
"""simple docstring"""





# 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


UpperCamelCase_       :    Any						      =			subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCamelCase_       :    List[Any]						      =			subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

UpperCamelCase_       :    Optional[int]						      =			'''|'''.join(sys.argv[1:])
UpperCamelCase_       :    Tuple						      =			re.compile(RF"""^({joined_dirs}).*?\.py$""")

UpperCamelCase_       :    Optional[Any]						      =			[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')






"""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_  (__a					, __a					, __a					, __a					, __a					):

					'''simple docstring'''





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

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

					# Load the entity vocab file
					A_								=    load_entity_vocab(__a					)

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

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

					A_								=    LukeTokenizer.from_pretrained(__a					)

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

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

					A_								=    LukeModel(config=__a					).eval()

					A_			,     A_								=    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
					A_								=    LukeTokenizer.from_pretrained(__a					, task="entity_classification"					)

					A_								=    (
					    "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 ."
					)
					A_								=    (39, 42)
					A_								=    tokenizer(__a					, entity_spans=[span]					, add_prefix_space=__a					, return_tensors="pt"					)

					A_								=    model(**__a					)

					# Verify word hidden states
					if model_size == "large":
										A_								=    torch.Size((1, 42, 1024)					)
										A_								=    torch.tensor(
										    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]					)
					else:  # base
										A_								=    torch.Size((1, 42, 768)					)
										A_								=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]					)

					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":
										A_								=    torch.Size((1, 1, 1024)					)
										A_								=    torch.tensor([[0.0466, -0.0106, -0.0179]]					)
					else:  # base
										A_								=    torch.Size((1, 1, 768)					)
										A_								=    torch.tensor([[0.1457, 0.1044, 0.0174]]					)

					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 				A_  (__a					):

					'''simple docstring'''





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

					return entity_vocab


if __name__ == "__main__":
		UpperCamelCase_       :    Dict						      =			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_       :    List[str]						      =			parser.parse_args()
		convert_luke_checkpoint(
		    args.checkpoint_path,
		    args.metadata_path,
		    args.entity_vocab_path,
		    args.pytorch_dump_folder_path,
		    args.model_size,
		)




