




'''simple docstring'''

import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				SwinConfig()
					snake_case_									=				swin_name.split('''_'''	)

					snake_case_									=				name_split[1]
					snake_case_									=				int(name_split[4]	)
					snake_case_									=				int(name_split[3][-1]	)

					if model_size == "tiny":
										snake_case_									=				96
										snake_case_									=				(2, 2, 6, 2)
										snake_case_									=				(3, 6, 12, 24)
					elif model_size == "small":
										snake_case_									=				96
										snake_case_									=				(2, 2, 18, 2)
										snake_case_									=				(3, 6, 12, 24)
					elif model_size == "base":
										snake_case_									=				128
										snake_case_									=				(2, 2, 18, 2)
										snake_case_									=				(4, 8, 16, 32)
					else:
										snake_case_									=				192
										snake_case_									=				(2, 2, 18, 2)
										snake_case_									=				(6, 12, 24, 48)

					if "in22k" in swin_name:
										snake_case_									=				2_1841
					else:
										snake_case_									=				1000
										snake_case_									=				'''huggingface/label-files'''
										snake_case_									=				'''imagenet-1k-id2label.json'''
										snake_case_									=				json.load(open(hf_hub_download(lowerCAmelCase__,      lowerCAmelCase__,      repo_type='''dataset'''	),      '''r'''	)	)
										snake_case_									=				{int(lowerCAmelCase__	): v for k, v in idalabel.items()}
										snake_case_									=				idalabel
										snake_case_									=				{v: k for k, v in idalabel.items()}

					snake_case_									=				img_size
					snake_case_									=				num_classes
					snake_case_									=				embed_dim
					snake_case_									=				depths
					snake_case_									=				num_heads
					snake_case_									=				window_size

					return config


def 		__magic_name__    (	__UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

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

					if name == "norm.weight":
										snake_case_									=				'''layernorm.weight'''
					if name == "norm.bias":
										snake_case_									=				'''layernorm.bias'''

					if "head" in name:
										snake_case_									=				name.replace('''head''',      '''classifier'''	)
					else:
										snake_case_									=				'''swin.''' + name

					return name


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					for key in orig_state_dict.copy().keys():
										snake_case_									=				orig_state_dict.pop(lowerCAmelCase__	)

										if "mask" in key:
															continue
										elif "qkv" in key:
															snake_case_									=				key.split('''.'''	)
															snake_case_									=				int(key_split[1]	)
															snake_case_									=				int(key_split[3]	)
															snake_case_									=				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

					return orig_state_dict


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				timm.create_model(lowerCAmelCase__,      pretrained=lowerCAmelCase__	)
					timm_model.eval()

					snake_case_									=				get_swin_config(lowerCAmelCase__	)
					snake_case_									=				SwinForImageClassification(lowerCAmelCase__	)
					model.eval()

					snake_case_									=				convert_state_dict(timm_model.state_dict(),      lowerCAmelCase__	)
					model.load_state_dict(lowerCAmelCase__	)

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

					snake_case_									=				AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_''',      '''-'''	)	)	)
					snake_case_									=				Image.open(requests.get(lowerCAmelCase__,      stream=lowerCAmelCase__	).raw	)
					snake_case_									=				image_processor(images=lowerCAmelCase__,      return_tensors='''pt'''	)

					snake_case_									=				timm_model(inputs['''pixel_values''']	)
					snake_case_									=				model(**lowerCAmelCase__	).logits

					assert torch.allclose(lowerCAmelCase__,      lowerCAmelCase__,      atol=1e-3	)

					print(F"Saving model {swin_name} to {pytorch_dump_folder_path}"	)
					model.save_pretrained(lowerCAmelCase__	)

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


if __name__ == "__main__":
							a  :				Union[str, Any]									=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--swin_name',
							    default='swin_tiny_patch4_window7_224',
							    type=str,
							    help='Name of the Swin timm model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)

							a  :				str									=  parser.parse_args()
							convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)

UpperCamelCase__    					=      '''▁'''

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

UpperCamelCase__    					=      {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

UpperCamelCase__    					=      {
    '''facebook/xglm-564M''': 2_0_4_8,
}




class 			lowerCamelCase_   (	__a      ):
      lowerCAmelCase__		     =     VOCAB_FILES_NAMES
      lowerCAmelCase__		     =     PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__		     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__		     =     ['input_ids', 'attention_mask']






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


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				{} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          UpperCAmelCase__			:		Optional[int]													=				7
          UpperCAmelCase__			:		Optional[int]													=				[f"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

          UpperCAmelCase__			:		Tuple													=				kwargs.get('''additional_special_tokens'''						,						[] )
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

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

          UpperCAmelCase__			:		Any													=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(str(_A ) )
          UpperCAmelCase__			:		List[str]													=				vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          UpperCAmelCase__			:		Any													=				1

          # Mimic fairseq token-to-id alignment for the first 4 token
          UpperCAmelCase__			:		Any													=				{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

          UpperCAmelCase__			:		int													=				len(self.sp_model )
          UpperCAmelCase__			:		Optional[int]													=				{f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
          self.fairseq_tokens_to_ids.update(_A )

          UpperCAmelCase__			:		Tuple													=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}






      def __getstate__(					self				:    Any ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.__dict__.copy()
          UpperCAmelCase__			:		Optional[int]													=				None
          UpperCAmelCase__			:		Optional[Any]													=				self.sp_model.serialized_model_proto()
          return state






      def __setstate__(					self				:    Union[str, Any]						,						_A				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[Any]													=				d

          # for backward compatibility
          if not hasattr(self						,						'''sp_model_kwargs''' ):
              UpperCAmelCase__			:		Dict													=				{}

          UpperCAmelCase__			:		str													=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto )






      def lowercase_							(					self				:    Any						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None ):


          '''simple docstring'''


          if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
          UpperCAmelCase__			:		Dict													=				[self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a






      def lowercase_							(					self				:    str						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None						,						_A				:    bool = False ):


          '''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 ))
          return [1] + ([0] * len(_A )) + [1, 1] + ([0] * len(_A ))






      def lowercase_							(					self				:    Any						,						_A				:    List[int]						,						_A				:    Optional[List[int]] = None ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				[self.sep_token_id]

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






      @property
      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words






      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				{self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    str ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    List[str]						,						_A				:    List[Any] ):


          '''simple docstring'''


          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          UpperCAmelCase__			:		Union[str, Any]													=				self.sp_model.PieceToId(_A )

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






      def lowercase_							(					self				:    List[Any]						,						_A				:    str ):


          '''simple docstring'''


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






      def lowercase_							(					self				:    int						,						_A				:    Optional[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				''''''.join(_A ).replace(_A						,						''' ''' ).strip()
          return out_string






      def lowercase_							(					self				:    Any						,						_A				:    str						,						_A				:    Optional[str] = None ):


          '''simple docstring'''


          if not os.path.isdir(_A ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
              return
          UpperCAmelCase__			:		List[str]													=				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:
                  UpperCAmelCase__			:		List[Any]													=				self.sp_model.serialized_model_proto()
                  fi.write(_A )

          return (out_vocab_file,)
