




'''simple docstring'''



import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


UpperCamelCase_    	=				"true"




def   lowercase__(			__UpperCamelCase:					Dict      ,__UpperCamelCase:					Any=82      ,__UpperCamelCase:					Union[str, Any]=16    ):

      """simple docstring"""






      set_seed(42    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						RegressionModel()
      SCREAMING_SNAKE_CASE   :      List[str]    					=						deepcopy(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						RegressionDataset(length=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						DataLoader(__UpperCamelCase      ,batch_size=__UpperCamelCase    )
      model.to(accelerator.device    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						accelerator.prepare(__UpperCamelCase      ,__UpperCamelCase    )
      return model, ddp_model, dataloader




def   lowercase__(			__UpperCamelCase:					Accelerator      ,__UpperCamelCase:					Tuple=False    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						load_dataset('glue'      ,'mrpc'      ,split='validation'    )

      def tokenize_function(__UpperCamelCase:					str    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenizer(examples['sentence1']      ,examples['sentence2']      ,truncation=__UpperCamelCase      ,max_length=__UpperCamelCase    )
            return outputs

      with accelerator.main_process_first():
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						dataset.map(
                __UpperCamelCase      ,batched=__UpperCamelCase      ,remove_columns=['idx', 'sentence1', 'sentence2']      ,)

      SCREAMING_SNAKE_CASE   :      Any    					=						tokenized_datasets.rename_column('label'      ,'labels'    )

      def collate_fn(__UpperCamelCase:					List[Any]    ):
            if use_longest:
                  return tokenizer.pad(__UpperCamelCase      ,padding='longest'      ,return_tensors='pt'    )
            return tokenizer.pad(__UpperCamelCase      ,padding='max_length'      ,max_length=1_28      ,return_tensors='pt'    )

      return DataLoader(__UpperCamelCase      ,shuffle=__UpperCamelCase      ,collate_fn=__UpperCamelCase      ,batch_size=16    )




def   lowercase__(			__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						Accelerator(dispatch_batches=__UpperCamelCase      ,split_batches=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						get_dataloader(__UpperCamelCase      ,not dispatch_batches    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						AutoModelForSequenceClassification.from_pretrained(
          'hf-internal-testing/mrpc-bert-base-cased'      ,return_dict=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						accelerator.prepare(__UpperCamelCase      ,__UpperCamelCase    )
      return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator




def   lowercase__(			__UpperCamelCase:					Tuple      ,__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					int    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[]
      for batch in dataloader:
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						batch.values()
            with torch.no_grad():
                  SCREAMING_SNAKE_CASE   :      int    					=						model(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						accelerator.gather_for_metrics((logit, target)    )
                  logits_and_targets.append((logit, target)    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[], []
      for logit, targ in logits_and_targets:
            logits.append(__UpperCamelCase    )
            targs.append(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Any    					=						torch.cat(__UpperCamelCase    ), torch.cat(__UpperCamelCase    )
      return logits, targs




def   lowercase__(			__UpperCamelCase:					Accelerator      ,__UpperCamelCase:					int=82      ,__UpperCamelCase:					List[Any]=False      ,__UpperCamelCase:					Optional[Any]=False      ,__UpperCamelCase:					str=16    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						get_basic_setup(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						generate_predictions(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      assert (
          len(__UpperCamelCase    ) == num_samples
      ), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__UpperCamelCase    )}"




def   lowercase__(			__UpperCamelCase:					bool = False      ,__UpperCamelCase:					bool = False    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						evaluate.load('glue'      ,'mrpc'    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						get_mrpc_setup(__UpperCamelCase      ,__UpperCamelCase    )
      # First do baseline
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[str]    					=						setup['no']
      model.to(__UpperCamelCase    )
      model.eval()
      for batch in dataloader:
            batch.to(__UpperCamelCase    )
            with torch.inference_mode():
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model(**__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						outputs.logits.argmax(dim=-1    )
            metric.add_batch(predictions=__UpperCamelCase      ,references=batch['labels']    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						metric.compute()

