'''simple docstring'''

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__     :	str				=			16
UpperCamelCase__     :	int				=			32







def __UpperCamelCase(   _A					:				Accelerator  ,			_A					:				int = 16    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
   UpperCAmelCase__							:				Tuple   				=  load_dataset('''glue'''  ,			'''mrpc'''    )

   def tokenize_function(_A					:				List[Any]    ):
      # max_length=None => use the model max length (it's actually the default)
      UpperCAmelCase__							:				int   				=  tokenizer(examples['''sentence1''']  ,			examples['''sentence2''']  ,			truncation=_A  ,			max_length=_A    )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      UpperCAmelCase__							:				str   				=  datasets.map(
          _A  ,			batched=_A  ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,			)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   UpperCAmelCase__							:				Any   				=  tokenized_datasets.rename_column('''label'''  ,			'''labels'''    )

   def collate_fn(_A					:				Optional[int]    ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      UpperCAmelCase__							:				Optional[Any]   				=  1_28 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         UpperCAmelCase__							:				Tuple   				=  16
      elif accelerator.mixed_precision != "no":
         UpperCAmelCase__							:				Optional[Any]   				=  8
      else:
         UpperCAmelCase__							:				Tuple   				=  None

      return tokenizer.pad(
          _A  ,			padding='''longest'''  ,			max_length=_A  ,			pad_to_multiple_of=_A  ,			return_tensors='''pt'''  ,			)

   # Instantiate dataloaders.
   UpperCAmelCase__							:				Dict   				=  DataLoader(
       tokenized_datasets['''train''']  ,			shuffle=_A  ,			collate_fn=_A  ,			batch_size=_A    )
   UpperCAmelCase__							:				Union[str, Any]   				=  DataLoader(
       tokenized_datasets['''validation''']  ,			shuffle=_A  ,			collate_fn=_A  ,			batch_size=_A    )

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  UpperCamelCase__     :	Optional[int]				=			mocked_dataloaders  # noqa: F811







def __UpperCamelCase(   _A					:				str  ,			_A					:				Tuple    ):
   '''simple docstring'''
   # For testing only
   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,			_A    ) == "1":
      UpperCAmelCase__							:				Union[str, Any]   				=  2
   # Initialize accelerator
   UpperCAmelCase__							:				int   				=  Accelerator(cpu=args.cpu  ,			mixed_precision=args.mixed_precision    )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCAmelCase__							:				Dict   				=  config['''lr''']
   UpperCAmelCase__							:				Optional[int]   				=  int(config['''num_epochs''']    )
   UpperCAmelCase__							:				Optional[int]   				=  int(config['''seed''']    )
   UpperCAmelCase__							:				Any   				=  int(config['''batch_size''']    )

   UpperCAmelCase__							:				str   				=  evaluate.load('''glue'''  ,			'''mrpc'''    )

   # If the batch size is too big we use gradient accumulation
   UpperCAmelCase__							:				Dict   				=  1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      UpperCAmelCase__							:				Optional[Any]   				=  batch_size // MAX_GPU_BATCH_SIZE
      UpperCAmelCase__							:				Tuple   				=  MAX_GPU_BATCH_SIZE

   set_seed(_A    )
   UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  get_dataloaders(_A  ,			_A    )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCAmelCase__							:				Union[str, Any]   				=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''  ,			return_dict=_A    )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   UpperCAmelCase__							:				str   				=  model.to(accelerator.device    )

   # Instantiate optimizer
   UpperCAmelCase__							:				str   				=  AdamW(params=model.parameters()  ,			lr=_A    )

   # Instantiate scheduler
   UpperCAmelCase__							:				int   				=  get_linear_schedule_with_warmup(
       optimizer=_A  ,			num_warmup_steps=1_00  ,			num_training_steps=(len(_A    ) * num_epochs) // gradient_accumulation_steps  ,			)

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  accelerator.prepare(
       _A  ,			_A  ,			_A  ,			_A  ,			_A    )

   # Now we train the model
   for epoch in range(_A    ):
      model.train()
      for step, batch in enumerate(_A    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device    )
         UpperCAmelCase__							:				List[str]   				=  model(**_A    )
         UpperCAmelCase__							:				Union[str, Any]   				=  outputs.loss
         UpperCAmelCase__							:				Dict   				=  loss / gradient_accumulation_steps
         accelerator.backward(_A    )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      UpperCAmelCase__							:				List[str]   				=  0
      for step, batch in enumerate(_A    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device    )
         with torch.no_grad():
            UpperCAmelCase__							:				Optional[Any]   				=  model(**_A    )
         UpperCAmelCase__							:				int   				=  outputs.logits.argmax(dim=-1    )
         UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  accelerator.gather((predictions, batch['''labels'''])    )
         # New Code #
         # First we check if it's a distributed system
         if accelerator.use_distributed:
            # Then see if we're on the last batch of our eval dataloader
            if step == len(_A    ) - 1:
               # Last batch needs to be truncated on distributed systems as it contains additional samples
               UpperCAmelCase__							:				List[Any]   				=  predictions[: len(eval_dataloader.dataset    ) - samples_seen]
               UpperCAmelCase__							:				Any   				=  references[: len(eval_dataloader.dataset    ) - samples_seen]
            else:
               # Otherwise we add the number of samples seen
               samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
         metric.add_batch(
             predictions=_A  ,			references=_A  ,			)

      UpperCAmelCase__							:				str   				=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''  ,			_A    )







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  argparse.ArgumentParser(description='''Simple example of training script.'''    )
   parser.add_argument(
       '''--mixed_precision'''  ,			type=_A  ,			default=_A  ,			choices=['''no''', '''fp16''', '''bf16''', '''fp8''']  ,			help='''Whether to use mixed precision. Choose'''
       '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
       '''and an Nvidia Ampere GPU.'''  ,			)
   parser.add_argument('''--cpu'''  ,			action='''store_true'''  ,			help='''If passed, will train on the CPU.'''    )
   UpperCAmelCase__							:				Any   				=  parser.parse_args()
   UpperCAmelCase__							:				Optional[int]   				=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(_A  ,			_A    )


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







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  []
   token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
   return token







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  []
   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 __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				Dict  ,			_A					:				str  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  '''imagenet-1k-id2label.json'''
   UpperCAmelCase__							:				str   				=  10_00

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

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

   UpperCAmelCase__							:				int   				=  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__							:				Tuple   				=  [1, 2, 10]

   # For depth size 21 (21 = 1+4+16)
   elif cvt_model.rsplit('''/'''  ,			1    )[-1][4:6] == "21":
      UpperCAmelCase__							:				List[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__							:				str   				=  [3, 12, 16]
      UpperCAmelCase__							:				Union[str, Any]   				=  [1_92, 7_68, 10_24]

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

   UpperCAmelCase__							:				Union[str, Any]   				=  OrderedDict()
   UpperCAmelCase__							:				str   				=  []

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

   UpperCAmelCase__							:				int   				=  list_of_state_dict + final()
   for gg in list_of_state_dict:
      print(_A    )
   for i in range(len(_A    )    ):
      UpperCAmelCase__							:				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__     :	Tuple				=			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__     :	Dict				=			parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




