

import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification






def 					__lowercase      (     _A			)     ->						int:
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   []
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
              F"stage{idx}.patch_embed.proj.weight",
          )			)
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
              F"stage{idx}.patch_embed.proj.bias",
          )			)
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
              F"stage{idx}.patch_embed.norm.weight",
          )			)
      embed.append(
          (
              F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
              F"stage{idx}.patch_embed.norm.bias",
          )			)
      return embed






def 					__lowercase      (     _A						,    _A			)     ->						Any:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   []
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
              F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
              F"stage{idx}.blocks.{cnt}.attn.proj.weight",
          )			)
      attention_weights.append(
          (
              F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
              F"stage{idx}.blocks.{cnt}.attn.proj.bias",
          )			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight")			)
      attention_weights.append(
          (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias")			)
      return attention_weights






def 					__lowercase      (     _A			)     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      List[Any]       =   []
      token.append((F"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")			)
      return token






def 					__lowercase      (     )     ->						Union[str, Any]:
      SCREAMING_SNAKE_CASE :      Dict       =   []
      head.append(("""layernorm.weight""", """norm.weight""")			)
      head.append(("""layernorm.bias""", """norm.bias""")			)
      head.append(("""classifier.weight""", """head.weight""")			)
      head.append(("""classifier.bias""", """head.bias""")			)
      return head






def 					__lowercase      (     _A						,    _A						,    _A						,    _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      List[str]       =   """imagenet-1k-id2label.json"""
      SCREAMING_SNAKE_CASE :      Tuple       =   1000

      SCREAMING_SNAKE_CASE :      str       =   """huggingface/label-files"""
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   num_labels
      SCREAMING_SNAKE_CASE :      int       =   json.load(open(cached_download(hf_hub_url(_A						,    _A						,    repo_type="""dataset"""			)			)						,    """r"""			)			)
      SCREAMING_SNAKE_CASE :      int       =   {int(_A			): v for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE :      List[Any]       =   idalabel
      SCREAMING_SNAKE_CASE :      int       =   {v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE :      Tuple       =   CvtConfig(num_labels=_A						,    idalabel=_A						,    labelaid=_A			)

      # For depth size 13 (13 = 1+2+10)
      if cvt_model.rsplit("""/"""						,    1			)[-1][4:6] == "13":
            SCREAMING_SNAKE_CASE :      List[str]       =   [1, 2, 10]

      # For depth size 21 (21 = 1+4+16)
      elif cvt_model.rsplit("""/"""						,    1			)[-1][4:6] == "21":
            SCREAMING_SNAKE_CASE :      int       =   [1, 4, 16]

      # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
      else:
            SCREAMING_SNAKE_CASE :      str       =   [2, 2, 20]
            SCREAMING_SNAKE_CASE :      Optional[int]       =   [3, 12, 16]
            SCREAMING_SNAKE_CASE :      str       =   [192, 768, 1024]

      SCREAMING_SNAKE_CASE :      List[Any]       =   CvtForImageClassification(_A			)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""			)
      SCREAMING_SNAKE_CASE :      List[str]       =   image_size
      SCREAMING_SNAKE_CASE :      Tuple       =   torch.load(_A						,    map_location=torch.device("""cpu"""			)			)

      SCREAMING_SNAKE_CASE :      Tuple       =   OrderedDict()
      SCREAMING_SNAKE_CASE :      Any       =   []

      for idx in range(len(config.depth			)			):
            if config.cls_token[idx]:
                  SCREAMING_SNAKE_CASE :      Any       =   list_of_state_dict + cls_token(_A			)
            SCREAMING_SNAKE_CASE :      int       =   list_of_state_dict + embeddings(_A			)
            for cnt in range(config.depth[idx]			):
                  SCREAMING_SNAKE_CASE :      str       =   list_of_state_dict + attention(_A						,    _A			)

      SCREAMING_SNAKE_CASE :      List[str]       =   list_of_state_dict + final()
      for gg in list_of_state_dict:
            print(_A			)
      for i in range(len(_A			)			):
            SCREAMING_SNAKE_CASE :      str       =   original_weights[list_of_state_dict[i][1]]

      model.load_state_dict(_A			)
      model.save_pretrained(_A			)
      image_processor.save_pretrained(_A			)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
  UpperCAmelCase__ :     List[str]				    =    argparse.ArgumentParser()
  parser.add_argument(
      """--cvt_model""",
      default="""cvt-w24""",
      type=str,
      help="""Name of the cvt model you'd like to convert.""",
  )
  parser.add_argument(
      """--image_size""",
      default=384,
      type=int,
      help="""Input Image Size""",
  )
  parser.add_argument(
      """--cvt_file_name""",
      default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
      type=str,
      help="""Input Image Size""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  UpperCAmelCase__ :     List[str]				    =    parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







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


UpperCAmelCase__ :     Tuple				    =    logging.get_logger(__name__)







