
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


A		: Any								=     get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')



@require_sentencepiece
@require_tokenizers
class    a_ (			_a					,			unittest.TestCase    ):
			a	:			Any					   =							PegasusTokenizer
			a	:			Optional[Any]					   =							PegasusTokenizerFast
			a	:			Tuple					   =							True
			a	:			Optional[Any]					   =							True






			def    UpperCamelCase_				(      self  ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									_lowercase					       =					PegasusTokenizer(__UpperCamelCase  )
									tokenizer.save_pretrained(self.tmpdirname  )






			@cached_property
			def    UpperCamelCase_				(      self  ):
									return PegasusTokenizer.from_pretrained("""google/pegasus-large"""  )






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									return PegasusTokenizer.from_pretrained(self.tmpdirname				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return ("This is a test", "This is a test")






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					"""</s>"""
									_lowercase					       =					1

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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					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(__UpperCamelCase  )				,			1_103  )






			def    UpperCamelCase_				(      self  ):
									self.assertEqual(self.get_tokenizer().vocab_size				,			1_103  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
									_lowercase					       =					self.tokenizer_class.from_pretrained(self.tmpdirname  )
									_lowercase					       =					(
									    """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>"""
									)
									_lowercase					       =					rust_tokenizer([raw_input_str]				,			return_tensors=__UpperCamelCase				,			add_special_tokens=__UpperCamelCase  ).input_ids[0]
									_lowercase					       =					py_tokenizer([raw_input_str]				,			return_tensors=__UpperCamelCase				,			add_special_tokens=__UpperCamelCase  ).input_ids[0]
									self.assertListEqual(__UpperCamelCase				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self._large_tokenizer
									# <mask_1> masks whole sentence while <mask_2> masks single word
									_lowercase					       =					"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
									_lowercase					       =					[2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
									_lowercase					       =					tokenizer([raw_input_str]				,			return_tensors=__UpperCamelCase  ).input_ids[0]
									self.assertListEqual(__UpperCamelCase				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self._large_tokenizer
									# The tracebacks for the following asserts are **better** without messages or self.assertEqual
									assert tokenizer.vocab_size == 96_103
									assert tokenizer.pad_token_id == 0
									assert tokenizer.eos_token_id == 1
									assert tokenizer.offset == 103
									assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
									assert tokenizer.unk_token == "<unk>"
									assert tokenizer.model_max_length == 1_024
									_lowercase					       =					"""To ensure a smooth flow of bank resolutions."""
									_lowercase					       =					[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
									_lowercase					       =					tokenizer([raw_input_str]				,			return_tensors=__UpperCamelCase  ).input_ids[0]
									self.assertListEqual(__UpperCamelCase				,			__UpperCamelCase  )
									assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






			@require_torch
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					["""This is going to be way too long.""" * 150, """short example"""]
									_lowercase					       =					["""not super long but more than 5 tokens""", """tiny"""]
									_lowercase					       =					self._large_tokenizer(__UpperCamelCase				,			padding=__UpperCamelCase				,			truncation=__UpperCamelCase				,			return_tensors="""pt"""  )
									_lowercase					       =					self._large_tokenizer(
									    text_target=__UpperCamelCase				,			max_length=5				,			padding=__UpperCamelCase				,			truncation=__UpperCamelCase				,			return_tensors="""pt"""  )

									assert batch.input_ids.shape == (2, 1_024)
									assert batch.attention_mask.shape == (2, 1_024)
									assert targets["input_ids"].shape == (2, 5)
									assert len(__UpperCamelCase  ) == 2  # input_ids, attention_mask.






			@slow
			def    UpperCamelCase_				(      self  ):
									# fmt: off
									_lowercase					       =					{"""input_ids""": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 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], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 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=__UpperCamelCase				,			model_name="""google/bigbird-pegasus-large-arxiv"""				,			revision="""ba85d0851d708441f91440d509690f1ab6353415"""				,			)







@require_sentencepiece
@require_tokenizers
class    a_ (			_a					,			unittest.TestCase    ):
			a	:			Dict					   =							PegasusTokenizer
			a	:			Tuple					   =							PegasusTokenizerFast
			a	:			Optional[Any]					   =							True
			a	:			List[str]					   =							True






			def    UpperCamelCase_				(      self  ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									_lowercase					       =					PegasusTokenizer(__UpperCamelCase				,			offset=0				,			mask_token_sent=__UpperCamelCase				,			mask_token="""[MASK]"""  )
									tokenizer.save_pretrained(self.tmpdirname  )






			@cached_property
			def    UpperCamelCase_				(      self  ):
									return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""  )






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									return PegasusTokenizer.from_pretrained(self.tmpdirname				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return ("This is a test", "This is a test")






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






			@require_torch
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					["""This is going to be way too long.""" * 1_000, """short example"""]
									_lowercase					       =					["""not super long but more than 5 tokens""", """tiny"""]
									_lowercase					       =					self._large_tokenizer(__UpperCamelCase				,			padding=__UpperCamelCase				,			truncation=__UpperCamelCase				,			return_tensors="""pt"""  )
									_lowercase					       =					self._large_tokenizer(
									    text_target=__UpperCamelCase				,			max_length=5				,			padding=__UpperCamelCase				,			truncation=__UpperCamelCase				,			return_tensors="""pt"""  )

									assert batch.input_ids.shape == (2, 4_096)
									assert batch.attention_mask.shape == (2, 4_096)
									assert targets["input_ids"].shape == (2, 5)
									assert len(__UpperCamelCase  ) == 2  # input_ids, attention_mask.






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					(
									    """This is an example string that is used to test the original TF implementation against the HF"""
									    """ implementation"""
									)

									_lowercase					       =					self._large_tokenizer(__UpperCamelCase  ).input_ids

									self.assertListEqual(
									    __UpperCamelCase				,			[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]				,			)

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)