
import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Any:
     UpperCamelCase						    =					old_name

     if "patch_embed" in old_name:
          UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase						    =					old_name.split('.'			)

          if layer == "0":
               UpperCamelCase						    =					old_name.replace('0'						,     'convolution1'			)
          elif layer == "1":
               UpperCamelCase						    =					old_name.replace('1'						,     'batchnorm_before'			)
          elif layer == "3":
               UpperCamelCase						    =					old_name.replace('3'						,     'convolution2'			)
          else:
               UpperCamelCase						    =					old_name.replace('4'						,     'batchnorm_after'			)

     if "network" in old_name and re.search(r'\d\.\d'						,     _lowercase			):
          UpperCamelCase						    =					r'\b\d{2}\b'
          if bool(re.search(_lowercase						,     _lowercase			)			):
               UpperCamelCase						    =					re.search(r'\d\.\d\d.'						,     _lowercase			).group()
          else:
               UpperCamelCase						    =					re.search(r'\d\.\d.'						,     _lowercase			).group()
          if int(match[0]			) < 6:
               UpperCamelCase						    =					old_name.replace(_lowercase						,     ''			)
               UpperCamelCase						    =					trimmed_name.replace('network'						,     match[0] + '.meta4D_layers.blocks.' + match[2:-1]			)
               UpperCamelCase						    =					'intermediate_stages.' + trimmed_name
          else:
               UpperCamelCase						    =					old_name.replace(_lowercase						,     ''			)
               if int(match[2]			) < num_meta4D_last_stage:
                    UpperCamelCase						    =					trimmed_name.replace('network'						,     'meta4D_layers.blocks.' + match[2]			)
               else:
                    UpperCamelCase						    =					str(int(match[2]			) - num_meta4D_last_stage			)
                    UpperCamelCase						    =					trimmed_name.replace('network'						,     'meta3D_layers.blocks.' + layer_index			)
                    if "norm1" in old_name:
                         UpperCamelCase						    =					trimmed_name.replace('norm1'						,     'layernorm1'			)
                    elif "norm2" in old_name:
                         UpperCamelCase						    =					trimmed_name.replace('norm2'						,     'layernorm2'			)
                    elif "fc1" in old_name:
                         UpperCamelCase						    =					trimmed_name.replace('fc1'						,     'linear_in'			)
                    elif "fc2" in old_name:
                         UpperCamelCase						    =					trimmed_name.replace('fc2'						,     'linear_out'			)

               UpperCamelCase						    =					'last_stage.' + trimmed_name

     elif "network" in old_name and re.search(r'.\d.'						,     _lowercase			):
          UpperCamelCase						    =					old_name.replace('network'						,     'intermediate_stages'			)

     if "fc" in new_name:
          UpperCamelCase						    =					new_name.replace('fc'						,     'convolution'			)
     elif ("norm1" in new_name) and ("layernorm1" not in new_name):
          UpperCamelCase						    =					new_name.replace('norm1'						,     'batchnorm_before'			)
     elif ("norm2" in new_name) and ("layernorm2" not in new_name):
          UpperCamelCase						    =					new_name.replace('norm2'						,     'batchnorm_after'			)
     if "proj" in new_name:
          UpperCamelCase						    =					new_name.replace('proj'						,     'projection'			)
     if "dist_head" in new_name:
          UpperCamelCase						    =					new_name.replace('dist_head'						,     'distillation_classifier'			)
     elif "head" in new_name:
          UpperCamelCase						    =					new_name.replace('head'						,     'classifier'			)
     elif "patch_embed" in new_name:
          UpperCamelCase						    =					'efficientformer.' + new_name
     elif new_name == "norm.weight" or new_name == "norm.bias":
          UpperCamelCase						    =					new_name.replace('norm'						,     'layernorm'			)
          UpperCamelCase						    =					'efficientformer.' + new_name
     else:
          UpperCamelCase						    =					'efficientformer.encoder.' + new_name

     return new_name

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Optional[Any]:
     for key in checkpoint.copy().keys():
          UpperCamelCase						    =					checkpoint.pop(_lowercase			)
          UpperCamelCase						    =					val

