'''simple docstring'''





import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			Dict:




						'''simple docstring'''
						# Load configuration defined in the metadata file
						with open(UpperCAmelCase					) as metadata_file:
												_UpperCamelCase			:			Optional[Any]   			=						json.load(UpperCAmelCase					)
						_UpperCamelCase			:			List[str]   			=						LukeConfig(use_entity_aware_attention=UpperCAmelCase     ,**metadata["model_config"]					)

						# Load in the weights from the checkpoint_path
						_UpperCamelCase			:			Tuple   			=						torch.load(UpperCAmelCase     ,map_location="cpu"					)

						# Load the entity vocab file
						_UpperCamelCase			:			List[Any]   			=						load_entity_vocab(UpperCAmelCase					)

						_UpperCamelCase			:			List[Any]   			=						RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]					)

						# Add special tokens to the token vocabulary for downstream tasks
						_UpperCamelCase			:			Tuple   			=						AddedToken("<ent>"     ,lstrip=UpperCAmelCase     ,rstrip=UpperCAmelCase					)
						_UpperCamelCase			:			Any   			=						AddedToken("<ent2>"     ,lstrip=UpperCAmelCase     ,rstrip=UpperCAmelCase					)
						tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}					)
						config.vocab_size += 2

						print(f'''Saving tokenizer to {pytorch_dump_folder_path}'''					)
						tokenizer.save_pretrained(UpperCAmelCase					)
						with open(os.path.join(UpperCAmelCase     ,LukeTokenizer.vocab_files_names["entity_vocab_file"]					)     ,"w"					) as f:
												json.dump(UpperCAmelCase     ,UpperCAmelCase					)

						_UpperCamelCase			:			Optional[Any]   			=						LukeTokenizer.from_pretrained(UpperCAmelCase					)

						# Initialize the embeddings of the special tokens
						_UpperCamelCase			:			str   			=						state_dict["embeddings.word_embeddings.weight"]
						_UpperCamelCase			:			Tuple   			=						word_emb[tokenizer.convert_tokens_to_ids(["@"]					)[0]].unsqueeze(0					)
						_UpperCamelCase			:			Tuple   			=						word_emb[tokenizer.convert_tokens_to_ids(["#"]					)[0]].unsqueeze(0					)
						_UpperCamelCase			:			Tuple   			=						torch.cat([word_emb, ent_emb, enta_emb]					)

						# Initialize the query layers of the entity-aware self-attention mechanism
						for layer_index in range(config.num_hidden_layers					):
												for matrix_name in ["query.weight", "query.bias"]:
																		_UpperCamelCase			:			List[str]   			=						f'''encoder.layer.{layer_index}.attention.self.'''
																		_UpperCamelCase			:			Union[str, Any]   			=						state_dict[prefix + matrix_name]
																		_UpperCamelCase			:			List[Any]   			=						state_dict[prefix + matrix_name]
																		_UpperCamelCase			:			str   			=						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
						_UpperCamelCase			:			Optional[Any]   			=						state_dict["entity_embeddings.entity_embeddings.weight"]
						_UpperCamelCase			:			Union[str, Any]   			=						entity_emb[entity_vocab["[MASK]"]]

						_UpperCamelCase			:			Dict   			=						LukeModel(config=UpperCAmelCase					).eval()

						_UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						model.load_state_dict(UpperCAmelCase     ,strict=UpperCAmelCase					)
						if not (len(UpperCAmelCase					) == 1 and missing_keys[0] == "embeddings.position_ids"):
												raise ValueError(f'''Missing keys {', '.join(UpperCAmelCase					)}. Expected only missing embeddings.position_ids'''					)
						if not (all(key.startswith("entity_predictions"					) or key.startswith("lm_head"					) for key in unexpected_keys					)):
												raise ValueError(
												    "Unexpected keys"
												    f''' {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'					) or key.startswith('lm_head'					))]					)}'''					)

						# Check outputs
						_UpperCamelCase			:			Optional[Any]   			=						LukeTokenizer.from_pretrained(UpperCAmelCase     ,task="entity_classification"					)

						_UpperCamelCase			:			Optional[int]   			=						(
						    "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
						    " new world number one avoid a humiliating second- round exit at Wimbledon ."
						)
						_UpperCamelCase			:			Dict   			=						(3_9, 4_2)
						_UpperCamelCase			:			int   			=						tokenizer(UpperCAmelCase     ,entity_spans=[span]     ,add_prefix_space=UpperCAmelCase     ,return_tensors="pt"					)

						_UpperCamelCase			:			Any   			=						model(**UpperCAmelCase					)

