
import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 							a__					(							snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Any					=		SwinConfig(image_size=192							)

  if "base" in model_name:
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		6
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		128
    __SCREAMING_SNAKE_CASE      :	List[str]					=		(2, 2, 18, 2)
    __SCREAMING_SNAKE_CASE      :	Dict					=		(4, 8, 16, 32)
  elif "large" in model_name:
    __SCREAMING_SNAKE_CASE      :	str					=		12
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		192
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		(2, 2, 18, 2)
    __SCREAMING_SNAKE_CASE      :	List[str]					=		(6, 12, 24, 48)
  else:
    raise ValueError('''Model not supported, only supports base and large variants'''							)

  __SCREAMING_SNAKE_CASE      :	List[str]					=		window_size
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		embed_dim
  __SCREAMING_SNAKE_CASE      :	Dict					=		depths
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		num_heads

  return config


def 							a__					(							snake_case							):





  """simple docstring"""
  if "encoder.mask_token" in name:
    __SCREAMING_SNAKE_CASE      :	Any					=		name.replace('''encoder.mask_token'''       ,			'''embeddings.mask_token'''							)
  if "encoder.patch_embed.proj" in name:
    __SCREAMING_SNAKE_CASE      :	List[str]					=		name.replace('''encoder.patch_embed.proj'''       ,			'''embeddings.patch_embeddings.projection'''							)
  if "encoder.patch_embed.norm" in name:
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		name.replace('''encoder.patch_embed.norm'''       ,			'''embeddings.norm'''							)
  if "attn.proj" in name:
    __SCREAMING_SNAKE_CASE      :	Tuple					=		name.replace('''attn.proj'''       ,			'''attention.output.dense'''							)
  if "attn" in name:
    __SCREAMING_SNAKE_CASE      :	Dict					=		name.replace('''attn'''       ,			'''attention.self'''							)
  if "norm1" in name:
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		name.replace('''norm1'''       ,			'''layernorm_before'''							)
  if "norm2" in name:
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		name.replace('''norm2'''       ,			'''layernorm_after'''							)
  if "mlp.fc1" in name:
    __SCREAMING_SNAKE_CASE      :	Tuple					=		name.replace('''mlp.fc1'''       ,			'''intermediate.dense'''							)
  if "mlp.fc2" in name:
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		name.replace('''mlp.fc2'''       ,			'''output.dense'''							)

  if name == "encoder.norm.weight":
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		'''layernorm.weight'''
  if name == "encoder.norm.bias":
    __SCREAMING_SNAKE_CASE      :	List[str]					=		'''layernorm.bias'''

  if "decoder" in name:
    pass
  else:
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		'''swin.''' + name

  return name


def 							a__					(							snake_case       ,			snake_case							):





  """simple docstring"""
  for key in orig_state_dict.copy().keys():
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		orig_state_dict.pop(snake_case							)

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

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

  return orig_state_dict


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	str					=		torch.load(snake_case       ,			map_location='''cpu'''							)['''model''']

  __SCREAMING_SNAKE_CASE      :	int					=		get_swin_config(snake_case							)
  __SCREAMING_SNAKE_CASE      :	List[str]					=		SwinForMaskedImageModeling(snake_case							)
  model.eval()

  __SCREAMING_SNAKE_CASE      :	int					=		convert_state_dict(snake_case       ,			snake_case							)
  model.load_state_dict(snake_case							)

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

  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		ViTImageProcessor(size={'''height''': 192, '''width''': 192}							)
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		Image.open(requests.get(snake_case       ,			stream=snake_case							).raw							)
  __SCREAMING_SNAKE_CASE      :	Tuple					=		image_processor(images=snake_case       ,			return_tensors='''pt'''							)

  with torch.no_grad():
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		model(**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(snake_case							)

    print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
    image_processor.save_pretrained(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__":
   lowercase_ 				=			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."""
   )

   lowercase_ 				=			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
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizer
       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizerFast
       __UpperCamelCase					:      str    =						True
       __UpperCamelCase					:      Any    =						True






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        super().setUp()

        lowerCAmelCase__   :				Tuple 			=				[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        lowerCAmelCase__   :				Dict 			=				os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES["vocab_file"]   )
        with open(self.vocab_file			,					"w"			,					encoding="utf-8"   ) as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				"UNwant\u00E9d,running"
        lowerCAmelCase__   :				Union[str, Any] 			=				"unwanted, running"
        return input_text, output_text






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.tokenizer_class(self.vocab_file   )

        lowerCAmelCase__   :				List[Any] 			=				tokenizer.tokenize("UNwant\u00E9d,running"   )
        self.assertListEqual(snake_case			,					["un", "##want", "##ed", ",", "runn", "##ing"]   )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case   )			,					[7, 4, 5, 10, 8, 9]   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        if not self.test_rust_tokenizer:
         return

        lowerCAmelCase__   :				int 			=				self.get_tokenizer()
        lowerCAmelCase__   :				List[str] 			=				self.get_rust_tokenizer()

        lowerCAmelCase__   :				Optional[Any] 			=				"I was born in 92000, and this is falsé."

        lowerCAmelCase__   :				int 			=				tokenizer.tokenize(snake_case   )
        lowerCAmelCase__   :				int 			=				rust_tokenizer.tokenize(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				str 			=				tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        lowerCAmelCase__   :				List[str] 			=				rust_tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				Tuple 			=				self.get_rust_tokenizer()
        lowerCAmelCase__   :				Tuple 			=				tokenizer.encode(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				rust_tokenizer.encode(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )






