


'''simple docstring'''



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A		=logging.get_logger(__name__)


__A		={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
__A		={
    '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',
    },
}

__A		={
    '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,
}







@lru_cache()
def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Any       				=       (
			    list(range(ord("""!"""   )	, ord("""~"""   ) + 1   )   ) + list(range(ord("""¡"""   )	, ord("""¬"""   ) + 1   )   ) + list(range(ord("""®"""   )	, ord("""ÿ"""   ) + 1   )   )
			)
			UpperCAmelCase__					:   List[Any]       				=       bs[:]
			UpperCAmelCase__					:   Tuple       				=       0
			for b in range(2**8   ):
						if b not in bs:
									bs.append(UpperCamelCase__   )
									cs.append(2**8 + n   )
									n += 1
			UpperCAmelCase__					:   List[Any]       				=       [chr(UpperCamelCase__   ) for n in cs]
			return dict(zip(UpperCamelCase__	, UpperCamelCase__   )   )







def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   str       				=       set()
			UpperCAmelCase__					:   Dict       				=       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)   )
						UpperCAmelCase__					:   Optional[Any]       				=       char
			return pairs


class 						_snake_case   (       a__		):
					lowerCAmelCase :List[str]						=   VOCAB_FILES_NAMES
					lowerCAmelCase :Tuple						=   PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase :str						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase :Optional[Any]						=   ['''input_ids''', '''attention_mask''']




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase="replace"							,							_lowerCamelCase="<s>"							,							_lowerCamelCase="</s>"							,							_lowerCamelCase="</s>"							,							_lowerCamelCase="<s>"							,							_lowerCamelCase="<unk>"							,							_lowerCamelCase="<pad>"							,							_lowerCamelCase="<mask>"							,							_lowerCamelCase=False							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Optional[int]       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else bos_token
								UpperCAmelCase__					:   int       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else eos_token
								UpperCAmelCase__					:   Any       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else sep_token
								UpperCAmelCase__					:   Any       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else cls_token
								UpperCAmelCase__					:   Optional[Any]       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else unk_token
								UpperCAmelCase__					:   List[str]       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase__					:   Tuple       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else mask_token

								super().__init__(
								    errors=_lowerCamelCase							,							bos_token=_lowerCamelCase							,							eos_token=_lowerCamelCase							,							unk_token=_lowerCamelCase							,							sep_token=_lowerCamelCase							,							cls_token=_lowerCamelCase							,							pad_token=_lowerCamelCase							,							mask_token=_lowerCamelCase							,							add_prefix_space=_lowerCamelCase							,							**_lowerCamelCase							,							)

								with open(_lowerCamelCase							,							encoding="""utf-8""") as vocab_handle:
											UpperCAmelCase__					:   Tuple       				=       json.load(_lowerCamelCase)
								UpperCAmelCase__					:   Union[str, Any]       				=       {v: k for k, v in self.encoder.items()}
								UpperCAmelCase__					:   List[Any]       				=       errors  # how to handle errors in decoding
								UpperCAmelCase__					:   Optional[Any]       				=       bytes_to_unicode()
								UpperCAmelCase__					:   int       				=       {v: k for k, v in self.byte_encoder.items()}
								with open(_lowerCamelCase							,							encoding="""utf-8""") as merges_handle:
											UpperCAmelCase__					:   List[Any]       				=       merges_handle.read().split("""\n""")[1:-1]
								UpperCAmelCase__					:   Tuple       				=       [tuple(merge.split()) for merge in bpe_merges]
								UpperCAmelCase__					:   Dict       				=       dict(zip(_lowerCamelCase							,							range(len(_lowerCamelCase))))
								UpperCAmelCase__					:   List[Any]       				=       {}
								UpperCAmelCase__					:   Any       				=       add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								UpperCAmelCase__					:   Tuple       				=       re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")




					@property
					def 			snake_case__  ( self):
								return len(self.encoder)




					def 			snake_case__  ( self):
								return dict(self.encoder							,							**self.added_tokens_encoder)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								if token in self.cache:
											return self.cache[token]
								UpperCAmelCase__					:   Any       				=       tuple(_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       get_pairs(_lowerCamelCase)

								if not pairs:
											return token

								while True:
											UpperCAmelCase__					:   Dict       				=       min(_lowerCamelCase							,							key=lambda _lowerCamelCase: self.bpe_ranks.get(_lowerCamelCase							,							float("""inf""")))
											if bigram not in self.bpe_ranks:
														break
											UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       bigram
											UpperCAmelCase__					:   List[Any]       				=       []
											UpperCAmelCase__					:   List[str]       				=       0
											while i < len(_lowerCamelCase):
														try:
																	UpperCAmelCase__					:   Optional[int]       				=       word.index(_lowerCamelCase							,							_lowerCamelCase)
														except ValueError:
																	new_word.extend(word[i:])
																	break
														else:
																	new_word.extend(word[i:j])
																	UpperCAmelCase__					:   Dict       				=       j

														if word[i] == first and i < len(_lowerCamelCase) - 1 and word[i + 1] == second:
																	new_word.append(first + second)
																	i += 2
														else:
																	new_word.append(word[i])
																	i += 1
											UpperCAmelCase__					:   List[Any]       				=       tuple(_lowerCamelCase)
											UpperCAmelCase__					:   Tuple       				=       new_word
											if len(_lowerCamelCase) == 1:
														break
											else:
														UpperCAmelCase__					:   Any       				=       get_pairs(_lowerCamelCase)
								UpperCAmelCase__					:   List[Any]       				=       """ """.join(_lowerCamelCase)
								UpperCAmelCase__					:   List[str]       				=       word
								return word