						# Verify word hidden states
						if model_size == "large":
												_UpperCamelCase			:			Optional[Any]   			=						torch.Size((1, 4_2, 1_0_2_4)					)
												_UpperCamelCase			:			List[str]   			=						torch.tensor(
												    [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]					)
						else:  # base
												_UpperCamelCase			:			int   			=						torch.Size((1, 4_2, 7_6_8)					)
												_UpperCamelCase			:			Dict   			=						torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]					)

						if not (outputs.last_hidden_state.shape == expected_shape):
												raise ValueError(
												    f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''					)
						if not torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,UpperCAmelCase     ,atol=1E-4					):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												_UpperCamelCase			:			Any   			=						torch.Size((1, 1, 1_0_2_4)					)
												_UpperCamelCase			:			Optional[Any]   			=						torch.tensor([[0.04_66, -0.01_06, -0.01_79]]					)
						else:  # base
												_UpperCamelCase			:			int   			=						torch.Size((1, 1, 7_6_8)					)
												_UpperCamelCase			:			Optional[Any]   			=						torch.tensor([[0.14_57, 0.10_44, 0.01_74]]					)

						if not (outputs.entity_last_hidden_state.shape != expected_shape):
												raise ValueError(
												    f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
												    f''' {expected_shape}'''					)
						if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]     ,UpperCAmelCase     ,atol=1E-4					):
												raise ValueError

						# Finally, save our PyTorch model and tokenizer
						print("Saving PyTorch model to {}".format(UpperCAmelCase					)					)
						model.save_pretrained(UpperCAmelCase					)
def 						__A							(     UpperCAmelCase					)     ->			Union[str, Any]:




						'''simple docstring'''
						_UpperCamelCase			:			Dict   			=						{}
						with open(UpperCAmelCase     ,"r"     ,encoding="utf-8"					) as f:
												for index, line in enumerate(UpperCAmelCase					):
																		_UpperCamelCase			,       _UpperCamelCase			:			Optional[int]   			=						line.rstrip().split("\t"					)
																		_UpperCamelCase			:			int   			=						index

						return entity_vocab


if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[int]			=     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
			parser.add_argument(
			    """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
			)
			parser.add_argument(
			    """--entity_vocab_path""",
			    default=None,
			    type=str,
			    help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
			)
			parser.add_argument(
			    """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
			)
			lowerCAmelCase_	:		Optional[int]			=     parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)



'''simple docstring'''





import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
			import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_	:		int			=     logging.get_logger(__name__)

lowerCAmelCase_	:		List[str]			=     {"""vocab_file""": """vocab.txt"""}

lowerCAmelCase_	:		Optional[Any]			=     {
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

lowerCAmelCase_	:		int			=     {
    """openbmb/cpm-ant-10b""": 1024,
}
def 						__A							(     UpperCAmelCase					)     ->			List[str]:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[int]   			=						collections.OrderedDict()
						with open(UpperCAmelCase     ,"r"     ,encoding="utf-8"					) as reader:
												_UpperCamelCase			:			int   			=						reader.readlines()
						for index, token in enumerate(UpperCAmelCase					):
												_UpperCamelCase			:			Optional[int]   			=						token.rstrip("\n"					)
												_UpperCamelCase			:			List[str]   			=						index
						return vocab




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Dict       ,		lowercase__					:						List[str]       ,		lowercase__					:						Any="<unk>"       ,		lowercase__					:						str=200					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						vocab
										_UpperCamelCase			:			str   			=						unk_token
										_UpperCamelCase			:			List[Any]   			=						max_input_chars_per_word







				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						Any					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						list(lowercase__					)
										if len(lowercase__					) > self.max_input_chars_per_word:
																return [self.unk_token]

										_UpperCamelCase			:			int   			=						0
										_UpperCamelCase			:			List[str]   			=						[]
										while start < len(lowercase__					):
																_UpperCamelCase			:			Union[str, Any]   			=						len(lowercase__					)
																_UpperCamelCase			:			Union[str, Any]   			=						None
																while start < end:
																						_UpperCamelCase			:			Dict   			=						"".join(chars[start:end]					)
																						if substr in self.vocab:
																												_UpperCamelCase			:			int   			=						substr
																												break
																						end -= 1
																if cur_substr is None:
																						sub_tokens.append(self.unk_token					)
																						start += 1
																else:
																						sub_tokens.append(lowercase__					)
																						_UpperCamelCase			:			List[str]   			=						end

										return sub_tokens





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							VOCAB_FILES_NAMES
				UpperCAmelCase__							=							PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase__							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase__							=							['''input_ids''', '''attention_mask''']
				UpperCAmelCase__							=							False





				def __init__(						self					:						int       ,		lowercase__					:						str       ,		lowercase__					:						int="<d>"       ,		lowercase__					:						Tuple="</d>"       ,		lowercase__					:						Optional[int]="<s>"       ,		lowercase__					:						Optional[int]="</s>"       ,		lowercase__					:						List[str]="<pad>"       ,		lowercase__					:						Optional[int]="<unk>"       ,		lowercase__					:						int="</n>"       ,		lowercase__					:						Optional[Any]="</_>"       ,		lowercase__					:						int="left"       ,		**lowercase__					:						List[str]       ,		)							->Union[str, Any]:


