






"""simple docstring"""
import os
import re
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''': '''spiece.model'''}

lowerCamelCase_					=      {
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

lowerCamelCase_					=      {
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				VOCAB_FILES_NAMES
	__magic_name__	      =				PRETRAINED_VOCAB_FILES_MAP
	__magic_name__	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__	      =				['''input_ids''', '''attention_mask''']
	__magic_name__	      =				[]





	def __init__(      self					: str	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Tuple="<unk>"	,	lowerCAmelCase_					: Union[str, Any]="<s>"	,	lowerCAmelCase_					: int="</s>"	,	lowerCAmelCase_					: List[Any]="<pad>"	,	lowerCAmelCase_					: Tuple="[SEP]"	,	lowerCAmelCase_					: int="[MASK]"	,	lowerCAmelCase_					: Optional[int]="[CLS]"	,	lowerCAmelCase_					: Optional[Dict[str, Any]] = None	,	**lowerCAmelCase_					: int	,	)						->			None:
				UpperCAmelCase_     : Tuple			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else bos_token
				UpperCAmelCase_     : Tuple			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else eos_token
				UpperCAmelCase_     : Optional[int]			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else unk_token
				UpperCAmelCase_     : List[str]			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else pad_token
				UpperCAmelCase_     : Any			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else cls_token
				UpperCAmelCase_     : int			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else sep_token

				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_     : Any			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else mask_token

				UpperCAmelCase_     : Tuple			     =					{} if sp_model_kwargs is None else sp_model_kwargs

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

				UpperCAmelCase_     : Any			     =					vocab_file

				UpperCAmelCase_     : Union[str, Any]			     =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.Load(lowerCAmelCase_						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Optional[int]:
				return self.sp_model.get_piece_size()





	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			Tuple:
				UpperCAmelCase_     : Any			     =					{self.convert_ids_to_tokens(lowerCAmelCase_						): i for i in range(self.vocab_size						)}
				vocab.update(self.added_tokens_encoder						)
				return vocab





	def __getstate__(      self					: int						)						->			List[str]:
				UpperCAmelCase_     : List[Any]			     =					self.__dict__.copy()
				UpperCAmelCase_     : List[Any]			     =					None
				return state





	def __setstate__(      self					: int	,	lowerCAmelCase_					: Optional[Any]						)						->			str:
				UpperCAmelCase_     : str			     =					d

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

				UpperCAmelCase_     : Optional[int]			     =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
				self.sp_model.Load(self.vocab_file						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: str						)						->			List[str]:
				return self.sp_model.encode(lowerCAmelCase_	,	out_type=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: Any						)						->			int:
				return self.sp_model.piece_to_id(lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: Any						)						->			Optional[Any]:
				UpperCAmelCase_     : List[str]			     =					self.sp_model.IdToPiece(lowerCAmelCase_						)
				return token





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: Dict						)						->			Union[str, Any]:
				UpperCAmelCase_     : Optional[int]			     =					[]
				UpperCAmelCase_     : List[str]			     =					""
				UpperCAmelCase_     : int			     =					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(lowerCAmelCase_						) + token
										UpperCAmelCase_     : Dict			     =					True
										UpperCAmelCase_     : str			     =					[]
							else:
										current_sub_tokens.append(lowerCAmelCase_						)
										UpperCAmelCase_     : Union[str, Any]			     =					False
				out_string += self.sp_model.decode(lowerCAmelCase_						)
				return out_string.strip()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: bool = False	,	lowerCAmelCase_					: bool = None	,	lowerCAmelCase_					: bool = True	,	**lowerCAmelCase_					: Optional[Any]	,	)						->			str:
				UpperCAmelCase_     : Any			     =					kwargs.pop("use_source_tokenizer"	,	lowerCAmelCase_						)

				UpperCAmelCase_     : int			     =					self.convert_ids_to_tokens(lowerCAmelCase_	,	skip_special_tokens=lowerCAmelCase_						)

