





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase   				=			logging.get_logger(__name__)

_lowerCamelCase   				=			{'vocab_file': 'sentencepiece.bpe.model'}

_lowerCamelCase   				=			{
    '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'
        ),
    },
}

_lowerCamelCase   				=			{
    'moussaKam/mbarthez': 1024,
    'moussaKam/barthez': 1024,
    'moussaKam/barthez-orangesum-title': 1024,
}

_lowerCamelCase   				=			'▁'




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					VOCAB_FILES_NAMES
							lowerCamelCase_           =					PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase_           =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase_           =					["input_ids", "attention_mask"]


							def __init__(							self    :List[Any]							,				__A    :Tuple							,				__A    :Any="<s>"							,				__A    :List[Any]="</s>"							,				__A    :Optional[Any]="</s>"							,				__A    :Optional[int]="<s>"							,				__A    :Tuple="<unk>"							,				__A    :List[Any]="<pad>"							,				__A    :Dict="<mask>"							,				__A    :Optional[Dict[str, Any]] = None							,				**__A    :Tuple							,				)   ->   None:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      AddedToken(__A							,				lstrip=__A							,				rstrip=__A		) if isinstance(__A							,				__A		) else mask_token

									SCREAMING_SNAKE_CASE__			=      {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=__A							,				eos_token=__A							,				unk_token=__A							,				sep_token=__A							,				cls_token=__A							,				pad_token=__A							,				mask_token=__A							,				sp_model_kwargs=self.sp_model_kwargs							,				**__A							,				)

									SCREAMING_SNAKE_CASE__			=      vocab_file
									SCREAMING_SNAKE_CASE__			=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(str(__A		)		)

									SCREAMING_SNAKE_CASE__			=      {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

									SCREAMING_SNAKE_CASE__			=      len(self.sp_model		) - 1
									SCREAMING_SNAKE_CASE__			=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :List[int]							,				__A    :Optional[List[int]] = None		)   ->   List[int]:



									"""simple docstring"""



									if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									SCREAMING_SNAKE_CASE__			=      [self.cls_token_id]
									SCREAMING_SNAKE_CASE__			=      [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep


							def      _snake_case  (							self    :Optional[Any]							,				__A    :List[int]							,				__A    :Optional[List[int]] = None							,				__A    :bool = False		)   ->   List[int]:



									"""simple docstring"""



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

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


							def      _snake_case  (							self    :Tuple							,				__A    :List[int]							,				__A    :Optional[List[int]] = None		)   ->   List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      [self.sep_token_id]
									SCREAMING_SNAKE_CASE__			=      [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]


							@property
							def      _snake_case  (							self    :List[str]		)   ->   Optional[int]:



									"""simple docstring"""



									return len(self.sp_model		)


							def      _snake_case  (							self    :Optional[Any]		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      {self.convert_ids_to_tokens(__A		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab


							def      _snake_case  (							self    :Dict							,				__A    :str		)   ->   List[str]:



									"""simple docstring"""



									return self.sp_model.encode(__A							,				out_type=__A		)


							def      _snake_case  (							self    :Dict							,				__A    :List[Any]		)   ->   Optional[Any]:



									"""simple docstring"""



									if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
									SCREAMING_SNAKE_CASE__			=      self.sp_model.PieceToId(__A		)

									return spm_id if spm_id else self.unk_token_id


							def      _snake_case  (							self    :str							,				__A    :Optional[Any]		)   ->   int:



									"""simple docstring"""



									if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(__A		)


							def      _snake_case  (							self    :List[str]							,				__A    :Union[str, Any]		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      []
									SCREAMING_SNAKE_CASE__			=      """"""
									SCREAMING_SNAKE_CASE__			=      False
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													if not prev_is_special:
															out_string += " "
													out_string += self.sp_model.decode(__A		) + token
													SCREAMING_SNAKE_CASE__			=      True
													SCREAMING_SNAKE_CASE__			=      []
											else:
													current_sub_tokens.append(__A		)
													SCREAMING_SNAKE_CASE__			=      False
									out_string += self.sp_model.decode(__A		)
									return out_string.strip()


							def __getstate__(							self    :List[str]		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.__dict__.copy()
									SCREAMING_SNAKE_CASE__			=      None
									return state


							def __setstate__(							self    :Optional[Any]							,				__A    :List[Any]		)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      d

