





"""simple docstring"""
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()
__UpperCamelCase     :						Union[str, Any]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Optional[int]						       =  {
    "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",
}
__UpperCamelCase     :						List[str]						       =  [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Dict			):




 for attribute in key.split('.'			):
  lowerCAmelCase    				= getattr(lowerCamelCase__  ,     lowerCamelCase__			)

 if weight_type is not None:
  lowerCAmelCase    				= getattr(lowerCamelCase__  ,     lowerCamelCase__			).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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    Dict			):
 lowerCAmelCase    				= []
 lowerCAmelCase    				= fairseq_model.state_dict()

 lowerCAmelCase    				= hf_model.feature_extractor
 lowerCAmelCase    				= hf_model.adapter

 for name, value in fairseq_dict.items():
  lowerCAmelCase    				= False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     hf_model.config.feat_extract_norm == 'group'  ,     )
   lowerCAmelCase    				= True
  elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']			):
   load_adapter(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__			)
   lowerCAmelCase    				= True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
     lowerCAmelCase    				= True
     if "*" in mapped_key:
      lowerCAmelCase    				= name.split(lowerCamelCase__			)[0].split('.'			)[-2]
      lowerCAmelCase    				= mapped_key.replace('*'  ,     lowerCamelCase__			)
     if "weight_g" in name:
      lowerCAmelCase    				= 'weight_g'
     elif "weight_v" in name:
      lowerCAmelCase    				= 'weight_v'
     elif "bias" in name:
      lowerCAmelCase    				= 'bias'
     elif "weight" in name:
      lowerCAmelCase    				= 'weight'
     else:
      lowerCAmelCase    				= None
     set_recursively(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__			)
    continue
  if not is_used:
   unused_weights.append(lowerCamelCase__			)

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

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Union[str, Any]  ,     _UpperCAmelCase					:    Any			):
 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(lowerCamelCase__			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    List[str]			):
 lowerCAmelCase    				= full_name.split('adaptor.'			)[-1]
 lowerCAmelCase    				= name.split('.'			)

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

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 lowerCAmelCase				,lowerCAmelCase    				= emb.weight.shape
 lowerCAmelCase    				= nn.Linear(lowerCamelCase__  ,     lowerCamelCase__  ,     bias=lowerCamelCase__			)
 lowerCAmelCase    				= emb.weight.data
 return lin_layer

@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Any  ,     _UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    Dict  ,     _UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Optional[int]  ,     _UpperCAmelCase					:    Optional[Any]  ,     _UpperCAmelCase					:    List[Any]  ,     _UpperCAmelCase					:    Optional[Any]  ,     ):
 lowerCAmelCase    				= WavaVecaConfig.from_pretrained(
     lowerCamelCase__  ,     add_adapter=lowerCamelCase__  ,     adapter_stride=lowerCamelCase__  ,     adapter_kernel_size=lowerCamelCase__  ,     use_auth_token=lowerCamelCase__  ,     output_hidden_size=lowerCamelCase__  ,     )
 lowerCAmelCase    				= MBartConfig.from_pretrained(lowerCamelCase__			)

 # load model
 lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= 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    				= model[0].eval()

 # load feature extractor
 lowerCAmelCase    				= WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__  ,     use_auth_token=lowerCamelCase__			)

 # set weights for wav2vec2 encoder
 lowerCAmelCase    				= WavaVecaModel(lowerCamelCase__			)

 recursively_load_weights_wavaveca(model.encoder  ,     lowerCamelCase__			)

 # load decoder weights
 lowerCAmelCase    				= MBartForCausalLM(lowerCamelCase__			)
 lowerCAmelCase				,lowerCAmelCase    				= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()  ,     strict=lowerCamelCase__			)
 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    				= SpeechEncoderDecoderModel(encoder=lowerCamelCase__  ,     decoder=lowerCamelCase__			)
 lowerCAmelCase    				= False

 lowerCAmelCase    				= MBartaaTokenizer(lowerCamelCase__			)
 tokenizer.save_pretrained(lowerCamelCase__			)

 lowerCAmelCase    				= hf_wavavec.config.to_dict()
 lowerCAmelCase    				= tokenizer.pad_token_id
 lowerCAmelCase    				= tokenizer.bos_token_id
 lowerCAmelCase    				= tokenizer.eos_token_id
 lowerCAmelCase    				= 'mbart50'
 lowerCAmelCase    				= 'wav2vec2'

 lowerCAmelCase    				= tokenizer.eos_token_id
 lowerCAmelCase    				= 25_0004
 lowerCAmelCase    				= tokenizer.eos_token_id

 lowerCAmelCase    				= SpeechEncoderDecoderConfig.from_dict(lowerCamelCase__			)

 hf_wavavec.save_pretrained(lowerCamelCase__			)
 feature_extractor.save_pretrained(lowerCamelCase__			)


if __name__ == "__main__":
       __UpperCamelCase     :						Dict						       =  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=1024, type=int, help='''encoder output dim''')
       parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')

       __UpperCamelCase     :						Dict						       =  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,
       )









import math

import tensorflow as tf
from packaging import version






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0      )  ,							x.dtype      )      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(math.pi  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi      ) * (x + coeff * tf.pow(lowerCamelCase__  ,							3      ))      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )

			return x * tf.tanh(tf.math.softplus(lowerCamelCase__      )      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.7_9_7_8_8_4_5_6_0_8  ,							x.dtype      )

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)      ))






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(1.7_0_2  ,							x.dtype      )
			return x * tf.math.sigmoid(coeff * x      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict      ):





			'''simple docstring'''




			return tf.clip_by_value(_gelu(lowerCamelCase__      )  ,							-10  ,							10      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Dict=-1      ):





			'''simple docstring'''




			lowerCamelCase		,		lowerCamelCase    		=  tf.split(lowerCamelCase__  ,							2  ,							axis=lowerCamelCase__      )
			return a * tf.math.sigmoid(lowerCamelCase__      )


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





					'''simple docstring'''




					return tf.keras.activations.gelu(lowerCamelCase__  ,							approximate=lowerCamelCase__      )

		UpperCAmelCase   :    Union[str, Any]     =		tf.keras.activations.gelu



		UpperCAmelCase   :    int     =		approximate_gelu_wrap
else:
		UpperCAmelCase   :    Dict     =		_gelu
		UpperCAmelCase   :    Dict     =		_gelu_new


UpperCAmelCase   :    List[str]     =		{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()      )}'      )

