
import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


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

snake_case__  							=							[
    (
        """protein1""",
        """MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA""",
    ),
    ("""protein2""", """MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"""),
    ("""protein3""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"""),
    ("""protein4""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"""),
]

snake_case__  							=							{
    """esm1b_t33_650M_UR50S""": esm_module.pretrained.esm1b_t33_650M_UR50S,
    """esm1v_t33_650M_UR90S_1""": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    """esm1v_t33_650M_UR90S_2""": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    """esm1v_t33_650M_UR90S_3""": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    """esm1v_t33_650M_UR90S_4""": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    """esm1v_t33_650M_UR90S_5""": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    """esm2_t48_15B_UR50D""": esm_module.pretrained.esm2_t48_15B_UR50D,
    """esm2_t36_3B_UR50D""": esm_module.pretrained.esm2_t36_3B_UR50D,
    """esm2_t33_650M_UR50D""": esm_module.pretrained.esm2_t33_650M_UR50D,
    """esm2_t30_150M_UR50D""": esm_module.pretrained.esm2_t30_150M_UR50D,
    """esm2_t12_35M_UR50D""": esm_module.pretrained.esm2_t12_35M_UR50D,
    """esm2_t6_8M_UR50D""": esm_module.pretrained.esm2_t6_8M_UR50D,
    """esmfold_v1""": esmfold_va,
}

snake_case__  							=							list("""ARNDCQEGHILKMFPSTWYV""")

snake_case__  							=							restypes + ["""X"""]
snake_case__  							=							restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]

def        lowerCamelCase_      (			):
      with TemporaryDirectory() as tempdir:
            lowercase       :    str											=  '''\n'''.join(UpperCAmelCase_       )
            lowercase       :    Dict											=  Path(UpperCAmelCase_       ) / '''vocab.txt'''
            vocab_file.write_text(UpperCAmelCase_       )
            lowercase       :    int											=  EsmTokenizer(vocab_file=str(UpperCAmelCase_       )       )
      lowercase       :    Union[str, Any]											=  0  # Overlaps with 'A' but that seems to be what they want
      return hf_tokenizer

def        lowerCamelCase_      (			UpperCAmelCase_							:				Union[str, Any]							,			UpperCAmelCase_							:				Tuple       ):
      lowercase       :    Optional[Any]											=  our_module.load_state_dict(original_module.state_dict()       )
      if status.missing_keys:
            raise ValueError(f'''Missing keys: {status.missing_keys}'''       )
      if status.unexpected_keys:
            raise ValueError(f'''Unexpected keys: {status.unexpected_keys}'''       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				bool							,			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				str       ):
      if model.startswith('''esmfold'''       ):
            lowercase       :    Union[str, Any]											=  MODEL_MAPPING[model]()
      else:
            lowercase       :    List[Any]											=  MODEL_MAPPING[model]()
      esm.eval()  # disable dropout

      if model.startswith('''esmfold'''       ):
            lowercase       :    Optional[int]											=  esm.esm.embed_dim
            lowercase       :    Optional[Any]											=  esm.esm.num_layers
            lowercase       :    Tuple											=  esm.esm.attention_heads
            lowercase       :    int											=  4 * embed_dim
            lowercase       :    int											=  esm.esm.token_dropout
            lowercase       :    List[str]											=  False  # This code path does not exist in ESM-2
            lowercase       :    int											=  '''rotary'''
            lowercase       :    Any											=  True
            lowercase       :    Any											=  EsmFoldConfig()
            for key, val in esm.cfg.items():
                  if hasattr(UpperCAmelCase_							,			UpperCAmelCase_       ) and key != "trunk":
                        setattr(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_       )
            for key, val in esm.cfg.trunk.items():
                  if hasattr(esmfold_config.trunk							,			UpperCAmelCase_       ) and key != "structure_module":
                        setattr(esmfold_config.trunk							,			UpperCAmelCase_							,			UpperCAmelCase_       )
            for key, val in esm.cfg.trunk.structure_module.items():
                  if hasattr(esmfold_config.trunk.structure_module							,			UpperCAmelCase_       ):
                        setattr(esmfold_config.trunk.structure_module							,			UpperCAmelCase_							,			UpperCAmelCase_       )
      elif hasattr(UpperCAmelCase_							,			'''args'''       ):
            # Indicates an ESM-1b or ESM-1v model
            lowercase       :    Any											=  esm.args.embed_dim
            lowercase       :    List[str]											=  esm.args.layers
            lowercase       :    List[Any]											=  esm.args.attention_heads
            lowercase       :    str											=  esm.args.ffn_embed_dim
            lowercase       :    str											=  esm.args.token_dropout
            lowercase       :    Tuple											=  True if esm.emb_layer_norm_before else False
            lowercase       :    Optional[int]											=  '''absolute'''
            lowercase       :    Optional[Any]											=  False
            lowercase       :    Any											=  None
      else:
            # Indicates an ESM-2 model
            lowercase       :    Optional[int]											=  esm.embed_dim
            lowercase       :    List[str]											=  esm.num_layers
            lowercase       :    Optional[int]											=  esm.attention_heads
            lowercase       :    Optional[int]											=  4 * embed_dim  # This is hardcoded in ESM-2
            lowercase       :    Dict											=  esm.token_dropout
            lowercase       :    int											=  False  # This code path does not exist in ESM-2
            lowercase       :    List[str]											=  '''rotary'''
            lowercase       :    Optional[int]											=  False
            lowercase       :    List[Any]											=  None

