



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

    return checkpoint



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

    return image



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

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

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

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

    assert torch.allclose(UpperCamelCase_ ,       UpperCamelCase_	)

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


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,
   )





import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		"▁"

_lowercase						  =		{"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

_lowercase						  =		{
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

_lowercase						  =		{
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

_lowercase						  =		{
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

_lowercase						  =		{
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				["input_ids"]

     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_INIT_CONFIGURATION
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				RESOURCE_FILES_NAMES




     def __init__(				self ,						a__ ,						a__=None ,						a__=False ,						a__="utf8" ,						a__="[UNK]" ,						a__="[SEP]" ,						a__="[PAD]" ,						a__="[CLS]" ,						a__="[MASK]" ,						a__ = None ,						**a__ ,						):
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.

         A__		 =					{} if sp_model_kwargs is None else sp_model_kwargs
         super().__init__(
             do_lower_case=a__ ,						unk_token=a__ ,						sep_token=a__ ,						pad_token=a__ ,						cls_token=a__ ,						mask_token=a__ ,						vocab_file=a__ ,						encoding=a__ ,						sp_model_kwargs=self.sp_model_kwargs ,						**a__ ,						)
         A__		 =					do_lower_case
         A__		 =					sentencepiece_model_ckpt
         A__		 =					spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(a__)

         # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
         if vocab_file is not None:
             A__		 =					self.load_vocab(filepath=a__)
         else:
             A__		 =					{self.sp_model.id_to_piece(a__): id for id in range(self.sp_model.get_piece_size())}
         A__		 =					{v: k for k, v in self.vocab.items()}




     def 		snake_case_		(				self ,						a__):
         if text is None:
             return None

         A__		 =					self.tokenize(a__)
         A__ ,	A__		 =					'''''', []

         for i, ch in enumerate(a__):
             if ch in self.SP_CHAR_MAPPING:
                 A__		 =					self.SP_CHAR_MAPPING.get(a__)
             else:
                 A__		 =					unicodedata.normalize('''NFKC''' ,						a__)
             if self.is_whitespace(a__):
                 continue
             normalized_text += ch
             char_mapping.extend([i] * len(a__))

         A__ ,	A__ ,	A__		 =					normalized_text, [], 0

         if self.do_lower_case:
             A__		 =					text.lower()

         for token in split_tokens:
             if token[:1] == "▁":
                 A__		 =					token[1:]
             A__		 =					text[offset:].index(a__) + offset
             A__		 =					start + len(a__)

             token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
             A__		 =					end
         return token_mapping




     @property
     def 		snake_case_		(				self):
         return len(self.vocab)




     def 		snake_case_		(				self):
         return dict(self.vocab ,						**self.added_tokens_encoder)




     def __getstate__(				self):
         A__		 =					self.__dict__.copy()
         A__		 =					None
         return state




     def __setstate__(				self ,						a__):
         A__		 =					d

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

         A__		 =					spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.sentencepiece_model_ckpt)




     def 		snake_case_		(				self ,						a__):
         return "".join((self.SP_CHAR_MAPPING.get(a__ ,						a__) for c in text))




     def 		snake_case_		(				self ,						a__ ,						a__=False ,						a__=6_4 ,						a__=0.1):

         if self.sp_model_kwargs.get('''enable_sampling''') is True:
             A__		 =					True
         if self.sp_model_kwargs.get('''alpha''') is not None:
             A__		 =					self.sp_model_kwargs.get('''alpha''')
         if self.sp_model_kwargs.get('''nbest_size''') is not None:
             A__		 =					self.sp_model_kwargs.get('''nbest_size''')

