



import os
from typing import List, Optional, Union

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


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {"vocab_file": "vocab.txt"}

lowerCamelCase__			= {
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

lowerCamelCase__			= {
    "facebook/esm2_t6_8M_UR50D": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any    ):
				"""simple docstring"""







				with open(_SCREAMING_SNAKE_CASE				,     """r"""    ) as f:
								__a       =		f.read().splitlines()
								return [l.strip() for l in lines]






class    SCREAMING_SNAKE_CASE			(							UpperCAmelCase_					):
							__lowerCamelCase	:	str    =VOCAB_FILES_NAMES
							__lowerCamelCase	:	int    =PRETRAINED_VOCAB_FILES_MAP
							__lowerCamelCase	:	List[str]    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCamelCase	:	List[str]    =['input_ids', 'attention_mask']





							def __init__(   self		:		int      ,				__lowercase		:		Tuple      ,				__lowercase		:		int="<unk>"      ,				__lowercase		:		str="<cls>"      ,				__lowercase		:		str="<pad>"      ,				__lowercase		:		Tuple="<mask>"      ,				__lowercase		:		Optional[Any]="<eos>"      ,				**__lowercase		:		Union[str, Any]      ,				):

											'''simple docstring'''
											super().__init__(**_lowercase	)
											__a       =		load_vocab_file(_lowercase	)
											__a       =		dict(enumerate(self.all_tokens	)	)
											__a       =		{tok: ind for ind, tok in enumerate(self.all_tokens	)}
											__a       =		unk_token
											__a       =		cls_token
											__a       =		pad_token
											__a       =		mask_token
											__a       =		eos_token
											__a       =		self.all_tokens
											self._create_trie(self.unique_no_split_tokens	)





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Optional[int]	):

											'''simple docstring'''
											return self._id_to_token.get(_lowercase      ,				self.unk_token	)





							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		str	):

											'''simple docstring'''
											return self._token_to_id.get(_lowercase      ,				self._token_to_id.get(self.unk_token	)	)





							def 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		Union[str, Any]      ,				**__lowercase		:		str	):

											'''simple docstring'''
											return text.split()





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		List[str]=False	):

											'''simple docstring'''
											return len(self._id_to_token	)





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											return {token: i for i, token in enumerate(self.all_tokens	)}





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		int	):

											'''simple docstring'''
											return self._token_to_id.get(_lowercase      ,				self._token_to_id.get(self.unk_token	)	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		str	):

											'''simple docstring'''
											return self._id_to_token.get(_lowercase      ,				self.unk_token	)





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Any      ,				__lowercase		:		str = None	):

											'''simple docstring'''
											__a       =		[self.cls_token_id]
											__a       =		[self.eos_token_id]  # No sep token in ESM vocabulary
											if token_ids_a is None:
															if self.eos_token_id is None:
																			return cls + token_ids_a
															else:
																			return cls + token_ids_a + sep
											elif self.eos_token_id is None:
															raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""	)
											return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Any      ,				__lowercase		:		str = None      ,				__lowercase		:		Dict = False	):

											'''simple docstring'''
											if already_has_special_tokens:
															if token_ids_a is not None:
																			raise ValueError(
																			    """You should not supply a second sequence if the provided sequence of """
																			    """ids is already formatted with special tokens for the model."""	)

															return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
											__a       =		[1] + ([0] * len(_lowercase	)) + [1]
											if token_ids_a is not None:
															mask += [0] * len(_lowercase	) + [1]
											return mask





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		os.path.join(_lowercase      ,				(filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""	)
											with open(_lowercase      ,				"""w"""	) as f:
															f.write("""\n""".join(self.all_tokens	)	)
											return (vocab_file,)





							@property
							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											return self.get_vocab_size(with_added_tokens=_lowercase	)







							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		List[str]      ,				__lowercase		:		List[str] = False	):

											'''simple docstring'''
											return super()._add_tokens(_lowercase      ,				special_tokens=_lowercase	)









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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			str=False				,     _SCREAMING_SNAKE_CASE       :			Tuple=False    ):
				"""simple docstring"""







				__a       =		"""backbone.""" if is_semantic else """"""

				__a       =		[]
				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__						(							_SCREAMING_SNAKE_CASE       :			Optional[Any]				,     _SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]=False				,     _SCREAMING_SNAKE_CASE       :			Any=False    ):
				"""simple docstring"""







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

								__a       =		in_proj_weight[
								    : config.hidden_size, :
								]
								__a       =		q_bias
								__a       =		in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__a       =		in_proj_weight[
								    -config.hidden_size :, :
								]
								__a       =		v_bias

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

								__a       =		gamma_a
								__a       =		gamma_a

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			Optional[int]    ):
				"""simple docstring"""







				__a       =		dct.pop(_SCREAMING_SNAKE_CASE    )
				__a       =		val

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__a       =		Image.open(requests.get(_SCREAMING_SNAKE_CASE				,     stream=_SCREAMING_SNAKE_CASE    ).raw    )
				return im

@torch.no_grad()
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			List[Any]				,     _SCREAMING_SNAKE_CASE       :			str=False    ):
				"""simple docstring"""







				__a       =		False if """rvlcdip""" in checkpoint_url else True
				__a       =		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:
								__a       =		1024
								__a       =		4096
								__a       =		24
								__a       =		16

				# labels
				if "rvlcdip" in checkpoint_url:
								__a       =		16
								__a       =		"""huggingface/label-files"""
								__a       =		"""rvlcdip-id2label.json"""
								__a       =		json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     repo_type="""dataset"""    )				,     """r"""    )    )
								__a       =		{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
								__a       =		idalabel
								__a       =		{v: k for k, v in idalabel.items()}

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

				__a       =		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
				__a       =		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
				__a       =		BeitImageProcessor(
				    size=config.image_size				,     resample=PILImageResampling.BILINEAR				,     do_center_crop=_SCREAMING_SNAKE_CASE    )
				__a       =		prepare_img()

				__a       =		image_processor(images=_SCREAMING_SNAKE_CASE				,     return_tensors="""pt"""    )
				__a       =		encoding["""pixel_values"""]

				__a       =		model(_SCREAMING_SNAKE_CASE    )
				__a       =		outputs.logits

				# verify logits
				__a       =		[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:
												__a       =		"""dit-base""" if """base""" in checkpoint_url else """dit-large"""
								else:
												__a       =		"""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__":
						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)