      if is_folding_model:
            lowercase       :    Optional[int]											=  esm.esm.alphabet
      lowercase       :    Optional[Any]											=  tuple(alphabet.all_toks       )
      lowercase       :    str											=  alphabet.mask_idx
      lowercase       :    List[Any]											=  alphabet.padding_idx

      if is_folding_model:
            lowercase       :    List[Any]											=  esm.esm
      else:
            lowercase       :    Dict											=  esm

      lowercase       :    List[str]											=  EsmConfig(
          vocab_size=original_esm_model.embed_tokens.num_embeddings							,			mask_token_id=UpperCAmelCase_							,			hidden_size=UpperCAmelCase_							,			num_hidden_layers=UpperCAmelCase_							,			num_attention_heads=UpperCAmelCase_							,			intermediate_size=UpperCAmelCase_							,			max_position_embeddings=10_26							,			layer_norm_eps=1E-5							,			attention_probs_dropout_prob=0.0							,			hidden_dropout_prob=0.0							,			pad_token_id=UpperCAmelCase_							,			emb_layer_norm_before=UpperCAmelCase_							,			token_dropout=UpperCAmelCase_							,			position_embedding_type=UpperCAmelCase_							,			is_folding_model=UpperCAmelCase_							,			esmfold_config=UpperCAmelCase_							,			vocab_list=UpperCAmelCase_							,			)
      if classification_head:
            lowercase       :    List[str]											=  esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
      print('''Our ESM config:'''							,			UpperCAmelCase_       )

      if model.startswith('''esmfold'''       ):
            lowercase       :    Optional[Any]											=  EsmForProteinFolding
      elif classification_head:
            lowercase       :    Dict											=  EsmForSequenceClassification
      else:
            lowercase       :    List[str]											=  EsmForMaskedLM
      lowercase       :    List[str]											=  model_class(UpperCAmelCase_       )
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      lowercase       :    Tuple											=  original_esm_model.embed_tokens.weight
      if position_embedding_type == "absolute":
            lowercase       :    Any											=  original_esm_model.embed_positions.weight

      if config.emb_layer_norm_before:
            lowercase       :    List[str]											=  original_esm_model.emb_layer_norm_before.weight
            lowercase       :    Union[str, Any]											=  original_esm_model.emb_layer_norm_before.bias

      lowercase       :    int											=  original_esm_model.emb_layer_norm_after.weight
      lowercase       :    List[Any]											=  original_esm_model.emb_layer_norm_after.bias

      for i in range(config.num_hidden_layers       ):
            # Encoder: start of layer
            lowercase       :    EsmLayer											=  model.esm.encoder.layer[i]
            # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
            lowercase       :    Optional[Any]											=  original_esm_model.layers[i]

            # self attention
            lowercase       :    EsmSelfAttention											=  layer.attention.self
            assert (
                esm_layer.self_attn.k_proj.weight.data.shape
                == esm_layer.self_attn.q_proj.weight.data.shape
                == esm_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size)       )
            )

            lowercase       :    List[Any]											=  esm_layer.self_attn.q_proj.weight
            lowercase       :    Any											=  esm_layer.self_attn.q_proj.bias
            lowercase       :    List[str]											=  esm_layer.self_attn.k_proj.weight
            lowercase       :    List[str]											=  esm_layer.self_attn.k_proj.bias
            lowercase       :    List[Any]											=  esm_layer.self_attn.v_proj.weight
            lowercase       :    int											=  esm_layer.self_attn.v_proj.bias

            if getattr(esm_layer.self_attn							,			'''rot_emb'''							,			UpperCAmelCase_       ) is not None:
                  # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
                  # During the training of ESM-2 the model was converted to float16 precision, which also converts
                  # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
                  # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
                  # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
                  # we make sure the new model copies the data from the old inv_freq.
                  lowercase       :    Union[str, Any]											=  esm_layer.self_attn.rot_emb.inv_freq

