

from argparse import ArgumentParser

from . import BaseTransformersCLICommand

def 						UpperCamelCase (lowercase_:			int )					->				str:
	return DownloadCommand(args.model   ,				args.cache_dir   ,				args.force   ,				args.trust_remote_code )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		parser.add_parser("""download"""      )
						download_parser.add_argument(
						    """--cache-dir"""   ,	type=A__   ,	default=A__   ,	help="""Path to location to store the models"""      )
						download_parser.add_argument(
						    """--force"""   ,	action="""store_true"""   ,	help="""Force the model to be download even if already in cache-dir"""      )
						download_parser.add_argument(
						    """--trust-remote-code"""   ,	action="""store_true"""   ,	help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""   ,	)
						download_parser.add_argument("""model"""   ,	type=A__   ,	help="""Name of the model to download"""      )
						download_parser.set_defaults(func=A__      )






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Union[str, Any]					       =		model
						A__							:   Dict					       =		cache
						A__							:   str					       =		force
						A__							:   Tuple					       =		trust_remote_code






					def   __A      (			self      ):
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )
						AutoTokenizer.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )






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 (lowercase_:			str   ,				lowercase_:			Optional[int] )					->				str:
	A__							:   Union[str, Any]					       =		old_name

	if "patch_embed" in old_name:
		A__       ,       A__       ,       A__							:   Any					       =		old_name.split(""".""" )

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

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

			A__							:   str					       =		"""last_stage.""" + trimmed_name

	elif "network" in old_name and re.search(r""".\d."""   ,				lowercase_ ):
		A__							:   List[str]					       =		old_name.replace("""network"""   ,				"""intermediate_stages""" )

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

	return new_name

def 						UpperCamelCase (lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any] )					->				Tuple:
	for key in checkpoint.copy().keys():
		A__							:   List[Any]					       =		checkpoint.pop(lowercase_ )
		A__							:   Dict					       =		val

	return checkpoint

def 						UpperCamelCase ()					->				Optional[int]:
	A__							:   List[str]					       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A__							:   List[str]					       =		Image.open(requests.get(lowercase_   ,				stream=lowercase_ ).raw )

	return image

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

	A__							:   Union[str, Any]					       =		config.depths[-1] - config.num_metaad_blocks + 1
	A__							:   Any					       =		convert_torch_checkpoint(lowercase_   ,				lowercase_ )

	model.load_state_dict(lowercase_ )
	model.eval()

	A__							:   Tuple					       =		{
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

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

	# original processing pipeline
	A__							:   List[Any]					       =		Compose(
	    [
	        Resize(lowercase_   ,				interpolation=pillow_resamplings["""bicubic"""] ),
	        CenterCrop(lowercase_ ),
	        ToTensor(),
	        Normalize(lowercase_   ,				lowercase_ ),
	    ] )
	A__							:   Any					       =		image_transforms(lowercase_ ).unsqueeze(0 )

	assert torch.allclose(lowercase_   ,				lowercase_ )

	A__							:   Optional[int]					       =		model(lowercase_ )
	A__							:   List[str]					       =		outputs.logits

	A__							:   Tuple					       =		(1, 1000)

	if "l1" in model_name:
		A__							:   List[str]					       =		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]   ,				lowercase_   ,				atol=1E-3 )
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		A__							:   Any					       =		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]   ,				lowercase_   ,				atol=1E-3 )
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		A__							:   Union[str, 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(lowercase_ ).mkdir(exist_ok=lowercase_ )
	model.save_pretrained(lowercase_ )
	print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
	processor.save_pretrained(lowercase_ )
	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=lowercase_   ,				)
		processor.push_to_hub(
		    repo_id=f"""Bearnardd/{pytorch_dump_path}"""   ,				commit_message="""Add image processor"""   ,				use_temp_dir=lowercase_   ,				)


if __name__ == "__main__":
	A_	:			List[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)

	A_	:			List[Any]  	=      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,
	)



