




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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[Any]	,       SCREAMING_SNAKE_CASE_:							int      )			->      Union[str, Any]:




				'''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"	,       SCREAMING_SNAKE_CASE_      ):
								A__        =			R"\b\d{2}\b"
								if bool(re.search(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )      ):
												A__        =			re.search(R"\d\.\d\d."	,       SCREAMING_SNAKE_CASE_      ).group()
								else:
												A__        =			re.search(R"\d\.\d."	,       SCREAMING_SNAKE_CASE_      ).group()
								if int(match[0]      ) < 6:
												A__        =			old_name.replace(SCREAMING_SNAKE_CASE_	,       ""      )
												A__        =			trimmed_name.replace("network"	,       match[0] + ".meta4D_layers.blocks." + match[2:-1]      )
												A__        =			"intermediate_stages." + trimmed_name
								else:
												A__        =			old_name.replace(SCREAMING_SNAKE_CASE_	,       ""      )
												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."	,       SCREAMING_SNAKE_CASE_      ):
								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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							List[Any]      )			->      int:




				'''simple docstring'''





				for key in checkpoint.copy().keys():
								A__        =			checkpoint.pop(SCREAMING_SNAKE_CASE_      )
								A__        =			val

				return checkpoint



def 					lowerCAmelCase__							(	)			->      Any:




				'''simple docstring'''





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

				return image



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							bool      )			->      Any:




				'''simple docstring'''





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

				A__        =			config.depths[-1] - config.num_metaad_blocks + 1
				A__        =			convert_torch_checkpoint(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				model.load_state_dict(SCREAMING_SNAKE_CASE_      )
				model.eval()

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

				# prepare image
				A__        =			prepare_img()
				A__        =			2_5_6
				A__        =			2_2_4
				A__        =			EfficientFormerImageProcessor(
				    size={"shortest_edge": image_size}	,       crop_size={"height": crop_size, "width": crop_size}	,       resample=pillow_resamplings["bicubic"]	,       )
				A__        =			processor(images=SCREAMING_SNAKE_CASE_	,       return_tensors="pt"      ).pixel_values

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

				assert torch.allclose(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				A__        =			model(SCREAMING_SNAKE_CASE_      )
				A__        =			outputs.logits

				A__        =			(1, 1_0_0_0)

				if "l1" in model_name:
								A__        =			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, :1_0]	,       SCREAMING_SNAKE_CASE_	,       atol=1e-3      )
								assert logits.shape == expected_shape
				elif "l3" in model_name:
								A__        =			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, :1_0]	,       SCREAMING_SNAKE_CASE_	,       atol=1e-3      )
								assert logits.shape == expected_shape
				elif "l7" in model_name:
								A__        =			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(SCREAMING_SNAKE_CASE_      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
				model.save_pretrained(SCREAMING_SNAKE_CASE_      )
				print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'      )
				processor.save_pretrained(SCREAMING_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=SCREAMING_SNAKE_CASE_	,       )
								processor.push_to_hub(
								    repo_id=F'Bearnardd/{pytorch_dump_path}'	,       commit_message="Add image processor"	,       use_temp_dir=SCREAMING_SNAKE_CASE_	,       )


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,
	)




from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser

@dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       field(
			    metadata={'help': 'The output directory where the model will be written.'}							,	)
			__lowerCamelCase        =       field(
			    metadata={
			        'help': (
			            'The encoder model checkpoint for weights initialization.'
			            'Don\'t set if you want to train an encoder model from scratch.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    metadata={
			        'help': (
			            'The decoder model checkpoint for weights initialization.'
			            'Don\'t set if you want to train a decoder model from scratch.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'}			)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'}			)



def 					lowerCAmelCase__							(	)			->      List[Any]:




				'''simple docstring'''





				A__        =			HfArgumentParser((ModelArguments,)      )
				((A__)    ,				)        =			parser.parse_args_into_dataclasses()

				# Load pretrained model and tokenizer

				# Use explicit specified encoder config
				if model_args.encoder_config_name:
								A__        =			AutoConfig.from_pretrained(model_args.encoder_config_name      )
				# Use pretrained encoder model's config
				else:
								A__        =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path      )

				# Use explicit specified decoder config
				if model_args.decoder_config_name:
								A__        =			AutoConfig.from_pretrained(model_args.decoder_config_name      )
				# Use pretrained decoder model's config
				else:
								A__        =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path      )

				# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
				A__        =			True
				A__        =			True

				A__        =			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
				    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path	,       decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path	,       encoder_config=SCREAMING_SNAKE_CASE_	,       decoder_config=SCREAMING_SNAKE_CASE_	,       )

				# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
				A__        =			decoder_config.decoder_start_token_id
				A__        =			decoder_config.pad_token_id
				if decoder_start_token_id is None:
								A__        =			decoder_config.bos_token_id
				if pad_token_id is None:
								A__        =			decoder_config.eos_token_id

				# This is necessary to make Flax's generate() work
				A__        =			decoder_config.eos_token_id
				A__        =			decoder_start_token_id
				A__        =			pad_token_id

				A__        =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path      )

				A__        =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path      )
				A__        =			tokenizer.convert_ids_to_tokens(model.config.pad_token_id      )

				model.save_pretrained(model_args.output_dir      )
				image_processor.save_pretrained(model_args.output_dir      )
				tokenizer.save_pretrained(model_args.output_dir      )


if __name__ == "__main__":
	main()

