



"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  List[Any] ):






				'''simple docstring'''



				_UpperCAmelCase							=			SwinConfig(image_size=192 )

				if "base" in model_name:
								_UpperCAmelCase							=			6
								_UpperCAmelCase							=			128
								_UpperCAmelCase							=			(2, 2, 18, 2)
								_UpperCAmelCase							=			(4, 8, 16, 32)
				elif "large" in model_name:
								_UpperCAmelCase							=			12
								_UpperCAmelCase							=			192
								_UpperCAmelCase							=			(2, 2, 18, 2)
								_UpperCAmelCase							=			(6, 12, 24, 48)
				else:
								raise ValueError('''Model not supported, only supports base and large variants''' )

				_UpperCAmelCase							=			window_size
				_UpperCAmelCase							=			embed_dim
				_UpperCAmelCase							=			depths
				_UpperCAmelCase							=			num_heads

				return config







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Optional[Any] ):






				'''simple docstring'''



				if "encoder.mask_token" in name:
								_UpperCAmelCase							=			name.replace('''encoder.mask_token'''    ,   '''embeddings.mask_token''' )
				if "encoder.patch_embed.proj" in name:
								_UpperCAmelCase							=			name.replace('''encoder.patch_embed.proj'''    ,   '''embeddings.patch_embeddings.projection''' )
				if "encoder.patch_embed.norm" in name:
								_UpperCAmelCase							=			name.replace('''encoder.patch_embed.norm'''    ,   '''embeddings.norm''' )
				if "attn.proj" in name:
								_UpperCAmelCase							=			name.replace('''attn.proj'''    ,   '''attention.output.dense''' )
				if "attn" in name:
								_UpperCAmelCase							=			name.replace('''attn'''    ,   '''attention.self''' )
				if "norm1" in name:
								_UpperCAmelCase							=			name.replace('''norm1'''    ,   '''layernorm_before''' )
				if "norm2" in name:
								_UpperCAmelCase							=			name.replace('''norm2'''    ,   '''layernorm_after''' )
				if "mlp.fc1" in name:
								_UpperCAmelCase							=			name.replace('''mlp.fc1'''    ,   '''intermediate.dense''' )
				if "mlp.fc2" in name:
								_UpperCAmelCase							=			name.replace('''mlp.fc2'''    ,   '''output.dense''' )

				if name == "encoder.norm.weight":
								_UpperCAmelCase							=			'layernorm.weight'
				if name == "encoder.norm.bias":
								_UpperCAmelCase							=			'layernorm.bias'

				if "decoder" in name:
								pass
				else:
								_UpperCAmelCase							=			'swin.' + name

				return name







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  List[str]    ,   _SCREAMING_SNAKE_CASE      :  str ):






				'''simple docstring'''



				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase							=			orig_state_dict.pop(__SCREAMING_SNAKE_CASE )

								if "attn_mask" in key:
												pass
								elif "qkv" in key:
												_UpperCAmelCase							=			key.split('''.''' )
												_UpperCAmelCase							=			int(key_split[2] )
												_UpperCAmelCase							=			int(key_split[4] )
												_UpperCAmelCase							=			model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

												if "weight" in key:
																_UpperCAmelCase							=			val[:dim, :]
																_UpperCAmelCase							=			val[
																    dim : dim * 2, :
																]
																_UpperCAmelCase							=			val[-dim:, :]
												else:
																_UpperCAmelCase							=			val[
																    :dim
																]
																_UpperCAmelCase							=			val[
																    dim : dim * 2
																]
																_UpperCAmelCase							=			val[
																    -dim:
																]
								else:
												_UpperCAmelCase							=			val

				return orig_state_dict







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Dict    ,   _SCREAMING_SNAKE_CASE      :  Optional[Any]    ,   _SCREAMING_SNAKE_CASE      :  Optional[Any]    ,   _SCREAMING_SNAKE_CASE      :  List[str] ):






				'''simple docstring'''



				_UpperCAmelCase							=			torch.load(__SCREAMING_SNAKE_CASE    ,   map_location='''cpu''' )['model']

				_UpperCAmelCase							=			get_swin_config(__SCREAMING_SNAKE_CASE )
				_UpperCAmelCase							=			SwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE )
				model.eval()

