




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__							:	Any											=		logging.get_logger(__name__)
lowercase__							:	Union[str, Any]											=		{"vocab_file": "spiece.model"}

lowercase__							:	Any											=		{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

lowercase__							:	Optional[int]											=		{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

lowercase__							:	Tuple											=		"▁"
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  VOCAB_FILES_NAMES
						lowerCAmelCase_							=  PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






						def __init__(		self				:	Any			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	List[str]=True			,      __lowercase				:	int=False			,      __lowercase				:	Tuple="[CLS]"			,      __lowercase				:	Dict="[SEP]"			,      __lowercase				:	Optional[int]="<unk>"			,      __lowercase				:	Tuple="[SEP]"			,      __lowercase				:	Dict="<pad>"			,      __lowercase				:	Optional[int]="[CLS]"			,      __lowercase				:	int="[MASK]"			,      __lowercase				:	Optional[Dict[str, Any]] = None			,      **__lowercase				:	Optional[Any]			,      ):






							"""simple docstring"""






							snake_case_						       =	(
							    AddedToken(__lowercase			,      lstrip=__lowercase			,      rstrip=__lowercase			,      normalized=__lowercase		)
							    if isinstance(__lowercase			,      __lowercase		)
							    else mask_token
							)

							snake_case_						       =	{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=__lowercase			,      remove_space=__lowercase			,      keep_accents=__lowercase			,      bos_token=__lowercase			,      eos_token=__lowercase			,      unk_token=__lowercase			,      sep_token=__lowercase			,      pad_token=__lowercase			,      cls_token=__lowercase			,      mask_token=__lowercase			,      sp_model_kwargs=self.sp_model_kwargs			,      **__lowercase			,      )

							snake_case_						       =	do_lower_case
							snake_case_						       =	remove_space
							snake_case_						       =	keep_accents
							snake_case_						       =	vocab_file

							snake_case_						       =	spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(__lowercase		)






						@property
						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							return len(self.sp_model		)






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	{self.convert_ids_to_tokens(__lowercase		): i for i in range(self.vocab_size		)}
							vocab.update(self.added_tokens_encoder		)
							return vocab






						def __getstate__(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	self.__dict__.copy()
							snake_case_						       =	None
							return state






						def __setstate__(		self				:	Union[str, Any]			,      __lowercase				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	d

							# for backward compatibility
							if not hasattr(self			,      "sp_model_kwargs"		):
								snake_case_						       =	{}

							snake_case_						       =	spm.SentencePieceProcessor(**self.sp_model_kwargs		)
							self.sp_model.Load(self.vocab_file		)






						def 			snake_case__		(		self				:	int			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							if self.remove_space:
								snake_case_						       =	" ".join(inputs.strip().split()		)
							else:
								snake_case_						       =	inputs
							snake_case_						       =	outputs.replace("``"			,      "\""		).replace("''"			,      "\""		)

							if not self.keep_accents:
								snake_case_						       =	unicodedata.normalize("NFKD"			,      __lowercase		)
								snake_case_						       =	"".join([c for c in outputs if not unicodedata.combining(__lowercase		)]		)
							if self.do_lower_case:
								snake_case_						       =	outputs.lower()

							return outputs






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.preprocess_text(__lowercase		)
							snake_case_						       =	self.sp_model.encode(__lowercase			,      out_type=__lowercase		)
							snake_case_						       =	[]
							for piece in pieces:
								if len(__lowercase		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
									snake_case_						       =	self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowercase			,      ""		)		)
									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
										if len(cur_pieces[0]		) == 1:
											snake_case_						       =	cur_pieces[1:]
										else:
											snake_case_						       =	cur_pieces[0][1:]
									cur_pieces.append(piece[-1]		)
									new_pieces.extend(__lowercase		)
								else:
									new_pieces.append(__lowercase		)

							return new_pieces






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							return self.sp_model.PieceToId(__lowercase		)






						def 			snake_case__		(		self				:	List[str]			,      __lowercase				:	str		):






							"""simple docstring"""






							return self.sp_model.IdToPiece(__lowercase		)






						def 			snake_case__		(		self				:	str			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	[]
							snake_case_						       =	""
							snake_case_						       =	False
							for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
									if not prev_is_special:
										out_string += " "
									out_string += self.sp_model.decode(__lowercase		) + token
									snake_case_						       =	True
									snake_case_						       =	[]
								else:
									current_sub_tokens.append(__lowercase		)
									snake_case_						       =	False
							out_string += self.sp_model.decode(__lowercase		)
							return out_string.strip()






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	List[int]			,      __lowercase				:	Optional[List[int]] = None		):






							"""simple docstring"""






							snake_case_						       =	[self.sep_token_id]
							snake_case_						       =	[self.cls_token_id]
							if token_ids_a is None:
								return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	List[int]			,      __lowercase				:	Optional[List[int]] = None			,      __lowercase				:	bool = False		):






							"""simple docstring"""






							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=__lowercase			,      token_ids_a=__lowercase			,      already_has_special_tokens=__lowercase		)

							if token_ids_a is not None:
								return [1] + ([0] * len(__lowercase		)) + [1] + ([0] * len(__lowercase		)) + [1]
							return [1] + ([0] * len(__lowercase		)) + [1]






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[int]			,      __lowercase				:	Optional[List[int]] = None		):






							"""simple docstring"""






							snake_case_						       =	[self.sep_token_id]
							snake_case_						       =	[self.cls_token_id]

							if token_ids_a is None:
								return len(cls + token_ids_a + sep		) * [0]
							return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	str			,      __lowercase				:	Optional[str] = None		):






							"""simple docstring"""






							if not os.path.isdir(__lowercase		):
								logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
								return
							snake_case_						       =	os.path.join(
							    __lowercase			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(__lowercase		) and os.path.isfile(self.vocab_file		):
								copyfile(self.vocab_file			,      __lowercase		)
							elif not os.path.isfile(self.vocab_file		):
								with open(__lowercase			,      "wb"		) as fi:
									snake_case_						       =	self.sp_model.serialized_model_proto()
									fi.write(__lowercase		)

							return (out_vocab_file,)






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__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	old_name

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

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

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

			snake_case_						       =	"last_stage." + trimmed_name

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

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

	return new_name


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	for key in checkpoint.copy().keys():
		snake_case_						       =	checkpoint.pop(_A				)
		snake_case_						       =	val

	return checkpoint


def 		lowerCamelCase__       (						):







	'''simple docstring'''





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

	return image


def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





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

	snake_case_						       =	config.depths[-1] - config.num_metaad_blocks + 1
	snake_case_						       =	convert_torch_checkpoint(_A	,      _A				)

	model.load_state_dict(_A				)
	model.eval()

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

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

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

	assert torch.allclose(_A	,      _A				)

	snake_case_						       =	model(_A				)
	snake_case_						       =	outputs.logits

	snake_case_						       =	(1, 1000)

	if "l1" in model_name:
		snake_case_						       =	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:
		snake_case_						       =	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:
		snake_case_						       =	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__":
		lowercase__							:	Tuple											=		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)

		lowercase__							:	Optional[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,
		)
