


"""simple docstring"""




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        lowercase							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     Any			) ->      str:
		__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'''    ,					lowerCAmelCase__			):
				__a           =			r'''\b\d{2}\b'''
				if bool(re.search(lowerCAmelCase__    ,					lowerCAmelCase__			)			):
						__a           =			re.search(r'''\d\.\d\d.'''    ,					lowerCAmelCase__			).group()
				else:
						__a           =			re.search(r'''\d\.\d.'''    ,					lowerCAmelCase__			).group()
				if int(match[0]			) < 6:
						__a           =			old_name.replace(lowerCAmelCase__    ,					''''''			)
						__a           =			trimmed_name.replace('''network'''    ,					match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]			)
						__a           =			'''intermediate_stages.''' + trimmed_name
				else:
						__a           =			old_name.replace(lowerCAmelCase__    ,					''''''			)
						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.'''    ,					lowerCAmelCase__			):
				__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        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     Tuple			) ->      Tuple:
		for key in checkpoint.copy().keys():
				__a           =			checkpoint.pop(lowerCAmelCase__			)
				__a           =			val

		return checkpoint

def        lowercase							(		) ->      Any:
		__a           =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		__a           =			Image.open(requests.get(lowerCAmelCase__    ,					stream=lowerCAmelCase__			).raw			)

		return image

def        lowercase							(		lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     Path    ,					lowerCAmelCase__ :     bool			) ->      Any:
		__a           =			torch.load(lowerCAmelCase__    ,					map_location='''cpu'''			)['''model''']
		__a           =			EfficientFormerConfig.from_json_file(lowerCAmelCase__			)
		__a           =			EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase__			)
		__a           =			'''_'''.join(checkpoint_path.split('''/'''			)[-1].split('''.'''			)[0].split('''_'''			)[:-1]			)

		__a           =			config.depths[-1] - config.num_metaad_blocks + 1
		__a           =			convert_torch_checkpoint(lowerCAmelCase__    ,					lowerCAmelCase__			)

		model.load_state_dict(lowerCAmelCase__			)
		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=lowerCAmelCase__    ,					return_tensors='''pt'''			).pixel_values

		# original processing pipeline
		__a           =			Compose(
		    [
		        Resize(lowerCAmelCase__    ,					interpolation=pillow_resamplings['''bicubic''']			),
		        CenterCrop(lowerCAmelCase__			),
		        ToTensor(),
		        Normalize(lowerCAmelCase__    ,					lowerCAmelCase__			),
		    ]			)
		__a           =			image_transforms(lowerCAmelCase__			).unsqueeze(0			)

		assert torch.allclose(lowerCAmelCase__    ,					lowerCAmelCase__			)

		__a           =			model(lowerCAmelCase__			)
		__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]    ,					lowerCAmelCase__    ,					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]    ,					lowerCAmelCase__    ,					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(lowerCAmelCase__			).mkdir(exist_ok=lowerCAmelCase__			)
		model.save_pretrained(lowerCAmelCase__			)
		print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''			)
		processor.save_pretrained(lowerCAmelCase__			)
		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=lowerCAmelCase__    ,					)
				processor.push_to_hub(
				    repo_id=f'''Bearnardd/{pytorch_dump_path}'''    ,					commit_message='''Add image processor'''    ,					use_temp_dir=lowerCAmelCase__    ,					)


if __name__ == "__main__":
				lowercase_			     =  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_			     =  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"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_rembert import RemBertTokenizer
else:
			__A						   =			None

__A						   =			logging.get_logger(__name__)
__A						   =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

__A						   =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

__A						   =			{
    "google/rembert": 256,
}

__A						   =			"▁"





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      Any         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Tuple         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Dict         =  RemBertTokenizer

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



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else mask_token

						super().__init__(
						    UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  do_lower_case=UpperCamelCase__     ,  remove_space=UpperCamelCase__     ,  keep_accents=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

						__lowerCAmelCase:       Optional[int]          =			do_lower_case
						__lowerCAmelCase:       int          =			remove_space
						__lowerCAmelCase:       int          =			keep_accents
						__lowerCAmelCase:       str          =			vocab_file
						__lowerCAmelCase:       Tuple          =			False if not self.vocab_file else True

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Any          =			[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 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None     ,  UpperCamelCase__							:				bool = False)->   List[int]:



						'''simple docstring'''


						if already_has_special_tokens:
								if token_ids_a is not None:
										raise ValueError(
										    "You should not supply a second sequence if the provided sequence of "
										    "ids is already formatted with special tokens for the model.")
								return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Dict          =			[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 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						if not os.path.isdir(UpperCamelCase__):
								logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__))
								return
						__lowerCAmelCase:       Optional[Any]          =			os.path.join(
						    UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

						if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase__):
								copyfile(self.vocab_file     ,  UpperCamelCase__)

						return (out_vocab_file,)


