





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


__lowerCamelCase			:	Optional[int]					    =	[
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
__lowerCamelCase			:	Dict					    =	[
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

__lowerCamelCase			:	List[Any]					    =	(
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

__lowerCamelCase			:	Dict					    =	(
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

__lowerCamelCase			:	List[str]					    =	[
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[Any]:
 for tf_name, hf_name in patterns:
  UpperCamelCase      :   Tuple			      =  k.replace(_lowerCAmelCase				,  _lowerCAmelCase	)
 return k



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					BigBirdPegasusForConditionalGeneration:
 UpperCamelCase      :   List[str]			      =  BigBirdPegasusConfig(**_lowerCAmelCase	)
 UpperCamelCase      :   Optional[int]			      =  BigBirdPegasusForConditionalGeneration(_lowerCAmelCase	)
 UpperCamelCase      :   int			      =  torch_model.state_dict()
 UpperCamelCase      :   Any			      =  {}

 # separating decoder weights
 UpperCamelCase      :   Any			      =  {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"	)}
 UpperCamelCase      :   Any			      =  {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder"	)}

 for k, v in tqdm(decoder_weights.items()				,  "tf -> hf conversion"	):
  UpperCamelCase      :   Any			      =  [k.endswith(_lowerCAmelCase	) for ending in KEYS_TO_IGNORE]
  if any(_lowerCAmelCase	):
   continue
  UpperCamelCase      :   Tuple			      =  DECODER_PATTERNS
  UpperCamelCase      :   List[str]			      =  rename_state_dict_key(_lowerCAmelCase				,  _lowerCAmelCase	)
  if new_k not in state_dict:
   raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""	)
  if any(True if i in k else False for i in ["dense", "query", "key", "value"]	):
   UpperCamelCase      :   int			      =  v.T
  UpperCamelCase      :   int			      =  torch.from_numpy(_lowerCAmelCase	)
  assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

 for k, v in tqdm(remaining_weights.items()				,  "tf -> hf conversion"	):
  UpperCamelCase      :   List[Any]			      =  [k.endswith(_lowerCAmelCase	) for ending in KEYS_TO_IGNORE]
  if any(_lowerCAmelCase	):
   continue
  UpperCamelCase      :   Optional[Any]			      =  REMAINING_PATTERNS
  UpperCamelCase      :   int			      =  rename_state_dict_key(_lowerCAmelCase				,  _lowerCAmelCase	)
  if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
   raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""	)
  if any(True if i in k else False for i in ["dense", "query", "key", "value"]	):
   UpperCamelCase      :   str			      =  v.T
  UpperCamelCase      :   List[str]			      =  torch.from_numpy(_lowerCAmelCase	)
  if k != "pegasus/embeddings/position_embeddings":
   assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

 UpperCamelCase      :   List[Any]			      =  mapping["model.embed_positions.weight"]
 UpperCamelCase      :   Union[str, Any]			      =  mapping.pop("model.embed_positions.weight"	)
 UpperCamelCase      ,       UpperCamelCase      :   Optional[Any]			      =  torch_model.load_state_dict(_lowerCAmelCase				,  strict=_lowerCAmelCase	)
 UpperCamelCase      :   Tuple			      =  [
     k
     for k in missing
     if k
     not in [
         "final_logits_bias",
         "model.encoder.embed_tokens.weight",
         "model.decoder.embed_tokens.weight",
         "lm_head.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 						A_							(	_lowerCAmelCase	)		->					Dict:
 UpperCamelCase      :   str			      =  tf.train.list_variables(_lowerCAmelCase	)
 UpperCamelCase      :   Dict			      =  {}
 UpperCamelCase      :   Union[str, Any]			      =  ["global_step"]
 for name, shape in tqdm(_lowerCAmelCase				,  desc="converting tf checkpoint to dict"	):
  UpperCamelCase      :   List[str]			      =  any(pat in name for pat in ignore_name	)
  if skip_key:
   continue
  UpperCamelCase      :   Tuple			      =  tf.train.load_variable(_lowerCAmelCase				,  _lowerCAmelCase	)
  UpperCamelCase      :   Optional[Any]			      =  array
 return tf_weights



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)		->					Dict:
 UpperCamelCase      :   Dict			      =  get_tf_weights_as_numpy(_lowerCAmelCase	)
 UpperCamelCase      :   Optional[int]			      =  convert_bigbird_pegasus(_lowerCAmelCase				,  _lowerCAmelCase	)
 torch_model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
    __lowerCamelCase			:	int					    =	argparse.ArgumentParser()
    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.""")
    __lowerCamelCase			:	List[Any]					    =	parser.parse_args()
    __lowerCamelCase			:	List[str]					    =	{}
    convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__lowerCamelCase			:	List[str]					    =	logging.get_logger(__name__)



class    A__  (			__snake_case     ):
      _UpperCAmelCase      :List[str]		       =  ['input_features', 'attention_mask']





      def __init__(					self			,				A_=80			,				A_=1_6000			,				A_=80			,				A_=0.0			,				A_=True			,				A_=True			,				A_=True			,				**A_			,				):
       '''simple docstring'''




       super().__init__(feature_size=A_			,				sampling_rate=A_			,				padding_value=A_			,				**A_  )
       UpperCamelCase      :   List[Any]			      =  num_mel_bins
       UpperCamelCase      :   Any			      =  do_ceptral_normalize
       UpperCamelCase      :   int			      =  normalize_means
       UpperCamelCase      :   Tuple			      =  normalize_vars
       UpperCamelCase      :   List[Any]			      =  True





      def 			__UpperCamelCase(					self			,				A_			,				):
       '''simple docstring'''




