





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def    __snake_case  (    _lowerCAmelCase       :       Dict , _lowerCAmelCase       :       Tuple , _lowerCAmelCase       :       int		)       ->  List[Any]:
   return params[f"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]

def    __snake_case  (    _lowerCAmelCase       :       Dict , _lowerCAmelCase       :       Any , _lowerCAmelCase       :       List[str] , _lowerCAmelCase       :       Any="attention"		)       ->  Optional[int]:
   A_    : List[Any]								=	np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]		)
   A_    : Union[str, Any]								=	k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2]		)
   A_    : Union[str, Any]								=	np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]		)
   A_    : Dict								=	o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2]		)
   A_    : int								=	np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]		)
   A_    : str								=	q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2]		)
   A_    : List[Any]								=	np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]		)
   A_    : Any								=	v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2]		)
   return k, o, q, v

def    __snake_case  (    _lowerCAmelCase       :       Optional[int] , _lowerCAmelCase       :       List[Any] , _lowerCAmelCase       :       int , _lowerCAmelCase       :       Union[str, Any]=False		)       ->  int:
   if split_mlp_wi:
      A_    : Dict								=	params[f"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
      A_    : List[Any]								=	params[f"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
      A_    : List[str]								=	(wi_a, wi_a)
   else:
      A_    : int								=	params[f"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

   A_    : List[str]								=	params[f"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
   return wi, wo

def    __snake_case  (    _lowerCAmelCase       :       str , _lowerCAmelCase       :       Dict , _lowerCAmelCase       :       Tuple , _lowerCAmelCase       :       List[Any]		)       ->  int:
   return params[f"{prefix}/{prefix}/{layer_name}/scale"][:, i]

def    __snake_case  (    _lowerCAmelCase       :       dict , *, _lowerCAmelCase       :       int , _lowerCAmelCase       :       bool , _lowerCAmelCase       :       bool = False		)       ->  Optional[Any]:
   A_    : Tuple								=	traverse_util.flatten_dict(variables["target"]		)
   A_    : List[str]								=	{'/'.join(_lowerCAmelCase		): v for k, v in old.items()}

   # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
   A_    : str								=	'encoder/encoder/mlp/wi_0/kernel' in old
   print("Split MLP:" , _lowerCAmelCase		)

   A_    : str								=	collections.OrderedDict()

   # Shared embeddings.
   A_    : Dict								=	old['token_embedder/embedding']

   # Encoder.
   for i in range(_lowerCAmelCase		):
      # Block i, layer 0 (Self Attention).
      A_    : Tuple								=	tax_layer_norm_lookup(_lowerCAmelCase , _lowerCAmelCase , "encoder" , "pre_attention_layer_norm"		)
      A_    : List[str]								=	tax_attention_lookup(_lowerCAmelCase , _lowerCAmelCase , "encoder" , "attention"		)
      A_    : int								=	layer_norm
      A_    : Dict								=	k.T
      A_    : str								=	o.T
      A_    : int								=	q.T
      A_    : Union[str, Any]								=	v.T

      # Block i, layer 1 (MLP).
      A_    : List[str]								=	tax_layer_norm_lookup(_lowerCAmelCase , _lowerCAmelCase , "encoder" , "pre_mlp_layer_norm"		)
      A_    : List[str]								=	tax_mlp_lookup(_lowerCAmelCase , _lowerCAmelCase , "encoder" , _lowerCAmelCase		)
      A_    : int								=	layer_norm
      if split_mlp_wi:
         A_    : int								=	wi[0].T
         A_    : Dict								=	wi[1].T
      else:
         A_    : Optional[Any]								=	wi.T
      A_    : str								=	wo.T
      if scalable_attention:
         # convert the rel_embedding of each layer
         A_    : Optional[int]								=	tax_relpos_bias_lookup(
             _lowerCAmelCase , _lowerCAmelCase , "encoder"		).T

   A_    : int								=	old['encoder/encoder_norm/scale']

   if not scalable_attention:
      A_    : Any								=	tax_relpos_bias_lookup(
          _lowerCAmelCase , 0 , "encoder"		).T
      A_    : str								=	tax_relpos_bias_lookup(
          _lowerCAmelCase , 0 , "decoder"		).T

   if not is_encoder_only:
      # Decoder.
      for i in range(_lowerCAmelCase		):
         # Block i, layer 0 (Self Attention).
         A_    : Any								=	tax_layer_norm_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , "pre_self_attention_layer_norm"		)
         A_    : str								=	tax_attention_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , "self_attention"		)
         A_    : List[Any]								=	layer_norm
         A_    : Tuple								=	k.T
         A_    : Any								=	o.T
         A_    : Optional[Any]								=	q.T
         A_    : Any								=	v.T

         # Block i, layer 1 (Cross Attention).
         A_    : List[str]								=	tax_layer_norm_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , "pre_cross_attention_layer_norm"		)
         A_    : List[str]								=	tax_attention_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , "encoder_decoder_attention"		)
         A_    : List[Any]								=	layer_norm
         A_    : List[Any]								=	k.T
         A_    : str								=	o.T
         A_    : List[str]								=	q.T
         A_    : Any								=	v.T

         # Block i, layer 2 (MLP).
         A_    : Dict								=	tax_layer_norm_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , "pre_mlp_layer_norm"		)
         A_    : Dict								=	tax_mlp_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder" , _lowerCAmelCase		)
         A_    : Union[str, Any]								=	layer_norm
         if split_mlp_wi:
            A_    : List[str]								=	wi[0].T
            A_    : Optional[int]								=	wi[1].T
         else:
            A_    : Optional[Any]								=	wi.T
         A_    : List[str]								=	wo.T

         if scalable_attention:
            # convert the rel_embedding of each layer
            A_    : Tuple								=	tax_relpos_bias_lookup(_lowerCAmelCase , _lowerCAmelCase , "decoder"		).T

