




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_		:				Optional[Any]									=     get_tests_dir("fixtures/test_sentencepiece_no_bos.model")




@require_sentencepiece
@require_tokenizers
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					PegasusTokenizer
						UpperCamelCase     						=					PegasusTokenizerFast
						UpperCamelCase     						=					True
						UpperCamelCase     						=					True

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										super().setUp()

										# We have a SentencePiece fixture for testing
										__magic_name__	:	Tuple								=PegasusTokenizer(__snake_case )
										tokenizer.save_pretrained(self.tmpdirname )

						@cached_property
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										return PegasusTokenizer.from_pretrained("""google/pegasus-large""" )

						def     A__						(		self   :str    , **__snake_case   :Dict ):
										'''simple docstring'''

										return PegasusTokenizer.from_pretrained(self.tmpdirname    , **__snake_case )

						def     A__						(		self   :Optional[int]    , __snake_case   :Union[str, Any] ):
										'''simple docstring'''

										return ("This is a test", "This is a test")

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								="""</s>"""
										__magic_name__	:	List[str]								=1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case )    , __snake_case )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case )    , __snake_case )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=list(self.get_tokenizer().get_vocab().keys() )

										self.assertEqual(vocab_keys[0]    , """<pad>""" )
										self.assertEqual(vocab_keys[1]    , """</s>""" )
										self.assertEqual(vocab_keys[-1]    , """v""" )
										self.assertEqual(len(__snake_case )    , 11_03 )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										self.assertEqual(self.get_tokenizer().vocab_size    , 11_03 )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
										__magic_name__	:	Tuple								=self.tokenizer_class.from_pretrained(self.tmpdirname )
										__magic_name__	:	Dict								=(
										    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
										    """ </s> <pad> <pad> <pad>"""
										)
										__magic_name__	:	int								=rust_tokenizer([raw_input_str]    , return_tensors=__snake_case    , add_special_tokens=__snake_case ).input_ids[0]
										__magic_name__	:	Dict								=py_tokenizer([raw_input_str]    , return_tensors=__snake_case    , add_special_tokens=__snake_case ).input_ids[0]
										self.assertListEqual(__snake_case    , __snake_case )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	str								=self._large_tokenizer
										# <mask_1> masks whole sentence while <mask_2> masks single word
										__magic_name__	:	Tuple								="""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
										__magic_name__	:	str								=[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
										__magic_name__	:	str								=tokenizer([raw_input_str]    , return_tensors=__snake_case ).input_ids[0]
										self.assertListEqual(__snake_case    , __snake_case )

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self._large_tokenizer
										# The tracebacks for the following asserts are **better** without messages or self.assertEqual
										assert tokenizer.vocab_size == 9_61_03
										assert tokenizer.pad_token_id == 0
										assert tokenizer.eos_token_id == 1
										assert tokenizer.offset == 1_03
										assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
										assert tokenizer.unk_token == "<unk>"
										assert tokenizer.model_max_length == 10_24
										__magic_name__	:	Optional[int]								="""To ensure a smooth flow of bank resolutions."""
										__magic_name__	:	Optional[Any]								=[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
										__magic_name__	:	Optional[int]								=tokenizer([raw_input_str]    , return_tensors=__snake_case ).input_ids[0]
										self.assertListEqual(__snake_case    , __snake_case )
										assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

						@require_torch
						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	int								=["""This is going to be way too long.""" * 1_50, """short example"""]
										__magic_name__	:	Union[str, Any]								=["""not super long but more than 5 tokens""", """tiny"""]
										__magic_name__	:	str								=self._large_tokenizer(__snake_case    , padding=__snake_case    , truncation=__snake_case    , return_tensors="""pt""" )
										__magic_name__	:	Tuple								=self._large_tokenizer(
										    text_target=__snake_case    , max_length=5    , padding=__snake_case    , truncation=__snake_case    , return_tensors="""pt""" )

										assert batch.input_ids.shape == (2, 10_24)
										assert batch.attention_mask.shape == (2, 10_24)
										assert targets["input_ids"].shape == (2, 5)
										assert len(__snake_case ) == 2  # input_ids, attention_mask.


						@slow
						def     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								={"""input_ids""": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=__snake_case    , model_name="""google/bigbird-pegasus-large-arxiv"""    , revision="""ba85d0851d708441f91440d509690f1ab6353415"""    , )




@require_sentencepiece
@require_tokenizers
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					PegasusTokenizer
						UpperCamelCase     						=					PegasusTokenizerFast
						UpperCamelCase     						=					True
						UpperCamelCase     						=					True

						def     A__						(		self   :str ):
										'''simple docstring'''

										super().setUp()

										# We have a SentencePiece fixture for testing
										__magic_name__	:	Optional[Any]								=PegasusTokenizer(__snake_case    , offset=0    , mask_token_sent=__snake_case    , mask_token="""[MASK]""" )
										tokenizer.save_pretrained(self.tmpdirname )

						@cached_property
						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv""" )

						def     A__						(		self   :int    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										return PegasusTokenizer.from_pretrained(self.tmpdirname    , **__snake_case )

						def     A__						(		self   :List[str]    , __snake_case   :Any ):
										'''simple docstring'''

										return ("This is a test", "This is a test")

