


"""simple docstring"""
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


__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      ["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
__SCREAMING_SNAKE_CASE							:      Optional[int]						=      {"""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()
__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      List[str]						=      """ Hello world! cécé herlolip"""

__SCREAMING_SNAKE_CASE							:      int						=      [
    ("""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   _A			( _SCREAMING_SNAKE_CASE			)      ->			Union[str, Any]:
							snake_case_          =						[
							    """encoder.version""",
							    """decoder.version""",
							    """model.encoder.version""",
							    """model.decoder.version""",
							    """_float_tensor""",
							]
							for k in ignore_keys:
														state_dict.pop(__snake_case			,	__snake_case			)







def   _A			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[str]:
							snake_case_          =						dct.pop(__snake_case			)
							snake_case_          =						val







def   _A			( _SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							snake_case_          =						torch.load(__snake_case			,	map_location="""cpu"""			)
							snake_case_          =						torch.hub.load("""pytorch/fairseq"""			,	"""bart.large.cnn"""			).eval()
							hub_interface.model.load_state_dict(sd["""model"""]			)
							return hub_interface







def   _A			( _SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_    ,  snake_case_          =						emb.weight.shape
							snake_case_          =						nn.Linear(__snake_case			,	__snake_case			,	bias=__snake_case			)
							snake_case_          =						emb.weight.data
							return lin_layer







@torch.no_grad()
def   _A			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE=None			)      ->			List[Any]:
							if not os.path.exists(__snake_case			):
														snake_case_          =						torch.hub.load("""pytorch/fairseq"""			,	__snake_case			).eval()
							else:
														snake_case_          =						load_xsum_checkpoint(__snake_case			)

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

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Union[str, 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'
				)
				__SCREAMING_SNAKE_CASE							:      List[str]						=      parser.parse_args()
				convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = 0			)      ->			list:
							snake_case_          =						length or len(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						False
							for i in range(length - 1			):
														if list_data[i] > list_data[i + 1]:
																					snake_case_    ,  snake_case_          =						list_data[i + 1], list_data[i]
																					snake_case_          =						True

							return list_data if not swapped else bubble_sort(_SCREAMING_SNAKE_CASE			,	length - 1			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



