





'''simple docstring'''

import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def  a__    (    lowercase :					int				)		->		Any:





     """simple docstring"""

     _UpperCamelCase       =	SwinConfig()
     _UpperCamelCase       =	swin_name.split('''_'''				)

     _UpperCamelCase       =	name_split[1]
     _UpperCamelCase       =	int(name_split[4]				)
     _UpperCamelCase       =	int(name_split[3][-1]				)

     if model_size == "tiny":
          _UpperCamelCase       =	96
          _UpperCamelCase       =	(2, 2, 6, 2)
          _UpperCamelCase       =	(3, 6, 12, 24)
     elif model_size == "small":
          _UpperCamelCase       =	96
          _UpperCamelCase       =	(2, 2, 18, 2)
          _UpperCamelCase       =	(3, 6, 12, 24)
     elif model_size == "base":
          _UpperCamelCase       =	128
          _UpperCamelCase       =	(2, 2, 18, 2)
          _UpperCamelCase       =	(4, 8, 16, 32)
     else:
          _UpperCamelCase       =	192
          _UpperCamelCase       =	(2, 2, 18, 2)
          _UpperCamelCase       =	(6, 12, 24, 48)

     if "in22k" in swin_name:
          _UpperCamelCase       =	21841
     else:
          _UpperCamelCase       =	1000
          _UpperCamelCase       =	'''huggingface/label-files'''
          _UpperCamelCase       =	'''imagenet-1k-id2label.json'''
          _UpperCamelCase       =	json.load(open(hf_hub_download(lowercase, lowercase, repo_type='''dataset'''				), '''r'''				)				)
          _UpperCamelCase       =	{int(lowercase				): v for k, v in idalabel.items()}
          _UpperCamelCase       =	idalabel
          _UpperCamelCase       =	{v: k for k, v in idalabel.items()}

     _UpperCamelCase       =	img_size
     _UpperCamelCase       =	num_classes
     _UpperCamelCase       =	embed_dim
     _UpperCamelCase       =	depths
     _UpperCamelCase       =	num_heads
     _UpperCamelCase       =	window_size

     return config







def  a__    (    lowercase :					Optional[int]				)		->		str:





     """simple docstring"""

     if "patch_embed.proj" in name:
          _UpperCamelCase       =	name.replace('''patch_embed.proj''', '''embeddings.patch_embeddings.projection'''				)
     if "patch_embed.norm" in name:
          _UpperCamelCase       =	name.replace('''patch_embed.norm''', '''embeddings.norm'''				)
     if "layers" in name:
          _UpperCamelCase       =	'''encoder.''' + name
     if "attn.proj" in name:
          _UpperCamelCase       =	name.replace('''attn.proj''', '''attention.output.dense'''				)
     if "attn" in name:
          _UpperCamelCase       =	name.replace('''attn''', '''attention.self'''				)
     if "norm1" in name:
          _UpperCamelCase       =	name.replace('''norm1''', '''layernorm_before'''				)
     if "norm2" in name:
          _UpperCamelCase       =	name.replace('''norm2''', '''layernorm_after'''				)
     if "mlp.fc1" in name:
          _UpperCamelCase       =	name.replace('''mlp.fc1''', '''intermediate.dense'''				)
     if "mlp.fc2" in name:
          _UpperCamelCase       =	name.replace('''mlp.fc2''', '''output.dense'''				)

     if name == "norm.weight":
          _UpperCamelCase       =	'''layernorm.weight'''
     if name == "norm.bias":
          _UpperCamelCase       =	'''layernorm.bias'''

     if "head" in name:
          _UpperCamelCase       =	name.replace('''head''', '''classifier'''				)
     else:
          _UpperCamelCase       =	'''swin.''' + name

     return name







def  a__    (    lowercase :					Optional[int], lowercase :					List[str]				)		->		int:





     """simple docstring"""

     for key in orig_state_dict.copy().keys():
          _UpperCamelCase       =	orig_state_dict.pop(lowercase				)

          if "mask" in key:
               continue
          elif "qkv" in key:
               _UpperCamelCase       =	key.split('''.'''				)
               _UpperCamelCase       =	int(key_split[1]				)
               _UpperCamelCase       =	int(key_split[3]				)
               _UpperCamelCase       =	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    _UpperCamelCase       =	val[:dim, :]
                    _UpperCamelCase       =	val[
                        dim : dim * 2, :
                    ]
                    _UpperCamelCase       =	val[-dim:, :]
               else:
                    _UpperCamelCase       =	val[
                        :dim
                    ]
                    _UpperCamelCase       =	val[
                        dim : dim * 2
                    ]
                    _UpperCamelCase       =	val[
                        -dim:
                    ]
          else:
               _UpperCamelCase       =	val

     return orig_state_dict







def  a__    (    lowercase :					List[str], lowercase :					Dict				)		->		Tuple:





     """simple docstring"""