				# To avoid mixing byte-level and unicode for byte-level BPT
				# we need to build string separately for added tokens and byte-level tokens
				# cf. https://github.com/huggingface/transformers/issues/1133
				UpperCAmelCase_     : int			     =					[]
				UpperCAmelCase_     : List[Any]			     =					[]
				for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
										continue
							if token in self.added_tokens_encoder:
										if current_sub_text:
													sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_						)						)
													UpperCAmelCase_     : Tuple			     =					[]
										sub_texts.append(lowerCAmelCase_						)
							else:
										current_sub_text.append(lowerCAmelCase_						)
				if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_						)						)

				# Mimic the behavior of the Rust tokenizer:
				# No space before [MASK] and [SEP]
				if spaces_between_special_tokens:
							UpperCAmelCase_     : List[str]			     =					re.sub(R" (\[(MASK|SEP)\])"	,	R"\1"	,	" ".join(lowerCAmelCase_						)						)
				else:
							UpperCAmelCase_     : Optional[Any]			     =					"".join(lowerCAmelCase_						)

				UpperCAmelCase_     : Dict			     =					(
				    clean_up_tokenization_spaces
				    if clean_up_tokenization_spaces is not None
				    else self.clean_up_tokenization_spaces
				)
				if clean_up_tokenization_spaces:
							UpperCAmelCase_     : str			     =					self.clean_up_tokenization(lowerCAmelCase_						)
							return clean_text
				else:
							return text





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Optional[str] = None						)						->			Tuple[str]:
				if not os.path.isdir(lowerCAmelCase_						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
				UpperCAmelCase_     : Any			     =					os.path.join(
				    lowerCAmelCase_	,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

				return (out_vocab_file,)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_     : Tuple			     =					[self.cls_token_id]
				UpperCAmelCase_     : Dict			     =					[self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None	,	lowerCAmelCase_					: bool = False						)						->			List[int]:
				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowerCAmelCase_	,	token_ids_a=lowerCAmelCase_	,	already_has_special_tokens=lowerCAmelCase_						)

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





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				UpperCAmelCase_     : List[str]			     =					[self.sep_token_id]
				UpperCAmelCase_     : Tuple			     =					[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						) * [0] + len(token_ids_a + sep						) * [1]











"""simple docstring"""
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Dict			     =					SwinConfig(image_size=1_92						)

			if "base" in model_name:
						UpperCAmelCase_     : Any			     =					6
						UpperCAmelCase_     : Optional[Any]			     =					1_28
						UpperCAmelCase_     : Optional[int]			     =					(2, 2, 18, 2)
						UpperCAmelCase_     : List[str]			     =					(4, 8, 16, 32)
			elif "large" in model_name:
						UpperCAmelCase_     : Dict			     =					12
						UpperCAmelCase_     : int			     =					1_92
						UpperCAmelCase_     : List[Any]			     =					(2, 2, 18, 2)
						UpperCAmelCase_     : int			     =					(6, 12, 24, 48)
			else:
						raise ValueError("Model not supported, only supports base and large variants"						)

			UpperCAmelCase_     : str			     =					window_size
			UpperCAmelCase_     : Any			     =					embed_dim
			UpperCAmelCase_     : int			     =					depths
			UpperCAmelCase_     : Any			     =					num_heads

			return config

def 				snake_case      (   A__						):
			if "encoder.mask_token" in name:
						UpperCAmelCase_     : str			     =					name.replace("encoder.mask_token"	,"embeddings.mask_token"						)
			if "encoder.patch_embed.proj" in name:
						UpperCAmelCase_     : Optional[int]			     =					name.replace("encoder.patch_embed.proj"	,"embeddings.patch_embeddings.projection"						)
			if "encoder.patch_embed.norm" in name:
						UpperCAmelCase_     : List[str]			     =					name.replace("encoder.patch_embed.norm"	,"embeddings.norm"						)
			if "attn.proj" in name:
						UpperCAmelCase_     : Optional[Any]			     =					name.replace("attn.proj"	,"attention.output.dense"						)
			if "attn" in name:
						UpperCAmelCase_     : Any			     =					name.replace("attn"	,"attention.self"						)
			if "norm1" in name:
						UpperCAmelCase_     : str			     =					name.replace("norm1"	,"layernorm_before"						)
			if "norm2" in name:
						UpperCAmelCase_     : Tuple			     =					name.replace("norm2"	,"layernorm_after"						)
			if "mlp.fc1" in name:
						UpperCAmelCase_     : List[str]			     =					name.replace("mlp.fc1"	,"intermediate.dense"						)
			if "mlp.fc2" in name:
						UpperCAmelCase_     : str			     =					name.replace("mlp.fc2"	,"output.dense"						)

