




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()
__lowerCamelCase   :						str				=			logging.get_logger(__name__)

__lowerCamelCase   :						str				=			{
    '''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''',
}
__lowerCamelCase   :						List[Any]				=			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]







def 		lowercase__   (		__A:	List[str]							,__A:	Any							,__A:	Optional[Any]							,__A:	List[str]							,__A:	Optional[Any]				):





					'''simple docstring'''






					for attribute in key.split('''.'''				):
										__magic_name__							:	List[Any]    							=							getattr(__A							,__A				)

					if weight_type is not None:
										__magic_name__							:	List[str]    							=							getattr(__A							,__A				).shape
					else:
										__magic_name__							:	Any    							=							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":
										__magic_name__							:	Tuple    							=							value
					elif weight_type == "weight_g":
										__magic_name__							:	Any    							=							value
					elif weight_type == "weight_v":
										__magic_name__							:	str    							=							value
					elif weight_type == "bias":
										__magic_name__							:	List[Any]    							=							value
					else:
										__magic_name__							:	Union[str, Any]    							=							value

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







def 		lowercase__   (		__A:	List[Any]							,__A:	Optional[Any]				):





					'''simple docstring'''






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

					__magic_name__							:	Any    							=							hf_model.feature_extractor
					__magic_name__							:	int    							=							hf_model.adapter

					for name, value in fairseq_dict.items():
										__magic_name__							:	List[Any]    							=							False
										if "conv_layers" in name:
															load_conv_layer(
															    __A							,__A							,__A							,__A							,hf_model.config.feat_extract_norm == '''group'''							,)
															__magic_name__							:	List[Any]    							=							True
										elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.''']				):
															load_adapter(__A							,__A							,__A							,__A				)
															__magic_name__							:	int    							=							True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
																									__magic_name__							:	List[Any]    							=							True
																									if "*" in mapped_key:
																														__magic_name__							:	Union[str, Any]    							=							name.split(__A				)[0].split('''.'''				)[-2]
																														__magic_name__							:	Union[str, Any]    							=							mapped_key.replace('''*'''							,__A				)
																									if "weight_g" in name:
																														__magic_name__							:	List[str]    							=							'''weight_g'''
																									elif "weight_v" in name:
																														__magic_name__							:	int    							=							'''weight_v'''
																									elif "bias" in name:
																														__magic_name__							:	Tuple    							=							'''bias'''
																									elif "weight" in name:
																														__magic_name__							:	List[str]    							=							'''weight'''
																									else:
																														__magic_name__							:	Tuple    							=							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 		lowercase__   (		__A:	Optional[int]							,__A:	int							,__A:	List[Any]							,__A:	Tuple							,__A:	Tuple				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							full_name.split('''conv_layers.'''				)[-1]
					__magic_name__							:	int    							=							name.split('''.'''				)
					__magic_name__							:	List[Any]    							=							int(items[0]				)
					__magic_name__							:	List[Any]    							=							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.'''
															)
															__magic_name__							:	str    							=							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.'''
															)
															__magic_name__							:	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:
															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."
															)
															__magic_name__							:	Union[str, 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.'''
															)
															__magic_name__							:	List[str]    							=							value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
					else:
										unused_weights.append(__A				)







def 		lowercase__   (		__A:	Optional[int]							,__A:	str							,__A:	List[str]							,__A:	List[str]				):





					'''simple docstring'''






					__magic_name__							:	int    							=							full_name.split('''adaptor.'''				)[-1]
					__magic_name__							:	str    							=							name.split('''.'''				)

					if items[1].isdigit():
										__magic_name__							:	Any    							=							int(items[1]				)
					else:
										__magic_name__							:	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.'''
																				__magic_name__							:	Dict    							=							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.'''
																				__magic_name__							:	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.'''
																				__magic_name__							:	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.'''
																				__magic_name__							:	List[Any]    							=							value
																				logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''				)
					elif isinstance(__A							,__A				):
										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.'''
															__magic_name__							:	int    							=							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.'''
															__magic_name__							:	List[Any]    							=							value
															logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''				)
					else:
										unused_weights.append(__A				)







def 		lowercase__   (		__A:	Tuple				):





