
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()
lowerCamelCase  =logging.get_logger(__name__)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__=False			,		UpperCamelCase__=False		):
      UpperCamelCase__     : Union[str, Any]											=					'''backbone.''' if is_semantic else ''''''

      UpperCamelCase__     : Tuple											=					[]
      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 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__=False			,		UpperCamelCase__=False		):
      for i in range(config.num_hidden_layers		):
            UpperCamelCase__     : List[Any]											=					'''backbone.''' if is_semantic else ''''''
            # queries, keys and values
            UpperCamelCase__     : int											=					state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''		)
            UpperCamelCase__     : Any											=					state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''		)
            UpperCamelCase__     : Any											=					state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''		)

            UpperCamelCase__     : Any											=					in_proj_weight[
                : config.hidden_size, :
            ]
            UpperCamelCase__     : Union[str, Any]											=					q_bias
            UpperCamelCase__     : List[str]											=					in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            UpperCamelCase__     : Optional[int]											=					in_proj_weight[
                -config.hidden_size :, :
            ]
            UpperCamelCase__     : Dict											=					v_bias

            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            UpperCamelCase__     : List[Any]											=					state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''		)
            UpperCamelCase__     : Optional[int]											=					state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''		)

            UpperCamelCase__     : Optional[int]											=					gamma_a
            UpperCamelCase__     : int											=					gamma_a


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : Optional[Any]											=					dct.pop(UpperCamelCase__		)
      UpperCamelCase__     : Tuple											=					val


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : Union[str, Any]											=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCamelCase__     : Any											=					Image.open(requests.get(UpperCamelCase__			,		stream=UpperCamelCase__		).raw		)
      return im


@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__=False		):
      UpperCamelCase__     : Dict											=					False if '''rvlcdip''' in checkpoint_url else True
      UpperCamelCase__     : Optional[int]											=					BeitConfig(use_absolute_position_embeddings=UpperCamelCase__			,		use_mask_token=UpperCamelCase__		)

      # size of the architecture
      if "large" in checkpoint_url or "dit-l" in checkpoint_url:
            UpperCamelCase__     : Tuple											=					1_0_2_4
            UpperCamelCase__     : Dict											=					4_0_9_6
            UpperCamelCase__     : List[str]											=					2_4
            UpperCamelCase__     : Union[str, Any]											=					1_6

      # labels
      if "rvlcdip" in checkpoint_url:
            UpperCamelCase__     : Union[str, Any]											=					1_6
            UpperCamelCase__     : Union[str, Any]											=					'''huggingface/label-files'''
            UpperCamelCase__     : Optional[Any]											=					'''rvlcdip-id2label.json'''
            UpperCamelCase__     : Optional[int]											=					json.load(open(hf_hub_download(UpperCamelCase__			,		UpperCamelCase__			,		repo_type='''dataset'''		)			,		'''r'''		)		)
            UpperCamelCase__     : Union[str, Any]											=					{int(UpperCamelCase__		): v for k, v in idalabel.items()}
            UpperCamelCase__     : Tuple											=					idalabel
            UpperCamelCase__     : str											=					{v: k for k, v in idalabel.items()}

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

      UpperCamelCase__     : str											=					create_rename_keys(UpperCamelCase__			,		has_lm_head=UpperCamelCase__		)
      for src, dest in rename_keys:
            rename_key(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)
      read_in_q_k_v(UpperCamelCase__			,		UpperCamelCase__			,		has_lm_head=UpperCamelCase__		)

      # load HuggingFace model
      UpperCamelCase__     : Optional[int]											=					BeitForMaskedImageModeling(UpperCamelCase__		) if has_lm_head else BeitForImageClassification(UpperCamelCase__		)
      model.eval()
      model.load_state_dict(UpperCamelCase__		)

      # Check outputs on an image
      UpperCamelCase__     : Optional[int]											=					BeitImageProcessor(
          size=config.image_size			,		resample=PILImageResampling.BILINEAR			,		do_center_crop=UpperCamelCase__		)
      UpperCamelCase__     : Any											=					prepare_img()

      UpperCamelCase__     : List[str]											=					image_processor(images=UpperCamelCase__			,		return_tensors='''pt'''		)
      UpperCamelCase__     : Union[str, Any]											=					encoding['''pixel_values''']

      UpperCamelCase__     : Any											=					model(UpperCamelCase__		)
      UpperCamelCase__     : List[Any]											=					outputs.logits

      # verify logits
      UpperCamelCase__     : List[str]											=					[1, 1_6] if '''rvlcdip''' in checkpoint_url else [1, 1_9_6, 8_1_9_2]
      assert logits.shape == torch.Size(UpperCamelCase__		), "Shape of logits not as expected"

      Path(UpperCamelCase__		).mkdir(exist_ok=UpperCamelCase__		)
      print(f'''Saving model to {pytorch_dump_folder_path}'''		)
      model.save_pretrained(UpperCamelCase__		)
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
      image_processor.save_pretrained(UpperCamelCase__		)

      if push_to_hub:
            if has_lm_head:
                  UpperCamelCase__     : str											=					'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
            else:
                  UpperCamelCase__     : List[str]											=					'''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(UpperCamelCase__			,		UpperCamelCase__		)			,		organization='''nielsr'''			,		commit_message='''Add image processor'''			,		use_temp_dir=UpperCamelCase__			,		)
            model.push_to_hub(
                repo_path_or_name=Path(UpperCamelCase__			,		UpperCamelCase__		)			,		organization='''nielsr'''			,		commit_message='''Add model'''			,		use_temp_dir=UpperCamelCase__			,		)


if __name__ == "__main__":
    lowerCamelCase  =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",
    )
    lowerCamelCase  =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 import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					'''ZinengTang/tvlt-base'''
           UpperCamelCase__     : Optional[Any]											=					tempfile.mkdtemp()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           return TvltImageProcessor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)
           processor.save_pretrained(self.tmpdirname	)
           UpperCamelCase__     : Optional[Any]											=					TvltProcessor.from_pretrained(self.tmpdirname	)

           self.assertIsInstance(processor.feature_extractor    ,    __SCREAMING_SNAKE_CASE	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.get_image_processor()
           UpperCamelCase__     : int											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : str											=					np.ones([1_2_0_0_0]	)

           UpperCamelCase__     : Dict											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Dict											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : str											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : Optional[int]											=					image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Tuple											=					processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[Any]											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Dict											=					np.ones([1_2_0_0_0]	)
           UpperCamelCase__     : str											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : List[Any]											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']	)

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






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.get_image_processor()
           UpperCamelCase__     : str											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(
               processor.model_input_names    ,    image_processor.model_input_names + feature_extractor.model_input_names    ,    msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''    ,    )


