





'''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 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		_UpperCamelCase       			=old_name

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

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

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

						_UpperCamelCase       			='''last_stage.''' + trimmed_name

		elif "network" in old_name and re.search(r'''.\d.'''  ,      __SCREAMING_SNAKE_CASE   ):
				_UpperCamelCase       			=old_name.replace('''network'''  ,      '''intermediate_stages'''   )

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

		return new_name


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		for key in checkpoint.copy().keys():
				_UpperCamelCase       			=checkpoint.pop(__SCREAMING_SNAKE_CASE   )
				_UpperCamelCase       			=val

		return checkpoint


def 					_a   ():


		"""simple docstring"""

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

		return image


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

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

		_UpperCamelCase       			=config.depths[-1] - config.num_metaad_blocks + 1
		_UpperCamelCase       			=convert_torch_checkpoint(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

		model.load_state_dict(__SCREAMING_SNAKE_CASE   )
		model.eval()

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

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

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

		assert torch.allclose(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

		_UpperCamelCase       			=model(__SCREAMING_SNAKE_CASE   )
		_UpperCamelCase       			=outputs.logits

		_UpperCamelCase       			=(1, 1000)

		if "l1" in model_name:
				_UpperCamelCase       			=torch.Tensor(
				    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]   )
				assert torch.allclose(logits[0, :10]  ,      __SCREAMING_SNAKE_CASE  ,      atol=1E-3   )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				_UpperCamelCase       			=torch.Tensor(
				    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]   )
				assert torch.allclose(logits[0, :10]  ,      __SCREAMING_SNAKE_CASE  ,      atol=1E-3   )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				_UpperCamelCase       			=torch.Tensor(
				    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]   )
				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						: List[str]		=   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						: int		=   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 tokenizers import processors

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


if is_sentencepiece_available():
      from .tokenization_mbart import MBartTokenizer
else:
      __lowerCamelCase						: List[Any]		=   None


__lowerCamelCase						: Optional[int]		=   logging.get_logger(__name__)


__lowerCamelCase						: Tuple		=   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase						: Union[str, Any]		=   {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

__lowerCamelCase						: List[str]		=   {
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
__lowerCamelCase						: Optional[int]		=   ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				VOCAB_FILES_NAMES
  lowerCAmelCase_					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_					=				PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_					=				["""input_ids""", """attention_mask"""]
  lowerCAmelCase_					=				MBartTokenizer

  lowerCAmelCase_					=				[]
  lowerCAmelCase_					=				[]


  def __init__(				self    :					Optional[int]							,	UpperCamelCase__    :					List[Any]=None							,	UpperCamelCase__    :					Tuple=None							,	UpperCamelCase__    :					List[str]="<s>"							,	UpperCamelCase__    :					Optional[Any]="</s>"							,	UpperCamelCase__    :					Dict="</s>"							,	UpperCamelCase__    :					str="<s>"							,	UpperCamelCase__    :					Tuple="<unk>"							,	UpperCamelCase__    :					Dict="<pad>"							,	UpperCamelCase__    :					str="<mask>"							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					List[str]=None							,	UpperCamelCase__    :					Dict=None							,	**UpperCamelCase__    :					Any							,	)     ->						List[str]:
    # Mask token behave like a normal word, i.e. include the space before it
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else mask_token

    super().__init__(
        vocab_file=UpperCamelCase__							,	tokenizer_file=UpperCamelCase__							,	bos_token=UpperCamelCase__							,	eos_token=UpperCamelCase__							,	sep_token=UpperCamelCase__							,	cls_token=UpperCamelCase__							,	unk_token=UpperCamelCase__							,	pad_token=UpperCamelCase__							,	mask_token=UpperCamelCase__							,	src_lang=UpperCamelCase__							,	tgt_lang=UpperCamelCase__							,	additional_special_tokens=UpperCamelCase__							,	**UpperCamelCase__							,	)