				_UpperCAmelCase							=			convert_state_dict(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE )
				model.load_state_dict(__SCREAMING_SNAKE_CASE )

				_UpperCAmelCase							=			'http://images.cocodataset.org/val2017/000000039769.jpg'

				_UpperCAmelCase							=			ViTImageProcessor(size={'''height''': 192, '''width''': 192} )
				_UpperCAmelCase							=			Image.open(requests.get(__SCREAMING_SNAKE_CASE    ,   stream=__SCREAMING_SNAKE_CASE ).raw )
				_UpperCAmelCase							=			image_processor(images=__SCREAMING_SNAKE_CASE    ,   return_tensors='''pt''' )

				with torch.no_grad():
								_UpperCAmelCase							=			model(**__SCREAMING_SNAKE_CASE ).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(__SCREAMING_SNAKE_CASE )

								print(f'Saving image processor to {pytorch_dump_folder_path}' )
								image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )

				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__":
					__A      :							Tuple   =      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."
					)

					__A      :							Any   =      parser.parse_args()
					convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




"""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:
       __SCREAMING_SNAKE_CASE    	=None


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE    	={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE    	={
    "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",
    },
}

__SCREAMING_SNAKE_CASE    	={
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
__SCREAMING_SNAKE_CASE    	=["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        UpperCamelCase		(			lowercase_  ):
   lowercase							=  VOCAB_FILES_NAMES
   lowercase							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=  PRETRAINED_VOCAB_FILES_MAP
   lowercase							=  ['input_ids', 'attention_mask']
   lowercase							=  MBartTokenizer

   lowercase							=  []
   lowercase							=  []
   def __init__(			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase="<s>"     ,__UpperCamelCase="</s>"     ,__UpperCamelCase="</s>"     ,__UpperCamelCase="<s>"     ,__UpperCamelCase="<unk>"     ,__UpperCamelCase="<pad>"     ,__UpperCamelCase="<mask>"     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase     ,)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				str													=	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     ,)

        lowercase_	:				str													=	vocab_file
        lowercase_	:				Optional[Any]													=	False if not self.vocab_file else True

        lowercase_	:				List[str]													=	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}			)
        lowercase_	:				List[Any]													=	{
            lang_code: self.convert_tokens_to_ids(__UpperCamelCase			) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowercase_	:				Dict													=	src_lang if src_lang is not None else 'en_XX'
        lowercase_	:				Union[str, Any]													=	self.convert_tokens_to_ids(self._src_lang			)
        lowercase_	:				int													=	tgt_lang
        self.set_src_lang_special_tokens(self._src_lang			)
   @property
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        return self._src_lang
   @src_lang.setter
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	new_src_lang
        self.set_src_lang_special_tokens(self._src_lang			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[int]:


        '''simple docstring'''


        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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	[self.sep_token_id]
        lowercase_	:				str													=	[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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
        lowercase_	:				Dict													=	src_lang
        lowercase_	:				List[Any]													=	self(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,return_tensors=__UpperCamelCase     ,**__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				Dict													=	tgt_lang_id
        return inputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = "en_XX"     ,__UpperCamelCase = None     ,__UpperCamelCase = "ro_RO"     ,**__UpperCamelCase     ,)	->      BatchEncoding:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	src_lang
        lowercase_	:				List[Any]													=	tgt_lang
        return super().prepare_seqaseq_batch(__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return self.set_src_lang_special_tokens(self.src_lang			)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        return self.set_tgt_lang_special_tokens(self.tgt_lang			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Any													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	[]
        lowercase_	:				int													=	[self.eos_token_id, self.cur_lang_code]

        lowercase_	:				Any													=	self.convert_ids_to_tokens(self.prefix_tokens			)
        lowercase_	:				int													=	self.convert_ids_to_tokens(self.suffix_tokens			)

        lowercase_	:				Union[str, Any]													=	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 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				str													=	[]
        lowercase_	:				Dict													=	[self.eos_token_id, self.cur_lang_code]

        lowercase_	:				str													=	self.convert_ids_to_tokens(self.prefix_tokens			)
        lowercase_	:				List[Any]													=	self.convert_ids_to_tokens(self.suffix_tokens			)

        lowercase_	:				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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      Tuple[str]:


        '''simple docstring'''


        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
        lowercase_	:				Tuple													=	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,)

