





'''simple docstring'''




import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


_SCREAMING_SNAKE_CASE							     =			"hf-internal-testing/tiny-random-bert"
_SCREAMING_SNAKE_CASE							     =			os.path.join(TRANSFORMERS_CACHE, "models--hf-internal-testing--tiny-random-bert")
_SCREAMING_SNAKE_CASE							     =			"9b8c223d42b2188cb49d29af482996f9d0f3e5a6"
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Optional[Any]:
        snake_case 						=   cached_file(__snake_case   ,       __snake_case						)
        # Should have downloaded the file in here
        self.assertTrue(os.path.isdir(__snake_case						)						)
        # Cache should contain at least those three subfolders:
        for subfolder in ["blobs", "refs", "snapshots"]:
           self.assertTrue(os.path.isdir(os.path.join(__snake_case   ,       __snake_case						)						)						)
        with open(os.path.join(__snake_case   ,       """refs"""   ,       """main"""						)						) as f:
           snake_case 						=   f.read()
        self.assertEqual(__snake_case   ,       os.path.join(__snake_case   ,       """snapshots"""   ,       __snake_case   ,       __snake_case						)						)
        self.assertTrue(os.path.isfile(__snake_case						)						)

        # File is cached at the same place the second time.
        snake_case 						=   cached_file(__snake_case   ,       __snake_case						)
        self.assertEqual(__snake_case   ,       __snake_case						)

        # Using a specific revision to test the full commit hash.
        snake_case 						=   cached_file(__snake_case   ,       __snake_case   ,       revision="""9b8c223"""						)
        self.assertEqual(__snake_case   ,       os.path.join(__snake_case   ,       """snapshots"""   ,       __snake_case   ,       __snake_case						)						)



     def      lowerCAmelCase			(   self							:			Dict						)->	Optional[int]:
        with self.assertRaisesRegex(__snake_case   ,       """is not a valid model identifier"""						):
           snake_case 						=   cached_file("""tiny-random-bert"""   ,       __snake_case						)

        with self.assertRaisesRegex(__snake_case   ,       """is not a valid git identifier"""						):
           snake_case 						=   cached_file(__snake_case   ,       __snake_case   ,       revision="""aaaa"""						)

        with self.assertRaisesRegex(__snake_case   ,       """does not appear to have a file named"""						):
           snake_case 						=   cached_file(__snake_case   ,       """conf"""						)



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Any:
        with self.assertRaisesRegex(__snake_case   ,       """does not appear to have a file named"""						):
           snake_case 						=   cached_file(__snake_case   ,       """conf"""						)

        with open(os.path.join(__snake_case   ,       """refs"""   ,       """main"""						)						) as f:
           snake_case 						=   f.read()
        self.assertTrue(os.path.isfile(os.path.join(__snake_case   ,       """.no_exist"""   ,       __snake_case   ,       """conf"""						)						)						)

        snake_case 						=   cached_file(__snake_case   ,       """conf"""   ,       _raise_exceptions_for_missing_entries=__snake_case						)
        self.assertIsNone(__snake_case						)

        snake_case 						=   cached_file(__snake_case   ,       """conf"""   ,       local_files_only=__snake_case   ,       _raise_exceptions_for_missing_entries=__snake_case						)
        self.assertIsNone(__snake_case						)

        snake_case 						=   mock.Mock()
        snake_case 						=   5_00
        snake_case 						=   {}
        snake_case 						=   HTTPError
        snake_case 						=   {}

        # Under the mock environment we get a 500 error when trying to reach the tokenizer.
        with mock.patch("""requests.Session.request"""   ,       return_value=__snake_case						) as mock_head:
           snake_case 						=   cached_file(__snake_case   ,       """conf"""   ,       _raise_exceptions_for_connection_errors=__snake_case						)
           self.assertIsNone(__snake_case						)
           # This check we did call the fake head request
           mock_head.assert_called()



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	List[Any]:
        self.assertTrue(has_file("""hf-internal-testing/tiny-bert-pt-only"""   ,       __snake_case						)						)
        self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only"""   ,       __snake_case						)						)
        self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only"""   ,       __snake_case						)						)



     def      lowerCAmelCase			(   self							:			Tuple						)->	Optional[int]:
        # `get_file_from_repo` returns None if the file does not exist
        self.assertIsNone(get_file_from_repo("""bert-base-cased"""   ,       """ahah.txt"""						)						)

        # The function raises if the repository does not exist.
        with self.assertRaisesRegex(__snake_case   ,       """is not a valid model identifier"""						):
           get_file_from_repo("""bert-base-case"""   ,       __snake_case						)

        # The function raises if the revision does not exist.
        with self.assertRaisesRegex(__snake_case   ,       """is not a valid git identifier"""						):
           get_file_from_repo("""bert-base-cased"""   ,       __snake_case   ,       revision="""ahaha"""						)

        snake_case 						=   get_file_from_repo("""bert-base-cased"""   ,       __snake_case						)
        # The name is the cached name which is not very easy to test, so instead we load the content.
        snake_case 						=   json.loads(open(__snake_case   ,       """r"""						).read()						)
        self.assertEqual(config["""hidden_size"""]   ,       7_68						)



