

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 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Optional[int]					=					old_name

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

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

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

                  __lowerCAmelCase					:						str					=					'''last_stage.''' + trimmed_name

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

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

      return new_name







def 		_lowercase (     lowercase__   ,	lowercase__						):
      for key in checkpoint.copy().keys():
            __lowerCAmelCase					:						Any					=					checkpoint.pop(lowercase__						)
            __lowerCAmelCase					:						Tuple					=					val

      return checkpoint







def 		_lowercase (     ):
      __lowerCAmelCase					:						str					=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __lowerCAmelCase					:						Tuple					=					Image.open(requests.get(lowercase__   ,	stream=lowercase__						).raw						)

      return image







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Optional[int]					=					torch.load(lowercase__   ,	map_location='''cpu'''						)['''model''']
      __lowerCAmelCase					:						Union[str, Any]					=					EfficientFormerConfig.from_json_file(lowercase__						)
      __lowerCAmelCase					:						Dict					=					EfficientFormerForImageClassificationWithTeacher(lowercase__						)
      __lowerCAmelCase					:						Optional[Any]					=					'''_'''.join(checkpoint_path.split('''/'''						)[-1].split('''.'''						)[0].split('''_'''						)[:-1]						)

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

      model.load_state_dict(lowercase__						)
      model.eval()

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

      # prepare image
      __lowerCAmelCase					:						List[str]					=					prepare_img()
      __lowerCAmelCase					:						int					=					2_5_6
      __lowerCAmelCase					:						Optional[Any]					=					2_2_4
      __lowerCAmelCase					:						Tuple					=					EfficientFormerImageProcessor(
          size={'''shortest_edge''': image_size}   ,	crop_size={'''height''': crop_size, '''width''': crop_size}   ,	resample=pillow_resamplings['''bicubic''']   ,	)
      __lowerCAmelCase					:						List[str]					=					processor(images=lowercase__   ,	return_tensors='''pt'''						).pixel_values

      # original processing pipeline
      __lowerCAmelCase					:						List[str]					=					Compose(
          [
              Resize(lowercase__   ,	interpolation=pillow_resamplings['''bicubic''']						),
              CenterCrop(lowercase__						),
              ToTensor(),
              Normalize(lowercase__   ,	lowercase__						),
          ]						)
      __lowerCAmelCase					:						List[str]					=					image_transforms(lowercase__						).unsqueeze(0						)

      assert torch.allclose(lowercase__   ,	lowercase__						)

      __lowerCAmelCase					:						int					=					model(lowercase__						)
      __lowerCAmelCase					:						Any					=					outputs.logits

      __lowerCAmelCase					:						List[str]					=					(1, 1_0_0_0)

      if "l1" in model_name:
            __lowerCAmelCase					:						Optional[int]					=					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]   ,	lowercase__   ,	atol=1E-3						)
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            __lowerCAmelCase					:						str					=					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]   ,	lowercase__   ,	atol=1E-3						)
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            __lowerCAmelCase					:						Optional[int]					=					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(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__":
 _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,
 )





import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
 import torch

if is_vision_available():
 import PIL


_UpperCamelCase 			=		logging.get_logger(__name__)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      def constraint_to_multiple_of(lowercase__   ,	lowercase__   ,	lowercase__=0   ,	lowercase__=None						):
            __lowerCAmelCase					:						int					=					round(val / multiple						) * multiple

            if max_val is not None and x > max_val:
                  __lowerCAmelCase					:						Optional[int]					=					math.floor(val / multiple						) * multiple

            if x < min_val:
                  __lowerCAmelCase					:						Any					=					math.ceil(val / multiple						) * multiple

            return x

      __lowerCAmelCase					:						Dict					=					(output_size, output_size) if isinstance(lowercase__   ,	lowercase__						) else output_size

      __lowerCAmelCase,							__lowerCAmelCase					:						Optional[Any]					=					get_image_size(lowercase__						)
      __lowerCAmelCase,							__lowerCAmelCase					:						int					=					output_size

      # determine new height and width
      __lowerCAmelCase					:						Optional[Any]					=					output_height / input_height
      __lowerCAmelCase					:						List[Any]					=					output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width						) < abs(1 - scale_height						):
                  # fit width
                  __lowerCAmelCase					:						str					=					scale_width
            else:
                  # fit height
                  __lowerCAmelCase					:						str					=					scale_height

      __lowerCAmelCase					:						Any					=					constraint_to_multiple_of(scale_height * input_height   ,	multiple=lowercase__						)
      __lowerCAmelCase					:						Union[str, Any]					=					constraint_to_multiple_of(scale_width * input_width   ,	multiple=lowercase__						)

      return (new_height, new_width)







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  ["""pixel_values"""]
 def __init__(							self      ,							A_ = True      ,							A_ = None      ,							A_ = PILImageResampling.BILINEAR      ,							A_ = False      ,							A_ = 1      ,							A_ = True      ,							A_ = 1 / 255      ,							A_ = True      ,							A_ = None      ,							A_ = None      ,							**A_      ,							)					->None:



       '''simple docstring'''





       super().__init__(**A_					)
       __lowerCAmelCase					:						Union[str, Any]					=					size if size is not None else {'''height''': 384, '''width''': 384}
       __lowerCAmelCase					:						Dict					=					get_size_dict(A_					)
       __lowerCAmelCase					:						Optional[Any]					=					do_resize
       __lowerCAmelCase					:						int					=					size
       __lowerCAmelCase					:						Dict					=					keep_aspect_ratio
       __lowerCAmelCase					:						List[Any]					=					ensure_multiple_of
       __lowerCAmelCase					:						Tuple					=					resample
       __lowerCAmelCase					:						Dict					=					do_rescale
       __lowerCAmelCase					:						Any					=					rescale_factor
       __lowerCAmelCase					:						List[Any]					=					do_normalize
       __lowerCAmelCase					:						Optional[int]					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       __lowerCAmelCase					:						Optional[int]					=					image_std if image_std is not None else IMAGENET_STANDARD_STD
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = False      ,							A_ = 1      ,							A_ = PILImageResampling.BICUBIC      ,							A_ = None      ,							**A_      ,							)					->np.ndarray:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					get_size_dict(A_					)
       if "height" not in size or "width" not in size:
             raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""					)
       __lowerCAmelCase					:						Union[str, Any]					=					get_resize_output_image_size(
           A_      ,							output_size=(size['''height'''], size['''width'''])      ,							keep_aspect_ratio=A_      ,							multiple=A_      ,							)
       return resize(A_      ,							size=A_      ,							resample=A_      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None      ,							**A_      ,							)					->Dict:



       '''simple docstring'''





       return rescale(A_      ,							scale=A_      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_ = None      ,							**A_      ,							)					->np.ndarray:



       '''simple docstring'''





       return normalize(A_      ,							mean=A_      ,							std=A_      ,							data_format=A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = None      ,							A_ = ChannelDimension.FIRST      ,							**A_      ,							)					->PIL.Image.Image:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					do_resize if do_resize is not None else self.do_resize
       __lowerCAmelCase					:						Optional[int]					=					size if size is not None else self.size
       __lowerCAmelCase					:						Union[str, Any]					=					get_size_dict(A_					)
       __lowerCAmelCase					:						List[Any]					=					keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
       __lowerCAmelCase					:						Optional[int]					=					ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
       __lowerCAmelCase					:						Tuple					=					resample if resample is not None else self.resample
       __lowerCAmelCase					:						Optional[int]					=					do_rescale if do_rescale is not None else self.do_rescale
       __lowerCAmelCase					:						Union[str, Any]					=					rescale_factor if rescale_factor is not None else self.rescale_factor
       __lowerCAmelCase					:						Tuple					=					do_normalize if do_normalize is not None else self.do_normalize
       __lowerCAmelCase					:						str					=					image_mean if image_mean is not None else self.image_mean
       __lowerCAmelCase					:						Optional[Any]					=					image_std if image_std is not None else self.image_std

       __lowerCAmelCase					:						Optional[Any]					=					make_list_of_images(A_					)

       if not valid_images(A_					):
             raise ValueError(
                 '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                 '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

       if do_resize and size is None or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''					)

       if do_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

       if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)

       # All transformations expect numpy arrays.
       __lowerCAmelCase					:						Any					=					[to_numpy_array(A_					) for image in images]

       if do_resize:
             __lowerCAmelCase					:						Optional[Any]					=					[self.resize(image=A_      ,							size=A_      ,							resample=A_					) for image in images]

       if do_rescale:
             __lowerCAmelCase					:						Tuple					=					[self.rescale(image=A_      ,							scale=A_					) for image in images]

       if do_normalize:
             __lowerCAmelCase					:						str					=					[self.normalize(image=A_      ,							mean=A_      ,							std=A_					) for image in images]

       __lowerCAmelCase					:						Union[str, Any]					=					[to_channel_dimension_format(A_      ,							A_					) for image in images]

       __lowerCAmelCase					:						Dict					=					{'''pixel_values''': images}
       return BatchFeature(data=A_      ,							tensor_type=A_					)





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
             if len(A_					) != len(A_					):
                   raise ValueError(
                       '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''					)

             if is_torch_tensor(A_					):
                   __lowerCAmelCase					:						Optional[int]					=					target_sizes.numpy()

             __lowerCAmelCase					:						List[str]					=					[]

             for idx in range(len(A_					)					):
                   __lowerCAmelCase					:						Any					=					torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0					)      ,							size=target_sizes[idx]      ,							mode='''bilinear'''      ,							align_corners=A_					)
                   __lowerCAmelCase					:						str					=					resized_logits[0].argmax(dim=0					)
                   semantic_segmentation.append(A_					)
       else:
             __lowerCAmelCase					:						Any					=					logits.argmax(dim=1					)
             __lowerCAmelCase					:						List[Any]					=					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

       return semantic_segmentation


