
"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__UpperCAmelCase       =  logging.get_logger(__name__)


__UpperCAmelCase       =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
__UpperCAmelCase       =  {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

__UpperCAmelCase       =  {
    'facebook/bart-base': 10_24,
    'facebook/bart-large': 10_24,
    'facebook/bart-large-mnli': 10_24,
    'facebook/bart-large-cnn': 10_24,
    'facebook/bart-large-xsum': 10_24,
    'yjernite/bart_eli5': 10_24,
}




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :List[str]				 =						VOCAB_FILES_NAMES
			UpperCAmelCase_  :Optional[Any]				 =						PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_  :int				 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_  :Optional[Any]				 =						["input_ids", "attention_mask"]
			UpperCAmelCase_  :int				 =						BartTokenizer






			def __init__(					self       ,   __A=None       ,   __A=None       ,   __A=None       ,   __A="replace"       ,   __A="<s>"       ,   __A="</s>"       ,   __A="</s>"       ,   __A="<s>"       ,   __A="<unk>"       ,   __A="<pad>"       ,   __A="<mask>"       ,   __A=False       ,   __A=True       ,   **__A       ,   )			->     Any:
						super().__init__(
						    __A       ,   __A       ,   tokenizer_file=__A       ,   errors=__A       ,   bos_token=__A       ,   eos_token=__A       ,   sep_token=__A       ,   cls_token=__A       ,   unk_token=__A       ,   pad_token=__A       ,   mask_token=__A       ,   add_prefix_space=__A       ,   trim_offsets=__A       ,   **__A       ,   )

						lowerCAmelCase_    :Dict			     =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
						if pre_tok_state.get("""add_prefix_space"""       ,   __A       ) != add_prefix_space:
									lowerCAmelCase_    :Optional[Any]			     =  getattr(__A       ,   pre_tok_state.pop("""type"""       )       )
									lowerCAmelCase_    :Optional[int]			     =  add_prefix_space
									lowerCAmelCase_    :Any			     =  pre_tok_class(**__A       )

						lowerCAmelCase_    :int			     =  add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						lowerCAmelCase_    :Dict			     =  """post_processor"""
						lowerCAmelCase_    :Optional[Any]			     =  getattr(self.backend_tokenizer       ,   __A       ,   __A       )
						if tokenizer_component_instance:
									lowerCAmelCase_    :List[str]			     =  json.loads(tokenizer_component_instance.__getstate__()       )

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
												lowerCAmelCase_    :str			     =  tuple(state["""sep"""]       )
									if "cls" in state:
												lowerCAmelCase_    :Any			     =  tuple(state["""cls"""]       )

									lowerCAmelCase_    :Dict			     =  False

									if state.get("""add_prefix_space"""       ,   __A       ) != add_prefix_space:
												lowerCAmelCase_    :int			     =  add_prefix_space
												lowerCAmelCase_    :Dict			     =  True

									if state.get("""trim_offsets"""       ,   __A       ) != trim_offsets:
												lowerCAmelCase_    :Tuple			     =  trim_offsets
												lowerCAmelCase_    :str			     =  True

									if changes_to_apply:
												lowerCAmelCase_    :Tuple			     =  getattr(__A       ,   state.pop("""type"""       )       )
												lowerCAmelCase_    :int			     =  component_class(**__A       )
												setattr(self.backend_tokenizer       ,   __A       ,   __A       )






			@property
			def  __lowerCAmelCase (					self       )			->     str:
						if self._mask_token is None:
									if self.verbose:
												logger.error("""Using mask_token, but it is not set yet."""       )
									return None
						return str(self._mask_token       )






			@mask_token.setter
			def  __lowerCAmelCase (					self       ,   __A       )			->     str:
						lowerCAmelCase_    :List[str]			     =  AddedToken(__A       ,   lstrip=__A       ,   rstrip=__A       ) if isinstance(__A       ,   __A       ) else value
						lowerCAmelCase_    :Optional[int]			     =  value






			def  __lowerCAmelCase (					self       ,   *__A       ,   **__A       )			->     BatchEncoding:
						lowerCAmelCase_    :Optional[int]			     =  kwargs.get("""is_split_into_words"""       ,   __A       )

						if is_split_into_words and not self.add_prefix_space:
									raise ValueError(
									    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
									    """to use it with pretokenized inputs."""       )