										'''simple docstring'''




										requires_backends(self       ,		["jieba"]					)
										super().__init__(
										    bod_token=lowercase__       ,		eod_token=lowercase__       ,		bos_token=lowercase__       ,		eos_token=lowercase__       ,		pad_token=lowercase__       ,		unk_token=lowercase__       ,		line_token=lowercase__       ,		space_token=lowercase__       ,		padding_side=lowercase__       ,		**lowercase__       ,		)
										_UpperCamelCase			:			Union[str, Any]   			=						bod_token
										_UpperCamelCase			:			Union[str, Any]   			=						eod_token
										_UpperCamelCase			:			int   			=						load_vocab(lowercase__					)
										_UpperCamelCase			:			Any   			=						self.encoder[space_token]
										_UpperCamelCase			:			List[str]   			=						self.encoder[line_token]

										del self.encoder[space_token]
										del self.encoder[line_token]

										_UpperCamelCase			:			Dict   			=						collections.OrderedDict(sorted(self.encoder.items()       ,		key=lambda lowercase__					: x[1]					)					)
										_UpperCamelCase			:			Any   			=						{v: k for k, v in self.encoder.items()}

										_UpperCamelCase			:			Any   			=						WordpieceTokenizer(vocab=self.encoder       ,		unk_token=self.unk_token					)





				@property
				def  snake_case__				(						self					:						Tuple					)							->List[str]:


										'''simple docstring'''




										return self.encoder[self.bod_token]





				@property
				def  snake_case__				(						self					:						Tuple					)							->List[Any]:


										'''simple docstring'''




										return self.encoder[self.eod_token]





				@property
				def  snake_case__				(						self					:						List[str]					)							->int:


										'''simple docstring'''




										return self.encoder["\n"]





				@property
				def  snake_case__				(						self					:						List[str]					)							->int:


										'''simple docstring'''




										return len(self.encoder					)





				def  snake_case__				(						self					:						Dict					)							->Tuple:


										'''simple docstring'''




										return dict(self.encoder       ,		**self.added_tokens_encoder					)





				def  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						Tuple					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						[]
										for x in jieba.cut(lowercase__       ,		cut_all=lowercase__					):
																output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowercase__					)					)
										return output_tokens





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						Any       ,		**lowercase__					:						Tuple					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						[i for i in token_ids if i >= 0]
										_UpperCamelCase			:			Union[str, Any]   			=						[
										    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
										]
										return super()._decode(lowercase__       ,		**lowercase__					)





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						Tuple					)							->Union[str, Any]:


										'''simple docstring'''




										return token in self.encoder





				def  snake_case__				(						self					:						str       ,		lowercase__					:						List[str]					)							->str:


										'''simple docstring'''




										return "".join(lowercase__					)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						Optional[Any]					)							->int:


										'''simple docstring'''




										return self.encoder.get(lowercase__       ,		self.encoder.get(self.unk_token					)					)





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						Dict					)							->Dict:


										'''simple docstring'''




										return self.decoder.get(lowercase__       ,		self.unk_token					)





				def  snake_case__				(						self					:						Tuple       ,		lowercase__					:						str       ,		lowercase__					:						Optional[str] = None					)							->Tuple[str]:


										'''simple docstring'''




										if os.path.isdir(lowercase__					):
																_UpperCamelCase			:			Tuple   			=						os.path.join(
																    lowercase__       ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
										else:
																_UpperCamelCase			:			Optional[int]   			=						(filename_prefix + "-" if filename_prefix else "") + save_directory
										_UpperCamelCase			:			List[str]   			=						0
										if " " in self.encoder:
																_UpperCamelCase			:			Dict   			=						self.encoder[" "]
																del self.encoder[" "]
										if "\n" in self.encoder:
																_UpperCamelCase			:			int   			=						self.encoder["\n"]
																del self.encoder["\n"]
										_UpperCamelCase			:			List[str]   			=						collections.OrderedDict(sorted(self.encoder.items()       ,		key=lambda lowercase__					: x[1]					)					)
										with open(lowercase__       ,		"w"       ,		encoding="utf-8"					) as writer:
																for token, token_index in self.encoder.items():
																						if index != token_index:
																												logger.warning(
																												    f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																												    " Please check that the vocabulary is not corrupted!"					)
																												_UpperCamelCase			:			str   			=						token_index
																						writer.write(token + "\n"					)
																						index += 1
										return (vocab_file,)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						List[int]       ,		lowercase__					:						List[int] = None					)							->List[int]:


										'''simple docstring'''




										if token_ids_a is None:
																return [self.bos_token_id] + token_ids_a
										return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a







				def  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						List[int]       ,		lowercase__					:						Optional[List[int]] = None       ,		lowercase__					:						bool = False					)							->List[int]:


										'''simple docstring'''




										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowercase__       ,		token_ids_a=lowercase__       ,		already_has_special_tokens=lowercase__					)

										if token_ids_a is not None:
																return [1] + ([0] * len(lowercase__					)) + [1] + ([0] * len(lowercase__					))
										return [1] + ([0] * len(lowercase__					))


