


'''simple docstring'''






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase       							=				get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
@require_tokenizers
class 						a__							(				a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  List[Any]      		= XLMRobertaTokenizer
							lowercase__     :  Optional[Any]      		= XLMRobertaTokenizerFast
							lowercase__     :  Union[str, Any]      		= True
							lowercase__     :  List[str]      		= True







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														super().setUp()

														# We have a SentencePiece fixture for testing
														lowerCAmelCase__			  =      XLMRobertaTokenizer(lowerCamelCase_   ,       keep_accents=lowerCamelCase_  )
														tokenizer.save_pretrained(self.tmpdirname  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      '''<pad>'''
														lowerCAmelCase__			  =      1

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      list(self.get_tokenizer().get_vocab().keys()  )

														self.assertEqual(vocab_keys[0]   ,       '''<s>'''  )
														self.assertEqual(vocab_keys[1]   ,       '''<pad>'''  )
														self.assertEqual(vocab_keys[-1]   ,       '''<mask>'''  )
														self.assertEqual(len(lowerCamelCase_  )   ,       10_02  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														self.assertEqual(self.get_tokenizer().vocab_size   ,       10_02  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      XLMRobertaTokenizer(lowerCamelCase_   ,       keep_accents=lowerCamelCase_  )

														lowerCAmelCase__			  =      tokenizer.tokenize('''This is a test'''  )
														self.assertListEqual(lowerCamelCase_   ,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']  )

														self.assertListEqual(
														    tokenizer.convert_tokens_to_ids(lowerCamelCase_  )   ,       [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]   ,       )

														lowerCAmelCase__			  =      tokenizer.tokenize('''I was born in 92000, and this is falsé.'''  )
														self.assertListEqual(
														    lowerCamelCase_   ,       [
														        SPIECE_UNDERLINE + '''I''',
														        SPIECE_UNDERLINE + '''was''',
														        SPIECE_UNDERLINE + '''b''',
														        '''or''',
														        '''n''',
														        SPIECE_UNDERLINE + '''in''',
														        SPIECE_UNDERLINE + '''''',
														        '''9''',
														        '''2''',
														        '''0''',
														        '''0''',
														        '''0''',
														        ''',''',
														        SPIECE_UNDERLINE + '''and''',
														        SPIECE_UNDERLINE + '''this''',
														        SPIECE_UNDERLINE + '''is''',
														        SPIECE_UNDERLINE + '''f''',
														        '''al''',
														        '''s''',
														        '''é''',
														        '''.''',
														    ]   ,       )
														lowerCAmelCase__			  =      tokenizer.convert_tokens_to_ids(lowerCamelCase_  )
														self.assertListEqual(
														    lowerCamelCase_   ,       [
														        value + tokenizer.fairseq_offset
														        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
														        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
														    ]   ,       )

														lowerCAmelCase__			  =      tokenizer.convert_ids_to_tokens(lowerCamelCase_  )
														self.assertListEqual(
														    lowerCamelCase_   ,       [
														        SPIECE_UNDERLINE + '''I''',
														        SPIECE_UNDERLINE + '''was''',
														        SPIECE_UNDERLINE + '''b''',
														        '''or''',
														        '''n''',
														        SPIECE_UNDERLINE + '''in''',
														        SPIECE_UNDERLINE + '''''',
														        '''<unk>''',
														        '''2''',
														        '''0''',
														        '''0''',
														        '''0''',
														        ''',''',
														        SPIECE_UNDERLINE + '''and''',
														        SPIECE_UNDERLINE + '''this''',
														        SPIECE_UNDERLINE + '''is''',
														        SPIECE_UNDERLINE + '''f''',
														        '''al''',
														        '''s''',
														        '''<unk>''',
														        '''.''',
														    ]   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														if not self.test_slow_tokenizer:
																					# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																					return

														lowerCAmelCase__			  =      (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																												lowerCAmelCase__			  =      self.rust_tokenizer_class.from_pretrained(lowerCamelCase_   ,       **lowerCamelCase_  )
																												lowerCAmelCase__			  =      self.tokenizer_class.from_pretrained(lowerCamelCase_   ,       **lowerCamelCase_  )

																												lowerCAmelCase__			  =      tempfile.mkdtemp()

																												lowerCAmelCase__			  =      tokenizer_r.save_pretrained(lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.save_pretrained(lowerCamelCase_  )

																												# Checks it save with the same files + the tokenizer.json file for the fast one
																												self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files  )  )
																												lowerCAmelCase__			  =      tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f  )
																												self.assertSequenceEqual(lowerCamelCase_   ,       lowerCamelCase_  )

																												# Checks everything loads correctly in the same way
																												lowerCAmelCase__			  =      tokenizer_r.from_pretrained(lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.from_pretrained(lowerCamelCase_  )

