



'''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						      =				None


__lowerCAmelCase						      =				logging.get_logger(__name__)


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

__lowerCAmelCase						      =				{
    '''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						      =				{
    '''facebook/mbart-large-en-ro''': 1_024,
    '''facebook/mbart-large-cc25''': 1_024,
}

# fmt: off
__lowerCAmelCase						      =				['''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      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   List[str]           = VOCAB_FILES_NAMES
							lowerCAmelCase	:   Any           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase	:   List[str]           = PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase	:   Dict           = ['input_ids', 'attention_mask']
							lowerCAmelCase	:   List[str]           = MBartTokenizer

							lowerCAmelCase	:   List[int]           = []
							lowerCAmelCase	:   List[int]           = []







							def __init__(							self :	Union[str, Any]     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	Any=None     ,_UpperCAmelCase :	Optional[int]="<s>"     ,_UpperCAmelCase :	List[Any]="</s>"     ,_UpperCAmelCase :	int="</s>"     ,_UpperCAmelCase :	Union[str, Any]="<s>"     ,_UpperCAmelCase :	List[str]="<unk>"     ,_UpperCAmelCase :	str="<pad>"     ,_UpperCAmelCase :	int="<mask>"     ,_UpperCAmelCase :	int=None     ,_UpperCAmelCase :	int=None     ,_UpperCAmelCase :	Tuple=None     ,**_UpperCAmelCase :	int     ,):
									# Mask token behave like a normal word, i.e. include the space before it
									_a					:				List[Any]				=							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     ,)

									_a					:				str				=							vocab_file
									_a					:				Dict				=							False if not self.vocab_file else True

									_a					:				List[Any]				=							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}		)
									_a					:				Dict				=							{
									    lang_code: self.convert_tokens_to_ids(_UpperCAmelCase		) for lang_code in FAIRSEQ_LANGUAGE_CODES
									}

									_a					:				Dict				=							src_lang if src_lang is not None else 'en_XX'
									_a					:				Any				=							self.convert_tokens_to_ids(self._src_lang		)
									_a					:				Dict				=							tgt_lang
									self.set_src_lang_special_tokens(self._src_lang		)







							@property
							def 							__lowercase      (							self :	Tuple		):
									return self._src_lang







							@src_lang.setter
							def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str		):
									_a					:				List[Any]				=							new_src_lang
									self.set_src_lang_special_tokens(self._src_lang		)







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







							def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	List[str]     ,_UpperCAmelCase :	str = "en_XX"     ,_UpperCAmelCase :	Optional[List[str]] = None     ,_UpperCAmelCase :	str = "ro_RO"     ,**_UpperCAmelCase :	List[Any]     ,):
									_a					:				Optional[int]				=							src_lang
									_a					:				Optional[int]				=							tgt_lang
									return super().prepare_seqaseq_batch(_UpperCAmelCase     ,_UpperCAmelCase     ,**_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any		):
									return self.set_src_lang_special_tokens(self.src_lang		)







							def 							__lowercase      (							self :	Dict		):
									return self.set_tgt_lang_special_tokens(self.tgt_lang		)







							def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	List[str]		):
									_a					:				Union[str, Any]				=							self.convert_tokens_to_ids(_UpperCAmelCase		)
									_a					:				Any				=							[]
									_a					:				Union[str, Any]				=							[self.eos_token_id, self.cur_lang_code]

									_a					:				Union[str, Any]				=							self.convert_ids_to_tokens(self.prefix_tokens		)
									_a					:				Tuple				=							self.convert_ids_to_tokens(self.suffix_tokens		)

									_a					:				Optional[int]				=							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 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	str		):
									_a					:				List[Any]				=							self.convert_tokens_to_ids(_UpperCAmelCase		)
									_a					:				List[str]				=							[]
									_a					:				int				=							[self.eos_token_id, self.cur_lang_code]

									_a					:				Optional[int]				=							self.convert_ids_to_tokens(self.prefix_tokens		)
									_a					:				Any				=							self.convert_ids_to_tokens(self.suffix_tokens		)

									_a					:				int				=							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 							__lowercase      (							self :	Tuple     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	Optional[str] = None		):
									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
									_a					:				Dict				=							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,)









'''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 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				int:
		_a					:				Optional[int]				=							old_name

