





'''simple docstring'''

import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_					= get_tests_dir("""fixtures/spiece.model""")


@require_sentencepiece
@require_tokenizers
class 					a_     (				snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = DebertaVaTokenizer
    UpperCamelCase     = DebertaVaTokenizerFast
    UpperCamelCase     = True
    UpperCamelCase     = True


    def 			snake_case_(		self       )		->   int:
        super().setUp()

        # We have a SentencePiece fixture for testing
        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizer(A ,		unk_token="""<unk>"""       )
        tokenizer.save_pretrained(self.tmpdirname       )


    def 			snake_case_(		self ,		A       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			"""this is a test"""
        _SCREAMING_SNAKE_CASE									=			"""this is a test"""
        return input_text, output_text


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			"""<pad>"""
        _SCREAMING_SNAKE_CASE									=			0

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


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			list(self.get_tokenizer().get_vocab().keys()       )
        self.assertEqual(vocab_keys[0] ,		"""<pad>"""       )
        self.assertEqual(vocab_keys[1] ,		"""<unk>"""       )
        self.assertEqual(vocab_keys[-1] ,		"""[PAD]"""       )
        self.assertEqual(len(A       ) ,		3_0001       )


    def 			snake_case_(		self       )		->   Any:
        self.assertEqual(self.get_tokenizer().vocab_size ,		3_0000       )


    def 			snake_case_(		self       )		->   Any:
        # fmt: off
        _SCREAMING_SNAKE_CASE									=			""" \tHeLLo!how  \n Are yoU?  """
        _SCREAMING_SNAKE_CASE									=			["""▁hello""", """!""", """how""", """▁are""", """▁you""", """?"""]
        # fmt: on

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizer(A ,		do_lower_case=A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(A ,		add_special_tokens=A       )       )

        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizerFast(A ,		do_lower_case=A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A ,		add_special_tokens=A       )       )

        self.assertListEqual(A ,		A       )


    @unittest.skip("""There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."""       )
    def 			snake_case_(		self       )		->   List[Any]:
        pass


    @unittest.skip("""There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."""       )
    def 			snake_case_(		self       )		->   Optional[int]:
        pass


    def 			snake_case_(		self       )		->   Optional[Any]:
        # fmt: off
        _SCREAMING_SNAKE_CASE									=			"""I was born in 92000, and this is falsé."""
        _SCREAMING_SNAKE_CASE									=			["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
        # fmt: on

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizer(A ,		split_by_punct=A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(A ,		add_special_tokens=A       )       )

        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizerFast(A ,		split_by_punct=A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A ,		add_special_tokens=A       )       )

        self.assertListEqual(A ,		A       )


    def 			snake_case_(		self       )		->   List[str]:
        # fmt: off
        _SCREAMING_SNAKE_CASE									=			"""I was born in 92000, and this is falsé."""
        _SCREAMING_SNAKE_CASE									=			["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
        # fmt: on

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizer(A ,		do_lower_case=A ,		split_by_punct=A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(A ,		add_special_tokens=A       )       )
        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizerFast(A ,		do_lower_case=A ,		split_by_punct=A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A ,		add_special_tokens=A       )       )
        self.assertListEqual(A ,		A       )


    def 			snake_case_(		self       )		->   str:
        # fmt: off
        _SCREAMING_SNAKE_CASE									=			"""I was born in 92000, and this is falsé."""
        _SCREAMING_SNAKE_CASE									=			["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """.""", ]
        # fmt: on

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizer(A ,		do_lower_case=A ,		split_by_punct=A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(A ,		add_special_tokens=A       )       )

        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizerFast(A ,		do_lower_case=A ,		split_by_punct=A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A ,		add_special_tokens=A       )       )

        self.assertListEqual(A ,		A       )


    def 			snake_case_(		self       )		->   Union[str, Any]:
        # fmt: off
        _SCREAMING_SNAKE_CASE									=			"""I was born in 92000, and this is falsé."""
        _SCREAMING_SNAKE_CASE									=			["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """▁""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """▁""", """.""", ]
        # fmt: on

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizer(A ,		do_lower_case=A ,		split_by_punct=A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(A ,		add_special_tokens=A       )       )

        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizerFast(A ,		do_lower_case=A ,		split_by_punct=A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A ,		add_special_tokens=A       )       )

        self.assertListEqual(A ,		A       )


    def 			snake_case_(		self       )		->   List[str]:
        # fmt: off
        _SCREAMING_SNAKE_CASE									=			""" \tHeLLo!how  \n Are yoU?  """
        _SCREAMING_SNAKE_CASE									=			["""▁""", """<unk>""", """e""", """<unk>""", """o""", """!""", """how""", """▁""", """<unk>""", """re""", """▁yo""", """<unk>""", """?"""]
        # fmt: on