         if not enable_sampling:
             A__		 =					self.sp_model.EncodeAsPieces(a__)
         else:
             A__		 =					self.sp_model.SampleEncodeAsPieces(a__ ,						a__ ,						a__)
         A__		 =					[]
         for pi, piece in enumerate(a__):
             if piece == SPIECE_UNDERLINE:
                 if not pieces[pi + 1].startswith(a__) and pi != 0:
                     new_pieces.append(a__)
                     continue
                 else:
                     continue
             A__		 =					0
             for i, chunk in enumerate(a__):
                 if chunk == SPIECE_UNDERLINE:
                     continue
                 if self.is_ch_char(a__) or self.is_punct(a__):
                     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                         new_pieces.append(piece[lst_i:i])
                     new_pieces.append(a__)
                     A__		 =					i + 1
                 elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                         new_pieces.append(piece[lst_i:i])
                     A__		 =					i
                 elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                         new_pieces.append(piece[lst_i:i])
                     A__		 =					i
             if len(a__) > lst_i:
                 new_pieces.append(piece[lst_i:])
         return new_pieces




     def 		snake_case_		(				self ,						a__):
         A__		 =					''''''.join(a__).replace(a__ ,						''' ''').strip()
         return out_string




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.convert_ids_to_tokens(a__)
         A__		 =					''''''.join(a__).replace(a__ ,						''' ''').strip()
         return out_string




     def 		snake_case_		(				self ,						a__):
         return self.vocab.get(a__ ,						self.vocab.get(self.unk_token))




     def 		snake_case_		(				self ,						a__):
         return self.reverse_vocab.get(a__ ,						self.unk_token)




     def 		snake_case_		(				self ,						a__ ,						a__=None):
         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A__		 =					[self.cls_token_id]
         A__		 =					[self.sep_token_id]
         return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep




     def 		snake_case_		(				self ,						a__ ,						a__=None):
         if offset_mapping_a is None:
             return [(0, 0)] + offset_mapping_a + [(0, 0)]

         return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]




     def 		snake_case_		(				self ,						a__ ,						a__=None ,						a__=False):

         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(a__)) + [1, 1] + ([0] * len(a__)) + [1]
         return [1] + ([0] * len(a__)) + [1]




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
         if token_ids_a is None:
             # [CLS] X [SEP]
             return (len(a__) + 2) * [0]

         # [CLS] A [SEP] [SEP] B [SEP]
         return [0] * (len(a__) + 1) + [1] * (len(a__) + 3)




     def 		snake_case_		(				self ,						a__):
         if "\u4e00" <= char <= "\u9fff":
             return True
         return False




     def 		snake_case_		(				self ,						a__):
         if ("a" <= char <= "z") or ("A" <= char <= "Z"):
             return True
         return False




     def 		snake_case_		(				self ,						a__):
         if char in ",;:.?!~，；：。？！《》【】":
             return True
         return False




     def 		snake_case_		(				self ,						a__):
         if char == " " or char == "\t" or char == "\n" or char == "\r":
             return True
         if len(a__) == 1:
             A__		 =					unicodedata.category(a__)
             if cat == "Zs":
                 return True
         return False




     def 		snake_case_		(				self ,						a__):
         A__		 =					{}
         with io.open(a__ ,						'''r''' ,						encoding='''utf-8''') as f:
             for index, line in enumerate(a__):
                 A__		 =					line.rstrip('''\n''')
                 A__		 =					int(a__)

         return token_to_idx




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         A__		 =					0
         if os.path.isdir(a__):
             A__		 =					os.path.join(
                 a__ ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
         else:
             A__		 =					(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
         with open(a__ ,						'''w''' ,						encoding='''utf-8''') as writer:
             for token, token_index in sorted(self.vocab.items() ,						key=lambda a__: kv[1]):
                 if index != token_index:
                     logger.warning(
                         F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                         ''' Please check that the vocabulary is not corrupted!''')
                     A__		 =					token_index
                 writer.write(token + '''\n''')
                 index += 1

         A__		 =					os.path.join(a__ ,						'''sentencepiece.bpe.model''')
         with open(a__ ,						'''wb''') as fi:
             A__		 =					self.sp_model.serialized_model_proto()
             fi.write(a__)

         return (vocab_file,)
