


'''simple docstring'''




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_a   :   str												=   logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")

_a   :   str												=   list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_a   :   Tuple												=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





def   _a		(lowercase__			:   str						)			->			int:






     """simple docstring"""
     with open(lowercase__							,   'rb'						) as f:
          __snake_case           =						Image.open(lowercase__						)
          return im.convert('RGB'						)




@dataclass
class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={
           "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(default=__lowercase						,					metadata={"help": "A folder containing the training data."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(default=__lowercase						,					metadata={"help": "A folder containing the validation data."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[float]          =	field(
       default=0.15						,					metadata={"help": "Percent to split off of train for validation."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of training examples to this "
               "value if set."
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
               "value if set."
           )
       }						,					)
   def a  (						self       :      List[Any]      ) ->							int:
        if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
             raise ValueError(
                 'You must specify either a dataset name from the hub or a train and/or validation directory.'      )




@dataclass
class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  str          =	field(
       default="google/vit-base-patch16-224-in21k"						,					metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__lowercase							)}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}							)
   _SCREAMING_SNAKE_CASE  :  str          =	field(
       default="main"						,					metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}						,					)
   _SCREAMING_SNAKE_CASE  :  str          =	field(default=__lowercase						,					metadata={"help": "Name or path of preprocessor config."}							)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
               "with private models)."
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}						,					)





def   _a		(lowercase__			:   int						)			->			Dict:






     """simple docstring"""
     __snake_case           =						torch.stack([example['pixel_values'] for example in examples]						)
     __snake_case           =						torch.tensor([example['labels'] for example in examples]						)
     return {"pixel_values": pixel_values, "labels": labels}





def   _a		()			->			Optional[int]:






     """simple docstring"""
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     __snake_case           =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
     if len(sys.argv						) == 2 and sys.argv[1].endswith('.json'						):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          __snake_case      ,       __snake_case      ,       __snake_case           =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          __snake_case      ,       __snake_case      ,       __snake_case           =						parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry('run_image_classification'							,   lowercase__							,   lowercase__						)

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,   datefmt='%m/%d/%Y %H:%M:%S'							,   handlers=[logging.StreamHandler(sys.stdout						)]							,   )

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     __snake_case           =						training_args.get_process_log_level()
     logger.setLevel(lowercase__						)
     transformers.utils.logging.set_verbosity(lowercase__						)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # Log on each process the small summary:
     logger.warning(
         f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + f'distributed training: {bool(training_args.local_rank != -1						)}, 16-bits training: {training_args.fpaa}'						)
     logger.info(f'Training/evaluation parameters {training_args}'						)

     # Detecting last checkpoint.
     __snake_case           =						None
     if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
          __snake_case           =						get_last_checkpoint(training_args.output_dir						)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir						)						) > 0:
               raise ValueError(
                   f'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   'Use --overwrite_output_dir to overcome.'						)
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
               logger.info(
                   f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'						)

    # Set seed before initializing model.
     set_seed(training_args.seed						)

     # Initialize our dataset and prepare it for the 'image-classification' task.
     if data_args.dataset_name is not None:
          __snake_case           =						load_dataset(
              data_args.dataset_name							,   data_args.dataset_config_name							,   cache_dir=model_args.cache_dir							,   task='image-classification'							,   use_auth_token=True if model_args.use_auth_token else None							,   )
     else:
          __snake_case           =						{}
          if data_args.train_dir is not None:
               __snake_case           =						os.path.join(data_args.train_dir							,   '**'						)
          if data_args.validation_dir is not None:
               __snake_case           =						os.path.join(data_args.validation_dir							,   '**'						)
          __snake_case           =						load_dataset(
              'imagefolder'							,   data_files=lowercase__							,   cache_dir=model_args.cache_dir							,   task='image-classification'							,   )

     # If we don't have a validation split, split off a percentage of train as validation.
     __snake_case           =						None if 'validation' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split							,   lowercase__						) and data_args.train_val_split > 0.0:
          __snake_case           =						dataset['train'].train_test_split(data_args.train_val_split						)
          __snake_case           =						split['train']
          __snake_case           =						split['test']

     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     __snake_case           =						dataset['train'].features['labels'].names
     __snake_case      ,       __snake_case           =						{}, {}
     for i, label in enumerate(lowercase__						):
          __snake_case           =						str(lowercase__						)
          __snake_case           =						label

     # Load the accuracy metric from the datasets package
     __snake_case           =						evaluate.load('accuracy'						)

     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.
     def compute_metrics(lowercase__			:   Any						):
          return metric.compute(predictions=np.argmax(p.predictions							,   axis=1						)							,   references=p.label_ids						)

     __snake_case           =						AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path							,   num_labels=len(lowercase__						)							,   labelaid=lowercase__							,   idalabel=lowercase__							,   finetuning_task='image-classification'							,   cache_dir=model_args.cache_dir							,   revision=model_args.model_revision							,   use_auth_token=True if model_args.use_auth_token else None							,   )
     __snake_case           =						AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path							,   from_tf=bool('.ckpt' in model_args.model_name_or_path						)							,   config=lowercase__							,   cache_dir=model_args.cache_dir							,   revision=model_args.model_revision							,   use_auth_token=True if model_args.use_auth_token else None							,   ignore_mismatched_sizes=model_args.ignore_mismatched_sizes							,   )
     __snake_case           =						AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path							,   cache_dir=model_args.cache_dir							,   revision=model_args.model_revision							,   use_auth_token=True if model_args.use_auth_token else None							,   )

