)

_A			:    int					  ={
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
							a          = """roc_bert"""




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Any=30_522    ,							UpperCamelCase__:       Optional[Any]=768    ,							UpperCamelCase__:       Union[str, Any]=12    ,							UpperCamelCase__:       Tuple=12    ,							UpperCamelCase__:       Tuple=3_072    ,							UpperCamelCase__:       str="gelu"    ,							UpperCamelCase__:       List[Any]=0.1    ,							UpperCamelCase__:       List[str]=0.1    ,							UpperCamelCase__:       Dict=512    ,							UpperCamelCase__:       str=2    ,							UpperCamelCase__:       str=0.02    ,							UpperCamelCase__:       Tuple=1e-12    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       Union[str, Any]=0    ,							UpperCamelCase__:       List[Any]="absolute"    ,							UpperCamelCase__:       Any=None    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       Optional[int]=True    ,							UpperCamelCase__:       Union[str, Any]=768    ,							UpperCamelCase__:       int=910    ,							UpperCamelCase__:       Tuple=512    ,							UpperCamelCase__:       int=24_858    ,							UpperCamelCase__:       Optional[Any]=True    ,							**UpperCamelCase__:       Optional[Any]    ,							):
														lowerCamelCase__      :			Optional[Any] 						= vocab_size
														lowerCamelCase__      :			Tuple 						= max_position_embeddings
														lowerCamelCase__      :			List[Any] 						= hidden_size
														lowerCamelCase__      :			int 						= num_hidden_layers
														lowerCamelCase__      :			Tuple 						= num_attention_heads
														lowerCamelCase__      :			Any 						= intermediate_size
														lowerCamelCase__      :			List[str] 						= hidden_act
														lowerCamelCase__      :			str 						= hidden_dropout_prob
														lowerCamelCase__      :			Dict 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Union[str, Any] 						= initializer_range
														lowerCamelCase__      :			Tuple 						= type_vocab_size
														lowerCamelCase__      :			Optional[Any] 						= layer_norm_eps
														lowerCamelCase__      :			List[Any] 						= use_cache
														lowerCamelCase__      :			Tuple 						= enable_pronunciation
														lowerCamelCase__      :			Union[str, Any] 						= enable_shape
														lowerCamelCase__      :			Union[str, Any] 						= pronunciation_embed_dim
														lowerCamelCase__      :			Any 						= pronunciation_vocab_size
														lowerCamelCase__      :			int 						= shape_embed_dim
														lowerCamelCase__      :			Tuple 						= shape_vocab_size
														lowerCamelCase__      :			Optional[Any] 						= concat_input
														lowerCamelCase__      :			str 						= position_embedding_type
														lowerCamelCase__      :			Dict 						= classifier_dropout
														super().__init__(pad_token_id=UpperCamelCase__    ,							**UpperCamelCase__  )

