






"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple

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


lowerCamelCase_					=      logging.get_logger(__name__)







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





	def __init__(      self					: List[Any]	,	lowerCAmelCase_					: Optional[Any]="</s>"	,	lowerCAmelCase_					: Union[str, Any]="<unk>"	,	lowerCAmelCase_					: List[Any]="<pad>"	,	lowerCAmelCase_					: Optional[Any]=125	,	lowerCAmelCase_					: Optional[int]=None	,	**lowerCAmelCase_					: Any	,	)						->			None:
				# Add extra_ids to the special token list
				if extra_ids > 0 and additional_special_tokens is None:
							UpperCAmelCase_     : List[Any]			     =					[f"""<extra_id_{i}>""" for i in range(lowerCAmelCase_						)]
				elif extra_ids > 0 and additional_special_tokens is not None:
							# Check that we have the right number of extra_id special tokens
							UpperCAmelCase_     : Dict			     =					len(set(filter(lambda lowerCAmelCase_						: bool("extra_id" in str(lowerCAmelCase_						)						)	,	lowerCAmelCase_						)						)						)
							if extra_tokens != extra_ids:
										raise ValueError(
										    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
										    " provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
										    " extra_ids tokens"						)

				UpperCAmelCase_     : Tuple			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else pad_token
				UpperCAmelCase_     : str			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else eos_token
				UpperCAmelCase_     : List[str]			     =					AddedToken(lowerCAmelCase_	,	lstrip=lowerCAmelCase_	,	rstrip=lowerCAmelCase_						) if isinstance(lowerCAmelCase_	,	lowerCAmelCase_						) else unk_token

				super().__init__(
				    eos_token=lowerCAmelCase_	,	unk_token=lowerCAmelCase_	,	pad_token=lowerCAmelCase_	,	extra_ids=lowerCAmelCase_	,	additional_special_tokens=lowerCAmelCase_	,	**lowerCAmelCase_	,	)

				UpperCAmelCase_     : Any			     =					extra_ids

				UpperCAmelCase_     : int			     =					2**8  # utf is 8 bits

				# define special tokens dict
				UpperCAmelCase_     : Dict[int, str]			     =					{
				    self.pad_token: 0,
				    self.eos_token: 1,
				    self.unk_token: 2,
				}
				UpperCAmelCase_     : Optional[int]			     =					len(self.special_tokens_encoder						)
				UpperCAmelCase_     : str			     =					len(lowerCAmelCase_						)
				for i, token in enumerate(lowerCAmelCase_						):
							UpperCAmelCase_     : Union[str, Any]			     =					self.vocab_size + i - n
				UpperCAmelCase_     : Dict[str, int]			     =					{v: k for k, v in self.special_tokens_encoder.items()}





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Union[str, Any]:
				return self._utf_vocab_size + self._num_special_tokens + self._extra_ids





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	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_						)

				# normal case: some special tokens
				if token_ids_a is None:
							return ([0] * len(lowerCAmelCase_						)) + [1]
				return ([0] * len(lowerCAmelCase_						)) + [1] + ([0] * len(lowerCAmelCase_						)) + [1]





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: List[int]						)						->			List[int]:
				if len(lowerCAmelCase_						) > 0 and token_ids[-1] == self.eos_token_id:
							warnings.warn(
							    f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
							    " eos tokens being added."						)
							return token_ids
				else:
							return token_ids + [self.eos_token_id]





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				UpperCAmelCase_     : int			     =					[self.eos_token_id]

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





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: List[int]	,	lowerCAmelCase_					: Optional[List[int]] = None						)						->			List[int]:
				UpperCAmelCase_     : Dict			     =					self._add_eos_if_not_present(lowerCAmelCase_						)
				if token_ids_a is None:
							return token_ids_a
				else:
							UpperCAmelCase_     : List[str]			     =					self._add_eos_if_not_present(lowerCAmelCase_						)
							return token_ids_a + token_ids_a





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: str						)						->			List[str]:
				UpperCAmelCase_     : Tuple			     =					[chr(lowerCAmelCase_						) for i in text.encode("utf-8"						)]
				return tokens





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: Dict						)						->			int:
				if token in self.special_tokens_encoder:
							UpperCAmelCase_     : Optional[Any]			     =					self.special_tokens_encoder[token]
				elif token in self.added_tokens_encoder:
							UpperCAmelCase_     : str			     =					self.added_tokens_encoder[token]
				elif len(lowerCAmelCase_						) != 1:
							UpperCAmelCase_     : int			     =					self.unk_token_id
				else:
							UpperCAmelCase_     : List[Any]			     =					ord(lowerCAmelCase_						) + self._num_special_tokens
				return token_id





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: List[str]						)						->			Tuple:
				if index in self.special_tokens_decoder:
							UpperCAmelCase_     : List[str]			     =					self.special_tokens_decoder[index]
				else:
							UpperCAmelCase_     : Optional[Any]			     =					chr(index - self._num_special_tokens						)
				return token





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: Optional[int]						)						->			Dict:
				UpperCAmelCase_     : Union[str, Any]			     =					b""
				for token in tokens:
							if token in self.special_tokens_decoder:
										UpperCAmelCase_     : Tuple			     =					self.special_tokens_decoder[token].encode("utf-8"						)
							elif token in self.added_tokens_decoder:
										UpperCAmelCase_     : Optional[Any]			     =					self.special_tokens_decoder[token].encode("utf-8"						)
							elif token in self.special_tokens_encoder:
										UpperCAmelCase_     : str			     =					token.encode("utf-8"						)
							elif token in self.added_tokens_encoder:
										UpperCAmelCase_     : List[str]			     =					token.encode("utf-8"						)
							else:
										UpperCAmelCase_     : Tuple			     =					bytes([ord(lowerCAmelCase_						)]						)
							bstring += tok_string
				UpperCAmelCase_     : Optional[int]			     =					bstring.decode("utf-8"	,	errors="ignore"						)
				return string





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Optional[str] = None						)						->			Tuple[str]:
				return ()











"""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)