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

									SCREAMING_SNAKE_CASE__			=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(self.vocab_file		)


							def      _snake_case  (							self    :Optional[int]							,				__A    :str							,				__A    :Optional[str] = None		)   ->   Tuple[str]:



									"""simple docstring"""



									if not os.path.isdir(__A		):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
											return
									SCREAMING_SNAKE_CASE__			=      os.path.join(
									    __A							,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(__A		) and os.path.isfile(self.vocab_file		):
											copyfile(self.vocab_file							,				__A		)
									elif not os.path.isfile(self.vocab_file		):
											with open(__A							,				"""wb"""		) as fi:
													SCREAMING_SNAKE_CASE__			=      self.sp_model.serialized_model_proto()
													fi.write(__A		)

									return (out_vocab_file,)






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase   				=			logging.get_logger(__name__)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      original_name.split("""."""    )[0]
		SCREAMING_SNAKE_CASE__			=      key.split("""."""    )
		SCREAMING_SNAKE_CASE__			=      int(key_list[key_list.index(UpperCamelCase__    ) - 2]    )
		SCREAMING_SNAKE_CASE__			=      int(key_list[key_list.index(UpperCamelCase__    ) - 1]    )
		SCREAMING_SNAKE_CASE__			=      orig_block_num - offset

		SCREAMING_SNAKE_CASE__			=      key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''					,   f'''block.{new_block_num}.{layer_num}.{new_name}'''    )
		return key






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int    ):
		SCREAMING_SNAKE_CASE__			=      OrderedDict()
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      0, 0
		for key, value in state_dict.items():
				if key.startswith("""network"""    ):
						SCREAMING_SNAKE_CASE__			=      key.replace("""network"""					,   """poolformer.encoder"""    )
				if "proj" in key:
						# Works for the first embedding as well as the internal embedding layers
						if key.endswith("""bias"""    ) and "patch_embed" not in key:
								patch_emb_offset += 1
						SCREAMING_SNAKE_CASE__			=      key[: key.find("""proj"""    )]
						SCREAMING_SNAKE_CASE__			=      key.replace(UpperCamelCase__					,   f'''patch_embeddings.{total_embed_found}.'''    )
						SCREAMING_SNAKE_CASE__			=      key.replace("""proj"""					,   """projection"""    )
						if key.endswith("""bias"""    ):
								total_embed_found += 1
				if "patch_embeddings" in key:
						SCREAMING_SNAKE_CASE__			=      """poolformer.encoder.""" + key
				if "mlp.fc1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """mlp.fc1"""					,   """output.conv1"""    )
				if "mlp.fc2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """mlp.fc2"""					,   """output.conv2"""    )
				if "norm1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """norm1"""					,   """before_norm"""    )
				if "norm2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """norm2"""					,   """after_norm"""    )
				if "layer_scale_1" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """layer_scale_1"""					,   """layer_scale_1"""    )
				if "layer_scale_2" in key:
						SCREAMING_SNAKE_CASE__			=      replace_key_with_offset(UpperCamelCase__					,   UpperCamelCase__					,   """layer_scale_2"""					,   """layer_scale_2"""    )
				if "head" in key:
						SCREAMING_SNAKE_CASE__			=      key.replace("""head"""					,   """classifier"""    )
				SCREAMING_SNAKE_CASE__			=      value
		return new_state_dict






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE__			=      Image.open(requests.get(UpperCamelCase__					,   stream=UpperCamelCase__    ).raw    )

		return image






@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      PoolFormerConfig()

		# set attributes based on model_name
		SCREAMING_SNAKE_CASE__			=      """huggingface/label-files"""
		SCREAMING_SNAKE_CASE__			=      model_name[-3:]
		SCREAMING_SNAKE_CASE__			=      1_000
		SCREAMING_SNAKE_CASE__			=      """imagenet-1k-id2label.json"""
		SCREAMING_SNAKE_CASE__			=      (1, 1_000)

