






'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCamelCase__			=      [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   for pegasus_name, hf_name in PATTERNS:
      lowercase_						:     Any							     =  k.replace(_UpperCamelCase    ,		_UpperCamelCase				)
   return k




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  DEFAULTS.copy()
   cfg_kwargs.update(_UpperCamelCase				)
   lowercase_						:     Dict							     =  PegasusConfig(**_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  PegasusForConditionalGeneration(_UpperCamelCase				)
   lowercase_						:     Dict							     =  torch_model.model.state_dict()
   lowercase_						:     str							     =  {}
   for k, v in tf_weights.items():
      lowercase_						:     Union[str, Any]							     =  rename_state_dict_key(_UpperCamelCase				)
      if new_k not in sd:
         raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""				)

      if "dense" in k or "proj" in new_k:
         lowercase_						:     List[Any]							     =  v.T
      lowercase_						:     Dict							     =  torch.tensor(_UpperCamelCase    ,		dtype=sd[new_k].dtype				)
      assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
   # make sure embedding.padding_idx is respected
   lowercase_						:     Tuple							     =  torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]				)
   lowercase_						:     Optional[Any]							     =  mapping["shared.weight"]
   lowercase_						:     Optional[int]							     =  mapping["shared.weight"]
   lowercase_						:     List[str]							     =  {k: torch.zeros_like(_UpperCamelCase				) for k, v in sd.items() if k.endswith("bias"				) and k not in mapping}
   mapping.update(**_UpperCamelCase				)
   lowercase_     ,		lowercase_						:     Optional[Any]							     =  torch_model.model.load_state_dict(_UpperCamelCase    ,		strict=_UpperCamelCase				)
   lowercase_						:     Union[str, Any]							     =  [
       k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
   ]
   assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
   assert extra == [], F"""no matches found for the following tf keys {extra}"""
   return torch_model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase="./ckpt/aeslc/model.ckpt-32000"				):


   """simple docstring"""




   lowercase_						:     Any							     =  tf.train.list_variables(_UpperCamelCase				)
   lowercase_						:     Union[str, Any]							     =  {}
   lowercase_						:     Optional[Any]							     =  ["Adafactor", "global_step"]
   for name, shape in tqdm(_UpperCamelCase    ,		desc="converting tf checkpoint to dict"				):
      lowercase_						:     Union[str, Any]							     =  any(pat in name for pat in ignore_name				)
      if skip_key:
         continue
      lowercase_						:     List[str]							     =  tf.train.load_variable(_UpperCamelCase    ,		_UpperCamelCase				)
      lowercase_						:     Optional[int]							     =  array
   return tf_weights




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Union[str, Any]							     =  Path(_UpperCamelCase				).parent.name
   lowercase_						:     str							     =  task_specific_params[F"""summarization_{dataset}"""]["max_position_embeddings"]
   lowercase_						:     int							     =  PegasusTokenizer.from_pretrained("sshleifer/pegasus"    ,		model_max_length=_UpperCamelCase				)
   assert tok.model_max_length == desired_max_model_length
   tok.save_pretrained(_UpperCamelCase				)

   # convert model
   lowercase_						:     List[str]							     =  get_tf_weights_as_numpy(_UpperCamelCase				)
   lowercase_						:     str							     =  task_specific_params[F"""summarization_{dataset}"""]
   if dataset == "large":
      lowercase_						:     List[str]							     =  task_specific_params
   lowercase_						:     Optional[int]							     =  convert_pegasus(_UpperCamelCase    ,		_UpperCamelCase				)
   torch_model.save_pretrained(_UpperCamelCase				)
   lowercase_						:     Tuple							     =  torch_model.state_dict()
   sd.pop("model.decoder.embed_positions.weight"				)
   sd.pop("model.encoder.embed_positions.weight"				)
   torch.save(_UpperCamelCase    ,		Path(_UpperCamelCase				) / "pytorch_model.bin"				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
 parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
 UpperCamelCase__			=      parser.parse_args()
 if args.save_dir is None:
  UpperCamelCase__			=      Path(args.tf_ckpt_path).parent.name
  UpperCamelCase__			=      os.path.join('pegasus', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)












'''simple docstring'''
import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  downstream_dict["projector.weight"]
   lowercase_						:     str							     =  downstream_dict["projector.bias"]
   lowercase_						:     int							     =  downstream_dict["model.post_net.linear.weight"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["model.post_net.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Any							     =  downstream_dict["model.linear.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["model.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  WavaVecaForXVector.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     str							     =  downstream_dict["connector.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      lowercase_						:     Union[str, Any]							     =  downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      lowercase_						:     Dict							     =  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   lowercase_						:     List[Any]							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["objective.W"]
   return model




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)

   lowercase_						:     Dict							     =  checkpoint["Downstream"]

   lowercase_						:     Optional[Any]							     =  WavaVecaConfig.from_pretrained(_UpperCamelCase				)
   lowercase_						:     Dict							     =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		do_normalize=_UpperCamelCase				)

   lowercase_						:     Dict							     =  hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"				):
      lowercase_						:     Any							     =  convert_classification(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForAudioFrameClassification"				):
      lowercase_						:     Optional[int]							     =  convert_diarization(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForXVector"				):
      lowercase_						:     List[Any]							     =  convert_xvector(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   else:
      raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

   if hf_config.use_weighted_layer_sum:
      lowercase_						:     List[str]							     =  checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(_UpperCamelCase				)
   hf_model.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
 UpperCamelCase__			=      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.')
 UpperCamelCase__			=      parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




