






"""simple docstring"""


import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_UpperCamelCase    :   Union[str, Any]   			=						logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_UpperCamelCase    :   int   			=						{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_UpperCamelCase    :   Any   			=						{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_UpperCamelCase    :   Optional[Any]   			=						sorted(arg_to_scheduler.keys())
_UpperCamelCase    :   Tuple   			=						'{' + ', '.join(arg_to_scheduler_choices) + '}'






class 						snake_case  (  pl.LightningModule	):





    def __init__(							self     : Optional[Any]    ,		A     : argparse.Namespace    ,		A     : List[str]=None    ,		A     : Optional[Any]="base"    ,		A     : int=None    ,		A     : Any=None    ,		A     : str=None    ,		**A     : str    ,		):


      '''simple docstring'''




      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading

      self.save_hyperparameters(A      )
      a					:      Any    =       0
      a					:      Optional[int]    =       Path(self.hparams.output_dir      )
      a					:      List[Any]    =       self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
        a					:      Any    =       AutoConfig.from_pretrained(
            self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path    ,		**({'num_labels': num_labels} if num_labels is not None else {})    ,		cache_dir=A    ,		**A    ,		)
      else:
        a					:      PretrainedConfig    =       config

      a					:      Optional[Any]    =       ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
      for p in extra_model_params:
        if getattr(self.hparams    ,		A    ,		A      ):
          assert hasattr(self.config    ,		A      ), F'''model config doesn\'t have a `{p}` attribute'''
          setattr(self.config    ,		A    ,		getattr(self.hparams    ,		A      )      )

      if tokenizer is None:
        a					:      int    =       AutoTokenizer.from_pretrained(
            self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,		cache_dir=A    ,		)
      else:
        a					:      PreTrainedTokenizer    =       tokenizer
      a					:      Optional[int]    =       MODEL_MODES[mode]
      if model is None:
        a					:      Union[str, Any]    =       self.model_type.from_pretrained(
            self.hparams.model_name_or_path    ,		from_tf=bool('.ckpt' in self.hparams.model_name_or_path      )    ,		config=self.config    ,		cache_dir=A    ,		)
      else:
        a					:      Optional[int]    =       model





    def     lowerCamelCase__				(							self     : Dict    ,		*A     : str    ,		**A     : Any      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       self.model_type.from_pretrained(*A    ,		**A      )





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       arg_to_scheduler[self.hparams.lr_scheduler]
      a					:      Any    =       get_schedule_func(
          self.opt    ,		num_warmup_steps=self.hparams.warmup_steps    ,		num_training_steps=self.total_steps()      )
      a					:      Optional[Any]    =       {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
      return scheduler





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      int    =       self.model
      a					:      str    =       ['bias', 'LayerNorm.weight']
      a					:      Union[str, Any]    =       [
          {
              'params': [
                  p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )
              ],  # check this named paramters
              'weight_decay': self.hparams.weight_decay,
          },
          {
              'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay      )],
              'weight_decay': 0.0,
          },
      ]
      if self.hparams.adafactor:
        a					:      Any    =       Adafactor(
            A    ,		lr=self.hparams.learning_rate    ,		scale_parameter=A    ,		relative_step=A      )

      else:
        a					:      List[Any]    =       AdamW(
            A    ,		lr=self.hparams.learning_rate    ,		eps=self.hparams.adam_epsilon      )
      a					:      List[Any]    =       optimizer

      a					:      int    =       self.get_lr_scheduler()

      return [optimizer], [scheduler]





    def     lowerCamelCase__				(							self     : Dict    ,		A     : Optional[Any]    ,		A     : Dict      ):


      '''simple docstring'''




      return self.validation_step(A    ,		A      )





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : str      ):


      '''simple docstring'''




      return self.validation_end(A      )





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a					:      int    =       max(1    ,		self.hparams.gpus      )  # TODO: consider num_tpu_cores
      a					:      Optional[Any]    =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





    def     lowerCamelCase__				(							self     : int    ,		A     : Any      ):


      '''simple docstring'''




      if stage == "test":
        a					:      Optional[Any]    =       len(self.test_dataloader().dataset      )
      else:
        a					:      Optional[int]    =       self.get_dataloader('train'    ,		self.hparams.train_batch_size    ,		shuffle=A      )
        a					:      Any    =       len(self.train_dataloader().dataset      )





    def     lowerCamelCase__				(							self     : str    ,		A     : str    ,		A     : int    ,		A     : bool = False      ):


      '''simple docstring'''




      raise NotImplementedError('You must implement this for your task'      )





