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  _lowerCamelCase					(   a_				: Optional[Any]						,			a_				: str						,			a_				: Any						,			a_				: Union[str, Any]						,			a_				: int):
			# Load configuration defined in the metadata file
			with open(a_) as metadata_file:
						lowerCamelCase						:Union[str, Any]						  =     json.load(a_)
			lowerCamelCase						:Union[str, Any]						  =     LukeConfig(use_entity_aware_attention=a_						,			**metadata['''model_config'''])

			# Load in the weights from the checkpoint_path
			lowerCamelCase						:List[str]						  =     torch.load(a_						,			map_location='''cpu''')

			# Load the entity vocab file
			lowerCamelCase						:List[str]						  =     load_entity_vocab(a_)

			lowerCamelCase						:Optional[int]						  =     RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''])

			# Add special tokens to the token vocabulary for downstream tasks
			lowerCamelCase						:int						  =     AddedToken('''<ent>'''						,			lstrip=a_						,			rstrip=a_)
			lowerCamelCase						:Optional[Any]						  =     AddedToken('''<ent2>'''						,			lstrip=a_						,			rstrip=a_)
			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(a_)
			with open(os.path.join(a_						,			LukeTokenizer.vocab_files_names['''entity_vocab_file'''])						,			'''w''') as f:
						json.dump(a_						,			a_)

			lowerCamelCase						:str						  =     LukeTokenizer.from_pretrained(a_)

			# Initialize the embeddings of the special tokens
			lowerCamelCase						:Union[str, Any]						  =     state_dict['''embeddings.word_embeddings.weight''']
			lowerCamelCase						:Any						  =     word_emb[tokenizer.convert_tokens_to_ids(['''@'''])[0]].unsqueeze(0)
			lowerCamelCase						:List[Any]						  =     word_emb[tokenizer.convert_tokens_to_ids(['''#'''])[0]].unsqueeze(0)
			lowerCamelCase						:Dict						  =     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"]:
									lowerCamelCase						:Tuple						  =     F"encoder.layer.{layer_index}.attention.self."
									lowerCamelCase						:List[Any]						  =     state_dict[prefix + matrix_name]
									lowerCamelCase						:Any						  =     state_dict[prefix + matrix_name]
									lowerCamelCase						:str						  =     state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
			lowerCamelCase						:Any						  =     state_dict['''entity_embeddings.entity_embeddings.weight''']
			lowerCamelCase						:List[Any]						  =     entity_emb[entity_vocab['''[MASK]''']]

			lowerCamelCase						:int						  =     LukeModel(config=a_).eval()

			lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     model.load_state_dict(a_						,			strict=a_)
			if not (len(a_) == 1 and missing_keys[0] == "embeddings.position_ids"):
						raise ValueError(F"Missing keys {', '.join(a_)}. 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
			lowerCamelCase						:Tuple						  =     LukeTokenizer.from_pretrained(a_						,			task='''entity_classification''')

			lowerCamelCase						: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 .'''
			)
			lowerCamelCase						:int						  =     (39, 42)
			lowerCamelCase						:Optional[Any]						  =     tokenizer(a_						,			entity_spans=[span]						,			add_prefix_space=a_						,			return_tensors='''pt''')

			lowerCamelCase						:List[str]						  =     model(**a_)

			# Verify word hidden states
			if model_size == "large":
						lowerCamelCase						:List[Any]						  =     torch.Size((1, 42, 10_24))
						lowerCamelCase						:Union[str, Any]						  =     torch.tensor(
						    [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]])
			else:  # base
						lowerCamelCase						:Optional[Any]						  =     torch.Size((1, 42, 7_68))
						lowerCamelCase						:List[Any]						  =     torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]])

			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]						,			a_						,			atol=1e-4):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						lowerCamelCase						:List[Any]						  =     torch.Size((1, 1, 10_24))
						lowerCamelCase						:int						  =     torch.tensor([[0.0_466, -0.0_106, -0.0_179]])
			else:  # base
						lowerCamelCase						:Optional[int]						  =     torch.Size((1, 1, 7_68))
						lowerCamelCase						:List[str]						  =     torch.tensor([[0.1_457, 0.1_044, 0.0_174]])

			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]						,			a_						,			atol=1e-4):
						raise ValueError