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

				if layer == "0":
						_a					:				Dict				=							old_name.replace('0'						,      'convolution1'  )
				elif layer == "1":
						_a					:				List[str]				=							old_name.replace('1'						,      'batchnorm_before'  )
				elif layer == "3":
						_a					:				List[str]				=							old_name.replace('3'						,      'convolution2'  )
				else:
						_a					:				Union[str, Any]				=							old_name.replace('4'						,      'batchnorm_after'  )

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

						_a					:				Dict				=							'last_stage.' + trimmed_name

		elif "network" in old_name and re.search(r'.\d.'						,      lowerCAmelCase_  ):
				_a					:				List[str]				=							old_name.replace('network'						,      'intermediate_stages'  )

		if "fc" in new_name:
				_a					:				Optional[int]				=							new_name.replace('fc'						,      'convolution'  )
		elif ("norm1" in new_name) and ("layernorm1" not in new_name):
				_a					:				Tuple				=							new_name.replace('norm1'						,      'batchnorm_before'  )
		elif ("norm2" in new_name) and ("layernorm2" not in new_name):
				_a					:				Union[str, Any]				=							new_name.replace('norm2'						,      'batchnorm_after'  )
		if "proj" in new_name:
				_a					:				Tuple				=							new_name.replace('proj'						,      'projection'  )
		if "dist_head" in new_name:
				_a					:				List[Any]				=							new_name.replace('dist_head'						,      'distillation_classifier'  )
		elif "head" in new_name:
				_a					:				Dict				=							new_name.replace('head'						,      'classifier'  )
		elif "patch_embed" in new_name:
				_a					:				Tuple				=							'efficientformer.' + new_name
		elif new_name == "norm.weight" or new_name == "norm.bias":
				_a					:				int				=							new_name.replace('norm'						,      'layernorm'  )
				_a					:				Any				=							'efficientformer.' + new_name
		else:
				_a					:				Optional[int]				=							'efficientformer.encoder.' + new_name

		return new_name



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Union[str, Any]:
		for key in checkpoint.copy().keys():
				_a					:				List[str]				=							checkpoint.pop(lowerCAmelCase_  )
				_a					:				List[str]				=							val

		return checkpoint



def 					__lowerCamelCase					(				)      ->				Any:
		_a					:				int				=							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a					:				List[str]				=							Image.open(requests.get(lowerCAmelCase_						,      stream=lowerCAmelCase_  ).raw  )

		return image



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[Any]:
		_a					:				int				=							torch.load(lowerCAmelCase_						,      map_location='cpu'  )['model']
		_a					:				Any				=							EfficientFormerConfig.from_json_file(lowerCAmelCase_  )
		_a					:				Optional[int]				=							EfficientFormerForImageClassificationWithTeacher(lowerCAmelCase_  )
		_a					:				List[Any]				=							'_'.join(checkpoint_path.split('/'  )[-1].split('.'  )[0].split('_'  )[:-1]  )

		_a					:				Dict				=							config.depths[-1] - config.num_metaad_blocks + 1
		_a					:				int				=							convert_torch_checkpoint(lowerCAmelCase_						,      lowerCAmelCase_  )

		model.load_state_dict(lowerCAmelCase_  )
		model.eval()

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

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

		# original processing pipeline
		_a					:				str				=							Compose(
		    [
		        Resize(lowerCAmelCase_						,      interpolation=pillow_resamplings['bicubic']  ),
		        CenterCrop(lowerCAmelCase_  ),
		        ToTensor(),
		        Normalize(lowerCAmelCase_						,      lowerCAmelCase_  ),
		    ]  )
		_a					:				Any				=							image_transforms(lowerCAmelCase_  ).unsqueeze(0  )

		assert torch.allclose(lowerCAmelCase_						,      lowerCAmelCase_  )

		_a					:				Tuple				=							model(lowerCAmelCase_  )
		_a					:				Tuple				=							outputs.logits

		_a					:				List[Any]				=							(1, 1000)

		if "l1" in model_name:
				_a					:				Optional[Any]				=							torch.Tensor(
				    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]  )
				assert torch.allclose(logits[0, :10]						,      lowerCAmelCase_						,      atol=1E-3  )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				_a					:				List[Any]				=							torch.Tensor(
				    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]  )
				assert torch.allclose(logits[0, :10]						,      lowerCAmelCase_						,      atol=1E-3  )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				_a					:				Tuple				=							torch.Tensor(
				    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]  )
				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__":
							__lowerCAmelCase						      =       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						      =       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,
							)




