




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__ (snake_case   :		int		,	snake_case   :		Optional[Any]				)     ->						str:
					__UpperCamelCase					:	List[Any]             =      old_name

					if "patch_embed" in old_name:
										__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      old_name.split("""."""				)

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

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

															__UpperCamelCase					:	List[Any]             =      """last_stage.""" + trimmed_name

					elif "network" in old_name and re.search(r""".\d."""		,	snake_case				):
										__UpperCamelCase					:	Dict             =      old_name.replace("""network"""		,	"""intermediate_stages"""				)

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

					return new_name





def 				A__ (snake_case   :		Optional[int]		,	snake_case   :		List[str]				)     ->						Tuple:
					for key in checkpoint.copy().keys():
										__UpperCamelCase					:	Any             =      checkpoint.pop(snake_case				)
										__UpperCamelCase					:	Tuple             =      val

					return checkpoint





def 				A__ ()     ->						Any:
					__UpperCamelCase					:	Union[str, Any]             =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__UpperCamelCase					:	Union[str, Any]             =      Image.open(requests.get(snake_case		,	stream=snake_case				).raw				)

					return image





def 				A__ (snake_case   :		Path		,	snake_case   :		Path		,	snake_case   :		Path		,	snake_case   :		bool				)     ->						str:
					__UpperCamelCase					:	str             =      torch.load(snake_case		,	map_location="""cpu"""				)["""model"""]
					__UpperCamelCase					:	Any             =      EfficientFormerConfig.from_json_file(snake_case				)
					__UpperCamelCase					:	Optional[Any]             =      EfficientFormerForImageClassificationWithTeacher(snake_case				)
					__UpperCamelCase					:	Optional[int]             =      """_""".join(checkpoint_path.split("""/"""				)[-1].split("""."""				)[0].split("""_"""				)[:-1]				)

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

					model.load_state_dict(snake_case				)
					model.eval()

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

					# prepare image
					__UpperCamelCase					:	str             =      prepare_img()
					__UpperCamelCase					:	Optional[Any]             =      2_56
					__UpperCamelCase					:	str             =      2_24
					__UpperCamelCase					:	Any             =      EfficientFormerImageProcessor(
					    size={"""shortest_edge""": image_size}		,	crop_size={"""height""": crop_size, """width""": crop_size}		,	resample=pillow_resamplings["""bicubic"""]		,	)
					__UpperCamelCase					:	Tuple             =      processor(images=snake_case		,	return_tensors="""pt"""				).pixel_values

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

					assert torch.allclose(snake_case		,	snake_case				)

					__UpperCamelCase					:	List[Any]             =      model(snake_case				)
					__UpperCamelCase					:	List[str]             =      outputs.logits

					__UpperCamelCase					:	Dict             =      (1, 10_00)

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


if __name__ == "__main__":
	a__		     =     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)

	a__		     =     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 tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Dict	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      SMALL_MODEL_IDENTIFIER
						__UpperCamelCase					:	List[str]             =      """pt"""
						__UpperCamelCase					:	List[Any]             =      """tf"""






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(lowerCAmelCase	)






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







						"""simple docstring"""







						__UpperCamelCase					:	List[str]             =      TFAutoModel.from_pretrained(self.test_model					,     from_pt=lowerCAmelCase	)
						model_tf.save_pretrained(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						str	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      """mock_framework"""

						# Framework provided - return whatever the user provides
						__UpperCamelCase					:	List[Any]             =      FeaturesManager.determine_framework(self.test_model					,     lowerCAmelCase	)
						self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      FeaturesManager.determine_framework(lowerCAmelCase					,     lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(lowerCAmelCase					,     lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       str:







						"""simple docstring"""







						with TemporaryDirectory() as local_pt_ckpt:
											self._setup_pt_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Optional[Any]             =      FeaturesManager.determine_framework(lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
											self._setup_tf_ckpt(lowerCAmelCase	)
											__UpperCamelCase					:	Union[str, Any]             =      FeaturesManager.determine_framework(lowerCAmelCase	)
											self.assertEqual(lowerCAmelCase					,     self.framework_tf	)

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
											with self.assertRaises(lowerCAmelCase	):
																__UpperCamelCase					:	int             =      FeaturesManager.determine_framework(lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Dict	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						__UpperCamelCase					:	Optional[int]             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	Any             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_tf	)

						# Both in environment -> use PyTorch
						__UpperCamelCase					:	Dict             =      MagicMock(return_value=lowerCAmelCase	)
						__UpperCamelCase					:	Any             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	), patch(
						    """transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											__UpperCamelCase					:	int             =      FeaturesManager.determine_framework(self.test_model	)
											self.assertEqual(lowerCAmelCase					,     self.framework_pt	)

						# Both not in environment -> raise error
						__UpperCamelCase					:	Union[str, Any]             =      MagicMock(return_value=lowerCAmelCase	)
						__UpperCamelCase					:	Tuple             =      MagicMock(return_value=lowerCAmelCase	)
						with patch("""transformers.onnx.features.is_tf_available"""					,     lowerCAmelCase	), patch(
						    """transformers.onnx.features.is_torch_available"""					,     lowerCAmelCase	):
											with self.assertRaises(lowerCAmelCase	):
																__UpperCamelCase					:	str             =      FeaturesManager.determine_framework(self.test_model	)






