






'''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()
lowerCamelCase_				: Tuple    	=							logging.get_logger(__name__)







def 	__magic_name__( _A						,      _A=False						,      _A=False					):
      '''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 	__magic_name__( _A						,      _A						,      _A=False						,      _A=False					):
      '''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 	__magic_name__( _A						,      _A						,      _A					):
      '''simple docstring'''





      UpperCamelCase__         = dct.pop(__lowerCAmelCase					)
      UpperCamelCase__         = val







def 	__magic_name__( ):
      '''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 	__magic_name__( _A						,      _A						,      _A=False					):
      '''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__         = 1024
            UpperCamelCase__         = 4096
            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, 196, 8192]
      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__":
     lowerCamelCase_				: Dict    	=							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_				: Optional[int]    	=							parser.parse_args()
     convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''


from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class    _SCREAMING_SNAKE_CASE							(		yaml.SafeLoader	):


			'''simple docstring'''





			def 				A      (   self							:						List[str] ,      lowercase							:						List[Any]			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = [self.constructed_objects[key_node] for key_node, _ in node.value]
									UpperCamelCase__         = [tuple(lowercase			) if isinstance(lowercase ,      lowercase			) else key for key in keys]
									UpperCamelCase__         = Counter(lowercase			)
									UpperCamelCase__         = [key for key in counter if counter[key] > 1]
									if duplicate_keys:
															raise TypeError(f"Got duplicate yaml keys: {duplicate_keys}"			)







			def 				A      (   self							:						List[str] ,      lowercase							:						int ,      lowercase							:						str=False			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = super().construct_mapping(lowercase ,      deep=lowercase			)
									self._check_no_duplicates_on_constructed_node(lowercase			)
									return mapping







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = list(readme_content.splitlines()					)
						if full_content and full_content[0] == "---" and "---" in full_content[1:]:
												UpperCamelCase__         = full_content[1:].index("""---"""					) + 1
												UpperCamelCase__         = """\n""".join(full_content[1:sep_idx]					)
												return yamlblock, "\n".join(full_content[sep_idx + 1 :]					)

						return None, "\n".join(_A					)
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Tuple					     =					{"train_eval_index"}  # train-eval-index in the YAML metadata






			@classmethod
			def 				A      (   cls							:						int ,      lowercase							:						Path			)							->    "DatasetMetadata":

									'''simple docstring'''







									with open(lowercase ,      encoding="""utf-8"""			) as readme_file:
															UpperCamelCase__    ,   UpperCamelCase__         = _split_yaml_from_readme(readme_file.read()			)
									if yaml_string is not None:
															return cls.from_yaml_string(lowercase			)
									else:
															return cls()






			def 				A      (   self							:						int ,      lowercase							:						Path			)							->    Dict:

									'''simple docstring'''







									if path.exists():
															with open(lowercase ,      encoding="""utf-8"""			) as readme_file:
																					UpperCamelCase__         = readme_file.read()
									else:
															UpperCamelCase__         = None
									UpperCamelCase__         = self._to_readme(lowercase			)
									with open(lowercase ,      """w""" ,      encoding="""utf-8"""			) as readme_file:
															readme_file.write(lowercase			)






			def 				A      (   self							:						Any ,      lowercase							:						Optional[str] = None			)							->    str:

									'''simple docstring'''







									if readme_content is not None:
															UpperCamelCase__    ,   UpperCamelCase__         = _split_yaml_from_readme(lowercase			)
															UpperCamelCase__         = """---\n""" + self.to_yaml_string() + """---\n""" + content
									else:
															UpperCamelCase__         = """---\n""" + self.to_yaml_string() + """---\n"""
									return full_content






			@classmethod
			def 				A      (   cls							:						Tuple ,      lowercase							:						str			)							->    "DatasetMetadata":

									'''simple docstring'''







									UpperCamelCase__         = yaml.load(lowercase ,      Loader=_NoDuplicateSafeLoader			) or {}

									# Convert the YAML keys to DatasetMetadata fields
									UpperCamelCase__         = {
									    (key.replace("""-""" ,      """_"""			) if key.replace("""-""" ,      """_"""			) in cls._FIELDS_WITH_DASHES else key): value
									    for key, value in metadata_dict.items()
									}
									return cls(**lowercase			)







			def 				A      (   self							:						Dict			)							->    str:

									'''simple docstring'''







									return yaml.safe_dump(
									    {
									        (key.replace("""_""" ,      """-"""			) if key in self._FIELDS_WITH_DASHES else key): value
									        for key, value in self.items()
									    } ,      sort_keys=lowercase ,      allow_unicode=lowercase ,      encoding="""utf-8""" ,      ).decode("""utf-8"""			)
lowerCamelCase_				: str    	=							{
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
					from argparse import ArgumentParser

					lowerCamelCase_				: Tuple    	=							ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
					ap.add_argument('''readme_filepath''')
					lowerCamelCase_				: str    	=							ap.parse_args()

					lowerCamelCase_				: List[str]    	=							Path(args.readme_filepath)
					lowerCamelCase_				: Tuple    	=							DatasetMetadata.from_readme(readme_filepath)
					print(dataset_metadata)
					dataset_metadata.to_readme(readme_filepath)
