"""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__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			int   )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     old_name

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

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

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

																		snake_case        =     'last_stage.' + trimmed_name

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

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

						return new_name





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			List[Any]   )   ->			Dict:





						"""simple docstring"""






						for key in checkpoint.copy().keys():
												snake_case        =     checkpoint.pop(_UpperCamelCase   )
												snake_case        =     val

						return checkpoint





def 				lowerCAmelCase__      (     )   ->			Dict:





						"""simple docstring"""






						snake_case        =     'http://images.cocodataset.org/val2017/000000039769.jpg'
						snake_case        =     Image.open(requests.get(_UpperCamelCase	,     stream=_UpperCamelCase   ).raw   )

						return image





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Path	,     _UpperCamelCase    :			Path	,     _UpperCamelCase    :			Path	,     _UpperCamelCase    :			bool   )   ->			Any:





						"""simple docstring"""






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

						snake_case        =     config.depths[-1] - config.num_metaad_blocks + 1
						snake_case        =     convert_torch_checkpoint(_UpperCamelCase	,     _UpperCamelCase   )

						model.load_state_dict(_UpperCamelCase   )
						model.eval()

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

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

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

						assert torch.allclose(_UpperCamelCase	,     _UpperCamelCase   )

						snake_case        =     model(_UpperCamelCase   )
						snake_case        =     outputs.logits

						snake_case        =     (1, 1_0_0_0)

						if "l1" in model_name:
												snake_case        =     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, :1_0]	,     _UpperCamelCase	,     atol=1e-3   )
												assert logits.shape == expected_shape
						elif "l3" in model_name:
												snake_case        =     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, :1_0]	,     _UpperCamelCase	,     atol=1e-3   )
												assert logits.shape == expected_shape
						elif "l7" in model_name:
												snake_case        =     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(_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__":
					SCREAMING_SNAKE_CASE__   =					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__   =					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 __future__ import annotations

from typing import Any



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				pass





class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     data
										snake_case        =     None

				def __iter__(			self      ):






										"""simple docstring"""

										snake_case        =     self
										snake_case        =     []
										while node:
																if node in visited:
																						raise ContainsLoopError
																visited.append(lowerCAmelCase      )
																yield node.data
																snake_case        =     node.next_node

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										try:
																list(self      )
																return False
										except ContainsLoopError:
																return True


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					Node(1)
					SCREAMING_SNAKE_CASE__   =					Node(2)
					SCREAMING_SNAKE_CASE__   =					Node(3)
					SCREAMING_SNAKE_CASE__   =					Node(4)
					print(root_node.has_loop)  # False
					SCREAMING_SNAKE_CASE__   =					root_node.next_node
					print(root_node.has_loop)  # True

					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					print(root_node.has_loop)  # False

					SCREAMING_SNAKE_CASE__   =					Node(1)
					print(root_node.has_loop)  # False