     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	int:
        with tempfile.TemporaryDirectory() as tmp_dir:
           snake_case 						=   Path(__snake_case						) / """a.txt"""
           filename.touch()
           self.assertEqual(get_file_from_repo(__snake_case   ,       """a.txt"""						)   ,       str(__snake_case						)						)

           self.assertIsNone(get_file_from_repo(__snake_case   ,       """b.txt"""						)						)








'''simple docstring'''




import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Tuple=False      ,    __lowerCAmelCase       :							Any=False		)		->  Optional[Any]:
   snake_case 						=   """backbone.""" if is_semantic else """"""

   snake_case 						=   []
   for i in range(config.num_hidden_layers		):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')		)
      rename_keys.append(
          (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')		)
      rename_keys.append(
          (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')		)
      rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')		)

   # projection layer + position embeddings
   rename_keys.extend(
       [
           (F'''{prefix}cls_token''', """beit.embeddings.cls_token"""),
           (F'''{prefix}patch_embed.proj.weight''', """beit.embeddings.patch_embeddings.projection.weight"""),
           (F'''{prefix}patch_embed.proj.bias''', """beit.embeddings.patch_embeddings.projection.bias"""),
           (F'''{prefix}pos_embed''', """beit.embeddings.position_embeddings"""),
       ]		)

   if has_lm_head:
      # mask token + layernorm
      rename_keys.extend(
          [
              ("""mask_token""", """beit.embeddings.mask_token"""),
              ("""norm.weight""", """layernorm.weight"""),
              ("""norm.bias""", """layernorm.bias"""),
          ]		)
   else:
      # layernorm + classification head
      rename_keys.extend(
          [
              ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
              ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
              ("""head.weight""", """classifier.weight"""),
              ("""head.bias""", """classifier.bias"""),
          ]		)

   return rename_keys





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							str=False      ,    __lowerCAmelCase       :							List[Any]=False		)		->  Optional[int]:
   for i in range(config.num_hidden_layers		):
      snake_case 						=   """backbone.""" if is_semantic else """"""
      # queries, keys and values
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

      snake_case 						=   in_proj_weight[
          : config.hidden_size, :
      ]
      snake_case 						=   q_bias
      snake_case 						=   in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      snake_case 						=   in_proj_weight[
          -config.hidden_size :, :
      ]
      snake_case 						=   v_bias

      # gamma_1 and gamma_2
      # we call them lambda because otherwise they are renamed when using .from_pretrained
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

      snake_case 						=   gamma_a
      snake_case 						=   gamma_a





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Tuple		)		->  Any:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      )		->  Dict:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Any=False		)		->  str:
   snake_case 						=   False if """rvlcdip""" in checkpoint_url else True
   snake_case 						=   BeitConfig(use_absolute_position_embeddings=__lowerCAmelCase      ,    use_mask_token=__lowerCAmelCase		)

   # size of the architecture
   if "large" in checkpoint_url or "dit-l" in checkpoint_url:
      snake_case 						=   10_24
      snake_case 						=   40_96
      snake_case 						=   24
      snake_case 						=   16

   # labels
   if "rvlcdip" in checkpoint_url:
      snake_case 						=   16
      snake_case 						=   """huggingface/label-files"""
      snake_case 						=   """rvlcdip-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()}

   # load state_dict of original model, remove and rename some keys
   snake_case 						=   torch.hub.load_state_dict_from_url(__lowerCAmelCase      ,    map_location="""cpu"""		)["""model"""]

   snake_case 						=   create_rename_keys(__lowerCAmelCase      ,    has_lm_head=__lowerCAmelCase		)
   for src, dest in rename_keys:
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   read_in_q_k_v(__lowerCAmelCase      ,    __lowerCAmelCase      ,    has_lm_head=__lowerCAmelCase		)

   # load HuggingFace model
   snake_case 						=   BeitForMaskedImageModeling(__lowerCAmelCase		) if has_lm_head else BeitForImageClassification(__lowerCAmelCase		)
   model.eval()
   model.load_state_dict(__lowerCAmelCase		)

   # Check outputs on an image
   snake_case 						=   BeitImageProcessor(
       size=config.image_size      ,    resample=PILImageResampling.BILINEAR      ,    do_center_crop=__lowerCAmelCase		)
   snake_case 						=   prepare_img()

   snake_case 						=   image_processor(images=__lowerCAmelCase      ,    return_tensors="""pt"""		)
   snake_case 						=   encoding["""pixel_values"""]

   snake_case 						=   model(__lowerCAmelCase		)
   snake_case 						=   outputs.logits

   # verify logits
   snake_case 						=   [1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
   assert logits.shape == torch.Size(__lowerCAmelCase		), "Shape of logits not as expected"

   Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
   print(F'''Saving model 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 push_to_hub:
      if has_lm_head:
         snake_case 						=   """dit-base""" if """base""" in checkpoint_url else """dit-large"""
      else:
         snake_case 						=   """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
      image_processor.push_to_hub(
          repo_path_or_name=Path(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    organization="""nielsr"""      ,    commit_message="""Add image processor"""      ,    use_temp_dir=__lowerCAmelCase      ,    )
      model.push_to_hub(
          repo_path_or_name=Path(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    organization="""nielsr"""      ,    commit_message="""Add model"""      ,    use_temp_dir=__lowerCAmelCase      ,    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_url",
     default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
     type=str,
     help="URL to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
 )
 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