																												# Check special tokens are set accordingly on Rust and Python
																												for key in tokenizer_pp.special_tokens_map:
																																			self.assertTrue(hasattr(lowerCamelCase_   ,       lowerCamelCase_  )  )
																																			# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																																			# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																												shutil.rmtree(lowerCamelCase_  )

																												# Save tokenizer rust, legacy_format=True
																												lowerCAmelCase__			  =      tempfile.mkdtemp()

																												lowerCAmelCase__			  =      tokenizer_r.save_pretrained(lowerCamelCase_   ,       legacy_format=lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.save_pretrained(lowerCamelCase_  )

																												# Checks it save with the same files
																												self.assertSequenceEqual(lowerCamelCase_   ,       lowerCamelCase_  )

																												# Checks everything loads correctly in the same way
																												lowerCAmelCase__			  =      tokenizer_r.from_pretrained(lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.from_pretrained(lowerCamelCase_  )

																												# Check special tokens are set accordingly on Rust and Python
																												for key in tokenizer_pp.special_tokens_map:
																																			self.assertTrue(hasattr(lowerCamelCase_   ,       lowerCamelCase_  )  )

																												shutil.rmtree(lowerCamelCase_  )

																												# Save tokenizer rust, legacy_format=False
																												lowerCAmelCase__			  =      tempfile.mkdtemp()

																												lowerCAmelCase__			  =      tokenizer_r.save_pretrained(lowerCamelCase_   ,       legacy_format=lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.save_pretrained(lowerCamelCase_  )

																												# Checks it saved the tokenizer.json file
																												self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files  )  )

																												# Checks everything loads correctly in the same way
																												lowerCAmelCase__			  =      tokenizer_r.from_pretrained(lowerCamelCase_  )
																												lowerCAmelCase__			  =      tokenizer_p.from_pretrained(lowerCamelCase_  )

																												# Check special tokens are set accordingly on Rust and Python
																												for key in tokenizer_pp.special_tokens_map:
																																			self.assertTrue(hasattr(lowerCamelCase_   ,       lowerCamelCase_  )  )

																												shutil.rmtree(lowerCamelCase_  )







							@cached_property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														with tempfile.NamedTemporaryFile() as f:
																					shutil.copyfile(lowerCamelCase_   ,       f.name  )
																					lowerCAmelCase__			  =      XLMRobertaTokenizer(f.name   ,       keep_accents=lowerCamelCase_  )
																					lowerCAmelCase__			  =      pickle.dumps(lowerCamelCase_  )
														pickle.loads(lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														if not self.test_rust_tokenizer:
																					return

														lowerCAmelCase__			  =      self.get_tokenizer()
														lowerCAmelCase__			  =      self.get_rust_tokenizer()

														lowerCAmelCase__			  =      '''I was born in 92000, and this is falsé.'''

														lowerCAmelCase__			  =      tokenizer.tokenize(lowerCamelCase_  )
														lowerCAmelCase__			  =      rust_tokenizer.tokenize(lowerCamelCase_  )
														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      tokenizer.encode(lowerCamelCase_   ,       add_special_tokens=lowerCamelCase_  )
														lowerCAmelCase__			  =      rust_tokenizer.encode(lowerCamelCase_   ,       add_special_tokens=lowerCamelCase_  )
														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      self.get_rust_tokenizer()
														lowerCAmelCase__			  =      tokenizer.encode(lowerCamelCase_  )
														lowerCAmelCase__			  =      rust_tokenizer.encode(lowerCamelCase_  )
														self.assertListEqual(lowerCamelCase_   ,       lowerCamelCase_  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      '''Hello World!'''
														lowerCAmelCase__			  =      [0, 3_53_78, 66_61, 38, 2]
														# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
														# xlmr.eval()
														# xlmr.encode(symbols)

														self.assertListEqual(lowerCamelCase_   ,       self.big_tokenizer.encode(lowerCamelCase_  )  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      (
														    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
														    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
														)
														lowerCAmelCase__			  =      [
														    0,
														    32_93,
														    83,
														    10,
														    45_52,
														    49_89,
														    79_86,
														    6_78,
														    10,
														    59_15,
														    1_11,
														    17_94_59,
														    12_48_50,
														    4,
														    60_44,
														    2_37,
														    12,
														    6,
														    5,
														    6,
														    4,
														    67_80,
														    7_05,
														    15,
														    13_88,
														    44,
														    3_78,
														    1_01_14,
														    7_11,
														    1_52,
														    20,
														    6,
														    5,
														    2_23_76,
														    6_42,
														    12_21,
														    1_51_90,
														    3_41_53,
														    4_50,
														    56_08,
														    9_59,
														    11_19,
														    5_77_02,
														    1_36,
														    1_86,
														    47,
														    10_98,
														    2_93_67,
														    47,
														    # 4426, # What fairseq tokenizes from "<unk>": "_<"
														    # 3678, # What fairseq tokenizes from "<unk>": "unk"
														    # 2740, # What fairseq tokenizes from "<unk>": ">"
														    3,  # What we tokenize from "<unk>": "<unk>"
														    6,  # Residue from the tokenization: an extra sentencepiece underline
														    4,
														    60_44,
														    2_37,
														    62_84,
														    5_09_01,
														    5_28,
														    31,
														    90,
														    34,
														    9_27,
														    2,
														]
														# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
														# xlmr.eval()
														# xlmr.encode(symbols)

