






"""simple docstring"""


import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
   from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
   _UpperCamelCase    :   Tuple   			=						True
   from torch.cuda.amp import autocast


_UpperCamelCase    :   str   			=						logging.getLogger(__name__)






@dataclass
class 						snake_case  :
    __magic_name__   					=					field(
        metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
    __magic_name__   					=					field(
        default=lowercase__       ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,			)
    __magic_name__   					=					field(
        default=lowercase__       ,			metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}	)
    __magic_name__   					=					field(
        default=lowercase__       ,			metadata={'''help''': '''Whether to log verbose messages or not.'''}       ,			)
    __magic_name__   					=					field(
        default=2.0       ,			metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}	)
    __magic_name__   					=					field(
        default=0.5       ,			metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}	)
    __magic_name__   					=					field(
        default=0.99_9995       ,			metadata={'''help''': '''Decay of gumbel temperature during training.'''}	)




def       snake_case  (A_				:ModelArguments     ,      A_				:TrainingArguments				):
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'     ,      datefmt='%m/%d/%Y %H:%M:%S'     ,      handlers=[logging.StreamHandler(sys.stdout				)]     ,      )
  a					:      Dict    =       logging.WARNING
  if model_args.verbose_logging:
    a					:      int    =       logging.DEBUG
  elif trainer_utils.is_main_process(training_args.local_rank				):
    a					:      int    =       logging.INFO
  logger.setLevel(lowerCamelCase_				)






@dataclass
class 						snake_case  :
    __magic_name__   					=					field(
        default=lowercase__       ,			metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}	)
    __magic_name__   					=					field(
        default=lowercase__       ,			metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}	)
    __magic_name__   					=					field(
        default='''train'''       ,			metadata={
            '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
        }       ,			)
    __magic_name__   					=					field(
        default='''validation'''       ,			metadata={
            '''help''': (
                '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
            )
        }       ,			)
    __magic_name__   					=					field(
        default='''file'''       ,			metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}       ,			)
    __magic_name__   					=					field(
        default=lowercase__       ,			metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}	)
    __magic_name__   					=					field(
        default=1       ,			metadata={
            '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
        }       ,			)
    __magic_name__   					=					field(
        default=lowercase__       ,			metadata={'''help''': '''The number of processes to use for the preprocessing.'''}       ,			)
    __magic_name__   					=					field(
        default=20.0       ,			metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}	)






@dataclass
class 						snake_case  :
    __magic_name__   					=					42
    __magic_name__   					=					42
    __magic_name__   					=					"longest"
    __magic_name__   					=					None
    __magic_name__   					=					None





    def __call__(							self     : str    ,		A     : List[Dict[str, Union[List[int], torch.Tensor]]]      ):


      '''simple docstring'''




      a					:      int    =       self.feature_extractor.pad(
          _UpperCamelCase    ,		max_length=self.max_length    ,		padding=self.padding    ,		pad_to_multiple_of=self.pad_to_multiple_of    ,		return_tensors='pt'    ,		)
      a					:      Tuple    =       self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]      )

      a					:      List[Any]    =       batch["""input_values"""].shape[0]

      # make sure that no loss is computed on padded inputs
      if batch["attention_mask"] is not None:
        # compute real output lengths according to convolution formula
        a					:      int    =       self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1      )      ).to(
            torch.long      )

        a					:      List[str]    =       torch.zeros(
            (batch_size, mask_indices_seq_length)    ,		dtype=torch.long    ,		device=batch['input_values'].device      )

        # these two operations makes sure that all values
        # before the output lengths indices are attended to
        a					:      Tuple    =       1
        a					:      Dict    =       attention_mask.flip([-1]      ).cumsum(-1      ).flip([-1]      ).bool()

      # sample randomly masked indices
      a					:      str    =       _compute_mask_indices(
          (batch_size, mask_indices_seq_length)    ,		self.model.config.mask_time_prob    ,		self.model.config.mask_time_length    ,		attention_mask=_UpperCamelCase    ,		min_masks=2    ,		)

      return batch






class 						snake_case  (  lowercase__	):





    def __init__(							self     : List[Any]    ,		*A     : List[Any]    ,		A     : Dict=1    ,		A     : Optional[int]=0    ,		A     : Optional[Any]=1.0    ,		**A     : Tuple      ):


