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		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = old_name

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

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

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

                     lowerCAmelCase__					     = 'last_stage.' + trimmed_name

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

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

       return new_name






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       for key in checkpoint.copy().keys():
              lowerCAmelCase__					     = checkpoint.pop(a__     )
              lowerCAmelCase__					     = val

       return checkpoint






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowerCAmelCase__					     = Image.open(requests.get(a__     ,  stream=a__     ).raw     )

       return image






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

       lowerCAmelCase__					     = config.depths[-1] - config.num_metaad_blocks + 1
       lowerCAmelCase__					     = convert_torch_checkpoint(a__     ,  a__     )

       model.load_state_dict(a__     )
       model.eval()

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

       # prepare image
       lowerCAmelCase__					     = prepare_img()
       lowerCAmelCase__					     = 2_5_6
       lowerCAmelCase__					     = 2_2_4
       lowerCAmelCase__					     = EfficientFormerImageProcessor(
           size={'shortest_edge': image_size}     ,  crop_size={'height': crop_size, 'width': crop_size}     ,  resample=pillow_resamplings['bicubic']     ,  )
       lowerCAmelCase__					     = processor(images=a__     ,  return_tensors='pt'     ).pixel_values

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

       assert torch.allclose(a__     ,  a__     )

       lowerCAmelCase__					     = model(a__     )
       lowerCAmelCase__					     = outputs.logits

       lowerCAmelCase__					     = (1, 1_0_0_0)

       if "l1" in model_name:
              lowerCAmelCase__					     = torch.Tensor(
                  [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]     )
              assert torch.allclose(logits[0, :1_0]     ,  a__     ,  atol=1e-3     )
              assert logits.shape == expected_shape
       elif "l3" in model_name:
              lowerCAmelCase__					     = torch.Tensor(
                  [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]     )
              assert torch.allclose(logits[0, :1_0]     ,  a__     ,  atol=1e-3     )
              assert logits.shape == expected_shape
       elif "l7" in model_name:
              lowerCAmelCase__					     = torch.Tensor(
                  [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]     )
              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(a__     ).mkdir(exist_ok=a__     )
       model.save_pretrained(a__     )
       print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )
       processor.save_pretrained(a__     )
       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=a__     ,  )
              processor.push_to_hub(
                  repo_id=F"""Bearnardd/{pytorch_dump_path}"""     ,  commit_message='Add image processor'     ,  use_temp_dir=a__     ,  )


if __name__ == "__main__":
    lowerCAmelCase__     						=			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)

    lowerCAmelCase__     						=			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 argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__     						=			logging.get_logger(__name__)






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = 'huggingface/label-files'
       lowerCAmelCase__					     = 'imagenet-1k-id2label.json'
       lowerCAmelCase__					     = json.load(open(hf_hub_download(lowerCAmelCase__     ,  lowerCAmelCase__     ,  repo_type='dataset'     )     ,  'r'     )     )
       lowerCAmelCase__					     = {int(lowerCAmelCase__     ): v for k, v in idalabel.items()}
       lowerCAmelCase__					     = {v: k for k, v in idalabel.items()}

       lowerCAmelCase__					     = 'std_conv' if 'bit' in model_name else False

       # note that when using BiT as backbone for ViT-hybrid checkpoints,
       # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
       # config.conv_layer = "std_conv_same"
       lowerCAmelCase__					     = BitConfig(
           conv_layer=lowerCAmelCase__     ,  num_labels=1_0_0_0     ,  idalabel=lowerCAmelCase__     ,  labelaid=lowerCAmelCase__     ,  )

       return config






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       if "stem.conv" in name:
              lowerCAmelCase__					     = name.replace('stem.conv'     ,  'bit.embedder.convolution'     )
       if "blocks" in name:
              lowerCAmelCase__					     = name.replace('blocks'     ,  'layers'     )
       if "head.fc" in name:
              lowerCAmelCase__					     = name.replace('head.fc'     ,  'classifier.1'     )
       if name.startswith('norm'     ):
              lowerCAmelCase__					     = 'bit.' + name
       if "bit" not in name and "classifier" not in name:
              lowerCAmelCase__					     = 'bit.encoder.' + name

       return name






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowerCAmelCase__					     = Image.open(requests.get(lowerCAmelCase__     ,  stream=lowerCAmelCase__     ).raw     )
       return im






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=False     ):
       lowerCAmelCase__					     = get_config(lowerCAmelCase__     )

       # load original model from timm
       lowerCAmelCase__					     = create_model(lowerCAmelCase__     ,  pretrained=lowerCAmelCase__     )
       timm_model.eval()

       # load state_dict of original model
       lowerCAmelCase__					     = timm_model.state_dict()
       for key in state_dict.copy().keys():
              lowerCAmelCase__					     = state_dict.pop(lowerCAmelCase__     )
              lowerCAmelCase__					     = val.squeeze() if 'head' in key else val

       # load HuggingFace model
       lowerCAmelCase__					     = BitForImageClassification(lowerCAmelCase__     )
       model.eval()
       model.load_state_dict(lowerCAmelCase__     )

       # create image processor
       lowerCAmelCase__					     = create_transform(**resolve_data_config({}     ,  model=lowerCAmelCase__     )     )
       lowerCAmelCase__					     = transform.transforms

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

       lowerCAmelCase__					     = BitImageProcessor(
           do_resize=lowerCAmelCase__     ,  size={'shortest_edge': timm_transforms[0].size}     ,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]     ,  do_center_crop=lowerCAmelCase__     ,  crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}     ,  do_normalize=lowerCAmelCase__     ,  image_mean=timm_transforms[-1].mean.tolist()     ,  image_std=timm_transforms[-1].std.tolist()     ,  )

       lowerCAmelCase__					     = prepare_img()
       lowerCAmelCase__					     = transform(lowerCAmelCase__     ).unsqueeze(0     )
       lowerCAmelCase__					     = processor(lowerCAmelCase__     ,  return_tensors='pt'     ).pixel_values

       # verify pixel values
       assert torch.allclose(lowerCAmelCase__     ,  lowerCAmelCase__     )

       # verify logits
       with torch.no_grad():
              lowerCAmelCase__					     = model(lowerCAmelCase__     )
              lowerCAmelCase__					     = outputs.logits

       print('Logits:'     ,  logits[0, :3]     )
       print('Predicted class:'     ,  model.config.idalabel[logits.argmax(-1     ).item()]     )
       lowerCAmelCase__					     = timm_model(lowerCAmelCase__     )
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(lowerCAmelCase__     ,  outputs.logits     ,  atol=1e-3     )
       print('Looks ok!'     )

       if pytorch_dump_folder_path is not None:
              Path(lowerCAmelCase__     ).mkdir(exist_ok=lowerCAmelCase__     )
              print(F"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""     )
              model.save_pretrained(lowerCAmelCase__     )
              processor.save_pretrained(lowerCAmelCase__     )

       if push_to_hub:
              print(F"""Pushing model {model_name} and processor to the hub"""     )
              model.push_to_hub(F"""ybelkada/{model_name}"""     )
              processor.push_to_hub(F"""ybelkada/{model_name}"""     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='resnetv2_50x1_bitm',
        type=str,
        help='Name of the BiT timm model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub',
        action='store_true',
        help='Whether to push the model to the hub.',
    )

    lowerCAmelCase__     						=			parser.parse_args()
    convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