     _UpperCamelCase       =	timm.create_model(lowercase, pretrained=lowercase				)
     timm_model.eval()

     _UpperCamelCase       =	get_swin_config(lowercase				)
     _UpperCamelCase       =	SwinForImageClassification(lowercase				)
     model.eval()

     _UpperCamelCase       =	convert_state_dict(timm_model.state_dict(), lowercase				)
     model.load_state_dict(lowercase				)

     _UpperCamelCase       =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

     _UpperCamelCase       =	AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_''', '''-'''				)				)				)
     _UpperCamelCase       =	Image.open(requests.get(lowercase, stream=lowercase				).raw				)
     _UpperCamelCase       =	image_processor(images=lowercase, return_tensors='''pt'''				)

     _UpperCamelCase       =	timm_model(inputs['''pixel_values''']				)
     _UpperCamelCase       =	model(**lowercase				).logits

     assert torch.allclose(lowercase, lowercase, atol=1e-3				)

     print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}"""				)
     model.save_pretrained(lowercase				)

     print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
     image_processor.save_pretrained(lowercase				)


if __name__ == "__main__":
   lowercase__						:      Tuple	=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--swin_name',
       default='swin_tiny_patch4_window7_224',
       type=str,
       help='Name of the Swin timm model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )

   lowercase__						:      Optional[Any]	=    parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)










'''simple docstring'''

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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase__						:      Any	=    logging.getLogger(__name__)







def  a__    (    lowercase :					Optional[Any], lowercase :					Tuple				)		->		Any:





     """simple docstring"""

     return (preds == labels).mean()
@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(
           metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
       _snake_case     :					Optional[str]						    =   field(
           default=__magic_name__						,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}						,						)

@dataclass
class     __lowerCAmelCase					:






       """simple docstring"""
       _snake_case     :					str						    =   field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()			)}			)
       _snake_case     :					str						    =   field(metadata={'help': 'Should contain the data files for the task.'}			)
       _snake_case     :					int						    =   field(
           default=1_2_8						,						metadata={
               'help': (
                   'The maximum total input sequence length after tokenization. Sequences longer '
                   'than this will be truncated, sequences shorter will be padded.'
               )
           }						,						)
       _snake_case     :					bool						    =   field(
           default=__magic_name__						,						metadata={'help': 'Overwrite the cached training and evaluation sets'}			)







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

     _UpperCamelCase       =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir				)
         and os.listdir(training_args.output_dir				)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
              ''' --overwrite_output_dir to overcome.'''				)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, )
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''', training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1				), training_args.fpaa, )
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank				):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('''Training/evaluation parameters %s''', lowercase				)

     # Set seed
     set_seed(training_args.seed				)

     try:
          _UpperCamelCase       =	processors[data_args.task_name]()
          _UpperCamelCase       =	processor.get_labels()
          _UpperCamelCase       =	len(lowercase				)
     except KeyError:
          raise ValueError('''Task not found: %s''' % (data_args.task_name)				)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     _UpperCamelCase       =	AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path, num_labels=lowercase, finetuning_task=data_args.task_name, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, )
     _UpperCamelCase       =	AutoModelForMultipleChoice.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, )

     # Get datasets
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.train, )
         if training_args.do_train
         else None
     )
     _UpperCamelCase       =	(
         MultipleChoiceDataset(
             data_dir=data_args.data_dir, tokenizer=lowercase, task=data_args.task_name, max_seq_length=data_args.max_seq_length, overwrite_cache=data_args.overwrite_cache, mode=Split.dev, )
         if training_args.do_eval
         else None
     )

     def compute_metrics(lowercase :					EvalPrediction				) -> Dict:
          _UpperCamelCase       =	np.argmax(p.predictions, axis=1				)
          return {"acc": simple_accuracy(lowercase, p.label_ids				)}

     # Data collator
     _UpperCamelCase       =	DataCollatorWithPadding(lowercase, pad_to_multiple_of=8				) if training_args.fpaa else None

     # Initialize our Trainer
     _UpperCamelCase       =	Trainer(
         model=lowercase, args=lowercase, train_dataset=lowercase, eval_dataset=lowercase, compute_metrics=lowercase, data_collator=lowercase, )

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
               tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
     _UpperCamelCase       =	{}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''				)

          _UpperCamelCase       =	trainer.evaluate()

          _UpperCamelCase       =	os.path.join(training_args.output_dir, '''eval_results.txt'''				)
          if trainer.is_world_master():
               with open(lowercase, '''w'''				) as writer:
                    logger.info('''***** Eval results *****'''				)
                    for key, value in result.items():
                         logger.info('''  %s = %s''', lowercase, lowercase				)
                         writer.write('''%s = %s\n''' % (key, value)				)

                    results.update(lowercase				)

     return results







def  a__    (    lowercase :					Tuple				)		->		List[Any]:





     """simple docstring"""

     main()


if __name__ == "__main__":
   main()



