



"""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_				= ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
SCREAMING_SNAKE_CASE_				= {"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_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= " Hello world! cécé herlolip"

SCREAMING_SNAKE_CASE_				= [
    ("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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Optional[int]:
	a_     :      int 						=     [
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "_float_tensor",
	]
	for k in ignore_keys:
		state_dict.pop(__lowerCAmelCase, __lowerCAmelCase	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Union[str, Any]:
	a_     :      Optional[int] 						=     dct.pop(__lowerCAmelCase	)
	a_     :      List[str] 						=     val



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      Any:
	a_     :      Optional[int] 						=     torch.load(__lowerCAmelCase, map_location="cpu"	)
	a_     :      str 						=     torch.hub.load("pytorch/fairseq", "bart.large.cnn"	).eval()
	hub_interface.model.load_state_dict(sd["model"]	)
	return hub_interface



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      List[str]:
	a_						,				a_     :      Tuple 						=     emb.weight.shape
	a_     :      Optional[Any] 						=     nn.Linear(__lowerCAmelCase, __lowerCAmelCase, bias=__lowerCAmelCase	)
	a_     :      int 						=     emb.weight.data
	return lin_layer



@torch.no_grad()
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      str:


	if not os.path.exists(__lowerCAmelCase	):
		a_     :      Dict 						=     torch.hub.load("pytorch/fairseq", __lowerCAmelCase	).eval()
	else:
		a_     :      List[str] 						=     load_xsum_checkpoint(__lowerCAmelCase	)

	bart.model.upgrade_state_dict(bart.model.state_dict()	)
	if hf_checkpoint_name is None:
		a_     :      Union[str, Any] 						=     checkpoint_path.replace(".", "-"	)
	a_     :      Dict 						=     BartConfig.from_pretrained(__lowerCAmelCase	)
	a_     :      List[str] 						=     bart.encode(__lowerCAmelCase	).unsqueeze(0	)
	a_     :      Any 						=     BartTokenizer.from_pretrained(__lowerCAmelCase	).encode(__lowerCAmelCase, return_tensors="pt"	).unsqueeze(0	)
	if not torch.eq(__lowerCAmelCase, __lowerCAmelCase	).all():
		raise ValueError(
		    F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""	)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= 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_				= parser.parse_args()
					convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(a - b	) for a, b in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(x - y	) for x, y in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()