





'''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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			__snake_case					:      str							      =     old_name

			if "patch_embed" in old_name:
						__snake_case      ,     __snake_case      ,     __snake_case					:      List[Any]							      =     old_name.split(""".""" )

						if layer == "0":
									__snake_case					:      int							      =     old_name.replace("""0"""    ,	"""convolution1""" )
						elif layer == "1":
									__snake_case					:      List[str]							      =     old_name.replace("""1"""    ,	"""batchnorm_before""" )
						elif layer == "3":
									__snake_case					:      List[str]							      =     old_name.replace("""3"""    ,	"""convolution2""" )
						else:
									__snake_case					:      str							      =     old_name.replace("""4"""    ,	"""batchnorm_after""" )

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

									__snake_case					:      Any							      =     """last_stage.""" + trimmed_name

			elif "network" in old_name and re.search(R""".\d."""    ,	_lowerCamelCase ):
						__snake_case					:      int							      =     old_name.replace("""network"""    ,	"""intermediate_stages""" )

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

			return new_name







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			for key in checkpoint.copy().keys():
						__snake_case					:      int							      =     checkpoint.pop(_lowerCamelCase )
						__snake_case					:      int							      =     val

			return checkpoint







def 					_a						(       ) ->				List[str]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      List[str]							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )

			return image







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


			__snake_case					:      List[Any]							      =     torch.load(_lowerCamelCase    ,	map_location="""cpu""" )["""model"""]
			__snake_case					:      Union[str, Any]							      =     EfficientFormerConfig.from_json_file(_lowerCamelCase )
			__snake_case					:      Dict							      =     EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     """_""".join(checkpoint_path.split("""/""" )[-1].split(""".""" )[0].split("""_""" )[:-1] )

			__snake_case					:      Dict							      =     config.depths[-1] - config.num_metaad_blocks + 1
			__snake_case					:      Any							      =     convert_torch_checkpoint(_lowerCamelCase    ,	_lowerCamelCase )

			model.load_state_dict(_lowerCamelCase )
			model.eval()

			__snake_case					:      Optional[int]							      =     {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

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

			# original processing pipeline
			__snake_case					:      int							      =     Compose(
			    [
			        Resize(_lowerCamelCase    ,	interpolation=pillow_resamplings["""bicubic"""] ),
			        CenterCrop(_lowerCamelCase ),
			        ToTensor(),
			        Normalize(_lowerCamelCase    ,	_lowerCamelCase ),
			    ] )
			__snake_case					:      int							      =     image_transforms(_lowerCamelCase ).unsqueeze(0 )

			assert torch.allclose(_lowerCamelCase    ,	_lowerCamelCase )

			__snake_case					:      Dict							      =     model(_lowerCamelCase )
			__snake_case					:      Dict							      =     outputs.logits

			__snake_case					:      List[str]							      =     (1, 1000)

			if "l1" in model_name:
						__snake_case					:      Dict							      =     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]    ,	_lowerCamelCase    ,	atol=1E-3 )
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						__snake_case					:      int							      =     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]    ,	_lowerCamelCase    ,	atol=1E-3 )
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						__snake_case					:      int							      =     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(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
			model.save_pretrained(_lowerCamelCase )
			print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
			processor.save_pretrained(_lowerCamelCase )
			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=_lowerCamelCase    ,	)
						processor.push_to_hub(
						    repo_id=F'''Bearnardd/{pytorch_dump_path}'''    ,	commit_message="""Add image processor"""    ,	use_temp_dir=_lowerCamelCase    ,	)


if __name__ == "__main__":
	__UpperCamelCase					    =	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)

	__UpperCamelCase					    =	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'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=18     ,	__SCREAMING_SNAKE_CASE=30     ,	__SCREAMING_SNAKE_CASE=400     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=True     ,	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	size if size is not None else {'''shortest_edge''': 20}
            __snake_case            =	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	num_channels
            __snake_case            =	image_size
            __snake_case            =	min_resolution
            __snake_case            =	max_resolution
            __snake_case            =	do_resize
            __snake_case            =	size
            __snake_case            =	do_center_crop
            __snake_case            =	crop_size
            __snake_case            =	do_flip_channel_order




       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_flip_channel_order": self.do_flip_channel_order,
            }





@require_torch
@require_vision
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Union[str, Any]										=      MobileViTImageProcessor if is_vision_available() else None


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	MobileViTImageProcessingTester(self	)


       @property
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_resize'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''size'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_center_crop'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''center_crop'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_flip_channel_order'''	)	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class.from_dict(self.image_processor_dict	)
            self.assertEqual(image_processor.size     ,	{'''shortest_edge''': 20}	)
            self.assertEqual(image_processor.crop_size     ,	{'''height''': 18, '''width''': 18}	)

            __snake_case            =	self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42     ,	crop_size=84	)
            self.assertEqual(image_processor.size     ,	{'''shortest_edge''': 42}	)
            self.assertEqual(image_processor.crop_size     ,	{'''height''': 84, '''width''': 84}	)


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            pass


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	Image.Image	)

            # Test not batched input
            __snake_case            =	image_processing(image_inputs[0]     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE     ,	numpify=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	np.ndarray	)

            # Test not batched input
            __snake_case            =	image_processing(image_inputs[0]     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)




       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE     ,	torchify=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	torch.Tensor	)

            # Test not batched input
            __snake_case            =	image_processing(image_inputs[0]     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)