       UpperCamelCase      :   Optional[int]			      =  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
       UpperCamelCase      :   Optional[Any]			      =  torch.from_numpy(A_  ).unsqueeze(0  )
       UpperCamelCase      :   Dict			      =  ta_kaldi.fbank(A_			,				num_mel_bins=self.num_mel_bins			,				sample_frequency=self.sampling_rate  )
       return features.numpy()





      @staticmethod
      def 			__UpperCamelCase(					A_			,				A_			,				A_ = True			,				A_ = True			,				A_ = 0.0			,				):
       '''simple docstring'''




       if normalize_means:
        UpperCamelCase      :   str			      =  x[:input_length].mean(axis=0  )
        UpperCamelCase      :   Any			      =  np.subtract(A_			,				A_  )
       if normalize_vars:
        UpperCamelCase      :   List[str]			      =  x[:input_length].std(axis=0  )
        UpperCamelCase      :   Optional[int]			      =  np.divide(A_			,				A_  )

       if input_length < x.shape[0]:
        UpperCamelCase      :   Dict			      =  padding_value

       # make sure array is in float32
       UpperCamelCase      :   Union[str, Any]			      =  x.astype(np.floataa  )

       return x





      def 			__UpperCamelCase(					self			,				A_			,				A_ = None  ):
       '''simple docstring'''




       UpperCamelCase      :   Optional[Any]			      =  attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [
           self.utterance_cmvn(A_			,				A_			,				self.normalize_means			,				self.normalize_vars			,				self.padding_value  )
           for x, n in zip(A_			,				A_  )
       ]





      def __call__(					self			,				A_			,				A_ = False			,				A_ = None			,				A_ = False			,				A_ = None			,				A_ = None			,				A_ = None			,				A_ = None			,				**A_			,				):
       '''simple docstring'''




       if sampling_rate is not None:
        if sampling_rate != self.sampling_rate:
         raise ValueError(
             F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
             F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
             F""" {self.sampling_rate} and not {sampling_rate}."""  )
       else:
        logger.warning(
            "It is strongly recommended to pass the `sampling_rate` argument to this function. "
            "Failing to do so can result in silent errors that might be hard to debug."  )

       UpperCamelCase      :   str			      =  isinstance(A_			,				np.ndarray  ) and len(raw_speech.shape  ) > 1
       if is_batched_numpy and len(raw_speech.shape  ) > 2:
        raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""  )
       UpperCamelCase      :   List[Any]			      =  is_batched_numpy or (
           isinstance(A_			,				(list, tuple)  ) and (isinstance(raw_speech[0]			,				(np.ndarray, tuple, list)  ))
       )

       if is_batched:
        UpperCamelCase      :   List[str]			      =  [np.asarray(A_			,				dtype=np.floataa  ) for speech in raw_speech]
       elif not is_batched and not isinstance(A_			,				np.ndarray  ):
        UpperCamelCase      :   int			      =  np.asarray(A_			,				dtype=np.floataa  )
       elif isinstance(A_			,				np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
        UpperCamelCase      :   Union[str, Any]			      =  raw_speech.astype(np.floataa  )

       # always return batch
       if not is_batched:
        UpperCamelCase      :   Optional[int]			      =  [raw_speech]

       # extract fbank features
       UpperCamelCase      :   Optional[int]			      =  [self._extract_fbank_features(A_  ) for waveform in raw_speech]

       # convert into correct format for padding
       UpperCamelCase      :   List[Any]			      =  BatchFeature({"input_features": features}  )

       UpperCamelCase      :   Any			      =  self.pad(
           A_			,				padding=A_			,				max_length=A_			,				truncation=A_			,				pad_to_multiple_of=A_			,				return_attention_mask=A_			,				**A_			,				)

       # make sure list is in array format
       UpperCamelCase      :   Optional[int]			      =  padded_inputs.get("input_features"  )
       if isinstance(input_features[0]			,				A_  ):
        UpperCamelCase      :   str			      =  [np.asarray(A_			,				dtype=np.floataa  ) for feature in input_features]

       UpperCamelCase      :   Optional[int]			      =  padded_inputs.get("attention_mask"  )
       if attention_mask is not None:
        UpperCamelCase      :   Optional[int]			      =  [np.asarray(A_			,				dtype=np.intaa  ) for array in attention_mask]

       # Utterance-level cepstral mean and variance normalization
       if self.do_ceptral_normalize:
        UpperCamelCase      :   List[Any]			      =  (
            np.array(A_			,				dtype=np.intaa  )
            if self._get_padding_strategies(A_			,				max_length=A_  ) is not PaddingStrategy.DO_NOT_PAD
            else None
        )
        UpperCamelCase      :   int			      =  self.normalize(
            padded_inputs["input_features"]			,				attention_mask=A_  )

       if return_tensors is not None:
        UpperCamelCase      :   List[str]			      =  padded_inputs.convert_to_tensors(A_  )

       return padded_inputs