      '''simple docstring'''




      super().__init__(*_UpperCamelCase    ,		**_UpperCamelCase      )
      a					:      List[str]    =       0
      a					:      List[str]    =       max_gumbel_temp
      a					:      Union[str, Any]    =       min_gumbel_temp
      a					:      List[str]    =       gumbel_temp_decay





    def     lowerCamelCase__				(							self     : int    ,		A     : nn.Module    ,		A     : Dict[str, Union[torch.Tensor, Any]]      ):


      '''simple docstring'''




      model.train()
      a					:      Any    =       self._prepare_inputs(_UpperCamelCase      )

      if self.use_amp:
        with autocast():
          a					:      Tuple    =       self.compute_loss(_UpperCamelCase    ,		_UpperCamelCase      )
      else:
        a					:      Optional[int]    =       self.compute_loss(_UpperCamelCase    ,		_UpperCamelCase      )

      if self.args.n_gpu > 1 or self.deepspeed:
        if model.module.config.ctc_loss_reduction == "mean":
          a					:      Dict    =       loss.mean()
        elif model.module.config.ctc_loss_reduction == "sum":
          a					:      Union[str, Any]    =       loss.sum() / (inputs["""mask_time_indices"""]).sum()
        else:
          raise ValueError(F'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''      )

      if self.args.gradient_accumulation_steps > 1:
        a					:      Optional[Any]    =       loss / self.args.gradient_accumulation_steps

      if self.use_amp:
        self.scaler.scale(_UpperCamelCase      ).backward()
      elif self.use_apex:
        with amp.scale_loss(_UpperCamelCase    ,		self.optimizer      ) as scaled_loss:
          scaled_loss.backward()
      elif self.deepspeed:
        self.deepspeed.backward(_UpperCamelCase      )
      else:
        loss.backward()

      self.num_update_step += 1
      # make sure gumbel softmax temperature is decayed
      if self.args.n_gpu > 1 or self.deepspeed:
        model.module.set_gumbel_temperature(
            max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step    ,		self.min_gumbel_temp      )      )
      else:
        model.set_gumbel_temperature(
            max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step    ,		self.min_gumbel_temp      )      )

      return loss.detach()




def       snake_case  ():
  a					:      List[str]    =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)

  a					:      str    =       parser.parse_args_into_dataclasses()
  configure_logger(lowerCamelCase_     ,      lowerCamelCase_				)

  # Downloading and loading a dataset from the hub.
  a					:      Optional[int]    =       load_dataset(data_args.dataset_name     ,      data_args.dataset_config_name     ,      cache_dir=model_args.cache_dir				)

  if "validation" not in datasets.keys():
    # make sure only "validation" and "train" keys remain"
    a					:      Tuple    =       DatasetDict()
    a					:      Optional[Any]    =       load_dataset(
        data_args.dataset_name     ,      data_args.dataset_config_name     ,      split=f'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'''     ,      cache_dir=model_args.cache_dir     ,      )
    a					:      Optional[int]    =       load_dataset(
        data_args.dataset_name     ,      data_args.dataset_config_name     ,      split=f'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'''     ,      cache_dir=model_args.cache_dir     ,      )
  else:
    # make sure only "validation" and "train" keys remain"
    a					:      List[Any]    =       DatasetDict()
    a					:      Optional[Any]    =       load_dataset(
        data_args.dataset_name     ,      data_args.dataset_config_name     ,      split='validation'     ,      cache_dir=model_args.cache_dir     ,      )
    a					:      List[Any]    =       load_dataset(
        data_args.dataset_name     ,      data_args.dataset_config_name     ,      split=f'''{data_args.train_split_name}'''     ,      cache_dir=model_args.cache_dir     ,      )

  # only normalized-inputs-training is supported
  a					:      Optional[int]    =       WavaVecaFeatureExtractor.from_pretrained(
      model_args.model_name_or_path     ,      cache_dir=model_args.cache_dir     ,      do_normalize=lowerCamelCase_				)

  def prepare_dataset(A_				:List[Any]				):
    # check that all files have the correct sampling rate
    a					:      str    =       librosa.load(batch[data_args.speech_file_column]     ,      sr=feature_extractor.sampling_rate				)
    return batch