						return super()._batch_encode_plus(*__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   *__A       ,   **__A       )			->     BatchEncoding:
						lowerCAmelCase_    :Optional[Any]			     =  kwargs.get("""is_split_into_words"""       ,   __A       )

						if is_split_into_words and not self.add_prefix_space:
									raise ValueError(
									    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
									    """to use it with pretokenized inputs."""       )

						return super()._encode_plus(*__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = None       )			->     Tuple[str]:
						lowerCAmelCase_    :Tuple			     =  self._tokenizer.model.save(__A       ,   name=__A       )
						return tuple(__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A=None       )			->     Tuple:
						lowerCAmelCase_    :Dict			     =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
									return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = None       )			->     List[int]:
						lowerCAmelCase_    :Any			     =  [self.sep_token_id]
						lowerCAmelCase_    :Optional[int]			     =  [self.cls_token_id]

						if token_ids_a is None:
									return len(cls + token_ids_a + sep       ) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]










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()
_snake_case             =     logging.get_logger(__name__)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					Tuple    =				"backbone." if is_semantic else ""

  lowerCamelCase :					int    =				[]
  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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

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

    lowerCamelCase :					str    =				in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCamelCase :					Any    =				q_bias
    lowerCamelCase :					Any    =				in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCamelCase :					Optional[int]    =				in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCamelCase :					int    =				v_bias

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

    lowerCamelCase :					int    =				gamma_a
    lowerCamelCase :					Optional[Any]    =				gamma_a






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				dct.pop(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					List[Any]    =				val






def 		lowercase_(  ):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase :					Optional[Any]    =				Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,						stream=SCREAMING_SNAKE_CASE_	).raw	)
  return im






@torch.no_grad()
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=False	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				False if "rvlcdip" in checkpoint_url else True
  lowerCamelCase :					str    =				BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_      ,						use_mask_token=SCREAMING_SNAKE_CASE_	)

  # size of the architecture
  if "large" in checkpoint_url or "dit-l" in checkpoint_url:
    lowerCamelCase :					Union[str, Any]    =				1024
    lowerCamelCase :					Any    =				4096
    lowerCamelCase :					str    =				24
    lowerCamelCase :					List[Any]    =				16

  # labels
  if "rvlcdip" in checkpoint_url:
    lowerCamelCase :					Optional[Any]    =				16
    lowerCamelCase :					Tuple    =				"huggingface/label-files"
    lowerCamelCase :					List[str]    =				"rvlcdip-id2label.json"
    lowerCamelCase :					str    =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						repo_type="dataset"	)      ,						"r"	)	)
    lowerCamelCase :					Any    =				{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
    lowerCamelCase :					Tuple    =				idalabel
    lowerCamelCase :					Dict    =				{v: k for k, v in idalabel.items()}

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

  lowerCamelCase :					Tuple    =				create_rename_keys(SCREAMING_SNAKE_CASE_      ,						has_lm_head=SCREAMING_SNAKE_CASE_	)
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
  read_in_q_k_v(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						has_lm_head=SCREAMING_SNAKE_CASE_	)

  # load HuggingFace model
  lowerCamelCase :					List[Any]    =				BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_	) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE_	)
  model.eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE_	)

  # Check outputs on an image
  lowerCamelCase :					str    =				BeitImageProcessor(
      size=config.image_size      ,						resample=PILImageResampling.BILINEAR      ,						do_center_crop=SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				prepare_img()

  lowerCamelCase :					Optional[int]    =				image_processor(images=SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	)
  lowerCamelCase :					Optional[Any]    =				encoding["pixel_values"]

  lowerCamelCase :					Optional[Any]    =				model(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Dict    =				outputs.logits

  # verify logits
  lowerCamelCase :					List[Any]    =				[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
  assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE_	), "Shape of logits not as expected"

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

  if push_to_hub:
    if has_lm_head:
      lowerCamelCase :					Optional[Any]    =				"dit-base" if "base" in checkpoint_url else "dit-large"
    else:
      lowerCamelCase :					Dict    =				"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(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						organization="nielsr"      ,						commit_message="Add image processor"      ,						use_temp_dir=SCREAMING_SNAKE_CASE_      ,						)
    model.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)      ,						organization="nielsr"      ,						commit_message="Add model"      ,						use_temp_dir=SCREAMING_SNAKE_CASE_      ,						)


if __name__ == "__main__":
   _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''',
   )
   _snake_case             =     parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



