






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_					:				Any    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[str]   ) ->   str:
						"""simple docstring"""







						with open(UpperCamelCase_   ) as metadata_file:
												lowerCAmelCase__       =						json.load(UpperCamelCase_   )
						lowerCAmelCase__       =						LukeConfig(use_entity_aware_attention=UpperCamelCase_    ,   **metadata["model_config"]   )

						# Load in the weights from the checkpoint_path
						lowerCAmelCase__       =						torch.load(UpperCamelCase_    ,   map_location="cpu"   )

						# Load the entity vocab file
						lowerCAmelCase__       =						load_entity_vocab(UpperCamelCase_   )

						lowerCAmelCase__       =						RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]   )

						# Add special tokens to the token vocabulary for downstream tasks
						lowerCAmelCase__       =						AddedToken("<ent>"    ,   lstrip=UpperCamelCase_    ,   rstrip=UpperCamelCase_   )
						lowerCAmelCase__       =						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_   )

						lowerCAmelCase__       =						LukeTokenizer.from_pretrained(UpperCamelCase_   )

						# Initialize the embeddings of the special tokens
						lowerCAmelCase__       =						state_dict["embeddings.word_embeddings.weight"]
						lowerCAmelCase__       =						word_emb[tokenizer.convert_tokens_to_ids(["@"]   )[0]].unsqueeze(0   )
						lowerCAmelCase__       =						word_emb[tokenizer.convert_tokens_to_ids(["#"]   )[0]].unsqueeze(0   )
						lowerCAmelCase__       =						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__       =						F"encoder.layer.{layer_index}.attention.self."
																		lowerCAmelCase__       =						state_dict[prefix + matrix_name]
																		lowerCAmelCase__       =						state_dict[prefix + matrix_name]
																		lowerCAmelCase__       =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
						lowerCAmelCase__       =						state_dict["entity_embeddings.entity_embeddings.weight"]
						lowerCAmelCase__       =						entity_emb[entity_vocab["[MASK]"]]

						lowerCAmelCase__       =						LukeModel(config=UpperCamelCase_   ).eval()

						lowerCAmelCase__      ,							lowerCAmelCase__       =						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
						lowerCAmelCase__       =						LukeTokenizer.from_pretrained(UpperCamelCase_    ,   task="entity_classification"   )

						lowerCAmelCase__       =						(
						    "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__       =						(39, 42)
						lowerCAmelCase__       =						tokenizer(UpperCamelCase_    ,   entity_spans=[span]    ,   add_prefix_space=UpperCamelCase_    ,   return_tensors="pt"   )

						lowerCAmelCase__       =						model(**UpperCamelCase_   )

						# Verify word hidden states
						if model_size == "large":
												lowerCAmelCase__       =						torch.Size((1, 42, 1_024)   )
												lowerCAmelCase__       =						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__       =						torch.Size((1, 42, 768)   )
												lowerCAmelCase__       =						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]    ,   UpperCamelCase_    ,   atol=1e-4   ):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												lowerCAmelCase__       =						torch.Size((1, 1, 1_024)   )
												lowerCAmelCase__       =						torch.tensor([[0.0_466, -0.0_106, -0.0_179]]   )
						else:  # base
												lowerCAmelCase__       =						torch.Size((1, 1, 768)   )
												lowerCAmelCase__       =						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]    ,   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_					:				Dict   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						{}
						with open(UpperCamelCase_    ,   "r"    ,   encoding="utf-8"   ) as f:
												for index, line in enumerate(UpperCamelCase_   ):
																		lowerCAmelCase__      ,							lowerCAmelCase__       =						line.rstrip().split("\t"   )
																		lowerCAmelCase__       =						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,
	)














# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


a_							=    subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
a_							=    subprocess.check_output(F"git diff --name-only {fork_point_sha}".split()).decode('''utf-8''').split()

a_							=    '''|'''.join(sys.argv[1:])
a_							=    re.compile(rF"^({joined_dirs}).*?\.py$")

a_							=    [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')






