



'''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     :    Union[str, Any]      						=					logging.get_logger(__name__)



def    __lowerCamelCase						(      _lowercase			,       _lowercase=False			,       _lowercase=False	)   ->    Dict:
							UpperCAmelCase  :	Any							  =	"""backbone.""" if is_semantic else """"""

							UpperCAmelCase  :	Union[str, 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    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=False			,       _lowercase=False	)   ->    Dict:
							for i in range(config.num_hidden_layers	):
														UpperCAmelCase  :	Tuple							  =	"""backbone.""" if is_semantic else """"""
														# queries, keys and values
														UpperCAmelCase  :	Union[str, Any]							  =	state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''	)
														UpperCAmelCase  :	Optional[int]							  =	state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''	)
														UpperCAmelCase  :	Any							  =	state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''	)

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

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

														UpperCAmelCase  :	Dict							  =	gamma_a
														UpperCAmelCase  :	Tuple							  =	gamma_a



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Any:
							UpperCAmelCase  :	Any							  =	dct.pop(_lowercase	)
							UpperCAmelCase  :	Optional[int]							  =	val



def    __lowerCamelCase						(      )   ->    Dict:
							UpperCAmelCase  :	Optional[Any]							  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase  :	Tuple							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	)
							return im



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=False	)   ->    str:
							UpperCAmelCase  :	Any							  =	False if """rvlcdip""" in checkpoint_url else True
							UpperCAmelCase  :	int							  =	BeitConfig(use_absolute_position_embeddings=_lowercase			,       use_mask_token=_lowercase	)

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

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

							UpperCAmelCase  :	Any							  =	create_rename_keys(_lowercase			,       has_lm_head=_lowercase	)
							for src, dest in rename_keys:
														rename_key(_lowercase			,       _lowercase			,       _lowercase	)
							read_in_q_k_v(_lowercase			,       _lowercase			,       has_lm_head=_lowercase	)

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

							# Check outputs on an image
							UpperCAmelCase  :	Dict							  =	BeitImageProcessor(
							    size=config.image_size			,       resample=PILImageResampling.BILINEAR			,       do_center_crop=_lowercase	)
							UpperCAmelCase  :	Union[str, Any]							  =	prepare_img()

							UpperCAmelCase  :	Optional[Any]							  =	image_processor(images=_lowercase			,       return_tensors="""pt"""	)
							UpperCAmelCase  :	Union[str, Any]							  =	encoding["""pixel_values"""]

							UpperCAmelCase  :	int							  =	model(_lowercase	)
							UpperCAmelCase  :	int							  =	outputs.logits

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

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

							if push_to_hub:
														if has_lm_head:
																					UpperCAmelCase  :	Union[str, Any]							  =	"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
														else:
																					UpperCAmelCase  :	int							  =	"""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(_lowercase			,       _lowercase	)			,       organization="""nielsr"""			,       commit_message="""Add image processor"""			,       use_temp_dir=_lowercase			,       )
														model.push_to_hub(
														    repo_path_or_name=Path(_lowercase			,       _lowercase	)			,       organization="""nielsr"""			,       commit_message="""Add model"""			,       use_temp_dir=_lowercase			,       )


if __name__ == "__main__":
							a     :    Optional[Any]      						=					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     :    int      						=					parser.parse_args()
							convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_barthez import BarthezTokenizer
else:
							a     :    int      						=					None

a     :    List[Any]      						=					logging.get_logger(__name__)

a     :    Dict      						=					{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a     :    Union[str, Any]      						=					{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

a     :    List[Any]      						=					{
    """moussaKam/mbarthez""": 1_0_2_4,
    """moussaKam/barthez""": 1_0_2_4,
    """moussaKam/barthez-orangesum-title""": 1_0_2_4,
}

a     :    int      						=					"""▁"""

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				VOCAB_FILES_NAMES
	lowercase    =				PRETRAINED_VOCAB_FILES_MAP
	lowercase    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase    =				['input_ids', 'attention_mask']
	lowercase    =				BarthezTokenizer





	def __init__(						self	,	A=None	,	A=None	,	A="<s>"	,	A="</s>"	,	A="</s>"	,	A="<s>"	,	A="<unk>"	,	A="<pad>"	,	A="<mask>"	,	**A	,	)    ->    List[Any]:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase  :	Tuple							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else mask_token

								super().__init__(
								    A	,	tokenizer_file=A	,	bos_token=A	,	eos_token=A	,	unk_token=A	,	sep_token=A	,	cls_token=A	,	pad_token=A	,	mask_token=A	,	**A	,	)

								UpperCAmelCase  :	Union[str, Any]							  =	vocab_file
								UpperCAmelCase  :	int							  =	False if not self.vocab_file else True





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase  :	Optional[int]							  =	[self.cls_token_id]
								UpperCAmelCase  :	List[str]							  =	[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
								UpperCAmelCase  :	Optional[int]							  =	[self.sep_token_id]
								UpperCAmelCase  :	List[Any]							  =	[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 				_lowercase(						self	,	A	,	A = None					)    ->    Tuple[str]:
								if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""					)

								if not os.path.isdir(A					):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								UpperCAmelCase  :	str							  =	os.path.join(
								    A	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(A					):
															copyfile(self.vocab_file	,	A					)

								return (out_vocab_file,)

