






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     (	a_				,	a_):
							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'				,	a_):
														snake_case_           =				R'\b\d{2}\b'
														if bool(re.search(a_				,	a_)):
																					snake_case_           =				re.search(R'\d\.\d\d.'				,	a_).group()
														else:
																					snake_case_           =				re.search(R'\d\.\d.'				,	a_).group()
														if int(match[0]) < 6:
																					snake_case_           =				old_name.replace(a_				,	'')
																					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(a_				,	'')
																					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.'				,	a_):
														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 		__UpperCAmelCase     (	a_				,	a_):
							for key in checkpoint.copy().keys():
														snake_case_           =				checkpoint.pop(a_)
														snake_case_           =				val

							return checkpoint





def 		__UpperCAmelCase     (	):
							snake_case_           =				'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_           =				Image.open(requests.get(a_				,	stream=a_).raw)

							return image





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							snake_case_           =				torch.load(a_				,	map_location='cpu')['model']
							snake_case_           =				EfficientFormerConfig.from_json_file(a_)
							snake_case_           =				EfficientFormerForImageClassificationWithTeacher(a_)
							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(a_				,	a_)

							model.load_state_dict(a_)
							model.eval()

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

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

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

							assert torch.allclose(a_				,	a_)

							snake_case_           =				model(a_)
							snake_case_           =				outputs.logits

							snake_case_           =				(1, 10_00)

							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, :10]				,	a_				,	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, :10]				,	a_				,	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(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,
	)













import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowercase    				=   4
lowercase    				=   3




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	pass






def 		__UpperCAmelCase     (	a_):
							for shard in shards:
														for i in range(a_):
																					yield {"i": i, "shard": shard}





def 		__UpperCAmelCase     (	):
							snake_case_           =				int(os.environ['RANK'])
							snake_case_           =				int(os.environ['WORLD_SIZE'])

							snake_case_           =				ArgumentParser()
							parser.add_argument('--streaming'				,	type=a_)
							parser.add_argument('--local_rank'				,	type=a_)
							parser.add_argument('--num_workers'				,	type=a_				,	default=0)
							snake_case_           =				parser.parse_args()
							snake_case_           =				args.streaming
							snake_case_           =				args.num_workers

							snake_case_           =				{'shards': [f'''shard_{shard_idx}''' for shard_idx in range(a_)]}
							snake_case_           =				IterableDataset.from_generator(a_				,	gen_kwargs=a_)
							if not streaming:
														snake_case_           =				Dataset.from_list(list(a_))

							snake_case_           =				split_dataset_by_node(a_				,	rank=a_				,	world_size=a_)
							snake_case_           =				torch.utils.data.DataLoader(a_				,	num_workers=a_)

							snake_case_           =				NUM_SHARDS * NUM_ITEMS_PER_SHARD
							snake_case_           =				full_size // world_size
							expected_local_size += int(rank < (full_size % world_size))

							snake_case_           =				sum(1 for _ in dataloader)
							if local_size != expected_local_size:
														raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''')


if __name__ == "__main__":
	main()