      # Then do distributed
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      str    					=						setup['ddp']
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						model(**__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      str    					=						outputs.logits.argmax(dim=-1    )
            SCREAMING_SNAKE_CASE   :      Tuple    					=						batch['labels']
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						accelerator.gather_for_metrics((preds, references)    )
            metric.add_batch(predictions=__UpperCamelCase      ,references=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						metric.compute()

      for key in "accuracy f1".split():
            assert math.isclose(
                baseline[key]      ,distributed[key]    ), f"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						Accelerator(split_batches=__UpperCamelCase      ,dispatch_batches=__UpperCamelCase    )
      if accelerator.is_local_main_process:
            datasets.utils.logging.set_verbosity_warning()
            transformers.utils.logging.set_verbosity_warning()
      else:
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      # These are a bit slower so they should only be ran on the GPU or TPU
      if torch.cuda.is_available() or is_tpu_available():
            if accelerator.is_local_main_process:
                  print('**Testing gather_for_metrics**'    )
            for split_batches in [True, False]:
                  for dispatch_batches in [True, False]:
                        if accelerator.is_local_main_process:
                              print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"    )
                        test_mrpc(__UpperCamelCase      ,__UpperCamelCase    )
                        accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('**Test torch metrics**'    )
      for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						Accelerator(split_batches=__UpperCamelCase      ,dispatch_batches=__UpperCamelCase    )
                  if accelerator.is_local_main_process:
                        print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"    )
                  test_torch_metrics(__UpperCamelCase      ,99    )
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('**Test last batch is not dropped when perfectly divisible**'    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						Accelerator()
      test_torch_metrics(__UpperCamelCase      ,5_12    )
      accelerator.state._reset_state()




def   lowercase__(			__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      main()


if __name__ == "__main__":
      main()









'''simple docstring'''




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()
a__					:			Any          =     logging.get_logger()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = True							)   ->Dict:
	print(f'''Converting {name}...'''							)

	with torch.no_grad():
		if hidden_sizes == 1_28:
			if name[-1] == "S":
				snake_case__	       =   timm.create_model('levit_128s'					,				pretrained=UpperCAmelCase_							)
			else:
				snake_case__	       =   timm.create_model('levit_128'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 1_92:
			snake_case__	       =   timm.create_model('levit_192'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 2_56:
			snake_case__	       =   timm.create_model('levit_256'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 3_84:
			snake_case__	       =   timm.create_model('levit_384'					,				pretrained=UpperCAmelCase_							)

		from_model.eval()
		snake_case__	       =   LevitForImageClassificationWithTeacher(UpperCAmelCase_							).eval()
		snake_case__	       =   OrderedDict()

		snake_case__	       =   from_model.state_dict()
		snake_case__	       =   list(from_model.state_dict().keys()							)
		snake_case__	       =   list(our_model.state_dict().keys()							)
		print(len(UpperCAmelCase_							)					,				len(UpperCAmelCase_							)							)
		for i in range(len(UpperCAmelCase_							)							):
			snake_case__	       =   weights[og_keys[i]]
		our_model.load_state_dict(UpperCAmelCase_							)

		snake_case__	       =   torch.randn((2, 3, 2_24, 2_24)							)
		snake_case__	       =   from_model(UpperCAmelCase_							)
		snake_case__	       =   our_model(UpperCAmelCase_							).logits

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

	snake_case__	       =   name
	print(UpperCAmelCase_							)

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name							)
		snake_case__	       =   LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name							)

		print(f'''Pushed {checkpoint_name}'''							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_ = None					,				UpperCAmelCase_ = True							)   ->Any:
	snake_case__	       =   'imagenet-1k-id2label.json'
	snake_case__	       =   10_00
	snake_case__	       =   (1, num_labels)

	snake_case__	       =   'huggingface/label-files'
	snake_case__	       =   num_labels
	snake_case__	       =   json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type='dataset'							)					,				'r'							)							)
	snake_case__	       =   {int(UpperCAmelCase_							): v for k, v in idalabel.items()}

	snake_case__	       =   idalabel
	snake_case__	       =   {v: k for k, v in idalabel.items()}

	snake_case__	       =   partial(UpperCAmelCase_					,				num_labels=UpperCAmelCase_					,				idalabel=UpperCAmelCase_					,				labelaid=UpperCAmelCase_							)

	snake_case__	       =   {
	    'levit-128S': 1_28,
	    'levit-128': 1_28,
	    'levit-192': 1_92,
	    'levit-256': 2_56,
	    'levit-384': 3_84,
	}

	snake_case__	       =   {
	    'levit-128S': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 6, 8]					,				depths=[2, 3, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-128': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 8, 12]					,				depths=[4, 4, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-192': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_92, 2_88, 3_84]					,				num_attention_heads=[3, 5, 6]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-256': ImageNetPreTrainedConfig(
	        hidden_sizes=[2_56, 3_84, 5_12]					,				num_attention_heads=[4, 6, 8]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-384': ImageNetPreTrainedConfig(
	        hidden_sizes=[3_84, 5_12, 7_68]					,				num_attention_heads=[6, 9, 12]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				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__":
			a__					:			Optional[int]          =     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''',
			)

			a__					:			Optional[Any]          =     parser.parse_args()
			a__					:			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)