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[int]			       =["""input_features""", """attention_mask"""]


  def __init__(			self    : List[str]				,				UpperCAmelCase__    : Optional[Any]=8_0				,				UpperCAmelCase__    : List[str]=1_6_0_0_0				,				UpperCAmelCase__    : Optional[int]=8_0				,				UpperCAmelCase__    : List[str]=0.0				,				UpperCAmelCase__    : List[Any]=True				,				UpperCAmelCase__    : Tuple=True				,				UpperCAmelCase__    : Tuple=True				,				**UpperCAmelCase__    : Optional[int]				,				)					->Optional[int]:
        """simple docstring"""







        super().__init__(feature_size=UpperCAmelCase__				,				sampling_rate=UpperCAmelCase__				,				padding_value=UpperCAmelCase__				,				**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_mel_bins
        SCREAMING_SNAKE_CASE :      Dict       =   do_ceptral_normalize
        SCREAMING_SNAKE_CASE :      Optional[int]       =   normalize_means
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   normalize_vars
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   True


  def  _lowercase      (			self    : Tuple				,				UpperCAmelCase__    : np.ndarray				,				)					->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
        SCREAMING_SNAKE_CASE :      Dict       =   torch.from_numpy(UpperCAmelCase__		).unsqueeze(0		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   ta_kaldi.fbank(UpperCAmelCase__				,				num_mel_bins=self.num_mel_bins				,				sample_frequency=self.sampling_rate		)
        return features.numpy()


  @staticmethod
  def  _lowercase      (			UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Optional[bool] = True				,				UpperCAmelCase__    : Optional[bool] = True				,				UpperCAmelCase__    : float = 0.0				,				)					->np.ndarray:
        """simple docstring"""







        if normalize_means:
              SCREAMING_SNAKE_CASE :      Tuple       =   x[:input_length].mean(axis=0		)
              SCREAMING_SNAKE_CASE :      List[Any]       =   np.subtract(UpperCAmelCase__				,				UpperCAmelCase__		)
        if normalize_vars:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   x[:input_length].std(axis=0		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.divide(UpperCAmelCase__				,				UpperCAmelCase__		)

        if input_length < x.shape[0]:
              SCREAMING_SNAKE_CASE :      Tuple       =   padding_value

        # make sure array is in float32
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   x.astype(np.floataa		)

        return x


  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : List[np.ndarray]				,				UpperCAmelCase__    : Optional[np.ndarray] = None		)					->List[np.ndarray]:
        """simple docstring"""







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




  def __call__(			self    : str				,				UpperCAmelCase__    : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,				UpperCAmelCase__    : Union[bool, str, PaddingStrategy] = False				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : bool = False				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Optional[Union[str, TensorType]] = None				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : Optional[bool] = None				,				**UpperCAmelCase__    : Optional[int]				,				)					->BatchFeature:
        """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."""		)

        SCREAMING_SNAKE_CASE :      Tuple       =   isinstance(UpperCAmelCase__				,				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}"		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   is_batched_numpy or (
            isinstance(UpperCAmelCase__				,				(list, tuple)		) and (isinstance(raw_speech[0]				,				(np.ndarray, tuple, list)		))
        )

        if is_batched:
              SCREAMING_SNAKE_CASE :      Dict       =   [np.asarray(UpperCAmelCase__				,				dtype=np.floataa		) for speech in raw_speech]
        elif not is_batched and not isinstance(UpperCAmelCase__				,				np.ndarray		):
              SCREAMING_SNAKE_CASE :      Any       =   np.asarray(UpperCAmelCase__				,				dtype=np.floataa		)
        elif isinstance(UpperCAmelCase__				,				np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
              SCREAMING_SNAKE_CASE :      Tuple       =   raw_speech.astype(np.floataa		)

        # always return batch
        if not is_batched:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   [raw_speech]

        # extract fbank features
        SCREAMING_SNAKE_CASE :      Tuple       =   [self._extract_fbank_features(UpperCAmelCase__		) for waveform in raw_speech]

        # convert into correct format for padding
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   BatchFeature({"""input_features""": features}		)

        SCREAMING_SNAKE_CASE :      str       =   self.pad(
            UpperCAmelCase__				,				padding=UpperCAmelCase__				,				max_length=UpperCAmelCase__				,				truncation=UpperCAmelCase__				,				pad_to_multiple_of=UpperCAmelCase__				,				return_attention_mask=UpperCAmelCase__				,				**UpperCAmelCase__				,				)

        # make sure list is in array format
        SCREAMING_SNAKE_CASE :      Dict       =   padded_inputs.get("""input_features"""		)
        if isinstance(input_features[0]				,				UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [np.asarray(UpperCAmelCase__				,				dtype=np.floataa		) for feature in input_features]

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

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

        if return_tensors is not None:
              SCREAMING_SNAKE_CASE :      Any       =   padded_inputs.convert_to_tensors(UpperCAmelCase__		)

        return padded_inputs