            # LayerNorm changes for pre-activation
            lowercase       :    Optional[Any]											=  esm_layer.self_attn_layer_norm.weight
            lowercase       :    List[Any]											=  esm_layer.self_attn_layer_norm.bias
            lowercase       :    Any											=  esm_layer.final_layer_norm.weight
            lowercase       :    Optional[Any]											=  esm_layer.final_layer_norm.bias

            # self-attention output
            lowercase       :    EsmSelfOutput											=  layer.attention.output
            assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
            lowercase       :    str											=  esm_layer.self_attn.out_proj.weight
            lowercase       :    Union[str, Any]											=  esm_layer.self_attn.out_proj.bias

            # intermediate
            lowercase       :    EsmIntermediate											=  layer.intermediate
            assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
            lowercase       :    List[str]											=  esm_layer.fca.weight
            lowercase       :    int											=  esm_layer.fca.bias

            # output
            lowercase       :    EsmOutput											=  layer.output
            assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
            lowercase       :    str											=  esm_layer.fca.weight
            lowercase       :    List[str]											=  esm_layer.fca.bias
            # end of layer

      if is_folding_model:
            lowercase       :    str											=  esm.esm_s_combine.data
            lowercase       :    Union[str, Any]											=  esm.afa_to_esm.data
            transfer_and_check_weights(esm.embedding							,			model.embedding       )
            transfer_and_check_weights(esm.esm_s_mlp							,			model.esm_s_mlp       )
            transfer_and_check_weights(esm.trunk							,			model.trunk       )
            transfer_and_check_weights(esm.distogram_head							,			model.distogram_head       )
            transfer_and_check_weights(esm.ptm_head							,			model.ptm_head       )
            transfer_and_check_weights(esm.lm_head							,			model.lm_head       )
            transfer_and_check_weights(esm.lddt_head							,			model.lddt_head       )

      elif classification_head:
            lowercase       :    str											=  esm.esm.classification_heads['''mnli'''].dense.weight
            lowercase       :    Union[str, Any]											=  esm.classification_heads['''mnli'''].dense.bias
            lowercase       :    Tuple											=  esm.classification_heads['''mnli'''].out_proj.weight
            lowercase       :    Optional[Any]											=  esm.classification_heads['''mnli'''].out_proj.bias
      else:
            # LM Head
            lowercase       :    Any											=  esm.lm_head.dense.weight
            lowercase       :    Dict											=  esm.lm_head.dense.bias
            lowercase       :    str											=  esm.lm_head.layer_norm.weight
            lowercase       :    Optional[Any]											=  esm.lm_head.layer_norm.bias
            lowercase       :    Optional[Any]											=  esm.lm_head.weight
            lowercase       :    Dict											=  esm.lm_head.bias

      # Contact prediction head
      transfer_and_check_weights(esm.contact_head							,			model.esm.contact_head       )

      # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
      if is_folding_model:
            # Folding models aren't trained on masked inputs and don't like mask tokens.
            lowercase       :    Optional[Any]											=  SAMPLE_DATA[:2]
      else:
            lowercase       :    Optional[int]											=  SAMPLE_DATA

      if is_folding_model:
            lowercase       :    List[str]											=  get_esmfold_tokenizer()
            lowercase       :    Any											=  hf_tokenizer(
                [row[1] for row in sample_data]							,			return_tensors='''pt'''							,			padding=UpperCAmelCase_							,			add_special_tokens=UpperCAmelCase_       )
            lowercase       :    int											=  esmfold_encode_sequences([row[1] for row in sample_data]       )
            lowercase       :    int											=  torch.all(hf_tokens['''input_ids'''] == esmfold_aas       ) and torch.all(
                hf_tokens['''attention_mask'''] == esmfold_mask       )
      else:
            # Let's check that we get the same results.
            lowercase       :    List[str]											=  alphabet.get_batch_converter()
            lowercase       :    Optional[Any]											=  batch_converter(UpperCAmelCase_       )
            # Prepare tokenizer and make sure it matches
            with TemporaryDirectory() as tempdir:
                  lowercase       :    List[Any]											=  '''\n'''.join(alphabet.all_toks       )
                  lowercase       :    Optional[Any]											=  Path(UpperCAmelCase_       ) / '''vocab.txt'''
                  vocab_file.write_text(UpperCAmelCase_       )
                  lowercase       :    str											=  EsmTokenizer(vocab_file=str(UpperCAmelCase_       )       )