		# set config attributes
		SCREAMING_SNAKE_CASE__			=      json.load(open(hf_hub_download(UpperCamelCase__					,   UpperCamelCase__					,   repo_type="""dataset"""    )					,   """r"""    )    )
		SCREAMING_SNAKE_CASE__			=      {int(UpperCamelCase__    ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE__			=      idalabel
		SCREAMING_SNAKE_CASE__			=      {v: k for k, v in idalabel.items()}
		if size == "s12":
				SCREAMING_SNAKE_CASE__			=      [2, 2, 6, 2]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "s24":
				SCREAMING_SNAKE_CASE__			=      [4, 4, 12, 4]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "s36":
				SCREAMING_SNAKE_CASE__			=      [6, 6, 18, 6]
				SCREAMING_SNAKE_CASE__			=      [64, 128, 320, 512]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      1e-6
				SCREAMING_SNAKE_CASE__			=      0.9
		elif size == "m36":
				SCREAMING_SNAKE_CASE__			=      [6, 6, 18, 6]
				SCREAMING_SNAKE_CASE__			=      [96, 192, 384, 768]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      1e-6
				SCREAMING_SNAKE_CASE__			=      0.9_5
		elif size == "m48":
				SCREAMING_SNAKE_CASE__			=      [8, 8, 24, 8]
				SCREAMING_SNAKE_CASE__			=      [96, 192, 384, 768]
				SCREAMING_SNAKE_CASE__			=      4.0
				SCREAMING_SNAKE_CASE__			=      1e-6
				SCREAMING_SNAKE_CASE__			=      0.9_5
		else:
				raise ValueError(f'''Size {size} not supported'''    )

		# load image processor
		SCREAMING_SNAKE_CASE__			=      PoolFormerImageProcessor(crop_pct=UpperCamelCase__    )

		# Prepare image
		SCREAMING_SNAKE_CASE__			=      prepare_img()
		SCREAMING_SNAKE_CASE__			=      image_processor(images=UpperCamelCase__					,   return_tensors="""pt"""    ).pixel_values

		logger.info(f'''Converting model {model_name}...'''    )

		# load original state dict
		SCREAMING_SNAKE_CASE__			=      torch.load(UpperCamelCase__					,   map_location=torch.device("""cpu"""    )    )

		# rename keys
		SCREAMING_SNAKE_CASE__			=      rename_keys(UpperCamelCase__    )

		# create HuggingFace model and load state dict
		SCREAMING_SNAKE_CASE__			=      PoolFormerForImageClassification(UpperCamelCase__    )
		model.load_state_dict(UpperCamelCase__    )
		model.eval()

		# Define image processor
		SCREAMING_SNAKE_CASE__			=      PoolFormerImageProcessor(crop_pct=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      image_processor(images=prepare_img()					,   return_tensors="""pt"""    ).pixel_values

		# forward pass
		SCREAMING_SNAKE_CASE__			=      model(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      outputs.logits

		# define expected logit slices for different models
		if size == "s12":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9]    )
		elif size == "s24":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5]    )
		elif size == "s36":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8]    )
		elif size == "m36":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8]    )
		elif size == "m48":
				SCREAMING_SNAKE_CASE__			=      torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3]    )
		else:
				raise ValueError(f'''Size {size} not supported'''    )

		# verify logits
		assert logits.shape == expected_shape
		assert torch.allclose(logits[0, :3]					,   UpperCamelCase__					,   atol=1e-2    )

		# finally, save model and image processor
		logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''    )
		Path(UpperCamelCase__    ).mkdir(exist_ok=UpperCamelCase__    )
		model.save_pretrained(UpperCamelCase__    )
		print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
		image_processor.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
	_lowerCamelCase   				=			argparse.ArgumentParser()

	parser.add_argument(
	    '--model_name',
	    default='poolformer_s12',
	    type=str,
	    help='Name of the model you\'d like to convert.',
	)
	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
	)
	_lowerCamelCase   				=			parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)