


import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE       :		Dict       =				logging.get_logger()




def     UpperCamelCase    (      _a       ,		_a       ,		_a       ,		_a       ,		_a = True		)					->							Dict:
     '''simple docstring'''





     print(f"Converting {name}..."		)

     with torch.no_grad():
          if hidden_sizes == 1_2_8:
               if name[-1] == "S":
                    lowercase_       :Optional[Any]     =       timm.create_model('''levit_128s'''       ,		pretrained=UpperCamelCase__		)
               else:
                    lowercase_       :Dict     =       timm.create_model('''levit_128'''       ,		pretrained=UpperCamelCase__		)
          if hidden_sizes == 1_9_2:
               lowercase_       :Tuple     =       timm.create_model('''levit_192'''       ,		pretrained=UpperCamelCase__		)
          if hidden_sizes == 2_5_6:
               lowercase_       :Optional[int]     =       timm.create_model('''levit_256'''       ,		pretrained=UpperCamelCase__		)
          if hidden_sizes == 3_8_4:
               lowercase_       :Dict     =       timm.create_model('''levit_384'''       ,		pretrained=UpperCamelCase__		)

          from_model.eval()
          lowercase_       :Optional[Any]     =       LevitForImageClassificationWithTeacher(UpperCamelCase__		).eval()
          lowercase_       :Tuple     =       OrderedDict()

          lowercase_       :Optional[Any]     =       from_model.state_dict()
          lowercase_       :str     =       list(from_model.state_dict().keys()		)
          lowercase_       :List[Any]     =       list(our_model.state_dict().keys()		)
          print(len(UpperCamelCase__		)       ,		len(UpperCamelCase__		)		)
          for i in range(len(UpperCamelCase__		)		):
               lowercase_       :str     =       weights[og_keys[i]]
          our_model.load_state_dict(UpperCamelCase__		)

          lowercase_       :int     =       torch.randn((2, 3, 2_2_4, 2_2_4)		)
          lowercase_       :Any     =       from_model(UpperCamelCase__		)
          lowercase_       :List[Any]     =       our_model(UpperCamelCase__		).logits

     assert torch.allclose(UpperCamelCase__       ,		UpperCamelCase__		), "The model logits don't match the original one."

     lowercase_       :Dict     =       name
     print(UpperCamelCase__		)

     if push_to_hub:
          our_model.save_pretrained(save_directory / checkpoint_name		)
          lowercase_       :Optional[int]     =       LevitImageProcessor()
          image_processor.save_pretrained(save_directory / checkpoint_name		)

          print(f"Pushed {checkpoint_name}"		)




def     UpperCamelCase    (      _a       ,		_a = None       ,		_a = True		)					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :Optional[Any]     =       """imagenet-1k-id2label.json"""
     lowercase_       :List[Any]     =       1_0_0_0
     lowercase_       :Dict     =       (1, num_labels)

     lowercase_       :List[Any]     =       """huggingface/label-files"""
     lowercase_       :Optional[int]     =       num_labels
     lowercase_       :List[str]     =       json.load(open(hf_hub_download(UpperCamelCase__       ,		UpperCamelCase__       ,		repo_type='''dataset'''		)       ,		'''r'''		)		)
     lowercase_       :Any     =       {int(UpperCamelCase__		): v for k, v in idalabel.items()}

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

     lowercase_       :Tuple     =       partial(UpperCamelCase__       ,		num_labels=UpperCamelCase__       ,		idalabel=UpperCamelCase__       ,		labelaid=UpperCamelCase__		)

     lowercase_       :Optional[int]     =       {
         """levit-128S""": 1_2_8,
         """levit-128""": 1_2_8,
         """levit-192""": 1_9_2,
         """levit-256""": 2_5_6,
         """levit-384""": 3_8_4,
     }

     lowercase_       :List[Any]     =       {
         """levit-128S""": ImageNetPreTrainedConfig(
             hidden_sizes=[1_2_8, 2_5_6, 3_8_4]       ,		num_attention_heads=[4, 6, 8]       ,		depths=[2, 3, 4]       ,		key_dim=[1_6, 1_6, 1_6]       ,		drop_path_rate=0       ,		),
         """levit-128""": ImageNetPreTrainedConfig(
             hidden_sizes=[1_2_8, 2_5_6, 3_8_4]       ,		num_attention_heads=[4, 8, 1_2]       ,		depths=[4, 4, 4]       ,		key_dim=[1_6, 1_6, 1_6]       ,		drop_path_rate=0       ,		),
         """levit-192""": ImageNetPreTrainedConfig(
             hidden_sizes=[1_9_2, 2_8_8, 3_8_4]       ,		num_attention_heads=[3, 5, 6]       ,		depths=[4, 4, 4]       ,		key_dim=[3_2, 3_2, 3_2]       ,		drop_path_rate=0       ,		),
         """levit-256""": ImageNetPreTrainedConfig(
             hidden_sizes=[2_5_6, 3_8_4, 5_1_2]       ,		num_attention_heads=[4, 6, 8]       ,		depths=[4, 4, 4]       ,		key_dim=[3_2, 3_2, 3_2]       ,		drop_path_rate=0       ,		),
         """levit-384""": ImageNetPreTrainedConfig(
             hidden_sizes=[3_8_4, 5_1_2, 7_6_8]       ,		num_attention_heads=[6, 9, 1_2]       ,		depths=[4, 4, 4]       ,		key_dim=[3_2, 3_2, 3_2]       ,		drop_path_rate=0.1       ,		),
     }