     # Define torchvision transforms to be applied to each image.
     if "shortest_edge" in image_processor.size:
          __snake_case           =						image_processor.size['shortest_edge']
     else:
          __snake_case           =						(image_processor.size['height'], image_processor.size['width'])
     __snake_case           =						Normalize(mean=image_processor.image_mean							,   std=image_processor.image_std						)
     __snake_case           =						Compose(
         [
             RandomResizedCrop(lowercase__						),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]						)
     __snake_case           =						Compose(
         [
             Resize(lowercase__						),
             CenterCrop(lowercase__						),
             ToTensor(),
             normalize,
         ]						)

     def train_transforms(lowercase__			:   Optional[Any]						):
          __snake_case           =						[
              _train_transforms(pil_img.convert('RGB'						)						) for pil_img in example_batch['image']
          ]
          return example_batch

     def val_transforms(lowercase__			:   Optional[Any]						):
          __snake_case           =						[_val_transforms(pil_img.convert('RGB'						)						) for pil_img in example_batch['image']]
          return example_batch

     if training_args.do_train:
          if "train" not in dataset:
               raise ValueError('--do_train requires a train dataset'						)
          if data_args.max_train_samples is not None:
               __snake_case           =						(
                   dataset['train'].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
               )
          # Set the training transforms
          dataset["train"].set_transform(lowercase__						)

     if training_args.do_eval:
          if "validation" not in dataset:
               raise ValueError('--do_eval requires a validation dataset'						)
          if data_args.max_eval_samples is not None:
               __snake_case           =						(
                   dataset['validation'].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
               )
          # Set the validation transforms
          dataset["validation"].set_transform(lowercase__						)

     # Initalize our trainer
     __snake_case           =						Trainer(
         model=lowercase__							,   args=lowercase__							,   train_dataset=dataset['train'] if training_args.do_train else None							,   eval_dataset=dataset['validation'] if training_args.do_eval else None							,   compute_metrics=lowercase__							,   tokenizer=lowercase__							,   data_collator=lowercase__							,   )

     # Training
     if training_args.do_train:
          __snake_case           =						None
          if training_args.resume_from_checkpoint is not None:
               __snake_case           =						training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               __snake_case           =						last_checkpoint
          __snake_case           =						trainer.train(resume_from_checkpoint=lowercase__						)
          trainer.save_model()
          trainer.log_metrics('train'							,   train_result.metrics						)
          trainer.save_metrics('train'							,   train_result.metrics						)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          __snake_case           =						trainer.evaluate()
          trainer.log_metrics('eval'							,   lowercase__						)
          trainer.save_metrics('eval'							,   lowercase__						)

     # Write model card and (optionally) push to hub
     __snake_case           =						{
         'finetuned_from': model_args.model_name_or_path,
         'tasks': 'image-classification',
         'dataset': data_args.dataset_name,
         'tags': ['image-classification', 'vision'],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**lowercase__						)
     else:
          trainer.create_model_card(**lowercase__						)


if __name__ == "__main__":
       main()











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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Dict:
    lowerCamelCase__       : Optional[int]   					=						[]
    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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[str]:
    lowerCamelCase__       : Optional[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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Union[str, Any]   					=						[]
    token.append((F"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')					)
    return token


def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    lowerCamelCase__       : Tuple   					=						[]
    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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Optional[int]:
    lowerCamelCase__       : Union[str, Any]   					=						'imagenet-1k-id2label.json'
    lowerCamelCase__       : Tuple   					=						1000

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

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

    lowerCamelCase__       : int   					=						CvtConfig(num_labels=_UpperCAmelCase		,      idalabel=_UpperCAmelCase		,      labelaid=_UpperCAmelCase					)

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

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

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        lowerCamelCase__       : Tuple   					=						[2, 2, 20]
        lowerCamelCase__       : Optional[int]   					=						[3, 12, 16]
        lowerCamelCase__       : int   					=						[192, 768, 1024]

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

    lowerCamelCase__       : Dict   					=						OrderedDict()
    lowerCamelCase__       : Union[str, Any]   					=						[]

    for idx in range(len(config.depth					)					):
        if config.cls_token[idx]:
            lowerCamelCase__       : str   					=						list_of_state_dict + cls_token(_UpperCAmelCase					)
        lowerCamelCase__       : Tuple   					=						list_of_state_dict + embeddings(_UpperCAmelCase					)
        for cnt in range(config.depth[idx]					):
            lowerCamelCase__       : List[str]   					=						list_of_state_dict + attention(_UpperCAmelCase		,      _UpperCAmelCase					)

    lowerCamelCase__       : Optional[Any]   					=						list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(_UpperCAmelCase					)
    for i in range(len(_UpperCAmelCase					)					):
        lowerCamelCase__       : List[str]   					=						original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(_UpperCAmelCase					)
    model.save_pretrained(_UpperCAmelCase					)
    image_processor.save_pretrained(_UpperCAmelCase					)


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

if __name__ == "__main__":
    _UpperCAmelCase							:			int        =							argparse.ArgumentParser()
    parser.add_argument(
        """--cvt_model""",
        default="""cvt-w24""",
        type=str,
        help="""Name of the cvt model you'd like to convert.""",
    )
    parser.add_argument(
        """--image_size""",
        default=3_84,
        type=int,
        help="""Input Image Size""",
    )
    parser.add_argument(
        """--cvt_file_name""",
        default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
        type=str,
        help="""Input Image Size""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
    )

    _UpperCAmelCase							:			Optional[int]        =							parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)