    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      return self.train_loader





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      return self.get_dataloader('dev'    ,		self.hparams.eval_batch_size    ,		shuffle=A      )





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      return self.get_dataloader('test'    ,		self.hparams.eval_batch_size    ,		shuffle=A      )





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : List[str]      ):


      '''simple docstring'''




      return os.path.join(
          self.hparams.data_dir    ,		'cached_{}_{}_{}'.format(
              A    ,		list(filter(A    ,		self.hparams.model_name_or_path.split('/'      )      )      ).pop()    ,		str(self.hparams.max_seq_length      )    ,		)    ,		)





    @pl.utilities.rank_zero_only
    def     lowerCamelCase__				(							self     : Any    ,		A     : Dict[str, Any]      ):


      '''simple docstring'''




      a					:      int    =       self.output_dir.joinpath('best_tfmr'      )
      a					:      Union[str, Any]    =       self.step_count
      self.model.save_pretrained(A      )
      self.tokenizer.save_pretrained(A      )





    @staticmethod
    def     lowerCamelCase__				(							A     : int    ,		A     : str      ):


      '''simple docstring'''




      parser.add_argument(
          '--model_name_or_path'    ,		default=A    ,		type=A    ,		required=A    ,		help='Path to pretrained model or model identifier from huggingface.co/models'    ,		)
      parser.add_argument(
          '--config_name'    ,		default=''    ,		type=A    ,		help='Pretrained config name or path if not the same as model_name'      )
      parser.add_argument(
          '--tokenizer_name'    ,		default=A    ,		type=A    ,		help='Pretrained tokenizer name or path if not the same as model_name'    ,		)
      parser.add_argument(
          '--cache_dir'    ,		default=str(Path(A      ).parent / 'test_run' / 'cache'      )    ,		type=A    ,		help='Where do you want to store the pre-trained models downloaded from huggingface.co'    ,		)
      parser.add_argument(
          '--encoder_layerdrop'    ,		type=A    ,		help='Encoder layer dropout probability (Optional). Goes into model.config'    ,		)
      parser.add_argument(
          '--decoder_layerdrop'    ,		type=A    ,		help='Decoder layer dropout probability (Optional). Goes into model.config'    ,		)
      parser.add_argument(
          '--dropout'    ,		type=A    ,		help='Dropout probability (Optional). Goes into model.config'    ,		)
      parser.add_argument(
          '--attention_dropout'    ,		type=A    ,		help='Attention dropout probability (Optional). Goes into model.config'    ,		)
      parser.add_argument('--learning_rate'    ,		default=5E-5    ,		type=A    ,		help='The initial learning rate for Adam.'      )
      parser.add_argument(
          '--lr_scheduler'    ,		default='linear'    ,		choices=A    ,		metavar=A    ,		type=A    ,		help='Learning rate scheduler'    ,		)
      parser.add_argument('--weight_decay'    ,		default=0.0    ,		type=A    ,		help='Weight decay if we apply some.'      )
      parser.add_argument('--adam_epsilon'    ,		default=1E-8    ,		type=A    ,		help='Epsilon for Adam optimizer.'      )
      parser.add_argument('--warmup_steps'    ,		default=0    ,		type=A    ,		help='Linear warmup over warmup_steps.'      )
      parser.add_argument('--num_workers'    ,		default=4    ,		type=A    ,		help='kwarg passed to DataLoader'      )
      parser.add_argument('--num_train_epochs'    ,		dest='max_epochs'    ,		default=3    ,		type=A      )
      parser.add_argument('--train_batch_size'    ,		default=3_2    ,		type=A      )
      parser.add_argument('--eval_batch_size'    ,		default=3_2    ,		type=A      )
      parser.add_argument('--adafactor'    ,		action='store_true'      )






class 						snake_case  (  pl.Callback	):





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : str    ,		A     : Tuple      ):


      '''simple docstring'''




      if (
          trainer.is_global_zero and trainer.global_rank == 0
      ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
        pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class 						snake_case  (  pl.Callback	):





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : Tuple    ,		A     : List[str]      ):


      '''simple docstring'''




      for name, param in pl_module.model.rag.named_parameters():
        if param.grad is None:
          print(A      )






class 						snake_case  (  pl.Callback	):





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Tuple    ,		A     : Optional[Any]      ):


      '''simple docstring'''




      a					:      str    =       trainer.lr_schedulers[0]['scheduler']
      a					:      Dict    =       {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
      pl_module.logger.log_metrics(A      )





