

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    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							old_name

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

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

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

															__lowerCAmelCase					=							'last_stage.' + trimmed_name

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

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

					return new_name


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					for key in checkpoint.copy().keys():
										__lowerCAmelCase					=							checkpoint.pop(UpperCAmelCase__      )
										__lowerCAmelCase					=							val

					return checkpoint


def        __lowercase    (  ):

					"""simple docstring"""






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

					return image


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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

					__lowerCAmelCase					=							config.depths[-1] - config.num_metaad_blocks + 1
					__lowerCAmelCase					=							convert_torch_checkpoint(UpperCAmelCase__		,					UpperCAmelCase__      )

					model.load_state_dict(UpperCAmelCase__      )
					model.eval()

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

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

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

					assert torch.allclose(UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							model(UpperCAmelCase__      )
					__lowerCAmelCase					=							outputs.logits

					__lowerCAmelCase					=							(1, 1_000)

					if "l1" in model_name:
										__lowerCAmelCase					=							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]		,					UpperCAmelCase__		,					atol=1E-3      )
										assert logits.shape == expected_shape
					elif "l3" in model_name:
										__lowerCAmelCase					=							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]		,					UpperCAmelCase__		,					atol=1E-3      )
										assert logits.shape == expected_shape
					elif "l7" in model_name:
										__lowerCAmelCase					=							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(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__":
		lowerCamelCase      				=     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      				=     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 warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowerCamelCase      				=     logging.get_logger(__name__)





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						*_A					,						**_A							):
												warnings.warn(
												    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
												    ' Please use PoolFormerImageProcessor instead.'					,						_A					,						)
												super().__init__(*_A					,						**_A							)