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Any       				=       []
								for token in re.findall(self.pat							,							_lowerCamelCase):
											UpperCAmelCase__					:   Any       				=       """""".join(
											    self.byte_encoder[b] for b in token.encode("""utf-8"""))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCamelCase).split(""" """))
								return bpe_tokens




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.encoder.get(_lowerCamelCase							,							self.encoder.get(self.unk_token))




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.decoder.get(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   List[Any]       				=       """""".join(_lowerCamelCase)
								UpperCAmelCase__					:   Tuple       				=       bytearray([self.byte_decoder[c] for c in text]).decode("""utf-8"""							,							errors=self.errors)
								return text




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not os.path.isdir(_lowerCamelCase):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
											return
								UpperCAmelCase__					:   str       				=       os.path.join(
								    _lowerCamelCase							,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
								UpperCAmelCase__					:   List[str]       				=       os.path.join(
								    _lowerCamelCase							,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])

								with open(_lowerCamelCase							,							"""w"""							,							encoding="""utf-8""") as f:
											f.write(json.dumps(self.encoder							,							indent=2							,							sort_keys=_lowerCamelCase							,							ensure_ascii=_lowerCamelCase) + """\n""")

								UpperCAmelCase__					:   Optional[int]       				=       0
								with open(_lowerCamelCase							,							"""w"""							,							encoding="""utf-8""") as writer:
											writer.write("""#version: 0.2\n""")
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,							key=lambda _lowerCamelCase: kv[1]):
														if index != token_index:
																	logger.warning(
																	    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																	    """ Please check that the tokenizer is not corrupted!""")
																	UpperCAmelCase__					:   Tuple       				=       token_index
														writer.write(""" """.join(_lowerCamelCase) + """\n""")
														index += 1

								return vocab_file, merge_file




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase__					:   List[str]       				=       [self.cls_token_id]
								UpperCAmelCase__					:   Optional[Any]       				=       [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = False):
								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=_lowerCamelCase							,							token_ids_a=_lowerCamelCase							,							already_has_special_tokens=_lowerCamelCase)

								if token_ids_a is None:
											return [1] + ([0] * len(_lowerCamelCase)) + [1]
								return [1] + ([0] * len(_lowerCamelCase)) + [1, 1] + ([0] * len(_lowerCamelCase)) + [1]




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   str       				=       [self.sep_token_id]
								UpperCAmelCase__					:   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]




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase=False							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Optional[int]       				=       kwargs.pop("""add_prefix_space"""							,							self.add_prefix_space)
								if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase) > 0 and not text[0].isspace()):
											UpperCAmelCase__					:   Union[str, Any]       				=       """ """ + text
								return (text, kwargs)



'''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()
__A		=logging.get_logger(__name__)







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__=False	, UpperCamelCase__=False   ):
			UpperCAmelCase__					:   str       				=       """backbone.""" if is_semantic else """"""

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

						UpperCAmelCase__					:   Optional[Any]       				=       in_proj_weight[
						    : config.hidden_size, :
						]
						UpperCAmelCase__					:   Any       				=       q_bias
						UpperCAmelCase__					:   Dict       				=       in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCAmelCase__					:   Any       				=       in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCAmelCase__					:   Any       				=       v_bias

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

						UpperCAmelCase__					:   Union[str, Any]       				=       gamma_a
						UpperCAmelCase__					:   str       				=       gamma_a







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       dct.pop(UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[Any]       				=       val







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Any       				=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase__					:   Dict       				=       Image.open(requests.get(UpperCamelCase__	, stream=UpperCamelCase__   ).raw   )
			return im







@torch.no_grad()
def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=False   ):
			UpperCAmelCase__					:   Optional[Any]       				=       False if """rvlcdip""" in checkpoint_url else True
			UpperCAmelCase__					:   Any       				=       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__					:   Optional[Any]       				=       1_0_2_4
						UpperCAmelCase__					:   Dict       				=       4_0_9_6
						UpperCAmelCase__					:   Any       				=       2_4
						UpperCAmelCase__					:   Tuple       				=       1_6

			# labels
			if "rvlcdip" in checkpoint_url:
						UpperCAmelCase__					:   int       				=       1_6
						UpperCAmelCase__					:   List[str]       				=       """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__					:   Optional[Any]       				=       idalabel
						UpperCAmelCase__					:   List[Any]       				=       {v: k for k, v in idalabel.items()}

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

			UpperCAmelCase__					:   List[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__					:   str       				=       BeitForMaskedImageModeling(UpperCamelCase__   ) if has_lm_head else BeitForImageClassification(UpperCamelCase__   )
			model.eval()
			model.load_state_dict(UpperCamelCase__   )

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

			UpperCAmelCase__					:   Optional[Any]       				=       image_processor(images=UpperCamelCase__	, return_tensors="""pt"""   )
			UpperCAmelCase__					:   Optional[Any]       				=       encoding["""pixel_values"""]

			UpperCAmelCase__					:   Optional[Any]       				=       model(UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       outputs.logits

			# verify logits
			UpperCAmelCase__					:   int       				=       [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__					:   Union[str, Any]       				=       """dit-base""" if """base""" in checkpoint_url else """dit-large"""
						else:
									UpperCAmelCase__					:   Tuple       				=       """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__":
		__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)