

"""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							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


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

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

			# Load the entity vocab file
			SCREAMING_SNAKE_CASE__										=   load_entity_vocab(snake_case__     )

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

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

			SCREAMING_SNAKE_CASE__										=   LukeTokenizer.from_pretrained(snake_case__     )

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

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

			SCREAMING_SNAKE_CASE__										=   LukeModel(config=snake_case__     ).eval()

			SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   model.load_state_dict(snake_case__		,					strict=snake_case__     )
			if not (len(snake_case__     ) == 1 and missing_keys[0] == "embeddings.position_ids"):
						raise ValueError(f"""Missing keys {", ".join(snake_case__     )}. 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
			SCREAMING_SNAKE_CASE__										=   LukeTokenizer.from_pretrained(snake_case__		,					task="""entity_classification"""     )

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

			SCREAMING_SNAKE_CASE__										=   model(**snake_case__     )

			# Verify word hidden states
			if model_size == "large":
						SCREAMING_SNAKE_CASE__										=   torch.Size((1, 42, 10_24)     )
						SCREAMING_SNAKE_CASE__										=   torch.tensor(
						    [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]     )
			else:  # base
						SCREAMING_SNAKE_CASE__										=   torch.Size((1, 42, 7_68)     )
						SCREAMING_SNAKE_CASE__										=   torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]     )

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

			# Verify entity hidden states
			if model_size == "large":
						SCREAMING_SNAKE_CASE__										=   torch.Size((1, 1, 10_24)     )
						SCREAMING_SNAKE_CASE__										=   torch.tensor([[0.04_66, -0.01_06, -0.01_79]]     )
			else:  # base
						SCREAMING_SNAKE_CASE__										=   torch.Size((1, 1, 7_68)     )
						SCREAMING_SNAKE_CASE__										=   torch.tensor([[0.14_57, 0.10_44, 0.01_74]]     )

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

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

def 						A							(  snake_case__     ):




			'''simple docstring'''


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

			return entity_vocab


if __name__ == "__main__":
						A_				:      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."
						)
						A_				:      Optional[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"""




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[str]       		=				PhobertTokenizer
			lowerCamelCase__ :		Union[str, Any]       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Any:
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						SCREAMING_SNAKE_CASE__										=   ["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
						SCREAMING_SNAKE_CASE__										=   dict(zip(__UpperCAmelCase     ,					range(len(__UpperCAmelCase							)							)							)							)
						SCREAMING_SNAKE_CASE__										=   ["""#version: 0.2""", """l à</w>"""]
						SCREAMING_SNAKE_CASE__										=   {"""unk_token""": """<unk>"""}

						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""vocab_file"""]							)
						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""merges_file"""]							)

						with open(self.vocab_file     ,					"""w"""     ,					encoding="""utf-8"""							) as fp:
									for token in vocab_tokens:
												fp.write(F"""{token} {vocab_tokens[token]}\n"""							)
						with open(self.merges_file     ,					"""w"""     ,					encoding="""utf-8"""							) as fp:
									fp.write("""\n""".join(__UpperCAmelCase							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					**__UpperCAmelCase					:    int							) ->     Optional[int]:
						kwargs.update(self.special_tokens_map							)
						return PhobertTokenizer.from_pretrained(self.tmpdirname     ,					**__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    int							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   """Tôi là VinAI Research"""
						SCREAMING_SNAKE_CASE__										=   """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
						return input_text, output_text





			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   PhobertTokenizer(self.vocab_file     ,					self.merges_file     ,					**self.special_tokens_map							)
						SCREAMING_SNAKE_CASE__										=   """Tôi là VinAI Research"""
						SCREAMING_SNAKE_CASE__										=   """T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
						SCREAMING_SNAKE_CASE__										=   tokenizer.tokenize(__UpperCAmelCase							)
						print(__UpperCAmelCase							)
						self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tokens + [tokenizer.unk_token]

						SCREAMING_SNAKE_CASE__										=   [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)     ,					__UpperCAmelCase							)


