





import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


UpperCAmelCase_							=				logging.get_logger(__name__)


UpperCAmelCase_							=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
UpperCAmelCase_							=				{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

UpperCAmelCase_							=				{
    '''allenai/led-base-16384''': 16_384,
}





@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def  __magic_name__  (				)				-> List[Any]:

							"""simple docstring"""



							lowercase_	:   Dict  				=     (
							    list(range(ord("""!"""				)				,       ord("""~"""				) + 1				)				) + list(range(ord("""¡"""				)				,       ord("""¬"""				) + 1				)				) + list(range(ord("""®"""				)				,       ord("""ÿ"""				) + 1				)				)
							)
							lowercase_	:   List[str]  				=     bs[:]
							lowercase_	:   str  				=     0
							for b in range(2**8				):
														if b not in bs:
																					bs.append(_lowerCamelCase				)
																					cs.append(2**8 + n				)
																					n += 1
							lowercase_	:   Any  				=     [chr(_lowerCamelCase				) for n in cs]
							return dict(zip(_lowerCamelCase				,       _lowerCamelCase				)				)





def  __magic_name__  (				lowercase				)				-> Dict:

							"""simple docstring"""



							lowercase_	:   Any  				=     set()
							lowercase_	:   str  				=     word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)				)
														lowercase_	:   Optional[int]  				=     char
							return pairs




class     UpperCamelCase__					(     __lowerCAmelCase				):






		'''simple docstring'''






		__a       :       Any       =	VOCAB_FILES_NAMES
		__a       :       Dict       =	PRETRAINED_VOCAB_FILES_MAP
		__a       :       int       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a       :       Any       =	['''input_ids''', '''attention_mask''']






		def __init__(							self,   snake_case__,   snake_case__,   snake_case__="replace",   snake_case__="<s>",   snake_case__="</s>",   snake_case__="</s>",   snake_case__="<s>",   snake_case__="<unk>",   snake_case__="<pad>",   snake_case__="<mask>",   snake_case__=False,   **snake_case__,   )       -> List[str]:






									"""simple docstring"""
									lowercase_	:   int  				=     AddedToken(__SCREAMING_SNAKE_CASE,   lstrip=__SCREAMING_SNAKE_CASE,   rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE,   __SCREAMING_SNAKE_CASE  ) else bos_token
									lowercase_	:   int  				=     AddedToken(__SCREAMING_SNAKE_CASE,   lstrip=__SCREAMING_SNAKE_CASE,   rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE,   __SCREAMING_SNAKE_CASE  ) else eos_token
									lowercase_	:   Union[str, Any]  				=     AddedToken(__SCREAMING_SNAKE_CASE,   lstrip=__SCREAMING_SNAKE_CASE,   rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE,   __SCREAMING_SNAKE_CASE  ) else sep_token
									lowercase_	:   int  				=     AddedToken(__SCREAMING_SNAKE_CASE,   lstrip=__SCREAMING_SNAKE_CASE,   rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE,   __SCREAMING_SNAKE_CASE  ) else cls_token
									lowercase_	:   str  				=     AddedToken(__SCREAMING_SNAKE_CASE,   lstrip=__SCREAMING_SNAKE_CASE,   rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE,   __SCREAMING_SNAKE_CASE  ) else unk_token
									lowercase_	:   Any  				=     AddedToken(__SCREAMING_SNAKE_CASE,   lstrip=__SCREAMING_SNAKE_CASE,   rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE,   __SCREAMING_SNAKE_CASE  ) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									lowercase_	:   List[str]  				=     AddedToken(__SCREAMING_SNAKE_CASE,   lstrip=__SCREAMING_SNAKE_CASE,   rstrip=__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE,   __SCREAMING_SNAKE_CASE  ) else mask_token