            lowercase       :    List[Any]											=  hf_tokenizer([row[1] for row in sample_data]							,			return_tensors='''pt'''							,			padding=UpperCAmelCase_       )
            lowercase       :    Optional[Any]											=  torch.all(hf_tokens['''input_ids'''] == batch_tokens       )

      print('''Do both models tokenizers output the same tokens?'''							,			'''🔥''' if success else '''💩'''       )
      if not success:
            raise Exception('''Tokenization does not match!'''       )

      with torch.no_grad():
            if is_folding_model:
                  # Let's test the model in parts
                  # ESMFold always converts the ESM stem to float16, which requires float16 ops
                  # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
                  # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
                  # original and the converted model on the GPU at the same time.
                  lowercase       :    Union[str, Any]											=  esm.cuda().infer([row[1] for row in sample_data]       )
                  lowercase       :    str											=  model.cuda()(
                      input_ids=hf_tokens['''input_ids'''].cuda()							,			attention_mask=hf_tokens['''attention_mask'''].cuda()       )
            else:
                  lowercase       :    List[Any]											=  model(**UpperCAmelCase_							,			output_hidden_states=UpperCAmelCase_       )
                  lowercase       :    Optional[Any]											=  our_output['''logits''']
                  if classification_head:
                        lowercase       :    Optional[int]											=  esm.model.classification_heads['''mnli'''](esm.extract_features(UpperCAmelCase_       )       )
                  else:
                        lowercase       :    List[str]											=  esm(hf_tokens['''input_ids''']							,			repr_layers=list(range(9_99       )       )       )
                        lowercase       :    List[Any]											=  their_output['''logits''']

            if is_folding_model:
                  lowercase       :    List[Any]											=  torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']       )       ).item()
                  lowercase       :    Dict											=  torch.allclose(our_output['''positions''']							,			their_output['''positions''']							,			atol=1E-5       )
            else:
                  lowercase       :    int											=  torch.max(torch.abs(our_output - their_output       )       ).item()
                  lowercase       :    Union[str, Any]											=  torch.allclose(UpperCAmelCase_							,			UpperCAmelCase_							,			atol=1E-5       )

            print(f'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-5
            print('''Do both models output the same tensors?'''							,			'''🔥''' if success else '''💩'''       )

            if not success:
                  raise Exception('''Something went wRoNg'''       )

            if not is_folding_model:
                  # Let's check contact prediction too
                  lowercase       :    List[str]											=  model.predict_contacts(hf_tokens['''input_ids''']							,			hf_tokens['''attention_mask''']       )
                  lowercase       :    Dict											=  esm.predict_contacts(hf_tokens['''input_ids''']       )
                  lowercase       :    str											=  torch.max(torch.abs(our_output - their_output       )       ).item()
                  lowercase       :    Union[str, Any]											=  torch.allclose(UpperCAmelCase_							,			UpperCAmelCase_							,			atol=1E-5       )

                  print('''Contact prediction testing:'''       )
                  print(f'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-5
                  print('''Do both models output the same tensors?'''							,			'''🔥''' if success else '''💩'''       )

                  if not success:
                        raise Exception('''Something went wRoNg'''       )

            pathlib.Path(UpperCAmelCase_       ).mkdir(parents=UpperCAmelCase_							,			exist_ok=UpperCAmelCase_       )
            print(f'''Saving model to {pytorch_dump_folder_path}'''       )
            model.save_pretrained(UpperCAmelCase_       )

            del esm  # Free up some memory before continuing

      print(f'''Saving tokenizer to {pytorch_dump_folder_path}'''       )
      hf_tokenizer.save_pretrained(UpperCAmelCase_       )

      if push_to_repo:
            model.push_to_hub(repo_id=UpperCAmelCase_							,			use_auth_token=UpperCAmelCase_       )
            hf_tokenizer.push_to_hub(repo_id=UpperCAmelCase_							,			use_auth_token=UpperCAmelCase_       )


if __name__ == "__main__":
    snake_case__  							=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--pytorch_dump_folder_path""", type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
    )
    parser.add_argument("""--model""", default=None, type=str, required=True, help="""Name of model to convert.""")
    parser.add_argument("""--push_to_repo""", type=str, help="""Repo to upload to (including username!).""")
    parser.add_argument("""--auth_token""", type=str, help="""HuggingFace auth token.""")
    snake_case__  							=							parser.parse_args()
    convert_esm_checkpoint_to_pytorch(
        args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
    )