     if model_name:
          convert_weight_and_push(
              names_to_hidden_sizes[model_name]       ,		UpperCamelCase__       ,		names_to_config[model_name]       ,		UpperCamelCase__       ,		UpperCamelCase__		)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(names_to_hidden_sizes[model_name]       ,		UpperCamelCase__       ,		UpperCamelCase__       ,		UpperCamelCase__       ,		UpperCamelCase__		)
     return config, expected_shape


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		Union[str, Any]       =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default=None,
       type=str,
       help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default="levit-dump-folder/",
       type=Path,
       required=False,
       help="Path to the output PyTorch model directory.",
   )
   parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
   parser.add_argument(
       "--no-push_to_hub",
       dest="push_to_hub",
       action="store_false",
       help="Do not push model and image processor to the hub",
   )

   SCREAMING_SNAKE_CASE       :		List[Any]       =				parser.parse_args()
   SCREAMING_SNAKE_CASE       :		Path       =				args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


SCREAMING_SNAKE_CASE       :		List[str]         =				logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer






   class      UpperCamelCase					(		lowercase__     ):


      '''simple docstring'''







      def __init__(     self   ,							UpperCamelCase_ = 101		):
           lowercase_       :Tuple     =       length
      def __len__(     self		):
           return self.length




      def __getitem__(     self   ,							UpperCamelCase_		):
           return i






   class      UpperCamelCase					:


      '''simple docstring'''







      def __call__(     self   ,							UpperCamelCase_		):
           return {"input_ids": torch.tensor(UpperCamelCase_		), "labels": torch.tensor(UpperCamelCase_		)}






   class      UpperCamelCase					(		nn.Module     ):


      '''simple docstring'''







      def __init__(     self		):
           super().__init__()
           # Add some (unused) params otherwise DDP will complain.
           lowercase_       :List[Any]     =       nn.Linear(120   ,							80		)




      def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=None		):
           if labels is not None:
                return torch.tensor(0.0   ,							device=input_ids.device		), input_ids
           else:
                return input_ids






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   @require_torch_neuroncore
   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       f"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
        lowercase_       :List[str]     =       self.get_auto_remove_tmp_dir()
        lowercase_       :List[str]     =       f"--output_dir {output_dir}".split()
        lowercase_       :Any     =       ['''torchrun'''] + distributed_args + args
        execute_subprocess_async(UpperCamelCase_   ,							env=self.get_env()		)
        # successful return here == success - any errors would have caused an error in the sub-call






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   @require_torch_multi_gpu
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       f"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
        lowercase_       :str     =       self.get_auto_remove_tmp_dir()
        lowercase_       :List[str]     =       f"--output_dir {output_dir}".split()
        lowercase_       :Dict     =       ['''torchrun'''] + distributed_args + args
        execute_subprocess_async(UpperCamelCase_   ,							env=self.get_env()		)
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
   # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
   #
   # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

   SCREAMING_SNAKE_CASE       :		Optional[int]         =				HfArgumentParser((TrainingArguments,))
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				parser.parse_args_into_dataclasses()[0]

   logger.warning(
       f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
       f"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
   )

   # Essentially, what we want to verify in the distributed case is that we get all samples back,
   # in the right order. (this is crucial for prediction for instance)
   for dataset_length in [101, 40, 7]:
      SCREAMING_SNAKE_CASE       :		Dict         =				DummyDataset(dataset_length)




      def     UpperCamelCase    (      _a		)					->							Dict:
           '''simple docstring'''





           lowercase_       :Optional[Any]     =       list(range(len(_a		)		)		)
           lowercase_       :Optional[Any]     =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
           if not success and training_args.local_rank == 0:
                logger.warning(
                    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
                    f"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"		)
           return {"success": success}

      SCREAMING_SNAKE_CASE       :		Optional[Any]         =				Trainer(
          model=DummyModel(),
          args=training_args,
          data_collator=DummyDataCollator(),
          eval_dataset=dataset,
          compute_metrics=compute_metrics,
      )
      SCREAMING_SNAKE_CASE       :		Any         =				trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

      SCREAMING_SNAKE_CASE       :		Optional[int]         =				trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)

      SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				2

      SCREAMING_SNAKE_CASE       :		Any         =				trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

      SCREAMING_SNAKE_CASE       :		List[str]         =				trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)
      SCREAMING_SNAKE_CASE       :		Optional[int]         =				None