      A_    : Optional[Any]								=	old['decoder/decoder_norm/scale']

      # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
      if "decoder/logits_dense/kernel" in old:
         A_    : int								=	old['decoder/logits_dense/kernel'].T

   return new

def    __snake_case  (    _lowerCAmelCase       :       Optional[int] , _lowerCAmelCase       :       bool		)       ->  Tuple:
   A_    : Any								=	collections.OrderedDict([(k, torch.from_numpy(v.copy()		)) for (k, v) in converted_params.items()]		)

   # Add what is missing.
   if "encoder.embed_tokens.weight" not in state_dict:
      A_    : str								=	state_dict['shared.weight']

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         A_    : Dict								=	state_dict['shared.weight']

      if "lm_head.weight" not in state_dict:  # For old 1.0 models.
         print("Using shared word embeddings as lm_head."		)
         A_    : Union[str, Any]								=	state_dict['shared.weight']

   return state_dict

def    __snake_case  (    _lowerCAmelCase       :       int , _lowerCAmelCase       :       Tuple , _lowerCAmelCase       :       List[str] , _lowerCAmelCase       :       Tuple , _lowerCAmelCase       :       int		)       ->  Dict:
   A_    : Optional[Any]								=	checkpoints.load_tax_checkpoint(_lowerCAmelCase		)
   A_    : List[Any]								=	convert_tax_to_pytorch(
       _lowerCAmelCase , num_layers=config.num_layers , is_encoder_only=_lowerCAmelCase , scalable_attention=_lowerCAmelCase		)
   A_    : str								=	make_state_dict(_lowerCAmelCase , _lowerCAmelCase		)
   model.load_state_dict(_lowerCAmelCase , strict=_lowerCAmelCase		)

def    __snake_case  (    _lowerCAmelCase       :       Any , _lowerCAmelCase       :       Optional[int] , _lowerCAmelCase       :       str , _lowerCAmelCase       :       bool = False , _lowerCAmelCase       :       bool = False , )       ->  Dict:
   A_    : List[Any]								=	MTaConfig.from_json_file(_lowerCAmelCase		)
   print(f"Building PyTorch model from configuration: {config}"		)
   # Non-v1.1 checkpoints could also use T5Model, but this works for all.
   # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
   if is_encoder_only:
      A_    : Optional[Any]								=	UMTaEncoderModel(_lowerCAmelCase		)
   else:
      A_    : List[Any]								=	UMTaForConditionalGeneration(_lowerCAmelCase		)

   # Load weights from tf checkpoint
   load_tax_weights_in_ta(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase		)

   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"		)
   model.save_pretrained(_lowerCAmelCase		)

   # Verify that we can load the checkpoint.
   model.from_pretrained(_lowerCAmelCase		)
   print("Done"		)


if __name__ == "__main__":
 _lowerCAmelCase							:					Any		    =		argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
 # Required parameters
 parser.add_argument(
     '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 parser.add_argument(
     '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
 )
 parser.add_argument(
     '''--scalable_attention''',
     action='''store_true''',
     help='''Whether the model uses scaled attention (umt5 model)''',
     default=False,
 )
 _lowerCAmelCase							:					Union[str, Any]		    =		parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path,
     args.config_file,
     args.pytorch_dump_path,
     args.is_encoder_only,
     args.scalable_attention,
 )

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Any =					downstream_dict['projector.weight']
 UpperCamelCase_					:						List[str] =					downstream_dict['projector.bias']
 UpperCamelCase_					:						Tuple =					downstream_dict['model.post_net.linear.weight']
 UpperCamelCase_					:						int =					downstream_dict['model.post_net.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Any =					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					downstream_dict['model.linear.weight']
 UpperCamelCase_					:						Any =					downstream_dict['model.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						str =					UniSpeechSatForXVector.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					downstream_dict['connector.weight']
 UpperCamelCase_					:						int =					downstream_dict['connector.bias']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
  UpperCamelCase_					:						Union[str, Any] =					downstream_dict[
      F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  ]
  UpperCamelCase_					:						Tuple =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

 UpperCamelCase_					:						int =					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
 UpperCamelCase_					:						Dict =					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
 UpperCamelCase_					:						List[Any] =					downstream_dict['objective.W']
 return model





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						int =					torch.load(lowerCamelCase  ,  map_location='cpu'	)

 UpperCamelCase_					:						Any =					checkpoint['Downstream']

 UpperCamelCase_					:						Dict =					UniSpeechSatConfig.from_pretrained(lowerCamelCase	)
 UpperCamelCase_					:						str =					WavaVecaFeatureExtractor.from_pretrained(
     lowerCamelCase  ,  return_attention_mask=lowerCamelCase  ,  do_normalize=lowerCamelCase	)

 UpperCamelCase_					:						int =					hf_config.architectures[0]
 if arch.endswith('ForSequenceClassification'	):
  UpperCamelCase_					:						Tuple =					convert_classification(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForAudioFrameClassification'	):
  UpperCamelCase_					:						List[str] =					convert_diarization(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForXVector'	):
  UpperCamelCase_					:						Union[str, Any] =					convert_xvector(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 else:
  raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"	)

 if hf_config.use_weighted_layer_sum:
  UpperCamelCase_					:						Tuple =					checkpoint['Featurizer']['weights']

 hf_feature_extractor.save_pretrained(lowerCamelCase	)
 hf_model.save_pretrained(lowerCamelCase	)


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser()
   parser.add_argument(
       '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
   )
   parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
   parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
   a_    					=       parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