     return checkpoint

def  __lowerCamelCase					( )       ->      Optional[Any]:
     UpperCamelCase						    =					'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCamelCase						    =					Image.open(requests.get(_lowercase						,     stream=_lowercase			).raw			)

     return image

def  __lowerCamelCase					( _lowercase						,     _lowercase						,     _lowercase						,     _lowercase			)       ->      str:
     UpperCamelCase						    =					torch.load(_lowercase						,     map_location='cpu'			)['model']
     UpperCamelCase						    =					EfficientFormerConfig.from_json_file(_lowercase			)
     UpperCamelCase						    =					EfficientFormerForImageClassificationWithTeacher(_lowercase			)
     UpperCamelCase						    =					'_'.join(checkpoint_path.split('/'			)[-1].split('.'			)[0].split('_'			)[:-1]			)

     UpperCamelCase						    =					config.depths[-1] - config.num_metaad_blocks + 1
     UpperCamelCase						    =					convert_torch_checkpoint(_lowercase						,     _lowercase			)

     model.load_state_dict(_lowercase			)
     model.eval()

     UpperCamelCase						    =					{
         'bilinear': PILImageResampling.BILINEAR,
         'bicubic': PILImageResampling.BICUBIC,
         'nearest': PILImageResampling.NEAREST,
     }

     # prepare image
     UpperCamelCase						    =					prepare_img()
     UpperCamelCase						    =					256
     UpperCamelCase						    =					224
     UpperCamelCase						    =					EfficientFormerImageProcessor(
         size={'shortest_edge': image_size}						,     crop_size={'height': crop_size, 'width': crop_size}						,     resample=pillow_resamplings['bicubic']						,     )
     UpperCamelCase						    =					processor(images=_lowercase						,     return_tensors='pt'			).pixel_values

     # original processing pipeline
     UpperCamelCase						    =					Compose(
         [
             Resize(_lowercase						,     interpolation=pillow_resamplings['bicubic']			),
             CenterCrop(_lowercase			),
             ToTensor(),
             Normalize(_lowercase						,     _lowercase			),
         ]			)
     UpperCamelCase						    =					image_transforms(_lowercase			).unsqueeze(0			)

     assert torch.allclose(_lowercase						,     _lowercase			)

     UpperCamelCase						    =					model(_lowercase			)
     UpperCamelCase						    =					outputs.logits

     UpperCamelCase						    =					(1, 1000)

     if "l1" in model_name:
          UpperCamelCase						    =					torch.Tensor(
              [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]			)
          assert torch.allclose(logits[0, :10]						,     _lowercase						,     atol=1e-3			)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          UpperCamelCase						    =					torch.Tensor(
              [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]			)
          assert torch.allclose(logits[0, :10]						,     _lowercase						,     atol=1e-3			)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          UpperCamelCase						    =					torch.Tensor(
              [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]			)
          assert logits.shape == expected_shape
     else:
          raise ValueError(
              F'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'			)

     # Save Checkpoints
     Path(_lowercase			).mkdir(exist_ok=_lowercase			)
     model.save_pretrained(_lowercase			)
     print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'			)
     processor.save_pretrained(_lowercase			)
     print(F'Processor successfuly saved at {pytorch_dump_path}'			)

     if push_to_hub:
          print('Pushing model to the hub...'			)

          model.push_to_hub(
              repo_id=F'Bearnardd/{pytorch_dump_path}'						,     commit_message='Add model'						,     use_temp_dir=_lowercase						,     )
          processor.push_to_hub(
              repo_id=F'Bearnardd/{pytorch_dump_path}'						,     commit_message='Add image processor'						,     use_temp_dir=_lowercase						,     )


if __name__ == "__main__":
 _snake_case      = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--pytorch_model_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to EfficientFormer pytorch checkpoint.''',
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The json file for EfficientFormer model config.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )

 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''',
 )
 parser.set_defaults(push_to_hub=True)

