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_           =				logging.get_logger(__name__)




class 			__magic_name__	(		__SCREAMING_SNAKE_CASE     ):





   """simple docstring"""




   lowerCAmelCase   :     Any	      = ["""input_features""", """attention_mask"""]



   def __init__(						self       :	Dict		,	_lowercase       :	str=80		,	_lowercase       :	Any=16_000		,	_lowercase       :	List[Any]=80		,	_lowercase       :	Any=0.0		,	_lowercase       :	List[str]=True		,	_lowercase       :	Any=True		,	_lowercase       :	int=True		,	**_lowercase       :	Optional[int]		,	):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	str		,	):



    """simple docstring"""



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



   @staticmethod
   def 	lowerCAmelCase    (						_lowercase       :	Optional[int]		,	_lowercase       :	Optional[Any]		,	_lowercase       :	Union[str, Any] = True		,	_lowercase       :	Tuple = True		,	_lowercase       :	int = 0.0		,	):



    """simple docstring"""



    if normalize_means:
     _UpperCamelCase:  Optional[int]    						=			x[:input_length].mean(axis=0				)
     _UpperCamelCase:  int    						=			np.subtract(A_		,	A_				)
    if normalize_vars:
     _UpperCamelCase:  Optional[int]    						=			x[:input_length].std(axis=0				)
     _UpperCamelCase:  Dict    						=			np.divide(A_		,	A_				)

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

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

    return x



   def 	lowerCAmelCase    (						self       :	Optional[Any]		,	_lowercase       :	Tuple		,	_lowercase       :	Tuple = None				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			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       :	List[str]		,	_lowercase       :	Dict		,	_lowercase       :	Optional[Any] = False		,	_lowercase       :	Optional[Any] = None		,	_lowercase       :	List[Any] = False		,	_lowercase       :	Optional[Any] = None		,	_lowercase       :	Optional[int] = None		,	_lowercase       :	Any = None		,	_lowercase       :	List[str] = None		,	**_lowercase       :	str		,	):



    """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:  Any    						=			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:  int    						=			is_batched_numpy or (
        isinstance(A_		,	(list, tuple)				) and (isinstance(raw_speech[0]		,	(np.ndarray, tuple, list)				))
    )

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

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

    # extract fbank features
    _UpperCamelCase:  List[str]    						=			[self._extract_fbank_features(A_				) for waveform in raw_speech]

    # convert into correct format for padding
    _UpperCamelCase:  List[str]    						=			BatchFeature({'''input_features''': features}				)

    _UpperCamelCase:  str    						=			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:  int    						=			padded_inputs.get('''input_features'''				)
    if isinstance(input_features[0]		,	A_				):
     _UpperCamelCase:  List[Any]    						=			[np.asarray(A_		,	dtype=np.floataa				) for feature in input_features]

    _UpperCamelCase:  List[str]    						=			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:  str    						=			(
         np.array(A_		,	dtype=np.intaa				)
         if self._get_padding_strategies(A_		,	max_length=A_				) is not PaddingStrategy.DO_NOT_PAD
         else None
     )
     _UpperCamelCase:  Optional[Any]    						=			self.normalize(
         padded_inputs['''input_features''']		,	attention_mask=A_				)

    if return_tensors is not None:
     _UpperCamelCase:  str    						=			padded_inputs.convert_to_tensors(A_				)

    return padded_inputs






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 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_	)				->						List[Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')	)
    return token


def 							__snake_case							(    )				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			[]
    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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''
    SCREAMING_SNAKE_CASE__											=			1_0_0_0

    SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
    SCREAMING_SNAKE_CASE__											=			num_labels
    SCREAMING_SNAKE_CASE__											=			json.load(open(cached_download(hf_hub_url(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset'''	)	) ,							'''r'''	)	)
    SCREAMING_SNAKE_CASE__											=			{int(lowerCAmelCase_	): v for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE__											=			idalabel
    SCREAMING_SNAKE_CASE__											=			{v: k for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE__											=			SCREAMING_SNAKE_CASE__											=			CvtConfig(num_labels=lowerCAmelCase_ ,							idalabel=lowerCAmelCase_ ,							labelaid=lowerCAmelCase_	)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit('''/''' ,							1	)[-1][4:6] == "13":
        SCREAMING_SNAKE_CASE__											=			[1, 2, 1_0]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit('''/''' ,							1	)[-1][4:6] == "21":
        SCREAMING_SNAKE_CASE__											=			[1, 4, 1_6]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        SCREAMING_SNAKE_CASE__											=			[2, 2, 2_0]
        SCREAMING_SNAKE_CASE__											=			[3, 1_2, 1_6]
        SCREAMING_SNAKE_CASE__											=			[1_9_2, 7_6_8, 1_0_2_4]

    SCREAMING_SNAKE_CASE__											=			CvtForImageClassification(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	)
    SCREAMING_SNAKE_CASE__											=			image_size
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location=torch.device('''cpu'''	)	)

    SCREAMING_SNAKE_CASE__											=			OrderedDict()
    SCREAMING_SNAKE_CASE__											=			[]

    for idx in range(len(config.depth	)	):
        if config.cls_token[idx]:
            SCREAMING_SNAKE_CASE__											=			list_of_state_dict + cls_token(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			list_of_state_dict + embeddings(lowerCAmelCase_	)
        for cnt in range(config.depth[idx]	):
            SCREAMING_SNAKE_CASE__											=			list_of_state_dict + attention(lowerCAmelCase_ ,							lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(lowerCAmelCase_	)
    for i in range(len(lowerCAmelCase_	)	):
        SCREAMING_SNAKE_CASE__											=			original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(lowerCAmelCase_	)
    model.save_pretrained(lowerCAmelCase_	)
    image_processor.save_pretrained(lowerCAmelCase_	)


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

if __name__ == "__main__":
  _A					:				Union[str, Any]				=			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=3_84,
      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."""
  )

  _A					:				int				=			parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