  # load audio files into numpy arrays
  a					:      Union[str, Any]    =       datasets.map(
      lowerCamelCase_     ,      num_proc=data_args.preprocessing_num_workers     ,      remove_columns=datasets['train'].column_names				)

  # filter audio files that are too long
  a					:      Optional[Any]    =       vectorized_datasets.filter(
      lambda A_				: len(data['speech']				) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate				)				)

  def normalize(A_				:Dict				):
    return feature_extractor(batch['speech']     ,      sampling_rate=feature_extractor.sampling_rate				)

  # normalize and transform to `BatchFeatures`
  a					:      Optional[Any]    =       vectorized_datasets.map(
      lowerCamelCase_     ,      batched=lowerCamelCase_     ,      num_proc=data_args.preprocessing_num_workers     ,      load_from_cache_file=not data_args.overwrite_cache     ,      remove_columns=vectorized_datasets['train'].column_names     ,      )

  # pretraining is only supported for "newer" stable layer norm architecture
  # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
  a					:      Any    =       WavaVecaConfig.from_pretrained(
      model_args.model_name_or_path     ,      cache_dir=model_args.cache_dir     ,      gradient_checkpointing=training_args.gradient_checkpointing     ,      )

  if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
    raise ValueError(
        'PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'
        ' ``config.feat_extract_norm=\'layer\''				)

  a					:      Optional[Any]    =       WavaVecaForPreTraining(lowerCamelCase_				)

  a					:      List[Any]    =       DataCollatorForWavaVecaPretraining(model=lowerCamelCase_     ,      feature_extractor=lowerCamelCase_				)

  a					:      Optional[int]    =       WavaVecaPreTrainer(
      model=lowerCamelCase_     ,      data_collator=lowerCamelCase_     ,      args=lowerCamelCase_     ,      train_dataset=vectorized_datasets['train']     ,      eval_dataset=vectorized_datasets['validation']     ,      tokenizer=lowerCamelCase_     ,      max_gumbel_temp=model_args.max_gumbel_temperature     ,      min_gumbel_temp=model_args.min_gumbel_temperature     ,      gumbel_temp_decay=model_args.gumbel_temperature_decay     ,      )
  trainer.train()


if __name__ == "__main__":
   main()












"""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_				:Dict				):







  '''simple docstring'''

  a					:      str    =       []
  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_				:Any     ,      A_				:List[Any]				):







  '''simple docstring'''

  a					:      Union[str, 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_				:Dict				):







  '''simple docstring'''

  a					:      int    =       []
  token.append((f'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token')				)
  return token




def       snake_case  ():







  '''simple docstring'''

  a					:      int    =       []
  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_				:int     ,      A_				:Optional[int]     ,      A_				:Dict     ,      A_				:Dict				):







  '''simple docstring'''

  a					:      Optional[Any]    =       'imagenet-1k-id2label.json'
  a					:      Dict    =       1_0_0_0

  a					:      Tuple    =       'huggingface/label-files'
  a					:      List[Any]    =       num_labels
  a					:      List[str]    =       json.load(open(cached_download(hf_hub_url(A_     ,      A_     ,      repo_type='dataset'				)				)     ,      'r'				)				)
  a					:      int    =       {int(A_				): v for k, v in idalabel.items()}

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

  a					:      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":
    a					:      int    =       [1, 2, 1_0]

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

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

  a					:      List[Any]    =       CvtForImageClassification(A_				)
  a					:      Optional[int]    =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
  a					:      Union[str, Any]    =       image_size
  a					:      Optional[Any]    =       torch.load(A_     ,      map_location=torch.device('cpu'				)				)

  a					:      int    =       OrderedDict()
  a					:      Any    =       []

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

  a					:      Any    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(A_				)
  for i in range(len(A_				)				):
    a					:      List[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__":
   _UpperCamelCase    :   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=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    :   int   			=						parser.parse_args()
   convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