														self.assertListEqual(lowerCamelCase_   ,       self.big_tokenizer.encode(lowerCamelCase_  )  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														# fmt: off
														lowerCAmelCase__			  =      {'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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]], '''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, 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, 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, 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, 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=lowerCamelCase_   ,       model_name='''xlm-roberta-base'''   ,       revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''   ,       )

import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


A		: int								=     ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
A		: Any								=     {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
A		: Optional[int]								=     logging.get_logger(__name__)

A		: int								=     ''' Hello world! cécé herlolip'''

A		: List[Any]								=     [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Tuple:
						_lowercase					       =					[
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """_float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     List[str]:
						_lowercase					       =					dct.pop(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					val




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     Any:
						_lowercase					       =					torch.load(SCREAMING_SNAKE_CASE_		,   map_location="""cpu"""       )
						_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   """bart.large.cnn"""       ).eval()
						hub_interface.model.load_state_dict(sd["""model"""]       )
						return hub_interface




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[int]:
						_lowercase       ,						_lowercase					       =					emb.weight.shape
						_lowercase					       =					nn.Linear(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   bias=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					emb.weight.data
						return lin_layer




@torch.no_grad()
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	int=None       )  ->     int:
						if not os.path.exists(SCREAMING_SNAKE_CASE_       ):
												_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   SCREAMING_SNAKE_CASE_       ).eval()
						else:
												_lowercase					       =					load_xsum_checkpoint(SCREAMING_SNAKE_CASE_       )

						bart.model.upgrade_state_dict(bart.model.state_dict()       )
						if hf_checkpoint_name is None:
												_lowercase					       =					checkpoint_path.replace("""."""		,   """-"""       )
						_lowercase					       =					BartConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					bart.encode(SCREAMING_SNAKE_CASE_       ).unsqueeze(0       )
						_lowercase					       =					BartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       ).encode(SCREAMING_SNAKE_CASE_		,   return_tensors="""pt"""       ).unsqueeze(0       )
						if not torch.eq(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ).all():
												raise ValueError(
												    f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""       )

						if checkpoint_path == "bart.large.mnli":
												_lowercase					       =					bart.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					state_dict["""model.decoder.embed_tokens.weight"""]
												for src, dest in mnli_rename_keys:
																		rename_key(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					BartForSequenceClassification(SCREAMING_SNAKE_CASE_       ).eval()
												model.load_state_dict(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					bart.predict("""mnli"""		,   SCREAMING_SNAKE_CASE_		,   return_logits=SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					model(SCREAMING_SNAKE_CASE_       )[0]  # logits
						else:  # no classification heads to worry about
												_lowercase					       =					bart.model.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					state_dict["""decoder.embed_tokens.weight"""]
												_lowercase					       =					bart.extract_features(SCREAMING_SNAKE_CASE_       )
												if hf_checkpoint_name == "facebook/bart-large":
																		_lowercase					       =					BartModel(SCREAMING_SNAKE_CASE_       ).eval()
																		model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		_lowercase					       =					model(SCREAMING_SNAKE_CASE_       ).model[0]
												else:
																		_lowercase					       =					BartForConditionalGeneration(SCREAMING_SNAKE_CASE_       ).eval()  # an existing summarization ckpt
																		model.model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		if hasattr(SCREAMING_SNAKE_CASE_		,   """lm_head"""       ):
																								_lowercase					       =					make_linear_from_emb(model.model.shared       )
																		_lowercase					       =					model.model(SCREAMING_SNAKE_CASE_       )[0]

    # Check results
						if fairseq_output.shape != new_model_outputs.shape:
												raise ValueError(
												    f"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""       )
						if (fairseq_output != new_model_outputs).any().item():
												raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`"""       )
						Path(SCREAMING_SNAKE_CASE_       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_       )
						model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
		A		: List[Any]								=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
		)
		parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument(
		    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
		)
		A		: Any								=     parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)