





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_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[int]   )   ->			Optional[int]:
    UpperCamelCase__							:       Any      =   old_name

    if "patch_embed" in old_name:
        UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   old_name.split('''.'''   )

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

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

            UpperCamelCase__							:       Optional[int]      =   '''last_stage.''' + trimmed_name

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

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

    return new_name




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    List[str]   )   ->			str:
    for key in checkpoint.copy().keys():
        UpperCamelCase__							:       List[str]      =   checkpoint.pop(__UpperCAmelCase   )
        UpperCamelCase__							:       int      =   val

    return checkpoint




def 	lowerCAmelCase_					(						)   ->			Optional[int]:
    UpperCamelCase__							:       str      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Dict      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )

    return image




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Path	,     __UpperCAmelCase:    Path	,     __UpperCAmelCase:    Path	,     __UpperCAmelCase:    bool   )   ->			str:
    UpperCamelCase__							:       str      =   torch.load(__UpperCAmelCase	,     map_location='''cpu'''   )['''model''']
    UpperCamelCase__							:       List[str]      =   EfficientFormerConfig.from_json_file(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   EfficientFormerForImageClassificationWithTeacher(__UpperCAmelCase   )
    UpperCamelCase__							:       List[str]      =   '''_'''.join(checkpoint_path.split('''/'''   )[-1].split('''.'''   )[0].split('''_'''   )[:-1]   )

    UpperCamelCase__							:       Optional[Any]      =   config.depths[-1] - config.num_metaad_blocks + 1
    UpperCamelCase__							:       Optional[Any]      =   convert_torch_checkpoint(__UpperCAmelCase	,     __UpperCAmelCase   )

    model.load_state_dict(__UpperCAmelCase   )
    model.eval()

    UpperCamelCase__							:       List[Any]      =   {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

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

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

    assert torch.allclose(__UpperCAmelCase	,     __UpperCAmelCase   )

    UpperCamelCase__							:       int      =   model(__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   outputs.logits

    UpperCamelCase__							:       List[str]      =   (1, 1000)

    if "l1" in model_name:
        UpperCamelCase__							:       Any      =   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]	,     __UpperCAmelCase	,     atol=1e-3   )
        assert logits.shape == expected_shape
    elif "l3" in model_name:
        UpperCamelCase__							:       List[Any]      =   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]	,     __UpperCAmelCase	,     atol=1e-3   )
        assert logits.shape == expected_shape
    elif "l7" in model_name:
        UpperCamelCase__							:       Optional[int]      =   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(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
    model.save_pretrained(__UpperCAmelCase   )
    print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )
    processor.save_pretrained(__UpperCAmelCase   )
    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=__UpperCAmelCase	,     )
        processor.push_to_hub(
            repo_id=f"Bearnardd/{pytorch_dump_path}"	,     commit_message='''Add image processor'''	,     use_temp_dir=__UpperCAmelCase	,     )


if __name__ == "__main__":
  UpperCAmelCase_  							=				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)

  UpperCAmelCase_  							=				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,
  )








def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict   )   ->			set:
    UpperCamelCase__							:       int      =   set()
    # edges = list of graph's edges
    UpperCamelCase__							:       str      =   get_edges(__UpperCAmelCase   )

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   edges.pop()
        chosen_vertices.add(__UpperCAmelCase   )
        chosen_vertices.add(__UpperCAmelCase   )
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(__UpperCAmelCase   )
    return chosen_vertices




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict   )   ->			set:
    UpperCamelCase__							:       Optional[int]      =   set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)   )
    return edges


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")