					'''simple docstring'''






					__magic_name__							:	Optional[Any]    							=							emb.weight.shape
					__magic_name__							:	Tuple    							=							nn.Linear(__A							,__A							,bias=__A				)
					__magic_name__							:	Optional[Any]    							=							emb.weight.data
					return lin_layer







@torch.no_grad()
def 		lowercase__   (		__A:	Optional[int]							,__A:	Dict							,__A:	Dict							,__A:	List[str]							,__A:	str							,__A:	Optional[int]							,__A:	Dict							,__A:	Optional[Any]							,__A:	Tuple							,__A:	Any							,__A:	Optional[int]							,):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							WavaVecaConfig.from_pretrained(
					    __A							,add_adapter=__A							,adapter_stride=__A							,adapter_kernel_size=__A							,use_auth_token=__A							,output_hidden_size=__A							,)
					__magic_name__							:	Dict    							=							MBartConfig.from_pretrained(__A				)

					# load model
					__magic_name__							:	List[Any]    							=							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,
					    }							,)
					__magic_name__							:	List[Any]    							=							model[0].eval()

					# load feature extractor
					__magic_name__							:	Optional[Any]    							=							WavaVecaFeatureExtractor.from_pretrained(__A							,use_auth_token=__A				)

					# set weights for wav2vec2 encoder
					__magic_name__							:	Any    							=							WavaVecaModel(__A				)

					recursively_load_weights_wavaveca(model.encoder							,__A				)

					# load decoder weights
					__magic_name__							:	List[str]    							=							MBartForCausalLM(__A				)
					__magic_name__							:	Union[str, Any]    							=							hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,strict=__A				)
					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}'''				)

					__magic_name__							:	str    							=							SpeechEncoderDecoderModel(encoder=__A							,decoder=__A				)
					__magic_name__							:	Any    							=							False

					__magic_name__							:	Optional[Any]    							=							MBartaaTokenizer(__A				)
					tokenizer.save_pretrained(__A				)

					__magic_name__							:	Optional[Any]    							=							hf_wavavec.config.to_dict()
					__magic_name__							:	Optional[int]    							=							tokenizer.pad_token_id
					__magic_name__							:	Dict    							=							tokenizer.bos_token_id
					__magic_name__							:	List[str]    							=							tokenizer.eos_token_id
					__magic_name__							:	List[str]    							=							'''mbart50'''
					__magic_name__							:	str    							=							'''wav2vec2'''

					__magic_name__							:	Optional[Any]    							=							tokenizer.eos_token_id
					__magic_name__							:	Tuple    							=							2_5_0_0_0_4
					__magic_name__							:	Dict    							=							tokenizer.eos_token_id

					__magic_name__							:	Union[str, Any]    							=							SpeechEncoderDecoderConfig.from_dict(__A				)

					hf_wavavec.save_pretrained(__A				)
					feature_extractor.save_pretrained(__A				)


if __name__ == "__main__":
			__lowerCamelCase   :						Union[str, 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=10_24, type=int, help='''encoder output dim''')
			parser.add_argument('''--start_token_id''', default=25_00_04, type=int, help='''`decoder_start_token_id` of model config''')

			__lowerCamelCase   :						Tuple				=			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,
			)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCamelCase   :						int				=			{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						int				=			[
	    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SwinForImageClassification''',
	    '''SwinForMaskedImageModeling''',
	    '''SwinModel''',
	    '''SwinPreTrainedModel''',
	    '''SwinBackbone''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[int]				=			[
	    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSwinForImageClassification''',
	    '''TFSwinForMaskedImageModeling''',
	    '''TFSwinModel''',
	    '''TFSwinPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Union[str, Any]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