    _UpperCamelCase       			=vocab_file
    _UpperCamelCase       			=False if not self.vocab_file else True

    _UpperCamelCase       			=FAIRSEQ_LANGUAGE_CODES.copy()

    if additional_special_tokens is not None:
      # Only add those special tokens if they are not already there.
      _additional_special_tokens.extend(
          [t for t in additional_special_tokens if t not in _additional_special_tokens]  )

    self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}  )
    _UpperCamelCase       			={
        lang_code: self.convert_tokens_to_ids(UpperCamelCase__  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
    }

    _UpperCamelCase       			=src_lang if src_lang is not None else '''en_XX'''
    _UpperCamelCase       			=self.convert_tokens_to_ids(self._src_lang  )
    _UpperCamelCase       			=tgt_lang
    self.set_src_lang_special_tokens(self._src_lang  )


  @property
  def   UpperCamelCase__      (				self    :					str  )     ->						str:
    return self._src_lang


  @src_lang.setter
  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=new_src_lang
    self.set_src_lang_special_tokens(self._src_lang  )


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    if token_ids_a is None:
      return self.prefix_tokens + token_ids_a + self.suffix_tokens
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = None  )     ->						List[int]:
    _UpperCamelCase       			=[self.sep_token_id]
    _UpperCamelCase       			=[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   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					Optional[int]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str]							,	UpperCamelCase__    :					Optional[str]							,	**UpperCamelCase__    :					List[str]  )     ->						str:
    if src_lang is None or tgt_lang is None:
      raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
    _UpperCamelCase       			=src_lang
    _UpperCamelCase       			=self(UpperCamelCase__							,	add_special_tokens=UpperCamelCase__							,	return_tensors=UpperCamelCase__							,	**UpperCamelCase__  )
    _UpperCamelCase       			=self.convert_tokens_to_ids(UpperCamelCase__  )
    _UpperCamelCase       			=tgt_lang_id
    return inputs


  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					str = "en_XX"							,	UpperCamelCase__    :					Optional[List[str]] = None							,	UpperCamelCase__    :					str = "ro_RO"							,	**UpperCamelCase__    :					Optional[int]							,	)     ->						BatchEncoding:
    _UpperCamelCase       			=src_lang
    _UpperCamelCase       			=tgt_lang
    return super().prepare_seqaseq_batch(UpperCamelCase__							,	UpperCamelCase__							,	**UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						List[str]:
    return self.set_src_lang_special_tokens(self.src_lang  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[str]:
    return self.set_tgt_lang_special_tokens(self.tgt_lang  )


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=self.convert_tokens_to_ids(UpperCamelCase__  )
    _UpperCamelCase       			=[]
    _UpperCamelCase       			=[self.eos_token_id, self.cur_lang_code]

    _UpperCamelCase       			=self.convert_ids_to_tokens(self.prefix_tokens  )
    _UpperCamelCase       			=self.convert_ids_to_tokens(self.suffix_tokens  )

    _UpperCamelCase       			=processors.TemplateProcessing(
        single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str							,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str							,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,	self.prefix_tokens + self.suffix_tokens  )  )							,	)


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=self.convert_tokens_to_ids(UpperCamelCase__  )
    _UpperCamelCase       			=[]
    _UpperCamelCase       			=[self.eos_token_id, self.cur_lang_code]

    _UpperCamelCase       			=self.convert_ids_to_tokens(self.prefix_tokens  )
    _UpperCamelCase       			=self.convert_ids_to_tokens(self.suffix_tokens  )

    _UpperCamelCase       			=processors.TemplateProcessing(
        single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str							,	pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str							,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str							,	self.prefix_tokens + self.suffix_tokens  )  )							,	)




  def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str] = None  )     ->						Tuple[str]:
    if not self.can_save_slow_tokenizer:
      raise ValueError(
          '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
          '''tokenizer.'''  )

    if not os.path.isdir(UpperCamelCase__  ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''  )
      return
    _UpperCamelCase       			=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,)






