





import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _A       (   __magic_name__	):
   lowercase__			       =  SwinConfig(image_size=192	)

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

   lowercase__			       =  window_size
   lowercase__			       =  embed_dim
   lowercase__			       =  depths
   lowercase__			       =  num_heads

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

   if name == "encoder.norm.weight":
      lowercase__			       =  "layernorm.weight"
   if name == "encoder.norm.bias":
      lowercase__			       =  "layernorm.bias"

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

   return name
def _A       (   __magic_name__  , __magic_name__	):
   for key in orig_state_dict.copy().keys():
      lowercase__			       =  orig_state_dict.pop(__magic_name__	)

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

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

   return orig_state_dict
def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  torch.load(__magic_name__  , map_location="cpu"	)["model"]

   lowercase__			       =  get_swin_config(__magic_name__	)
   lowercase__			       =  SwinForMaskedImageModeling(__magic_name__	)
   model.eval()

   lowercase__			       =  convert_state_dict(__magic_name__  , __magic_name__	)
   model.load_state_dict(__magic_name__	)

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

   lowercase__			       =  ViTImageProcessor(size={"height": 192, "width": 192}	)
   lowercase__			       =  Image.open(requests.get(__magic_name__  , stream=__magic_name__	).raw	)
   lowercase__			       =  image_processor(images=__magic_name__  , return_tensors="pt"	)

   with torch.no_grad():
      lowercase__			       =  model(**__magic_name__	).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(__magic_name__	)

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

   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__":
  _snake_case           =			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."""
  )

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













import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
  import torch


if is_vision_available():
  from PIL import Image

  from transformers import MgpstrProcessor, ViTImageProcessor





@require_torch
@require_vision
class  lowerCAmelCase							(       unittest.TestCase ):
  __lowerCamelCase		=      ViTImageProcessor if is_vision_available() else None







  @property
  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     return self.image_processor_tester.prepare_image_processor_dict()







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  (3, 32, 1_28)
     lowercase__			       =  tempfile.mkdtemp()

     # fmt: off
     lowercase__			       =  ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
     # fmt: on
     lowercase__			       =  dict(zip(_lowercase   ,							range(len(_lowercase ) ) ) )

     lowercase__			       =  os.path.join(self.tmpdirname   ,							VOCAB_FILES_NAMES["vocab_file"] )
     with open(self.vocab_file   ,							"w"   ,							encoding="utf-8" ) as fp:
        fp.write(json.dumps(_lowercase ) + "\n" )

     lowercase__			       =  {
         "do_normalize": False,
         "do_resize": True,
         "image_processor_type": "ViTImageProcessor",
         "resample": 3,
         "size": {"height": 32, "width": 1_28},
     }
     lowercase__			       =  os.path.join(self.tmpdirname   ,							_lowercase )
     with open(self.image_processor_file   ,							"w"   ,							encoding="utf-8" ) as fp:
        json.dump(_lowercase   ,							_lowercase )







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							**_lowercase  :str ):






     '''simple docstring'''





     return MgpstrTokenizer.from_pretrained(self.tmpdirname   ,							**_lowercase )







  def   UpperCAmelCase					(      self  :List[Any]   ,							**_lowercase  :List[str] ):






     '''simple docstring'''





     return ViTImageProcessor.from_pretrained(self.tmpdirname   ,							**_lowercase )







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     shutil.rmtree(self.tmpdirname )







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  np.random.randint(2_55   ,							size=(3, 30, 4_00)   ,							dtype=np.uinta )

     lowercase__			       =  Image.fromarray(np.moveaxis(_lowercase   ,							0   ,							-1 ) )

     return image_input







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_tokenizer()
     lowercase__			       =  self.get_image_processor()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )
     processor.save_pretrained(self.tmpdirname )
     lowercase__			       =  MgpstrProcessor.from_pretrained(self.tmpdirname   ,							use_fast=_lowercase )

     self.assertEqual(processor.char_tokenizer.get_vocab()   ,							tokenizer.get_vocab() )
     self.assertIsInstance(processor.char_tokenizer   ,							_lowercase )

     self.assertEqual(processor.image_processor.to_json_string()   ,							image_processor.to_json_string() )
     self.assertIsInstance(processor.image_processor   ,							_lowercase )







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_tokenizer()
     lowercase__			       =  self.get_image_processor()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )
     processor.save_pretrained(self.tmpdirname )

     lowercase__			       =  self.get_tokenizer(bos_token="(BOS)"   ,							eos_token="(EOS)" )
     lowercase__			       =  self.get_image_processor(do_normalize=_lowercase   ,							padding_value=1.0 )

     lowercase__			       =  MgpstrProcessor.from_pretrained(
         self.tmpdirname   ,							bos_token="(BOS)"   ,							eos_token="(EOS)"   ,							do_normalize=_lowercase   ,							padding_value=1.0 )

     self.assertEqual(processor.char_tokenizer.get_vocab()   ,							tokenizer_add_kwargs.get_vocab() )
     self.assertIsInstance(processor.char_tokenizer   ,							_lowercase )

     self.assertEqual(processor.image_processor.to_json_string()   ,							image_processor_add_kwargs.to_json_string() )
     self.assertIsInstance(processor.image_processor   ,							_lowercase )







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  self.prepare_image_inputs()

     lowercase__			       =  image_processor(_lowercase   ,							return_tensors="np" )
     lowercase__			       =  processor(images=_lowercase   ,							return_tensors="np" )

     for key in input_image_proc.keys():
        self.assertAlmostEqual(input_image_proc[key].sum()   ,							input_processor[key].sum()   ,							delta=1e-2 )







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  "test"

     lowercase__			       =  processor(text=_lowercase )

     lowercase__			       =  tokenizer(_lowercase )
     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]   ,							encoded_processor[key] )







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  "test"
     lowercase__			       =  self.prepare_image_inputs()

     lowercase__			       =  processor(text=_lowercase   ,							images=_lowercase )

     self.assertListEqual(list(inputs.keys() )   ,							["pixel_values", "labels"] )

     # test if it raises when no input is passed
     with pytest.raises(_lowercase ):
        processor()







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

     lowercase__			       =  processor.char_decode(_lowercase )
     lowercase__			       =  tokenizer.batch_decode(_lowercase )
     lowercase__			       =  [seq.replace(" "   ,							"" ) for seq in decoded_tok]

     self.assertListEqual(_lowercase   ,							_lowercase )







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  None
     lowercase__			       =  self.prepare_image_inputs()

     lowercase__			       =  processor(text=_lowercase   ,							images=_lowercase )

     self.assertListEqual(list(inputs.keys() )   ,							processor.model_input_names )







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     lowercase__			       =  self.get_image_processor()
     lowercase__			       =  self.get_tokenizer()

     lowercase__			       =  MgpstrProcessor(tokenizer=_lowercase   ,							image_processor=_lowercase )

     lowercase__			       =  torch.randn(1   ,							27   ,							38 )
     lowercase__			       =  torch.randn(1   ,							27   ,							5_02_57 )
     lowercase__			       =  torch.randn(1   ,							27   ,							3_05_22 )

     lowercase__			       =  processor.batch_decode([char_input, bpe_input, wp_input] )

     self.assertListEqual(list(results.keys() )   ,							["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"] )






