
'''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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		snake_case  :       str     			=						old_name

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

				if layer == "0":
						snake_case  :       Dict     			=						old_name.replace('''0''' ,       '''convolution1'''			)
				elif layer == "1":
						snake_case  :       Tuple     			=						old_name.replace('''1''' ,       '''batchnorm_before'''			)
				elif layer == "3":
						snake_case  :       Optional[Any]     			=						old_name.replace('''3''' ,       '''convolution2'''			)
				else:
						snake_case  :       Optional[int]     			=						old_name.replace('''4''' ,       '''batchnorm_after'''			)

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

						snake_case  :       List[Any]     			=						'''last_stage.''' + trimmed_name

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

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

		return new_name




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Optional[int]:


		'''simple docstring'''


		for key in checkpoint.copy().keys():
				snake_case  :       Union[str, Any]     			=						checkpoint.pop(a_			)
				snake_case  :       List[Any]     			=						val

		return checkpoint




def    _UpperCamelCase						(       )  ->							Tuple:


		'''simple docstring'''


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

		return image




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							str:


		'''simple docstring'''


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

		snake_case  :       List[Any]     			=						config.depths[-1] - config.num_metaad_blocks + 1
		snake_case  :       List[Any]     			=						convert_torch_checkpoint(a_ ,       a_			)

		model.load_state_dict(a_			)
		model.eval()

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

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

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

		assert torch.allclose(a_ ,       a_			)

		snake_case  :       Dict     			=						model(a_			)
		snake_case  :       Optional[int]     			=						outputs.logits

		snake_case  :       Optional[int]     			=						(1, 1000)

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

						lowercase__						       =      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,
						)










from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowercase    				=   TypeVar("T")




class 					UpperCamelCase_ (     Generic[T]							):



	'''simple docstring'''




	lowerCAmelCase						=					42  # Cache store of keys
	lowerCAmelCase						=					42  # References of the keys in cache
	lowerCAmelCase						=					1_0  # Maximum capacity of cache




	def __init__(			self			,     a   )			->       None:
								snake_case_           =				deque()
								snake_case_           =				set()
								if not n:
															snake_case_           =				sys.maxsize
								elif n < 0:
															raise ValueError('n should be an integer greater than 0.'   )
								else:
															snake_case_           =				n




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								if x not in self.key_reference:
															if len(self.dq_store   ) == LRUCache._MAX_CAPACITY:
																						snake_case_           =				self.dq_store.pop()
																						self.key_reference.remove(a   )
								else:
															self.dq_store.remove(a   )

								self.dq_store.appendleft(a   )
								self.key_reference.add(a   )




	def 				_UpperCamelCase     (			self   )			->       None:
								for k in self.dq_store:
															print(a   )






	def __repr__(			self   )			->       str:
								return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store   )}'''


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	lowercase    				=   LRUCache(4)
	lru_cache.refer("A")
	lru_cache.refer(2)
	lru_cache.refer(3)
	lru_cache.refer("A")
	lru_cache.refer(4)
	lru_cache.refer(5)
	lru_cache.display()

	print(lru_cache)
	assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





