




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 			__magic_name__				( A		:					int,  A		:					List[str]   ):






			'''simple docstring'''







			a    =			old_name

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

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

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

									a    =			"last_stage." + trimmed_name

			elif "network" in old_name and re.search(R".\d.",  A   ):
						a    =			old_name.replace("network",  "intermediate_stages"   )

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

			return new_name




def 			__magic_name__				( A		:					Any,  A		:					List[Any]   ):






			'''simple docstring'''







			for key in checkpoint.copy().keys():
						a    =			checkpoint.pop(A   )
						a    =			val

			return checkpoint




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			"http://images.cocodataset.org/val2017/000000039769.jpg"
			a    =			Image.open(requests.get(A,  stream=A   ).raw   )

			return image




def 			__magic_name__				( A		:					Path,  A		:					Path,  A		:					Path,  A		:					bool   ):






			'''simple docstring'''







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

			a    =			config.depths[-1] - config.num_metaad_blocks + 1
			a    =			convert_torch_checkpoint(A,  A   )

			model.load_state_dict(A   )
			model.eval()

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

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

			# original processing pipeline
			a    =			Compose(
			    [
			        Resize(A,  interpolation=pillow_resamplings["bicubic"]   ),
			        CenterCrop(A   ),
			        ToTensor(),
			        Normalize(A,  A   ),
			    ]   )
			a    =			image_transforms(A   ).unsqueeze(0   )

			assert torch.allclose(A,  A   )

			a    =			model(A   )
			a    =			outputs.logits

			a    =			(1, 1000)

			if "l1" in model_name:
						a    =			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:
						a    =			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:
						a    =			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__":
		__lowerCAmelCase		:				List[Any]       			=		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		:				Union[str, 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,
		)














"""simple docstring"""
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCamelCase_					=      logging.get_logger(__name__)







@add_end_docstrings(__A     )
class 			UpperCamelCase_       (__A     ):





	def __init__(      self					: int	,	*lowerCAmelCase_					: Tuple	,	**lowerCAmelCase_					: List[str]						)						->			Optional[Any]:
				super().__init__(*lowerCAmelCase_	,	**lowerCAmelCase_						)
				requires_backends(self	,	"vision"						)
				self.check_model_type(
				    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
				    if self.framework == "tf"
				    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: Optional[int]=None						)						->			List[Any]:
				UpperCAmelCase_     : str			     =					{}
				if top_k is not None:
							UpperCAmelCase_     : List[str]			     =					top_k
				return {}, {}, postprocess_params





	def __call__(      self					: str	,	lowerCAmelCase_					: Union[str, List[str], "Image.Image", List["Image.Image"]]	,	**lowerCAmelCase_					: Any						)						->			Tuple:
				return super().__call__(lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: str						)						->			Any:
				UpperCAmelCase_     : Tuple			     =					load_image(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					self.image_processor(images=lowerCAmelCase_	,	return_tensors=self.framework						)
				return model_inputs





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: Dict						)						->			str:
				UpperCAmelCase_     : Any			     =					self.model(**lowerCAmelCase_						)
				return model_outputs





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: Union[str, Any]	,	lowerCAmelCase_					: Optional[int]=5						)						->			Any:
				if top_k > self.model.config.num_labels:
							UpperCAmelCase_     : int			     =					self.model.config.num_labels

				if self.framework == "pt":
							UpperCAmelCase_     : str			     =					model_outputs.logits.softmax(-1						)[0]
							UpperCAmelCase_     ,	UpperCAmelCase_     : Tuple			     =					probs.topk(lowerCAmelCase_						)
				elif self.framework == "tf":
							UpperCAmelCase_     : str			     =					stable_softmax(model_outputs.logits	,	axis=-1						)[0]
							UpperCAmelCase_     : Union[str, Any]			     =					tf.math.top_k(lowerCAmelCase_	,	k=lowerCAmelCase_						)
							UpperCAmelCase_     ,	UpperCAmelCase_     : List[Any]			     =					topk.values.numpy(), topk.indices.numpy()
				else:
							raise ValueError(f"""Unsupported framework: {self.framework}"""						)

				UpperCAmelCase_     : int			     =					scores.tolist()
				UpperCAmelCase_     : Optional[Any]			     =					ids.tolist()
				return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_	,	lowerCAmelCase_						)]



