'''simple docstring'''





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_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Any      )   ->				List[str]:


     '''simple docstring'''




     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" ,   snake_case_      ):
          UpperCAmelCase_	   =       R"\b\d{2}\b"
          if bool(re.search(snake_case_ ,   snake_case_      )      ):
               UpperCAmelCase_	   =       re.search(R"\d\.\d\d." ,   snake_case_      ).group()
          else:
               UpperCAmelCase_	   =       re.search(R"\d\.\d." ,   snake_case_      ).group()
          if int(match[0]      ) < 6:
               UpperCAmelCase_	   =       old_name.replace(snake_case_ ,   ""      )
               UpperCAmelCase_	   =       trimmed_name.replace("network" ,   match[0] + ".meta4D_layers.blocks." + match[2:-1]      )
               UpperCAmelCase_	   =       "intermediate_stages." + trimmed_name
          else:
               UpperCAmelCase_	   =       old_name.replace(snake_case_ ,   ""      )
               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." ,   snake_case_      ):
          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_    ( snake_case_	:			List[Any] ,   snake_case_	:			List[Any]      )   ->				int:


     '''simple docstring'''




     for key in checkpoint.copy().keys():
          UpperCAmelCase_	   =       checkpoint.pop(snake_case_      )
          UpperCAmelCase_	   =       val

     return checkpoint





def      lowerCAmelCase_    ( )   ->				List[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_	   =       Image.open(requests.get(snake_case_ ,   stream=snake_case_      ).raw      )

     return image





def      lowerCAmelCase_    ( snake_case_	:			Path ,   snake_case_	:			Path ,   snake_case_	:			Path ,   snake_case_	:			bool      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       torch.load(snake_case_ ,   map_location="cpu"      )["model"]
     UpperCAmelCase_	   =       EfficientFormerConfig.from_json_file(snake_case_      )
     UpperCAmelCase_	   =       EfficientFormerForImageClassificationWithTeacher(snake_case_      )
     UpperCAmelCase_	   =       "_".join(checkpoint_path.split("/"      )[-1].split("."      )[0].split("_"      )[:-1]      )

     UpperCAmelCase_	   =       config.depths[-1] - config.num_metaad_blocks + 1
     UpperCAmelCase_	   =       convert_torch_checkpoint(snake_case_ ,   snake_case_      )

     model.load_state_dict(snake_case_      )
     model.eval()

     UpperCAmelCase_	   =       {
         "bilinear": PILImageResampling.BILINEAR,
         "bicubic": PILImageResampling.BICUBIC,
         "nearest": PILImageResampling.NEAREST,
     }

     # prepare image
     UpperCAmelCase_	   =       prepare_img()
     UpperCAmelCase_	   =       2_56
     UpperCAmelCase_	   =       2_24
     UpperCAmelCase_	   =       EfficientFormerImageProcessor(
         size={"shortest_edge": image_size} ,   crop_size={"height": crop_size, "width": crop_size} ,   resample=pillow_resamplings["bicubic"] ,   )
     UpperCAmelCase_	   =       processor(images=snake_case_ ,   return_tensors="pt"      ).pixel_values

     # original processing pipeline
     UpperCAmelCase_	   =       Compose(
         [
             Resize(snake_case_ ,   interpolation=pillow_resamplings["bicubic"]      ),
             CenterCrop(snake_case_      ),
             ToTensor(),
             Normalize(snake_case_ ,   snake_case_      ),
         ]      )
     UpperCAmelCase_	   =       image_transforms(snake_case_      ).unsqueeze(0      )

     assert torch.allclose(snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       model(snake_case_      )
     UpperCAmelCase_	   =       outputs.logits

     UpperCAmelCase_	   =       (1, 10_00)

     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] ,   snake_case_ ,   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] ,   snake_case_ ,   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(snake_case_      ).mkdir(exist_ok=snake_case_      )
     model.save_pretrained(snake_case_      )
     print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""      )
     processor.save_pretrained(snake_case_      )
     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=snake_case_ ,   )
          processor.push_to_hub(
              repo_id=f"""Bearnardd/{pytorch_dump_path}""" ,   commit_message="Add image processor" ,   use_temp_dir=snake_case_ ,   )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					List[Any]  =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)

    SCREAMING_SNAKE_CASE_:					Tuple  =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,
    )





'''simple docstring'''





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters





def      lowerCAmelCase_    ( )   ->				str:


     '''simple docstring'''




     UpperCAmelCase_	   =       {
         "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
         "path": ["test_1.py", "test_2.py", "unit_test.py"],
         "content": ["a " * 20, "a " * 30, "b " * 7],
     }
     UpperCAmelCase_	   =       Dataset.from_dict(snake_case_      )
     return dataset






class 	__A				(			UpperCamelCase__     ):



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_	   =       make_duplicate_clusters(__a       ,      0.85			)
         self.assertEqual(len(duplicate_clusters[0]			)       ,      2			)






    def  _lowercase						(self   :   Optional[Any]			):
         UpperCAmelCase_	   =       get_dataset()
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       deduplicate_dataset(__a			)
         self.assertEqual(len(__a			)       ,      2			)
         print(__a			)
         self.assertEqual(duplicate_clusters[0][0]["copies"]       ,      2			)
         self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,      __a			)