									super().__init__(
									    errors=__SCREAMING_SNAKE_CASE,   bos_token=__SCREAMING_SNAKE_CASE,   eos_token=__SCREAMING_SNAKE_CASE,   unk_token=__SCREAMING_SNAKE_CASE,   sep_token=__SCREAMING_SNAKE_CASE,   cls_token=__SCREAMING_SNAKE_CASE,   pad_token=__SCREAMING_SNAKE_CASE,   mask_token=__SCREAMING_SNAKE_CASE,   add_prefix_space=__SCREAMING_SNAKE_CASE,   **__SCREAMING_SNAKE_CASE,   )

									with open(__SCREAMING_SNAKE_CASE,   encoding="""utf-8"""  ) as vocab_handle:
																lowercase_	:   Optional[int]  				=     json.load(__SCREAMING_SNAKE_CASE  )
									lowercase_	:   List[Any]  				=     {v: k for k, v in self.encoder.items()}
									lowercase_	:   Dict  				=     errors  # how to handle errors in decoding
									lowercase_	:   Tuple  				=     bytes_to_unicode()
									lowercase_	:   int  				=     {v: k for k, v in self.byte_encoder.items()}
									with open(__SCREAMING_SNAKE_CASE,   encoding="""utf-8"""  ) as merges_handle:
																lowercase_	:   Dict  				=     merges_handle.read().split("""\n"""  )[1:-1]
									lowercase_	:   str  				=     [tuple(merge.split()  ) for merge in bpe_merges]
									lowercase_	:   Optional[Any]  				=     dict(zip(__SCREAMING_SNAKE_CASE,   range(len(__SCREAMING_SNAKE_CASE  )  )  )  )
									lowercase_	:   Optional[Any]  				=     {}
									lowercase_	:   Optional[int]  				=     add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									lowercase_	:   Dict  				=     re.compile(r"""\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""  )






		@property
		# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									return len(self.encoder  )






		def 						snake_case__				(							self  )       -> Optional[Any]:






									"""simple docstring"""
									return dict(self.encoder,   **self.added_tokens_encoder  )






		def 						snake_case__				(							self,   snake_case__  )       -> List[str]:






									"""simple docstring"""




									if token in self.cache:
																return self.cache[token]
									lowercase_	:   Optional[int]  				=     tuple(__SCREAMING_SNAKE_CASE  )
									lowercase_	:   Dict  				=     get_pairs(__SCREAMING_SNAKE_CASE  )

									if not pairs:
																return token

									while True:
																lowercase_	:   List[str]  				=     min(__SCREAMING_SNAKE_CASE,   key=lambda snake_case__  : self.bpe_ranks.get(__SCREAMING_SNAKE_CASE,   float("""inf"""  )  )  )
																if bigram not in self.bpe_ranks:
																							break
																lowercase_				,		lowercase_	:   Tuple  				=     bigram
																lowercase_	:   Optional[Any]  				=     []
																lowercase_	:   List[str]  				=     0
																while i < len(__SCREAMING_SNAKE_CASE  ):
																							try:
																														lowercase_	:   Optional[Any]  				=     word.index(__SCREAMING_SNAKE_CASE,   __SCREAMING_SNAKE_CASE  )
																							except ValueError:
																														new_word.extend(word[i:]  )
																														break
																							else:
																														new_word.extend(word[i:j]  )
																														lowercase_	:   str  				=     j

																							if word[i] == first and i < len(__SCREAMING_SNAKE_CASE  ) - 1 and word[i + 1] == second:
																														new_word.append(first + second  )
																														i += 2
																							else:
																														new_word.append(word[i]  )
																														i += 1
																lowercase_	:   Optional[Any]  				=     tuple(__SCREAMING_SNAKE_CASE  )
																lowercase_	:   str  				=     new_word
																if len(__SCREAMING_SNAKE_CASE  ) == 1:
																							break
																else:
																							lowercase_	:   List[str]  				=     get_pairs(__SCREAMING_SNAKE_CASE  )
									lowercase_	:   Union[str, Any]  				=     """ """.join(__SCREAMING_SNAKE_CASE  )
									lowercase_	:   List[Any]  				=     word
									return word






		def 						snake_case__				(							self,   snake_case__  )       -> Any:






									"""simple docstring"""
									lowercase_	:   Any  				=     []
									for token in re.findall(self.pat,   __SCREAMING_SNAKE_CASE  ):
																lowercase_	:   List[str]  				=     """""".join(
																    self.byte_encoder[b] for b in token.encode("""utf-8"""  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__SCREAMING_SNAKE_CASE  ).split(""" """  )  )
									return bpe_tokens






		def 						snake_case__				(							self,   snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									return self.encoder.get(__SCREAMING_SNAKE_CASE,   self.encoder.get(self.unk_token  )  )






		def 						snake_case__				(							self,   snake_case__  )       -> int:






									"""simple docstring"""
									return self.decoder.get(__SCREAMING_SNAKE_CASE  )






		def 						snake_case__				(							self,   snake_case__  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   int  				=     """""".join(__SCREAMING_SNAKE_CASE  )
									lowercase_	:   Union[str, Any]  				=     bytearray([self.byte_decoder[c] for c in text]  ).decode("""utf-8""",   errors=self.errors  )
									return text






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> Tuple[str]:






									"""simple docstring"""




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

									with open(__SCREAMING_SNAKE_CASE,   """w""",   encoding="""utf-8"""  ) as f:
																f.write(json.dumps(self.encoder,   indent=2,   sort_keys=__SCREAMING_SNAKE_CASE,   ensure_ascii=__SCREAMING_SNAKE_CASE  ) + """\n"""  )

									lowercase_	:   str  				=     0
									with open(__SCREAMING_SNAKE_CASE,   """w""",   encoding="""utf-8"""  ) as writer:
																writer.write("""#version: 0.2\n"""  )
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),   key=lambda snake_case__  : kv[1]  ):
																							if index != token_index:
																														logger.warning(
																														    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																														    """ Please check that the tokenizer is not corrupted!"""  )
																														lowercase_	:   Optional[int]  				=     token_index
																							writer.write(""" """.join(__SCREAMING_SNAKE_CASE  ) + """\n"""  )
																							index += 1

									return vocab_file, merge_file






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> List[int]:






									"""simple docstring"""




									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									lowercase_	:   List[Any]  				=     [self.cls_token_id]
									lowercase_	:   int  				=     [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = False  )       -> List[int]:






									"""simple docstring"""




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

									if token_ids_a is None:
																return [1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1]
									return [1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE  )) + [1]






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None  )       -> List[int]:






									"""simple docstring"""
									lowercase_	:   Any  				=     [self.sep_token_id]
									lowercase_	:   List[str]  				=     [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 + sep + token_ids_a + sep  ) * [0]






		def 						snake_case__				(							self,   snake_case__,   snake_case__=False,   **snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   Optional[Any]  				=     kwargs.pop("""add_prefix_space""",   self.add_prefix_space  )
									if (is_split_into_words or add_prefix_space) and (len(__SCREAMING_SNAKE_CASE  ) > 0 and not text[0].isspace()):
																lowercase_	:   List[str]  				=     """ """ + text
									return (text, kwargs)






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = PaddingStrategy.DO_NOT_PAD,   snake_case__ = None,   snake_case__ = None,   )       -> dict:






									"""simple docstring"""
									lowercase_	:   Dict  				=     super()._pad(
									    encoded_inputs=__SCREAMING_SNAKE_CASE,   max_length=__SCREAMING_SNAKE_CASE,   padding_strategy=__SCREAMING_SNAKE_CASE,   pad_to_multiple_of=__SCREAMING_SNAKE_CASE,   return_attention_mask=__SCREAMING_SNAKE_CASE,   )

