






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_       			=   logging.get_logger(__name__)







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       ['''input_features''', '''attention_mask''']





	def __init__(   self      :				Dict			,			lowerCAmelCase_      :				Tuple=80			,			lowerCAmelCase_      :				Any=16_000			,			lowerCAmelCase_      :				Tuple=80			,			lowerCAmelCase_      :				Any=0.0			,			lowerCAmelCase_      :				Dict=True			,			lowerCAmelCase_      :				Dict=True			,			lowerCAmelCase_      :				Optional[int]=True			,			**lowerCAmelCase_      :				Any			,			)		->    Tuple:
				super().__init__(feature_size=lowerCAmelCase_			,			sampling_rate=lowerCAmelCase_			,			padding_value=lowerCAmelCase_			,			**lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      num_mel_bins
				UpperCAmelCase_  :				Any      =      do_ceptral_normalize
				UpperCAmelCase_  :				Union[str, Any]      =      normalize_means
				UpperCAmelCase_  :				int      =      normalize_vars
				UpperCAmelCase_  :				Any      =      True





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				np.ndarray			,			)		->    np.ndarray:
				UpperCAmelCase_  :				List[str]      =      waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
				UpperCAmelCase_  :				Optional[Any]      =      torch.from_numpy(lowerCAmelCase_		).unsqueeze(0		)
				UpperCAmelCase_  :				List[Any]      =      ta_kaldi.fbank(lowerCAmelCase_			,			num_mel_bins=self.num_mel_bins			,			sample_frequency=self.sampling_rate		)
				return features.numpy()





	@staticmethod
	def   _SCREAMING_SNAKE_CASE   (   lowerCAmelCase_      :				np.ndarray			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Optional[bool] = True			,			lowerCAmelCase_      :				Optional[bool] = True			,			lowerCAmelCase_      :				float = 0.0			,			)		->    np.ndarray:
				# make sure we normalize float32 arrays
				if normalize_means:
							UpperCAmelCase_  :				List[str]      =      x[:input_length].mean(axis=0		)
							UpperCAmelCase_  :				Any      =      np.subtract(lowerCAmelCase_			,			lowerCAmelCase_		)
				if normalize_vars:
							UpperCAmelCase_  :				Optional[int]      =      x[:input_length].std(axis=0		)
							UpperCAmelCase_  :				List[str]      =      np.divide(lowerCAmelCase_			,			lowerCAmelCase_		)

				if input_length < x.shape[0]:
							UpperCAmelCase_  :				List[Any]      =      padding_value

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

				return x





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				List[np.ndarray]			,			lowerCAmelCase_      :				Optional[np.ndarray] = None		)		->    List[np.ndarray]:
				UpperCAmelCase_  :				Tuple      =      attention_mask.sum(-1		) if attention_mask is not None else [x.shape[0] for x in input_features]
				return [
				    self.utterance_cmvn(lowerCAmelCase_			,			lowerCAmelCase_			,			self.normalize_means			,			self.normalize_vars			,			self.padding_value		)
				    for x, n in zip(lowerCAmelCase_			,			lowerCAmelCase_		)
				]





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

				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_  :				Tuple      =      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}"""		)
				UpperCAmelCase_  :				str      =      is_batched_numpy or (
				    isinstance(lowerCAmelCase_			,			(list, tuple)		) and (isinstance(raw_speech[0]			,			(np.ndarray, tuple, list)		))
				)

				if is_batched:
							UpperCAmelCase_  :				Dict      =      [np.asarray(lowerCAmelCase_			,			dtype=np.floataa		) for speech in raw_speech]
				elif not is_batched and not isinstance(lowerCAmelCase_			,			np.ndarray		):
							UpperCAmelCase_  :				List[str]      =      np.asarray(lowerCAmelCase_			,			dtype=np.floataa		)
				elif isinstance(lowerCAmelCase_			,			np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
							UpperCAmelCase_  :				Tuple      =      raw_speech.astype(np.floataa		)

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

				# extract fbank features
				UpperCAmelCase_  :				Dict      =      [self._extract_fbank_features(lowerCAmelCase_		) for waveform in raw_speech]

				# convert into correct format for padding
				UpperCAmelCase_  :				Any      =      BatchFeature({"input_features": features}		)

				UpperCAmelCase_  :				List[str]      =      self.pad(
				    lowerCAmelCase_			,			padding=lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			pad_to_multiple_of=lowerCAmelCase_			,			return_attention_mask=lowerCAmelCase_			,			**lowerCAmelCase_			,			)

				# make sure list is in array format
				UpperCAmelCase_  :				List[Any]      =      padded_inputs.get("input_features"		)
				if isinstance(input_features[0]			,			lowerCAmelCase_		):
							UpperCAmelCase_  :				List[Any]      =      [np.asarray(lowerCAmelCase_			,			dtype=np.floataa		) for feature in input_features]

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

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

				if return_tensors is not None:
							UpperCAmelCase_  :				Optional[int]      =      padded_inputs.convert_to_tensors(lowerCAmelCase_		)

				return padded_inputs











"""simple docstring"""
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   (     A__		):
			UpperCAmelCase_  :				Tuple      =      []
			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   (     A__			,A__		):
			UpperCAmelCase_  :				List[Any]      =      []
			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   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      []
			token.append((F"""cvt.encoder.stages.{idx}.cls_token""", "stage2.cls_token")		)
			return token

def        snake_case   (     ):
			UpperCAmelCase_  :				str      =      []
			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   (     A__			,A__			,A__			,A__		):
			UpperCAmelCase_  :				int      =      "imagenet-1k-id2label.json"
			UpperCAmelCase_  :				Union[str, Any]      =      10_00

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

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

			UpperCAmelCase_  :				str      =      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":
						UpperCAmelCase_  :				Optional[int]      =      [1, 2, 10]

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

			# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
			else:
						UpperCAmelCase_  :				Optional[int]      =      [2, 2, 20]
						UpperCAmelCase_  :				List[str]      =      [3, 12, 16]
						UpperCAmelCase_  :				Optional[Any]      =      [1_92, 7_68, 10_24]

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

			UpperCAmelCase_  :				int      =      OrderedDict()
			UpperCAmelCase_  :				Optional[Any]      =      []

			for idx in range(len(config.depth		)		):
						if config.cls_token[idx]:
									UpperCAmelCase_  :				Optional[Any]      =      list_of_state_dict + cls_token(A__		)
						UpperCAmelCase_  :				Any      =      list_of_state_dict + embeddings(A__		)
						for cnt in range(config.depth[idx]		):
									UpperCAmelCase_  :				Dict      =      list_of_state_dict + attention(A__			,A__		)

			UpperCAmelCase_  :				Union[str, Any]      =      list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(A__		)
			for i in range(len(A__		)		):
						UpperCAmelCase_  :				Any      =      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__":
			lowerCamelCase_       			=   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.'''
			)

			lowerCamelCase_       			=   parser.parse_args()
			convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)