			if name == "encoder.norm.weight":
						UpperCAmelCase_     : List[str]			     =					"layernorm.weight"
			if name == "encoder.norm.bias":
						UpperCAmelCase_     : int			     =					"layernorm.bias"

			if "decoder" in name:
						pass
			else:
						UpperCAmelCase_     : Any			     =					"swin." + name

			return name

def 				snake_case      (   A__	,A__						):
			for key in orig_state_dict.copy().keys():
						UpperCAmelCase_     : Tuple			     =					orig_state_dict.pop(A__						)

						if "attn_mask" in key:
									pass
						elif "qkv" in key:
									UpperCAmelCase_     : Optional[int]			     =					key.split("."						)
									UpperCAmelCase_     : str			     =					int(key_split[2]						)
									UpperCAmelCase_     : Union[str, Any]			     =					int(key_split[4]						)
									UpperCAmelCase_     : Optional[Any]			     =					model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												UpperCAmelCase_     : List[Any]			     =					val[:dim, :]
												UpperCAmelCase_     : str			     =					val[
												    dim : dim * 2, :
												]
												UpperCAmelCase_     : str			     =					val[-dim:, :]
									else:
												UpperCAmelCase_     : List[str]			     =					val[
												    :dim
												]
												UpperCAmelCase_     : str			     =					val[
												    dim : dim * 2
												]
												UpperCAmelCase_     : Optional[Any]			     =					val[
												    -dim:
												]
						else:
									UpperCAmelCase_     : Tuple			     =					val

			return orig_state_dict

def 				snake_case      (   A__	,A__	,A__	,A__						):
			UpperCAmelCase_     : List[Any]			     =					torch.load(A__	,map_location="cpu"						)["model"]

			UpperCAmelCase_     : Optional[Any]			     =					get_swin_config(A__						)
			UpperCAmelCase_     : List[Any]			     =					SwinForMaskedImageModeling(A__						)
			model.eval()

			UpperCAmelCase_     : str			     =					convert_state_dict(A__	,A__						)
			model.load_state_dict(A__						)

			UpperCAmelCase_     : int			     =					"http://images.cocodataset.org/val2017/000000039769.jpg"

			UpperCAmelCase_     : int			     =					ViTImageProcessor(size={"height": 1_92, "width": 1_92}						)
			UpperCAmelCase_     : Any			     =					Image.open(requests.get(A__	,stream=A__						).raw						)
			UpperCAmelCase_     : Any			     =					image_processor(images=A__	,return_tensors="pt"						)

			with torch.no_grad():
						UpperCAmelCase_     : List[Any]			     =					model(**A__						).logits

			print(outputs.keys()						)
			print("Looks ok!"						)

			if pytorch_dump_folder_path is not None:
						print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""						)
						model.save_pretrained(A__						)

						print(F"""Saving image processor to {pytorch_dump_folder_path}"""						)
						image_processor.save_pretrained(A__						)

			if push_to_hub:
						print(F"""Pushing model and image processor for {model_name} to hub"""						)
						model.push_to_hub(F"""microsoft/{model_name}"""						)
						image_processor.push_to_hub(F"""microsoft/{model_name}"""						)


if __name__ == "__main__":
			lowerCamelCase_					=      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''swin-base-simmim-window6-192''',
			    type=str,
			    choices=['''swin-base-simmim-window6-192''', '''swin-large-simmim-window12-192'''],
			    help='''Name of the Swin SimMIM model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--checkpoint_path''',
			    default='''/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth''',
			    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 output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			lowerCamelCase_					=      parser.parse_args()
			convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



