






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,
	)








# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase_ :						Any      =      '''Create a default config file for Accelerate with only a few flags set.'''

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]="no"	,			__magic_name__					:  str = default_json_config_file	,			__magic_name__					:  bool = False					)		->			str:






	"""simple docstring"""



	UpperCamelCase     :Any          =	Path(__magic_name__					)
	path.parent.mkdir(parents=__magic_name__	,			exist_ok=__magic_name__					)
	if path.exists():
		print(
		    f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""					)
		return False
	UpperCamelCase     :Dict          =	mixed_precision.lower()
	if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
		raise ValueError(
		    f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""					)
	UpperCamelCase     :Optional[Any]          =	{
	    """compute_environment""": """LOCAL_MACHINE""",
	    """mixed_precision""": mixed_precision,
	}
	if torch.cuda.is_available():
		UpperCamelCase     :Union[str, Any]          =	torch.cuda.device_count()
		UpperCamelCase     :List[Any]          =	num_gpus
		UpperCamelCase     :Dict          =	False
		if num_gpus > 1:
			UpperCamelCase     :Any          =	"""MULTI_GPU"""
		else:
			UpperCamelCase     :Any          =	"""NO"""
	elif is_xpu_available() and use_xpu:
		UpperCamelCase     :Optional[Any]          =	torch.xpu.device_count()
		UpperCamelCase     :Optional[int]          =	num_xpus
		UpperCamelCase     :int          =	False
		if num_xpus > 1:
			UpperCamelCase     :Union[str, Any]          =	"""MULTI_XPU"""
		else:
			UpperCamelCase     :Union[str, Any]          =	"""NO"""
	elif is_npu_available():
		UpperCamelCase     :List[Any]          =	torch.npu.device_count()
		UpperCamelCase     :Optional[Any]          =	num_npus
		UpperCamelCase     :Tuple          =	False
		if num_npus > 1:
			UpperCamelCase     :Optional[Any]          =	"""MULTI_NPU"""
		else:
			UpperCamelCase     :List[Any]          =	"""NO"""
	else:
		UpperCamelCase     :Any          =	0
		UpperCamelCase     :Optional[Any]          =	True
		UpperCamelCase     :Optional[Any]          =	1
		UpperCamelCase     :List[str]          =	"""NO"""
	UpperCamelCase     :int          =	ClusterConfig(**__magic_name__					)
	config.to_json_file(__magic_name__					)
	return path

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Tuple					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	parser.add_parser("""default"""	,			parents=__magic_name__	,			help=__magic_name__	,			formatter_class=__magic_name__					)
	parser.add_argument(
	    """--config_file"""	,			default=__magic_name__	,			help=(
	        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
	        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
	        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
	        """with 'huggingface'."""
	    )	,			dest="""save_location"""	,			)

	parser.add_argument(
	    """--mixed_precision"""	,			choices=["""no""", """fp16""", """bf16"""]	,			type=__magic_name__	,			help="""Whether or not to use mixed precision training. """
	    """Choose between FP16 and BF16 (bfloat16) training. """
	    """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""	,			default="""no"""	,			)
	parser.set_defaults(func=__magic_name__					)
	return parser

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






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	write_basic_config(args.mixed_precision	,			args.save_location					)
	if config_file:
		print(f"""accelerate configuration saved at {config_file}"""					)
