



"""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      __lowercase       (			a__							,       a__       )    ->			List[Any]:
							__SCREAMING_SNAKE_CASE									= old_name

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

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

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

																					__SCREAMING_SNAKE_CASE									= 'last_stage.' + trimmed_name

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

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

							return new_name






def      __lowercase       (			a__							,       a__       )    ->			Optional[Any]:
							for key in checkpoint.copy().keys():
														__SCREAMING_SNAKE_CASE									= checkpoint.pop(a__       )
														__SCREAMING_SNAKE_CASE									= val

							return checkpoint






def      __lowercase       (			)    ->			Dict:
							__SCREAMING_SNAKE_CASE									= 'http://images.cocodataset.org/val2017/000000039769.jpg'
							__SCREAMING_SNAKE_CASE									= Image.open(requests.get(a__							,       stream=a__       ).raw       )

							return image






def      __lowercase       (			a__							,       a__							,       a__							,       a__       )    ->			str:
							__SCREAMING_SNAKE_CASE									= torch.load(a__							,       map_location='cpu'       )['model']
							__SCREAMING_SNAKE_CASE									= EfficientFormerConfig.from_json_file(a__       )
							__SCREAMING_SNAKE_CASE									= EfficientFormerForImageClassificationWithTeacher(a__       )
							__SCREAMING_SNAKE_CASE									= '_'.join(checkpoint_path.split('/'       )[-1].split('.'       )[0].split('_'       )[:-1]       )

							__SCREAMING_SNAKE_CASE									= config.depths[-1] - config.num_metaad_blocks + 1
							__SCREAMING_SNAKE_CASE									= convert_torch_checkpoint(a__							,       a__       )

							model.load_state_dict(a__       )
							model.eval()

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

							# prepare image
							__SCREAMING_SNAKE_CASE									= prepare_img()
							__SCREAMING_SNAKE_CASE									= 2_56
							__SCREAMING_SNAKE_CASE									= 2_24
							__SCREAMING_SNAKE_CASE									= EfficientFormerImageProcessor(
							    size={'shortest_edge': image_size}							,       crop_size={'height': crop_size, 'width': crop_size}							,       resample=pillow_resamplings['bicubic']							,       )
							__SCREAMING_SNAKE_CASE									= processor(images=a__							,       return_tensors='pt'       ).pixel_values

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

							assert torch.allclose(a__							,       a__       )

							__SCREAMING_SNAKE_CASE									= model(a__       )
							__SCREAMING_SNAKE_CASE									= outputs.logits

							__SCREAMING_SNAKE_CASE									= (1, 10_00)

							if "l1" in model_name:
														__SCREAMING_SNAKE_CASE									= 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:
														__SCREAMING_SNAKE_CASE									= 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:
														__SCREAMING_SNAKE_CASE									= 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__":
			lowerCAmelCase__		:					str							=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)

			lowerCAmelCase__		:					int							=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 collections import deque

from .hash_table import HashTable

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													super().__init__(*_A				,							**_A		)






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= deque([]		) if self.values[key] is None else self.values[key]
													self.values[key].appendleft(_A		)
													__SCREAMING_SNAKE_CASE									= self.values[key]






						def 	_A     (   self		):







													'''simple docstring'''

													return (
													    sum(self.charge_factor - len(_A		) for slot in self.values		)
													    / self.size_table
													    * self.charge_factor
													)






						def 	_A     (   self				,							_A				,							_A=None		):







													'''simple docstring'''

													if not (
													    len(self.values[key]		) == self.charge_factor and self.values.count(_A		) == 0
													):
																				return key
													return super()._collision_resolution(_A				,							_A		)