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 TensorType, logging


snake_case__  							=							logging.get_logger(__name__)







class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: str			  =				["""input_values""", """attention_mask"""]

					def __init__(			self      :					Union[str, Any] ,      lowerCAmelCase      :					Tuple=1 ,      lowerCAmelCase      :					Dict=1_6000 ,      lowerCAmelCase      :					Optional[Any]=128 ,      lowerCAmelCase      :					Dict=1024 ,      lowerCAmelCase      :					List[Any]=0.0 ,      lowerCAmelCase      :					Any=True ,      lowerCAmelCase      :					Tuple=-4.2_67_73_93 ,      lowerCAmelCase      :					Dict=4.5_68_99_74 ,      lowerCAmelCase      :					int=False ,      **lowerCAmelCase      :					Optional[Any] ,      ):
											super().__init__(feature_size=lowerCAmelCase ,      sampling_rate=lowerCAmelCase ,      padding_value=lowerCAmelCase ,      **lowerCAmelCase					)
											lowercase       :    Optional[int]											=  num_mel_bins
											lowercase       :    List[Any]											=  max_length
											lowercase       :    Any											=  do_normalize
											lowercase       :    Any											=  mean
											lowercase       :    Union[str, Any]											=  std
											lowercase       :    int											=  return_attention_mask

					def        _lowerCAmelCase	(			self      :					int ,      lowerCAmelCase      :					np.ndarray ,      lowerCAmelCase      :					int ,      ):
											lowercase       :    List[Any]											=  torch.from_numpy(lowerCAmelCase					).unsqueeze(0					)
											lowercase       :    List[str]											=  ta_kaldi.fbank(
											    lowerCAmelCase ,      htk_compat=lowerCAmelCase ,      sample_frequency=self.sampling_rate ,      use_energy=lowerCAmelCase ,      window_type='''hanning''' ,      num_mel_bins=self.num_mel_bins ,      dither=0.0 ,      frame_shift=10 ,      )

											lowercase       :    Tuple											=  fbank.shape[0]
											lowercase       :    Optional[Any]											=  max_length - n_frames

											# pad or truncate, depending on difference
											if difference > 0:
																	lowercase       :    int											=  torch.nn.ZeroPadad((0, 0, 0, difference)					)
																	lowercase       :    str											=  pad_module(lowerCAmelCase					)
											elif difference < 0:
																	lowercase       :    Optional[int]											=  fbank[0:max_length, :]

											lowercase       :    List[str]											=  fbank.numpy()

											return fbank

					def        _lowerCAmelCase	(			self      :					Tuple ,      lowerCAmelCase      :					np.ndarray					):
											return (input_values - (self.mean)) / (self.std * 2)




					def __call__(			self      :					Any ,      lowerCAmelCase      :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,      lowerCAmelCase      :					Optional[int] = None ,      lowerCAmelCase      :					Optional[Union[str, TensorType]] = None ,      **lowerCAmelCase      :					Tuple ,      ):

											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.'''					)

											lowercase       :    Optional[Any]											=  isinstance(lowerCAmelCase ,      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}'''					)
											lowercase       :    Tuple											=  is_batched_numpy or (
											    isinstance(lowerCAmelCase ,      (list, tuple)					) and (isinstance(raw_speech[0] ,      (np.ndarray, tuple, list)					))
											)

											if is_batched:
																	lowercase       :    Optional[int]											=  [np.asarray(lowerCAmelCase ,      dtype=np.floataa					) for speech in raw_speech]
											elif not is_batched and not isinstance(lowerCAmelCase ,      np.ndarray					):
																	lowercase       :    Dict											=  np.asarray(lowerCAmelCase ,      dtype=np.floataa					)
											elif isinstance(lowerCAmelCase ,      np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
																	lowercase       :    int											=  raw_speech.astype(np.floataa					)

											# always return batch
											if not is_batched:
																	lowercase       :    Dict											=  [raw_speech]

											# extract fbank features and pad/truncate to max_length
											lowercase       :    Dict											=  [self._extract_fbank_features(lowerCAmelCase ,      max_length=self.max_length					) for waveform in raw_speech]

											# convert into BatchFeature
											lowercase       :    Any											=  BatchFeature({'''input_values''': features}					)

											# make sure list is in array format
											lowercase       :    str											=  padded_inputs.get('''input_values'''					)
											if isinstance(input_values[0] ,      lowerCAmelCase					):
																	lowercase       :    Any											=  [np.asarray(lowerCAmelCase ,      dtype=np.floataa					) for feature in input_values]

											# normalization
											if self.do_normalize:
																	lowercase       :    List[str]											=  [self.normalize(lowerCAmelCase					) for feature in input_values]

											if return_tensors is not None:
																	lowercase       :    str											=  padded_inputs.convert_to_tensors(lowerCAmelCase					)

											return padded_inputs


