



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()
__A       =						logging.get_logger(__name__)



def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Any=False      ,lowerCAmelCase_       : List[Any]=False    )				->			Any:






      '''simple docstring'''



      UpperCAmelCase_=       'backbone.' if is_semantic else ''

      UpperCAmelCase_=       []
      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 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Dict=False      ,lowerCAmelCase_       : Tuple=False    )				->			int:






      '''simple docstring'''



      for i in range(config.num_hidden_layers    ):
            UpperCAmelCase_=       'backbone.' if is_semantic else ''
            # queries, keys and values
            UpperCAmelCase_=       state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""    )
            UpperCAmelCase_=       state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""    )
            UpperCAmelCase_=       state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""    )

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

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

            UpperCAmelCase_=       gamma_a
            UpperCAmelCase_=       gamma_a



def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Any    )				->			Dict:






      '''simple docstring'''



      UpperCAmelCase_=       dct.pop(lowerCAmelCase_    )
      UpperCAmelCase_=       val



def 	__a					(   )				->			Dict:






      '''simple docstring'''



      UpperCAmelCase_=       'http://images.cocodataset.org/val2017/000000039769.jpg'
      UpperCAmelCase_=       Image.open(requests.get(lowerCAmelCase_      ,stream=lowerCAmelCase_    ).raw    )
      return im



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : Optional[int]=False    )				->			Union[str, Any]:






      '''simple docstring'''



      UpperCAmelCase_=       False if 'rvlcdip' in checkpoint_url else True
      UpperCAmelCase_=       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:
            UpperCAmelCase_=       10_24
            UpperCAmelCase_=       40_96
            UpperCAmelCase_=       24
            UpperCAmelCase_=       16

      # labels
      if "rvlcdip" in checkpoint_url:
            UpperCAmelCase_=       16
            UpperCAmelCase_=       'huggingface/label-files'
            UpperCAmelCase_=       'rvlcdip-id2label.json'
            UpperCAmelCase_=       json.load(open(hf_hub_download(lowerCAmelCase_      ,lowerCAmelCase_      ,repo_type="""dataset"""    )      ,"""r"""    )    )
            UpperCAmelCase_=       {int(lowerCAmelCase_    ): v for k, v in idalabel.items()}
            UpperCAmelCase_=       idalabel
            UpperCAmelCase_=       {v: k for k, v in idalabel.items()}

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

      UpperCAmelCase_=       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
      UpperCAmelCase_=       BeitForMaskedImageModeling(lowerCAmelCase_    ) if has_lm_head else BeitForImageClassification(lowerCAmelCase_    )
      model.eval()
      model.load_state_dict(lowerCAmelCase_    )

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

      UpperCAmelCase_=       image_processor(images=lowerCAmelCase_      ,return_tensors="""pt"""    )
      UpperCAmelCase_=       encoding['pixel_values']

      UpperCAmelCase_=       model(lowerCAmelCase_    )
      UpperCAmelCase_=       outputs.logits

      # verify logits
      UpperCAmelCase_=       [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:
                  UpperCAmelCase_=       'dit-base' if 'base' in checkpoint_url else 'dit-large'
            else:
                  UpperCAmelCase_=       '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__":
 __A       =						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''',
 )
 __A       =						parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			List[Any]:
							UpperCAmelCase_=       tempfile.mkdtemp()

							UpperCAmelCase_=       BlipImageProcessor()
							UpperCAmelCase_=       GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""					)

							UpperCAmelCase_=       BlipaProcessor(__UpperCAmelCase						,  __UpperCAmelCase					)

							processor.save_pretrained(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  **__UpperCAmelCase     :						Union[str, Any]					)      ->			int:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).tokenizer




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  **__UpperCAmelCase     :						str					)      ->			Optional[int]:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).image_processor




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							shutil.rmtree(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Any:
							UpperCAmelCase_=       [np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta					)]

							UpperCAmelCase_=       [Image.fromarray(np.moveaxis(__UpperCAmelCase						,  0						,  -1					)					) for x in image_inputs]

							return image_inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       BlipaProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor()					)
							processor.save_pretrained(self.tmpdirname					)

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

							UpperCAmelCase_=       BlipaProcessor.from_pretrained(
							    self.tmpdirname						,  bos_token="""(BOS)"""						,  eos_token="""(EOS)"""						,  do_normalize=__UpperCAmelCase						,  padding_value=1.0					)

							self.assertEqual(processor.tokenizer.get_vocab()						,  tokenizer_add_kwargs.get_vocab()					)
							self.assertIsInstance(processor.tokenizer						,  __UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[int]:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       image_processor(__UpperCAmelCase						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processor(images=__UpperCAmelCase						,  return_tensors="""np"""					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""

							UpperCAmelCase_=       processor(text=__UpperCAmelCase					)

							UpperCAmelCase_=       tokenizer(__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase					)

							for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]						,  encoded_processor[key]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Dict:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""
							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

							UpperCAmelCase_=       processor.batch_decode(__UpperCAmelCase					)
							UpperCAmelCase_=       tokenizer.batch_decode(__UpperCAmelCase					)

							self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			int:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""
							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

							# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
							self.assertListEqual(list(inputs.keys()					)						,  ["""pixel_values""", """input_ids""", """attention_mask"""]					)