 _snake_case      = parser.parse_args()
 convert_efficientformer_checkpoint(
     checkpoint_path=args.pytorch_model_path,
     efficientformer_config_file=args.config_file,
     pytorch_dump_path=args.pytorch_dump_path,
     push_to_hub=args.push_to_hub,
 )




import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=JukeboxTokenizer
 SCREAMING_SNAKE_CASE_				:					str       						={
     "artist": "Zac Brown Band",
     "genres": "Country",
     "lyrics": "I met a traveller from an antique land,\n        Who said \"Two vast and trunkless legs of stone\n        Stand in the desert. . . . Near them, on the sand,\n        Half sunk a shattered visage lies, whose frown,\n        And wrinkled lip, and sneer of cold command,\n        Tell that its sculptor well those passions read\n        Which yet survive, stamped on these lifeless things,\n        The hand that mocked them, and the heart that fed;\n        And on the pedestal, these words appear:\n        My name is Ozymandias, King of Kings;\n        Look on my Works, ye Mighty, and despair!\n        Nothing beside remains. Round the decay\n        Of that colossal Wreck, boundless and bare\n        The lone and level sands stretch far away\n        ",
 }

 @require_torch
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      import torch

      UpperCamelCase						    =					JukeboxTokenizer.from_pretrained('openai/jukebox-1b-lyrics'   )
      UpperCamelCase						    =					tokenizer(**self.metas   )['input_ids']
      # fmt: off
      UpperCamelCase						    =					[
          torch.tensor([[
              0, 0, 0, 71_69, 5_07, 9, 76, 39, 31, 46, 76, 27,
              76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
              44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
              47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
              76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
              30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
              27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
              45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
              41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
              76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
              76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
              64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
              30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
              27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
              34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
              27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
              41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
              76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
              44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
              76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
              32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
              40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
              45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
              31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
              45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
              76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
              34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
              31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
              40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
              38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
              76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
              41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
              27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
              46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
              76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
              41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
              46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
              41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
              40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
              27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
              76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
              76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
              41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
              76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
              27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
              34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
              44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
              40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
              78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
              46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
              38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
              40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
              27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
              20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
              76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
              76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
              76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
              76, 76]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11]]   ),
      ]
      # fmt: on
      self.assertTrue(torch.allclose(tokens[0]					, EXPECTED_OUTPUT[0]   )   )
      self.assertTrue(torch.allclose(tokens[1]					, EXPECTED_OUTPUT[1]   )   )
      self.assertTrue(torch.allclose(tokens[2]					, EXPECTED_OUTPUT[2]   )   )






 @require_torch
 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      import torch

      UpperCamelCase						    =					JukeboxTokenizer.from_pretrained('openai/jukebox-5b-lyrics'   )
      UpperCamelCase						    =					tokenizer(**self.metas   )['input_ids']
      # fmt: off
      UpperCamelCase						    =					[
          torch.tensor([[
              0, 0, 0, 10_69, 11, -1, -1, -1, -1, 9, 77, 39,
              31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
              31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
              40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
              79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
              77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
              27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
              37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
              32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
              77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
              77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
              46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
              77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
              77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
              77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
              77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
              64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
              40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
              40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
              38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
              31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
              41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
              46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
              41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
              31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
              31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
              23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
              44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
              31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
              38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
              40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
              27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
              31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
              34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
              31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
              1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
              31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
              45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
              31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
              15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
              11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
              45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
              41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
              44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
              46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
              27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
              35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
              77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
              31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
              77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
              41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
              77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
              40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
              77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
              77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
              27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
              77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
              77, 77, 77, 77, 77, 77]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]   ),
          torch.tensor([[0, 0, 0, 10_69, 11, -1, -1, -1, -1]]   ),
      ]
      # fmt: on
      self.assertTrue(torch.allclose(tokens[0]					, EXPECTED_OUTPUT[0]   )   )
      self.assertTrue(torch.allclose(tokens[1]					, EXPECTED_OUTPUT[1]   )   )
      self.assertTrue(torch.allclose(tokens[2]					, EXPECTED_OUTPUT[2]   )   )