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
										__magic_name__	:	int								=self.tokenizer_class.from_pretrained(self.tmpdirname )
										__magic_name__	:	Optional[Any]								=(
										    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
										    """ <pad> <pad> <pad>"""
										)
										__magic_name__	:	Optional[Any]								=rust_tokenizer([raw_input_str]    , return_tensors=__snake_case    , add_special_tokens=__snake_case ).input_ids[0]
										__magic_name__	:	int								=py_tokenizer([raw_input_str]    , return_tensors=__snake_case    , add_special_tokens=__snake_case ).input_ids[0]
										self.assertListEqual(__snake_case    , __snake_case )

						@require_torch
						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	str								=["""This is going to be way too long.""" * 10_00, """short example"""]
										__magic_name__	:	int								=["""not super long but more than 5 tokens""", """tiny"""]
										__magic_name__	:	Dict								=self._large_tokenizer(__snake_case    , padding=__snake_case    , truncation=__snake_case    , return_tensors="""pt""" )
										__magic_name__	:	Any								=self._large_tokenizer(
										    text_target=__snake_case    , max_length=5    , padding=__snake_case    , truncation=__snake_case    , return_tensors="""pt""" )

										assert batch.input_ids.shape == (2, 40_96)
										assert batch.attention_mask.shape == (2, 40_96)
										assert targets["input_ids"].shape == (2, 5)
										assert len(__snake_case ) == 2  # input_ids, attention_mask.


						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=(
										    """This is an example string that is used to test the original TF implementation against the HF"""
										    """ implementation"""
										)

										__magic_name__	:	Optional[int]								=self._large_tokenizer(__snake_case ).input_ids

										self.assertListEqual(
										    __snake_case    , [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]    , )










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 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				# Load configuration defined in the metadata file
				with open(lowerCamelCase   ) as metadata_file:
								__magic_name__	:	List[Any]								=json.load(lowerCamelCase   )
				__magic_name__	:	Optional[int]								=LukeConfig(use_entity_aware_attention=lowerCamelCase	,     **metadata["""model_config"""]   )

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

				# Load the entity vocab file
				__magic_name__	:	Dict								=load_entity_vocab(lowerCamelCase   )

				__magic_name__	:	Union[str, Any]								=RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]   )

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

				__magic_name__	:	Optional[Any]								=LukeTokenizer.from_pretrained(lowerCamelCase   )

				# Initialize the embeddings of the special tokens
				__magic_name__	:	List[str]								=state_dict["""embeddings.word_embeddings.weight"""]
				__magic_name__	:	Optional[int]								=word_emb[tokenizer.convert_tokens_to_ids(["""@"""]   )[0]].unsqueeze(0   )
				__magic_name__	:	Optional[int]								=word_emb[tokenizer.convert_tokens_to_ids(["""#"""]   )[0]].unsqueeze(0   )
				__magic_name__	:	Tuple								=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"]:
												__magic_name__	:	Optional[Any]								=F"encoder.layer.{layer_index}.attention.self."
												__magic_name__	:	Optional[Any]								=state_dict[prefix + matrix_name]
												__magic_name__	:	Any								=state_dict[prefix + matrix_name]
												__magic_name__	:	Dict								=state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				__magic_name__	:	str								=state_dict["""entity_embeddings.entity_embeddings.weight"""]
				__magic_name__	:	Union[str, Any]								=entity_emb[entity_vocab["""[MASK]"""]]

				__magic_name__	:	Optional[int]								=LukeModel(config=lowerCamelCase   ).eval()

				__magic_name__				, __magic_name__	:	Any								=model.load_state_dict(lowerCamelCase	,     strict=lowerCamelCase   )
				if not (len(lowerCamelCase   ) == 1 and missing_keys[0] == "embeddings.position_ids"):
								raise ValueError(F"Missing keys {', '.join(lowerCamelCase   )}. 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
				__magic_name__	:	Dict								=LukeTokenizer.from_pretrained(lowerCamelCase	,     task="""entity_classification"""   )

				__magic_name__	:	int								=(
				    """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 ."""
				)
				__magic_name__	:	Optional[Any]								=(39, 42)
				__magic_name__	:	Dict								=tokenizer(lowerCamelCase	,     entity_spans=[span]	,     add_prefix_space=lowerCamelCase	,     return_tensors="""pt"""   )

				__magic_name__	:	str								=model(**lowerCamelCase   )

				# Verify word hidden states
				if model_size == "large":
								__magic_name__	:	Tuple								=torch.Size((1, 42, 1024)   )
								__magic_name__	:	int								=torch.tensor(
								    [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]   )
				else:  # base
								__magic_name__	:	Dict								=torch.Size((1, 42, 768)   )
								__magic_name__	:	List[Any]								=torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]   )

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

				# Verify entity hidden states
				if model_size == "large":
								__magic_name__	:	Optional[Any]								=torch.Size((1, 1, 1024)   )
								__magic_name__	:	Tuple								=torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]   )
				else:  # base
								__magic_name__	:	List[Any]								=torch.Size((1, 1, 768)   )
								__magic_name__	:	Dict								=torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]   )

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

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




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Tuple								={}
				with open(lowerCamelCase	,     """r"""	,     encoding="""utf-8"""   ) as f:
								for index, line in enumerate(lowerCamelCase   ):
												__magic_name__				, __magic_name__	:	Optional[Any]								=line.rstrip().split("""\t"""   )
												__magic_name__	:	Union[str, Any]								=index

				return entity_vocab


if __name__ == "__main__":
							UpperCAmelCase_		:				Optional[Any]									=     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,
							)




