'''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    :     Tuple         =       logging.get_logger()





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			str   ,		__snake_case							:			LevitConfig   ,		__snake_case							:			Path   ,		__snake_case							:			bool = True				):
     print(F'Converting {name}...'				)

     with torch.no_grad():
          if hidden_sizes == 1_2_8:
               if name[-1] == "S":
                    _A    		=  timm.create_model('levit_128s'   ,		pretrained=SCREAMING_SNAKE_CASE__				)
               else:
                    _A    		=  timm.create_model('levit_128'   ,		pretrained=SCREAMING_SNAKE_CASE__				)
          if hidden_sizes == 1_9_2:
               _A    		=  timm.create_model('levit_192'   ,		pretrained=SCREAMING_SNAKE_CASE__				)
          if hidden_sizes == 2_5_6:
               _A    		=  timm.create_model('levit_256'   ,		pretrained=SCREAMING_SNAKE_CASE__				)
          if hidden_sizes == 3_8_4:
               _A    		=  timm.create_model('levit_384'   ,		pretrained=SCREAMING_SNAKE_CASE__				)

          from_model.eval()
          _A    		=  LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__				).eval()
          _A    		=  OrderedDict()

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

          _A    		=  torch.randn((2, 3, 2_2_4, 2_2_4)				)
          _A    		=  from_model(SCREAMING_SNAKE_CASE__				)
          _A    		=  our_model(SCREAMING_SNAKE_CASE__				).logits

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

     _A    		=  name
     print(SCREAMING_SNAKE_CASE__				)

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

          print(F'Pushed {checkpoint_name}'				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Path   ,		__snake_case							:			str = None   ,		__snake_case							:			bool = True				):
     _A    		=  'imagenet-1k-id2label.json'
     _A    		=  1_0_0_0
     _A    		=  (1, num_labels)

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

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

     _A    		=  partial(SCREAMING_SNAKE_CASE__   ,		num_labels=SCREAMING_SNAKE_CASE__   ,		idalabel=SCREAMING_SNAKE_CASE__   ,		labelaid=SCREAMING_SNAKE_CASE__				)

     _A    		=  {
         '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,
     }

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


if __name__ == "__main__":
   _UpperCAmelCase    :     Tuple         =       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    :     Optional[Any]         =       parser.parse_args()
   _UpperCAmelCase    :     Tuple         =       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)







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor




class A    (  unittest.TestCase	):






      def __init__(   self		:	Any     ,  lowercase_		:	List[str]     ,  lowercase_		:	Union[str, Any]=7     ,  lowercase_		:	Dict=3     ,  lowercase_		:	List[Any]=30     ,  lowercase_		:	int=400     ,  lowercase_		:	List[Any]=True     ,  lowercase_		:	str=None     ,  lowercase_		:	str=0.9     ,  lowercase_		:	Union[str, Any]=None     ,  lowercase_		:	List[str]=True     ,  lowercase_		:	List[str]=[0.5, 0.5, 0.5]     ,  lowercase_		:	int=[0.5, 0.5, 0.5]     ,  )   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						int            =size if size is not None else {'shortest_edge': 30}
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else {'height': 30, 'width': 30}
             _lowerCamelCase			:						List[Any]            =parent
             _lowerCamelCase			:						Union[str, Any]            =batch_size
             _lowerCamelCase			:						Dict            =num_channels
             _lowerCamelCase			:						Any            =min_resolution
             _lowerCamelCase			:						Union[str, Any]            =max_resolution
             _lowerCamelCase			:						Any            =do_resize_and_center_crop
             _lowerCamelCase			:						Any            =size
             _lowerCamelCase			:						Optional[Any]            =crop_pct
             _lowerCamelCase			:						Optional[Any]            =crop_size
             _lowerCamelCase			:						List[Any]            =do_normalize
             _lowerCamelCase			:						int            =image_mean
             _lowerCamelCase			:						Optional[int]            =image_std






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Tuple:
             """simple docstring"""




             return {
                 "size": self.size,
                 "do_resize_and_center_crop": self.do_resize_and_center_crop,
                 "crop_pct": self.crop_pct,
                 "crop_size": self.crop_size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
             }




@require_torch
@require_vision
class A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    Dict =PoolFormerImageProcessor if is_vision_available() else None






      def 			lowerCamelCase			(   self		:	int					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =PoolFormerImageProcessingTester(self					)






      @property
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  'do_resize_and_center_crop'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'size'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'crop_pct'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'do_normalize'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_mean'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_std'					)					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						Any            =self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'shortest_edge': 30}					)
             self.assertEqual(image_processor.crop_size     ,  {'height': 30, 'width': 30}					)

             _lowerCamelCase			:						List[Any]            =self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42     ,  crop_size=84					)
             self.assertEqual(image_processor.size     ,  {'shortest_edge': 42}					)
             self.assertEqual(image_processor.crop_size     ,  {'height': 84, 'width': 84}					)






      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _lowerCamelCase			:						int            =image_processing(image_inputs[0]     ,  return_tensors='pt'					).pixel_values

             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )

             # Test batched
             _lowerCamelCase			:						Optional[int]            =image_processing(lowercase_     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _lowerCamelCase			:						Optional[Any]            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  np.ndarray					)

             # Test not batched input
             _lowerCamelCase			:						Dict            =image_processing(image_inputs[0]     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )

             # Test batched
             _lowerCamelCase			:						Optional[int]            =image_processing(lowercase_     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

             # Test not batched input
             _lowerCamelCase			:						str            =image_processing(image_inputs[0]     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )

             # Test batched
             _lowerCamelCase			:						Tuple            =image_processing(lowercase_     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )


