


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 	SCREAMING_SNAKE_CASE__    (				snake_case_,						snake_case_						)      ->				Dict:
				A__  :				int 					=old_name

				if "patch_embed" in old_name:
								A__  :				Optional[int] 					=old_name.split('''.'''						)

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

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

												A__  :				List[Any] 					='last_stage.' + trimmed_name

				elif "network" in old_name and re.search(R'''.\d.''',						lowerCamelCase_						):
								A__  :				str 					=old_name.replace('''network''',						'''intermediate_stages'''						)

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

				return new_name



def 	SCREAMING_SNAKE_CASE__    (				snake_case_,						snake_case_						)      ->				Dict:





				for key in checkpoint.copy().keys():
								A__  :				Optional[Any] 					=checkpoint.pop(lowerCamelCase_						)
								A__  :				Union[str, Any] 					=val

				return checkpoint



def 	SCREAMING_SNAKE_CASE__    (				)      ->				List[Any]:
				A__  :				Dict 					='http://images.cocodataset.org/val2017/000000039769.jpg'
				A__  :				Union[str, Any] 					=Image.open(requests.get(lowerCamelCase_,						stream=lowerCamelCase_						).raw						)

				return image



def 	SCREAMING_SNAKE_CASE__    (				snake_case_,						snake_case_,						snake_case_,						snake_case_						)      ->				int:
				A__  :				Dict 					=torch.load(lowerCamelCase_,						map_location='''cpu'''						)['model']
				A__  :				Any 					=EfficientFormerConfig.from_json_file(lowerCamelCase_						)
				A__  :				List[Any] 					=EfficientFormerForImageClassificationWithTeacher(lowerCamelCase_						)
				A__  :				Optional[int] 					='_'.join(checkpoint_path.split('''/'''						)[-1].split('''.'''						)[0].split('''_'''						)[:-1]						)

				A__  :				Dict 					=config.depths[-1] - config.num_metaad_blocks + 1
				A__  :				Tuple 					=convert_torch_checkpoint(lowerCamelCase_,						lowerCamelCase_						)

				model.load_state_dict(lowerCamelCase_						)
				model.eval()

				A__  :				Optional[int] 					={
				    'bilinear': PILImageResampling.BILINEAR,
				    'bicubic': PILImageResampling.BICUBIC,
				    'nearest': PILImageResampling.NEAREST,
				}

				# prepare image
				A__  :				List[str] 					=prepare_img()
				A__  :				List[Any] 					=2_5_6
				A__  :				Any 					=2_2_4
				A__  :				Any 					=EfficientFormerImageProcessor(
				    size={'''shortest_edge''': image_size},						crop_size={'''height''': crop_size, '''width''': crop_size},						resample=pillow_resamplings['''bicubic'''],						)
				A__  :				Union[str, Any] 					=processor(images=lowerCamelCase_,						return_tensors='''pt'''						).pixel_values

				# original processing pipeline
				A__  :				List[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__  :				Dict 					=model(lowerCamelCase_						)
				A__  :				str 					=outputs.logits

				A__  :				Any 					=(1, 1_0_0_0)

				if "l1" in model_name:
								A__  :				str 					=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, :1_0],						lowerCamelCase_,						atol=1e-3						)
								assert logits.shape == expected_shape
				elif "l3" in model_name:
								A__  :				Optional[Any] 					=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, :1_0],						lowerCamelCase_,						atol=1e-3						)
								assert logits.shape == expected_shape
				elif "l7" in model_name:
								A__  :				Optional[Any] 					=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(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							:							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)

						__lowerCamelCase							:							List[str]					 =      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 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_barthez import BarthezTokenizer
else:
						UpperCamelCase__ :   Dict						=							None

UpperCamelCase__ :   Tuple						=							logging.get_logger(__name__)

UpperCamelCase__ :   Union[str, Any]						=							{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :   Dict						=							{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase__ :   Dict						=							{
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

UpperCamelCase__ :   Any						=							'''▁'''







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[str]						=     VOCAB_FILES_NAMES
				__a     :       int						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       Optional[int]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       List[Any]						=     ["input_ids", "attention_mask"]
				__a     :       Dict						=     BarthezTokenizer






				def __init__(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__="<s>"      ,snake_case__="</s>"      ,snake_case__="</s>"      ,snake_case__="<s>"      ,snake_case__="<unk>"      ,snake_case__="<pad>"      ,snake_case__="<mask>"      ,**snake_case__      ,):
							# Mask token behave like a normal word, i.e. include the space before it
							SCREAMING_SNAKE_CASE_   : str					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else mask_token

							super().__init__(
							    snake_case__      ,tokenizer_file=snake_case__      ,bos_token=snake_case__      ,eos_token=snake_case__      ,unk_token=snake_case__      ,sep_token=snake_case__      ,cls_token=snake_case__      ,pad_token=snake_case__      ,mask_token=snake_case__      ,**snake_case__      ,)

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      vocab_file
							SCREAMING_SNAKE_CASE_   : int					  =      False if not self.vocab_file else True






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):

							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : List[str]					  =      [self.cls_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[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 ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : 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 		snake_case	(			self      ,snake_case__      ,snake_case__ = 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(snake_case__ ):
										logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
										return
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      os.path.join(
							    snake_case__      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

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

							return (out_vocab_file,)