			# Finally, save our PyTorch model and tokenizer
			print('''Saving PyTorch model to {}'''.format(a_))
			model.save_pretrained(a_)





def  _lowerCamelCase					(   a_				: Tuple):
			lowerCamelCase						:Optional[Any]						  =     {}
			with open(a_						,			'''r'''						,			encoding='''utf-8''') as f:
						for index, line in enumerate(a_):
									lowerCamelCase  ,    lowerCamelCase						:Tuple						  =     line.rstrip().split('''\t''')
									lowerCamelCase						:str						  =     index

			return entity_vocab


if __name__ == "__main__":
			A__           =							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."""
			)
			A__           =							parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     ''
							_UpperCAmelCase						    =     (
							    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
							)
							_UpperCAmelCase						    =     None  # compression type in fsspec. ex: "gzip"
							_UpperCAmelCase						    =     None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz







							def __init__(				self     :    str	,					__snake_case     :    str = ""	,					__snake_case     :    Optional[str] = None	,					__snake_case     :    Optional[dict] = None	,					**__snake_case     :    Dict		):
										super().__init__(self	,					**__snake_case		)
										# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
										lowerCamelCase						:Optional[Any]						  =     fsspec.open(
										    __snake_case	,					mode='''rb'''	,					protocol=__snake_case	,					compression=self.compression	,					client_kwargs={
										        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
										        '''trust_env''': True,  # Enable reading proxy env variables.
										        **(target_options or {}).pop('''client_kwargs'''	,					{}		),  # To avoid issues if it was already passed.
										    }	,					**(target_options or {})	,					)
										lowerCamelCase						:List[str]						  =     os.path.basename(self.file.path.split('''::'''		)[0]		)
										lowerCamelCase						:Dict						  =     (
										    self.compressed_name[: self.compressed_name.rindex('''.'''		)]
										    if '''.''' in self.compressed_name
										    else self.compressed_name
										)
										lowerCamelCase						:List[str]						  =     None







							@classmethod
							def 				snake_case  (				cls     :    Any	,					__snake_case     :    Any		):
										# compressed file paths are always relative to the archive root
										return super()._strip_protocol(__snake_case		).lstrip('''/'''		)







							def 				snake_case  (				self     :    Any		):
										if self.dir_cache is None:
													lowerCamelCase						:Optional[Any]						  =     {**self.file.fs.info(self.file.path		), '''name''': self.uncompressed_name}
													lowerCamelCase						:Optional[Any]						  =     {f['''name''']: f}







							def 				snake_case  (				self     :    Union[str, Any]	,					__snake_case     :    str		):
										return self.file.open().read()







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    str	,					__snake_case     :    str = "rb"	,					__snake_case     :    int=None	,					__snake_case     :    Optional[int]=True	,					__snake_case     :    str=None	,					**__snake_case     :    str	,					):
										lowerCamelCase						:List[str]						  =     self._strip_protocol(__snake_case		)
										if mode != "rb":
													raise ValueError(F"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"		)
										return self.file.open()


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'bz2'
							_UpperCAmelCase						    =     'bz2'
							_UpperCAmelCase						    =     '.bz2'


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'gzip'
							_UpperCAmelCase						    =     'gzip'
							_UpperCAmelCase						    =     '.gz'


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'lz4'
							_UpperCAmelCase						    =     'lz4'
							_UpperCAmelCase						    =     '.lz4'


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'xz'
							_UpperCAmelCase						    =     'xz'
							_UpperCAmelCase						    =     '.xz'


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'zstd'
							_UpperCAmelCase						    =     'zstd'
							_UpperCAmelCase						    =     '.zst'







							def __init__(				self     :    str	,					__snake_case     :    str	,					__snake_case     :    str = "rb"	,					__snake_case     :    Optional[str] = None	,					__snake_case     :    Optional[dict] = None	,					__snake_case     :    int = DEFAULT_BLOCK_SIZE	,					**__snake_case     :    int	,					):
										super().__init__(
										    fo=__snake_case	,					mode=__snake_case	,					target_protocol=__snake_case	,					target_options=__snake_case	,					block_size=__snake_case	,					**__snake_case	,					)
										# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
										#
										# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
										#     out.close = close
										# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
										#
										# see https://github.com/intake/filesystem_spec/issues/725
										lowerCamelCase						:Tuple						  =     self.file.__enter__
										class   _lowerCAmelCase		:







																	def __init__(				self     :    Dict	,					__snake_case     :    Tuple		):
																				lowerCamelCase						:Optional[int]						  =     file_







																	def __enter__(				self     :    Optional[int]		):
																				self._file.__enter__()
																				return self







																	def __exit__(				self     :    str	,					*__snake_case     :    Optional[Any]	,					**__snake_case     :    List[Any]		):
																				self._file.__exit__(*__snake_case	,					**__snake_case		)







																	def __iter__(				self     :    Optional[Any]		):
																				return iter(self._file		)







																	def 				snake_case  (				self     :    List[Any]		):
																				return next(self._file		)







																	def __getattr__(				self     :    Any	,					__snake_case     :    str		):
																				return getattr(self._file	,					__snake_case		)

										def fixed_enter(*__snake_case     :    Optional[int]	,					**__snake_case     :    str		):
													return WrappedFile(_enter(*__snake_case	,					**__snake_case		)		)

										lowerCamelCase						:Dict						  =     fixed_enter



