'''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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ={
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}




class 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):
    a__	:						Optional[Any]	       =  """resnet"""
    a__	:						Tuple	       =  ["""basic""", """bottleneck"""]



    def __init__(self   :   List[Any]       ,      __a   :   Any=3       ,      __a   :   Dict=64       ,      __a   :   Union[str, Any]=[256, 512, 1024, 2048]       ,      __a   :   str=[3, 4, 6, 3]       ,      __a   :   Optional[Any]="bottleneck"       ,      __a   :   Tuple="relu"       ,      __a   :   int=False       ,      __a   :   Optional[int]=None       ,      __a   :   str=None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         if layer_type not in self.layer_types:
              raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types			)}"""			)
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embedding_size
         UpperCAmelCase_	   =       hidden_sizes
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       layer_type
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       downsample_in_first_stage
         UpperCAmelCase_	   =       ["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(__a			) + 1			)]
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_aligned_output_features_output_indices(
             out_features=__a       ,      out_indices=__a       ,      stage_names=self.stage_names			)




class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  version.parse("""1.11"""     )



    @property
    def  _lowercase						(self   :   Optional[int]			):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]			)






    @property
    def  _lowercase						(self   :   str			):
         return 1E-3