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizer(A ,		do_lower_case=A ,		split_by_punct=A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(A ,		add_special_tokens=A       )       )

        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizerFast(A ,		do_lower_case=A ,		split_by_punct=A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A ,		add_special_tokens=A       )       )

        self.assertListEqual(A ,		A       )


    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()
        _SCREAMING_SNAKE_CASE									=			self.get_rust_tokenizer()

        _SCREAMING_SNAKE_CASE									=			"""I was born in 92000, and this is falsé."""

        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(tokenizer.encode(A ,		add_special_tokens=A       )       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(A ,		add_special_tokens=A       )       )
        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A ,		add_special_tokens=A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.encode(A ,		add_special_tokens=A       )
        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			self.get_rust_tokenizer()
        _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.encode(A       )
        self.assertListEqual(A ,		A       )


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			"""This is a test"""
        _SCREAMING_SNAKE_CASE									=			[13, 1, 4398, 25, 21, 1289]
        _SCREAMING_SNAKE_CASE									=			["""▁""", """T""", """his""", """▁is""", """▁a""", """▁test"""]
        _SCREAMING_SNAKE_CASE									=			["""▁""", """<unk>""", """his""", """▁is""", """▁a""", """▁test"""]

        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizer(A ,		keep_accents=A       )
        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizerFast(A ,		keep_accents=A       )

        _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A ,		add_special_tokens=A       )
        self.assertListEqual(A ,		A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.tokenize(A       )
        self.assertListEqual(A ,		A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(A       )
        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.encode(A ,		add_special_tokens=A       )
        self.assertListEqual(A ,		A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.tokenize(A       )
        self.assertListEqual(A ,		A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.convert_ids_to_tokens(A       )
        self.assertListEqual(A ,		A       )

        # fmt: off
        _SCREAMING_SNAKE_CASE									=			"""I was born in 92000, and this is falsé."""
        _SCREAMING_SNAKE_CASE									=			[13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
        _SCREAMING_SNAKE_CASE									=			["""▁""", """I""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """.""", ]
        _SCREAMING_SNAKE_CASE									=			["""▁""", """<unk>""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """.""", ]
        # fmt: on

        _SCREAMING_SNAKE_CASE									=			tokenizer.encode(A ,		add_special_tokens=A       )
        self.assertListEqual(A ,		A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.tokenize(A       )
        self.assertListEqual(A ,		A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(A       )
        self.assertListEqual(A ,		A       )

        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.encode(A ,		add_special_tokens=A       )
        self.assertListEqual(A ,		A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.tokenize(A       )
        self.assertListEqual(A ,		A       )
        _SCREAMING_SNAKE_CASE									=			rust_tokenizer.convert_ids_to_tokens(A       )
        self.assertListEqual(A ,		A       )


    def 			snake_case_(		self       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			DebertaVaTokenizer(A       )

        _SCREAMING_SNAKE_CASE									=			tokenizer.encode("""sequence builders"""       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.encode("""multi-sequence build"""       )

        _SCREAMING_SNAKE_CASE									=			tokenizer.build_inputs_with_special_tokens(A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.build_inputs_with_special_tokens(A ,		A       )

        self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] ,		A       )
        self.assertEqual(
            [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id] ,		A ,		)





    @slow
    def 			snake_case_(		self       )		->   List[str]:
        # fmt: off
        _SCREAMING_SNAKE_CASE									=			{"""input_ids""": [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 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], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 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]], """token_type_ids""": [[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, 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, 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, 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, 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], [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, 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=A ,		model_name="""microsoft/deberta-v2-xlarge""" ,		revision="""ad6e42c1532ddf3a15c39246b63f5559d558b670""" ,		)










