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 A__	(      lowercase:  int,		lowercase:  Any       )					->		Any:
       A    :  Tuple								=old_name

       if "patch_embed" in old_name:
              A    :  Optional[int]								=old_name.split('.'       )

              if layer == "0":
                     A    :  List[str]								=old_name.replace('0',		'convolution1'       )
              elif layer == "1":
                     A    :  str								=old_name.replace('1',		'batchnorm_before'       )
              elif layer == "3":
                     A    :  Optional[Any]								=old_name.replace('3',		'convolution2'       )
              else:
                     A    :  Tuple								=old_name.replace('4',		'batchnorm_after'       )

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

                     A    :  List[Any]								="last_stage." + trimmed_name

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

       if "fc" in new_name:
              A    :  Dict								=new_name.replace('fc',		'convolution'       )
       elif ("norm1" in new_name) and ("layernorm1" not in new_name):
              A    :  List[str]								=new_name.replace('norm1',		'batchnorm_before'       )
       elif ("norm2" in new_name) and ("layernorm2" not in new_name):
              A    :  Union[str, Any]								=new_name.replace('norm2',		'batchnorm_after'       )
       if "proj" in new_name:
              A    :  Dict								=new_name.replace('proj',		'projection'       )
       if "dist_head" in new_name:
              A    :  List[str]								=new_name.replace('dist_head',		'distillation_classifier'       )
       elif "head" in new_name:
              A    :  List[str]								=new_name.replace('head',		'classifier'       )
       elif "patch_embed" in new_name:
              A    :  str								="efficientformer." + new_name
       elif new_name == "norm.weight" or new_name == "norm.bias":
              A    :  List[str]								=new_name.replace('norm',		'layernorm'       )
              A    :  Optional[int]								="efficientformer." + new_name
       else:
              A    :  List[Any]								="efficientformer.encoder." + new_name

       return new_name



def A__	(      lowercase:  List[str],		lowercase:  str       )					->		str:
       for key in checkpoint.copy().keys():
              A    :  Union[str, Any]								=checkpoint.pop(_lowerCAmelCase       )
              A    :  Optional[int]								=val

       return checkpoint



def A__	(      )					->		Dict:
       A    :  Dict								="http://images.cocodataset.org/val2017/000000039769.jpg"
       A    :  str								=Image.open(requests.get(_lowerCAmelCase,		stream=_lowerCAmelCase       ).raw       )

       return image



def A__	(      lowercase:  List[str],		lowercase:  int,		lowercase:  int,		lowercase:  Tuple       )					->		Union[str, Any]:
       A    :  List[str]								=torch.load(_lowerCAmelCase,		map_location='cpu'       )["model"]
       A    :  int								=EfficientFormerConfig.from_json_file(_lowerCAmelCase       )
       A    :  int								=EfficientFormerForImageClassificationWithTeacher(_lowerCAmelCase       )
       A    :  List[Any]								="_".join(checkpoint_path.split('/'       )[-1].split('.'       )[0].split('_'       )[:-1]       )

       A    :  Optional[Any]								=config.depths[-1] - config.num_metaad_blocks + 1
       A    :  Optional[int]								=convert_torch_checkpoint(_lowerCAmelCase,		_lowerCAmelCase       )

       model.load_state_dict(_lowerCAmelCase       )
       model.eval()

       A    :  Union[str, Any]								={
           "bilinear": PILImageResampling.BILINEAR,
           "bicubic": PILImageResampling.BICUBIC,
           "nearest": PILImageResampling.NEAREST,
       }

       # prepare image
       A    :  Optional[Any]								=prepare_img()
       A    :  Optional[int]								=256
       A    :  Optional[int]								=224
       A    :  List[str]								=EfficientFormerImageProcessor(
           size={'shortest_edge': image_size},		crop_size={'height': crop_size, 'width': crop_size},		resample=pillow_resamplings['bicubic'],		)
       A    :  Optional[Any]								=processor(images=_lowerCAmelCase,		return_tensors='pt'       ).pixel_values

       # original processing pipeline
       A    :  int								=Compose(
           [
               Resize(_lowerCAmelCase,		interpolation=pillow_resamplings['bicubic']       ),
               CenterCrop(_lowerCAmelCase       ),
               ToTensor(),
               Normalize(_lowerCAmelCase,		_lowerCAmelCase       ),
           ]       )
       A    :  Union[str, Any]								=image_transforms(_lowerCAmelCase       ).unsqueeze(0       )

       assert torch.allclose(_lowerCAmelCase,		_lowerCAmelCase       )

       A    :  Union[str, Any]								=model(_lowerCAmelCase       )
       A    :  str								=outputs.logits

       A    :  Union[str, Any]								=(1, 1_000)

       if "l1" in model_name:
              A    :  Optional[Any]								=torch.Tensor(
                  [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]       )
              assert torch.allclose(logits[0, :10],		_lowerCAmelCase,		atol=1e-3       )
              assert logits.shape == expected_shape
       elif "l3" in model_name:
              A    :  Optional[int]								=torch.Tensor(
                  [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]       )
              assert torch.allclose(logits[0, :10],		_lowerCAmelCase,		atol=1e-3       )
              assert logits.shape == expected_shape
       elif "l7" in model_name:
              A    :  str								=torch.Tensor(
                  [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]       )
              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(_lowerCAmelCase       ).mkdir(exist_ok=_lowerCAmelCase       )
       model.save_pretrained(_lowerCAmelCase       )
       print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'       )
       processor.save_pretrained(_lowerCAmelCase       )
       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=_lowerCAmelCase,		)
              processor.push_to_hub(
                  repo_id=F'Bearnardd/{pytorch_dump_path}',		commit_message='Add image processor',		use_temp_dir=_lowerCAmelCase,		)


if __name__ == "__main__":
    _lowercase				:   Union[str, 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)

    _lowercase				:   Union[str, Any]   				=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,
    )













from __future__ import annotations

import math



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					float:
 UpperCamelCase      :   Tuple			      =  u
 for i in range(1				,  _lowerCAmelCase	):
  UpperCamelCase      :   Any			      =  temp * (u - i)
 return temp



def 						A_							(	)		->					None:
 UpperCamelCase      :   Union[str, Any]			      =  int(input("enter the numbers of values: "	)	)
 UpperCamelCase      :   list[list[float]]			      =  []
 for _ in range(_lowerCAmelCase	):
  y.append([]	)
 for i in range(_lowerCAmelCase	):
  for j in range(_lowerCAmelCase	):
   y[i].append(_lowerCAmelCase	)
   UpperCamelCase      :   List[str]			      =  0

 print("enter the values of parameters in a list: "	)
 UpperCamelCase      :   Optional[Any]			      =  list(map(_lowerCAmelCase				,  input().split()	)	)

 print("enter the values of corresponding parameters: "	)
 for i in range(_lowerCAmelCase	):
  UpperCamelCase      :   Tuple			      =  float(input()	)

 UpperCamelCase      :   Any			      =  int(input("enter the value to interpolate: "	)	)
 UpperCamelCase      :   List[Any]			      =  (value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1				,  _lowerCAmelCase	):
  for j in range(n - i	):
   UpperCamelCase      :   Optional[Any]			      =  y[j + 1][i - 1] - y[j][i - 1]

 UpperCamelCase      :   List[str]			      =  y[0][0]
 for i in range(1				,  _lowerCAmelCase	):
  summ += (ucal(_lowerCAmelCase				,  _lowerCAmelCase	) * y[0][i]) / math.factorial(_lowerCAmelCase	)

 print(F"""the value at {value} is {summ}"""	)


if __name__ == "__main__":
    main()