									# Load from model defaults
									if return_attention_mask is None:
																lowercase_	:   Dict  				=     """attention_mask""" in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
																lowercase_	:   Any  				=     encoded_inputs[self.model_input_names[0]]
																# `global_attention_mask` need to have the same length as other (sequential) inputs.
																lowercase_	:   str  				=     len(encoded_inputs["""global_attention_mask"""]  ) != len(__SCREAMING_SNAKE_CASE  )

																if needs_to_be_padded:
																							lowercase_	:   List[Any]  				=     len(__SCREAMING_SNAKE_CASE  ) - len(encoded_inputs["""global_attention_mask"""]  )

																							if self.padding_side == "right":
																														# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																														lowercase_	:   List[Any]  				=     (
																														    encoded_inputs["""global_attention_mask"""] + [-1] * difference
																														)
																							elif self.padding_side == "left":
																														lowercase_	:   Optional[int]  				=     [-1] * difference + encoded_inputs[
																														    """global_attention_mask"""
																														]
																							else:
																														raise ValueError("""Invalid padding strategy:""" + str(self.padding_side  )  )

									return encoded_inputs





'''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       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  str						)-> List[str]:




     '''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''' ,						_lowerCamelCase						):
          __snake_case            =	R'''\b\d{2}\b'''
          if bool(re.search(_lowerCamelCase ,						_lowerCamelCase						)						):
               __snake_case            =	re.search(R'''\d\.\d\d.''' ,						_lowerCamelCase						).group()
          else:
               __snake_case            =	re.search(R'''\d\.\d.''' ,						_lowerCamelCase						).group()
          if int(match[0]						) < 6:
               __snake_case            =	old_name.replace(_lowerCamelCase ,						''''''						)
               __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(_lowerCamelCase ,						''''''						)
               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.''' ,						_lowerCamelCase						):
          __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       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Tuple						)-> List[str]:




     '''simple docstring'''





     for key in checkpoint.copy().keys():
          __snake_case            =	checkpoint.pop(_lowerCamelCase						)
          __snake_case            =	val

     return checkpoint







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __snake_case            =	Image.open(requests.get(_lowerCamelCase ,						stream=_lowerCamelCase						).raw						)

     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Path ,						_lowerCamelCase				:  Path ,						_lowerCamelCase				:  Path ,						_lowerCamelCase				:  bool						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	torch.load(_lowerCamelCase ,						map_location='''cpu'''						)['''model''']
     __snake_case            =	EfficientFormerConfig.from_json_file(_lowerCamelCase						)
     __snake_case            =	EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase						)
     __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(_lowerCamelCase ,						_lowerCamelCase						)

     model.load_state_dict(_lowerCamelCase						)
     model.eval()

     __snake_case            =	{
         '''bilinear''': PILImageResampling.BILINEAR,
         '''bicubic''': PILImageResampling.BICUBIC,
         '''nearest''': PILImageResampling.NEAREST,
     }

     # prepare image
     __snake_case            =	prepare_img()
     __snake_case            =	2_56
     __snake_case            =	2_24
     __snake_case            =	EfficientFormerImageProcessor(
         size={'''shortest_edge''': image_size} ,						crop_size={'''height''': crop_size, '''width''': crop_size} ,						resample=pillow_resamplings['''bicubic'''] ,						)
     __snake_case            =	processor(images=_lowerCamelCase ,						return_tensors='''pt'''						).pixel_values

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

     assert torch.allclose(_lowerCamelCase ,						_lowerCamelCase						)

     __snake_case            =	model(_lowerCamelCase						)
     __snake_case            =	outputs.logits

     __snake_case            =	(1, 10_00)

     if "l1" in model_name:
          __snake_case            =	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, :10] ,						_lowerCamelCase ,						atol=1E-3						)
          assert logits.shape == expected_shape
     elif "l3" in model_name:
          __snake_case            =	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, :10] ,						_lowerCamelCase ,						atol=1E-3						)
          assert logits.shape == expected_shape
     elif "l7" in model_name:
          __snake_case            =	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(_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__":
     UpperCAmelCase_							:	Optional[int]	     =		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)

     UpperCAmelCase_							:	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,
     )