'''simple docstring'''

import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
lowercase_					= logging.getLogger(__name__)


lowercase_					= """Hello world! cécé herlolip"""


lowercase_					= namedtuple(
    """BertAbsConfig""",
    [
        """temp_dir""",
        """large""",
        """use_bert_emb""",
        """finetune_bert""",
        """encoder""",
        """share_emb""",
        """max_pos""",
        """enc_layers""",
        """enc_hidden_size""",
        """enc_heads""",
        """enc_ff_size""",
        """enc_dropout""",
        """dec_layers""",
        """dec_hidden_size""",
        """dec_heads""",
        """dec_ff_size""",
        """dec_dropout""",
    ],
)





def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Union[str, Any]    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			BertAbsConfig(
        temp_dir="""."""					,       finetune_bert=__lowerCamelCase					,       large=__lowerCamelCase					,       share_emb=__lowerCamelCase					,       use_bert_emb=__lowerCamelCase					,       encoder="""bert"""					,       max_pos=512					,       enc_layers=6					,       enc_hidden_size=512					,       enc_heads=8					,       enc_ff_size=512					,       enc_dropout=0.2					,       dec_layers=6					,       dec_hidden_size=768					,       dec_heads=8					,       dec_ff_size=2048					,       dec_dropout=0.2					,       )
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       lambda __lowerCamelCase					,       __lowerCamelCase    : storage    )
    _SCREAMING_SNAKE_CASE									=			AbsSummarizer(__lowerCamelCase					,       torch.device("""cpu"""    )					,       __lowerCamelCase    )
    original.eval()

    _SCREAMING_SNAKE_CASE									=			BertAbsSummarizer(__lowerCamelCase					,       torch.device("""cpu"""    )    )
    new_model.eval()

    # -------------------
    # Convert the weights
    # -------------------

    logging.info("""convert the model"""    )
    new_model.bert.load_state_dict(original.bert.state_dict()    )
    new_model.decoder.load_state_dict(original.decoder.state_dict()    )
    new_model.generator.load_state_dict(original.generator.state_dict()    )

    # ----------------------------------
    # Make sure the outpus are identical
    # ----------------------------------

    logging.info("""Make sure that the models' outputs are identical"""    )
    _SCREAMING_SNAKE_CASE									=			BertTokenizer.from_pretrained("""bert-base-uncased"""    )

    # prepare the model inputs
    _SCREAMING_SNAKE_CASE									=			tokenizer.encode("""This is sample éàalj'-."""    )
    encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase    ))    )
    _SCREAMING_SNAKE_CASE									=			torch.tensor(__lowerCamelCase    ).unsqueeze(0    )
    _SCREAMING_SNAKE_CASE									=			tokenizer.encode("""This is sample 3 éàalj'-."""    )
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase    ))    )
    _SCREAMING_SNAKE_CASE									=			torch.tensor(__lowerCamelCase    ).unsqueeze(0    )

    # failsafe to make sure the weights reset does not affect the
    # loaded weights.
    assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight    )    ) == 0

    # forward pass
    _SCREAMING_SNAKE_CASE									=			encoder_input_ids
    _SCREAMING_SNAKE_CASE									=			decoder_input_ids
    _SCREAMING_SNAKE_CASE									=			_SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			_SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			_SCREAMING_SNAKE_CASE									=			None
    _SCREAMING_SNAKE_CASE									=			None

    # The original model does not apply the geneator layer immediatly but rather in
    # the beam search (where it combines softmax + linear layer). Since we already
    # apply the softmax in our generation process we only apply the linear layer here.
    # We make sure that the outputs of the full stack are identical
    _SCREAMING_SNAKE_CASE									=			original(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )[0]
    _SCREAMING_SNAKE_CASE									=			original.generator(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			new_model(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )[0]
    _SCREAMING_SNAKE_CASE									=			new_model.generator(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			torch.max(torch.abs(output_converted_model - output_original_model    )    ).item()
    print("""Maximum absolute difference beween weights: {:.2f}""".format(__lowerCamelCase    )    )
    _SCREAMING_SNAKE_CASE									=			torch.max(torch.abs(output_converted_generator - output_original_generator    )    ).item()
    print("""Maximum absolute difference beween weights: {:.2f}""".format(__lowerCamelCase    )    )

    _SCREAMING_SNAKE_CASE									=			torch.allclose(__lowerCamelCase					,       __lowerCamelCase					,       atol=1e-3    )
    if are_identical:
        logging.info("""all weights are equal up to 1e-3"""    )
    else:
        raise ValueError("""the weights are different. The new model is likely different from the original one."""    )

    # The model has been saved with torch.save(model) and this is bound to the exact
    # directory structure. We save the state_dict instead.
    logging.info("""saving the model's state dictionary"""    )
    torch.save(
        new_model.state_dict()					,       """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"""    )


if __name__ == "__main__":
      lowercase_					= argparse.ArgumentParser()
      parser.add_argument(
          """--bertabs_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help="""Path the official PyTorch dump.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the output PyTorch model.""",
      )
      lowercase_					= parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )



