






import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  Optional[Any]	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Dict	,			__magic_name__					:  str					)		->			int:






	"""simple docstring"""



	with open(__magic_name__					) as metadata_file:
		UpperCamelCase     :int          =	json.load(__magic_name__					)
	UpperCamelCase     :List[Any]          =	LukeConfig(use_entity_aware_attention=__magic_name__	,			**metadata["""model_config"""]					)

	# Load in the weights from the checkpoint_path
	UpperCamelCase     :List[str]          =	torch.load(__magic_name__	,			map_location="""cpu"""					)["""module"""]

	# Load the entity vocab file
	UpperCamelCase     :Optional[int]          =	load_original_entity_vocab(__magic_name__					)
	# add an entry for [MASK2]
	UpperCamelCase     :Optional[Any]          =	max(entity_vocab.values()					) + 1
	config.entity_vocab_size += 1

	UpperCamelCase     :Any          =	XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]					)

	# Add special tokens to the token vocabulary for downstream tasks
	UpperCamelCase     :Union[str, Any]          =	AddedToken("""<ent>"""	,			lstrip=__magic_name__	,			rstrip=__magic_name__					)
	UpperCamelCase     :Optional[int]          =	AddedToken("""<ent2>"""	,			lstrip=__magic_name__	,			rstrip=__magic_name__					)
	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(__magic_name__					)
	with open(os.path.join(__magic_name__	,			"""tokenizer_config.json"""					)	,			"""r"""					) as f:
		UpperCamelCase     :str          =	json.load(__magic_name__					)
	UpperCamelCase     :Optional[int]          =	"""MLukeTokenizer"""
	with open(os.path.join(__magic_name__	,			"""tokenizer_config.json"""					)	,			"""w"""					) as f:
		json.dump(__magic_name__	,			__magic_name__					)

	with open(os.path.join(__magic_name__	,			MLukeTokenizer.vocab_files_names["""entity_vocab_file"""]					)	,			"""w"""					) as f:
		json.dump(__magic_name__	,			__magic_name__					)

	UpperCamelCase     :Optional[int]          =	MLukeTokenizer.from_pretrained(__magic_name__					)

	# Initialize the embeddings of the special tokens
	UpperCamelCase     :str          =	tokenizer.convert_tokens_to_ids(["""@"""]					)[0]
	UpperCamelCase     :Optional[int]          =	tokenizer.convert_tokens_to_ids(["""#"""]					)[0]

	UpperCamelCase     :str          =	state_dict["""embeddings.word_embeddings.weight"""]
	UpperCamelCase     :int          =	word_emb[ent_init_index].unsqueeze(0					)
	UpperCamelCase     :Dict          =	word_emb[enta_init_index].unsqueeze(0					)
	UpperCamelCase     :Union[str, Any]          =	torch.cat([word_emb, ent_emb, enta_emb]					)
	# add special tokens for 'entity_predictions.bias'
	for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
		UpperCamelCase     :Dict          =	state_dict[bias_name]
		UpperCamelCase     :str          =	decoder_bias[ent_init_index].unsqueeze(0					)
		UpperCamelCase     :Dict          =	decoder_bias[enta_init_index].unsqueeze(0					)
		UpperCamelCase     :Optional[int]          =	torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]					)

	# 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     :str          =	f"""encoder.layer.{layer_index}.attention.self."""
			UpperCamelCase     :str          =	state_dict[prefix + matrix_name]
			UpperCamelCase     :Any          =	state_dict[prefix + matrix_name]
			UpperCamelCase     :List[Any]          =	state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
	UpperCamelCase     :int          =	state_dict["""entity_embeddings.entity_embeddings.weight"""]
	UpperCamelCase     :Dict          =	entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0					)
	UpperCamelCase     :int          =	torch.cat([entity_emb, entity_mask_emb]					)
	# add [MASK2] for 'entity_predictions.bias'
	UpperCamelCase     :Union[str, Any]          =	state_dict["""entity_predictions.bias"""]
	UpperCamelCase     :List[Any]          =	entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0					)
	UpperCamelCase     :Dict          =	torch.cat([entity_prediction_bias, entity_mask_bias]					)

	UpperCamelCase     :Union[str, Any]          =	LukeForMaskedLM(config=__magic_name__					).eval()

	state_dict.pop("""entity_predictions.decoder.weight"""					)
	state_dict.pop("""lm_head.decoder.weight"""					)
	state_dict.pop("""lm_head.decoder.bias"""					)
	UpperCamelCase     :int          =	OrderedDict()
	for key, value in state_dict.items():
		if not (key.startswith("""lm_head"""					) or key.startswith("""entity_predictions"""					)):
			UpperCamelCase     :Union[str, Any]          =	state_dict[key]
		else:
			UpperCamelCase     :Optional[Any]          =	state_dict[key]

	UpperCamelCase     ,      UpperCamelCase     :List[Any]          =	model.load_state_dict(__magic_name__	,			strict=__magic_name__					)

	if set(__magic_name__					) != {"luke.embeddings.position_ids"}:
		raise ValueError(f"""Unexpected unexpected_keys: {unexpected_keys}"""					)
	if set(__magic_name__					) != {
	    "lm_head.decoder.weight",
	    "lm_head.decoder.bias",
	    "entity_predictions.decoder.weight",
	}:
		raise ValueError(f"""Unexpected missing_keys: {missing_keys}"""					)

	model.tie_weights()
	assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
	assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

	# Check outputs
	UpperCamelCase     :Dict          =	MLukeTokenizer.from_pretrained(__magic_name__	,			task="""entity_classification"""					)

	UpperCamelCase     :Dict          =	"""ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."""
	UpperCamelCase     :Union[str, Any]          =	(0, 9)
	UpperCamelCase     :Optional[Any]          =	tokenizer(__magic_name__	,			entity_spans=[span]	,			return_tensors="""pt"""					)

	UpperCamelCase     :Union[str, Any]          =	model(**__magic_name__					)

	# Verify word hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		UpperCamelCase     :Union[str, Any]          =	torch.Size((1, 33, 768)					)
		UpperCamelCase     :Tuple          =	torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]					)

	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]	,			__magic_name__	,			atol=1E-4					):
		raise ValueError

	# Verify entity hidden states
	if model_size == "large":
		raise NotImplementedError
	else:  # base
		UpperCamelCase     :Union[str, Any]          =	torch.Size((1, 1, 768)					)
		UpperCamelCase     :Tuple          =	torch.tensor([[-0.1482, 0.0609, 0.0322]]					)

	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]	,			__magic_name__	,			atol=1E-4					):
		raise ValueError

	# Verify masked word/entity prediction
	UpperCamelCase     :Dict          =	MLukeTokenizer.from_pretrained(__magic_name__					)
	UpperCamelCase     :Optional[int]          =	"""Tokyo is the capital of <mask>."""
	UpperCamelCase     :Optional[int]          =	(24, 30)
	UpperCamelCase     :Optional[Any]          =	tokenizer(__magic_name__	,			entity_spans=[span]	,			return_tensors="""pt"""					)

	UpperCamelCase     :List[str]          =	model(**__magic_name__					)

	UpperCamelCase     :Optional[Any]          =	encoding["""input_ids"""][0].tolist()
	UpperCamelCase     :Any          =	input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>"""					)					)
	UpperCamelCase     :str          =	outputs.logits[0][mask_position_id].argmax(dim=-1					)
	assert "Japan" == tokenizer.decode(__magic_name__					)

	UpperCamelCase     :Tuple          =	outputs.entity_logits[0][0].argmax().item()
	UpperCamelCase     :str          =	[
	    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
	]
	assert [e for e in multilingual_predicted_entities if e.startswith("""en:"""					)][0] == "en:Japan"

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	["""[MASK]""", """[PAD]""", """[UNK]"""]

	UpperCamelCase     :Dict          =	[json.loads(__magic_name__					) for line in open(__magic_name__					)]

	UpperCamelCase     :int          =	{}
	for entry in data:
		UpperCamelCase     :Dict          =	entry["""id"""]
		for entity_name, language in entry["entities"]:
			if entity_name in SPECIAL_TOKENS:
				UpperCamelCase     :int          =	entity_id
				break
			UpperCamelCase     :Dict          =	f"""{language}:{entity_name}"""
			UpperCamelCase     :Optional[int]          =	entity_id
	return new_mapping


if __name__ == "__main__":
	UpperCAmelCase_ :						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.'''
	)
	UpperCAmelCase_ :						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'''



# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


UpperCAmelCase :       Any			   =     [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]







def    a__       (  a__      ,    a__=None      ,    a__=None      ,    a__=None						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      True
     while ask_again:
          __SCREAMING_SNAKE_CASE     =      input(a__						)
          try:
               if default is not None and len(a__						) == 0:
                    return default
               return convert_value(a__						) if convert_value is not None else result
          except Exception:
               if error_message is not None:
                    print(a__						)







def    a__       (  a__      ,    a__=[]      ,    a__=None      ,    a__=0						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      BulletMenu(a__      ,    a__						)
     __SCREAMING_SNAKE_CASE     =      menu.run(default_choice=a__						)
     return convert_value(a__						) if convert_value is not None else result







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return DynamoBackend(DYNAMO_BACKENDS[value]						).value







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)
     return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value]						)







def    a__       (  a__						):




     """simple docstring"""


     return {"yes": True, "no": False}[value.lower()]







class 						lowerCAmelCase__   (      argparse.RawDescriptionHelpFormatter					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       str					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      super()._format_usage(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      usage.replace("""<command> [<args>] """					,							""""""			)
      return usage






