'''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()
UpperCamelCase__     :	Optional[Any]				=			logging.get_logger()







def __UpperCamelCase(   _A					:				int  ,			_A					:				str  ,			_A					:				LevitConfig  ,			_A					:				Path  ,			_A					:				bool = True    ):
   '''simple docstring'''
   print(F'''Converting {name}...'''    )

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            UpperCAmelCase__							:				Union[str, Any]   				=  timm.create_model('''levit_128s'''  ,			pretrained=_A    )
         else:
            UpperCAmelCase__							:				Optional[int]   				=  timm.create_model('''levit_128'''  ,			pretrained=_A    )
      if hidden_sizes == 1_92:
         UpperCAmelCase__							:				Union[str, Any]   				=  timm.create_model('''levit_192'''  ,			pretrained=_A    )
      if hidden_sizes == 2_56:
         UpperCAmelCase__							:				str   				=  timm.create_model('''levit_256'''  ,			pretrained=_A    )
      if hidden_sizes == 3_84:
         UpperCAmelCase__							:				str   				=  timm.create_model('''levit_384'''  ,			pretrained=_A    )

      from_model.eval()
      UpperCAmelCase__							:				Dict   				=  LevitForImageClassificationWithTeacher(_A    ).eval()
      UpperCAmelCase__							:				Optional[int]   				=  OrderedDict()

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

      UpperCAmelCase__							:				Optional[int]   				=  torch.randn((2, 3, 2_24, 2_24)    )
      UpperCAmelCase__							:				Dict   				=  from_model(_A    )
      UpperCAmelCase__							:				Dict   				=  our_model(_A    ).logits

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

   UpperCAmelCase__							:				Optional[int]   				=  name
   print(_A    )

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

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







def __UpperCamelCase(   _A					:				Path  ,			_A					:				str = None  ,			_A					:				bool = True    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  '''imagenet-1k-id2label.json'''
   UpperCAmelCase__							:				Union[str, Any]   				=  10_00
   UpperCAmelCase__							:				Any   				=  (1, num_labels)

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

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

   UpperCAmelCase__							:				Optional[Any]   				=  partial(_A  ,			num_labels=_A  ,			idalabel=_A  ,			labelaid=_A    )

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

   UpperCAmelCase__							:				Any   				=  {
       '''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]  ,			_A  ,			names_to_config[model_name]  ,			_A  ,			_A    )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]  ,			_A  ,			_A  ,			_A  ,			_A    )
   return config, expected_shape


if __name__ == "__main__":
  UpperCamelCase__     :	Dict				=			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',
  )

  UpperCamelCase__     :	str				=			parser.parse_args()
  UpperCamelCase__     :	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)





'''simple docstring'''

import unittest

import numpy as np







def __UpperCamelCase(   _A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray | None = None  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  np.shape(_A    )
   UpperCAmelCase__							:				List[Any]   				=  np.shape(_A    )
   UpperCAmelCase__							:				Tuple   				=  np.shape(_A    )

   if shape_a[0] != shape_b[0]:
      UpperCAmelCase__							:				Any   				=  (
          '''Expected the same number of rows for A and B. '''
          F'''Instead found A of size {shape_a} and B of size {shape_b}'''
      )
      raise ValueError(_A    )

   if shape_b[1] != shape_c[1]:
      UpperCAmelCase__							:				List[str]   				=  (
          '''Expected the same number of columns for B and C. '''
          F'''Instead found B of size {shape_b} and C of size {shape_c}'''
      )
      raise ValueError(_A    )

   UpperCAmelCase__							:				Optional[Any]   				=  pseudo_inv
   if a_inv is None:
      try:
         UpperCAmelCase__							:				str   				=  np.linalg.inv(_A    )
      except np.linalg.LinAlgError:
         raise ValueError(
             '''Input matrix A is not invertible. Cannot compute Schur complement.'''    )

   return mat_c - mat_b.T @ a_inv @ mat_b


class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				List[Any]   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[2, 1], [6, 3]]							)

         UpperCAmelCase__							:				Tuple   				=  schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  np.block([[a, b], [b.T, c]]							)

         UpperCAmelCase__							:				int   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  np.linalg.det(lowerCamelCase_							)

         self.assertAlmostEqual(lowerCamelCase_					,det_a * det_s							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[2, 1], [6, 3]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				str   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Any   				=  np.array([[2, 1, 3], [6, 3, 5]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()