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : pl.Trainer    ,		A     : pl.LightningModule      ):


      '''simple docstring'''




      rank_zero_info('***** Validation results *****'      )
      a					:      int    =       trainer.callback_metrics
      # Log results
      for key in sorted(A      ):
        if key not in ["log", "progress_bar"]:
          rank_zero_info('{} = {}\n'.format(A    ,		str(metrics[key]      )      )      )





    def     lowerCamelCase__				(							self     : int    ,		A     : pl.Trainer    ,		A     : pl.LightningModule      ):


      '''simple docstring'''




      rank_zero_info('***** Test results *****'      )
      a					:      List[Any]    =       trainer.callback_metrics
      # Log and save results to file
      a					:      Tuple    =       os.path.join(pl_module.hparams.output_dir    ,		'test_results.txt'      )
      with open(A    ,		'w'      ) as writer:
        for key in sorted(A      ):
          if key not in ["log", "progress_bar"]:
            rank_zero_info('{} = {}\n'.format(A    ,		str(metrics[key]      )      )      )
            writer.write('{} = {}\n'.format(A    ,		str(metrics[key]      )      )      )




def       snake_case  (A_				:List[Any]     ,      A_				:List[str]				):
  parser.add_argument(
      '--output_dir'     ,      default=str(Path(A_				).parent / 'test_run' / 'model_checkpoints'				)     ,      type=A_     ,      help='The output directory where the model predictions and checkpoints will be written.'     ,      )
  parser.add_argument(
      '--fp16'     ,      action='store_true'     ,      help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'     ,      )

  parser.add_argument(
      '--fp16_opt_level'     ,      type=A_     ,      default='O2'     ,      help=(
          'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
          'See details at https://nvidia.github.io/apex/amp.html'
      )     ,      )
  parser.add_argument('--n_tpu_cores'     ,      dest='tpu_cores'     ,      type=A_				)
  parser.add_argument('--max_grad_norm'     ,      dest='gradient_clip_val'     ,      default=1.0     ,      type=A_     ,      help='Max gradient norm'				)
  parser.add_argument('--do_train'     ,      action='store_true'     ,      help='Whether to run training.'				)
  parser.add_argument('--do_predict'     ,      action='store_true'     ,      help='Whether to run predictions on the test set.'				)
  parser.add_argument(
      '--gradient_accumulation_steps'     ,      dest='accumulate_grad_batches'     ,      type=A_     ,      default=1     ,      help='Number of updates steps to accumulate before performing a backward/update pass.'     ,      )
  parser.add_argument('--seed'     ,      type=A_     ,      default=4_2     ,      help='random seed for initialization'				)
  parser.add_argument(
      '--data_dir'     ,      default=str(Path(A_				).parent / 'test_run' / 'dummy-train-data'				)     ,      type=A_     ,      help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'     ,      )






def       snake_case  (A_				:BaseTransformer     ,      A_				:argparse.Namespace     ,      A_				:Tuple=None     ,      A_				:Optional[int]=True     ,      A_				:Dict=[]     ,      A_				:Optional[int]=None     ,      A_				:Optional[Any]=None     ,      **A_				:Union[str, Any]     ,      ):
  pl.seed_everything(args.seed				)

  # init model
  a					:      Union[str, Any]    =       Path(model.hparams.output_dir				)
  odir.mkdir(exist_ok=A_				)

  # add custom checkpoints
  if checkpoint_callback is None:
    a					:      List[Any]    =       pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir     ,      prefix='checkpoint'     ,      monitor='val_loss'     ,      mode='min'     ,      save_top_k=1				)
  if early_stopping_callback:
    extra_callbacks.append(A_				)
  if logging_callback is None:
    a					:      Union[str, Any]    =       LoggingCallback()

  a					:      List[Any]    =       {}

  if args.fpaa:
    a					:      Union[str, Any]    =       1_6

  if args.gpus > 1:
    a					:      List[Any]    =       'auto'
    a					:      Dict    =       'ddp'

  a					:      Tuple    =       args.accumulate_grad_batches
  a					:      int    =       None
  a					:      List[str]    =       'auto'

  a					:      Tuple    =       pl.Trainer.from_argparse_args(
      A_     ,      weights_summary=A_     ,      callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]     ,      logger=A_     ,      val_check_interval=1     ,      num_sanity_val_steps=2     ,      **A_     ,      )

  if args.do_train:
    trainer.fit(A_				)

  else:
    print('RAG modeling tests with new set functions successfuly executed!'				)
  return trainer












"""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)




