






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


__UpperCamelCase   :   List[Any]      =    ["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
__UpperCamelCase   :   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()
__UpperCamelCase   :   List[str]      =    logging.get_logger(__name__)

__UpperCamelCase   :   Tuple      =    """ Hello world! cécé herlolip"""

__UpperCamelCase   :   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 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	[
	    """encoder.version""",
	    """decoder.version""",
	    """model.encoder.version""",
	    """model.decoder.version""",
	    """_float_tensor""",
	]
	for k in ignore_keys:
		state_dict.pop(lowerCamelCase					,	lowerCamelCase       )





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	dct.pop(lowerCamelCase       )
	__lowercase   							=	val





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	torch.load(lowerCamelCase					,	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 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase							,    __lowercase   							=	emb.weight.shape
	__lowercase   							=	nn.Linear(lowerCamelCase					,	lowerCamelCase					,	bias=lowerCamelCase       )
	__lowercase   							=	emb.weight.data
	return lin_layer





@torch.no_grad()
def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase=None       ):




	'''simple docstring'''







	if not os.path.exists(lowerCamelCase       ):
		__lowercase   							=	torch.hub.load("""pytorch/fairseq"""					,	lowerCamelCase       ).eval()
	else:
		__lowercase   							=	load_xsum_checkpoint(lowerCamelCase       )

	bart.model.upgrade_state_dict(bart.model.state_dict()       )
	if hf_checkpoint_name is None:
		__lowercase   							=	checkpoint_path.replace("""."""					,	"""-"""       )
	__lowercase   							=	BartConfig.from_pretrained(lowerCamelCase       )
	__lowercase   							=	bart.encode(lowerCamelCase       ).unsqueeze(0       )
	__lowercase   							=	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":
		__lowercase   							=	bart.state_dict()
		remove_ignore_keys_(lowerCamelCase       )
		__lowercase   							=	state_dict["""model.decoder.embed_tokens.weight"""]
		for src, dest in mnli_rename_keys:
			rename_key(lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       )
		__lowercase   							=	BartForSequenceClassification(lowerCamelCase       ).eval()
		model.load_state_dict(lowerCamelCase       )
		__lowercase   							=	bart.predict("""mnli"""					,	lowerCamelCase					,	return_logits=lowerCamelCase       )
		__lowercase   							=	model(lowerCamelCase       )[0]  # logits
	else:  # no classification heads to worry about
		__lowercase   							=	bart.model.state_dict()
		remove_ignore_keys_(lowerCamelCase       )
		__lowercase   							=	state_dict["""decoder.embed_tokens.weight"""]
		__lowercase   							=	bart.extract_features(lowerCamelCase       )
		if hf_checkpoint_name == "facebook/bart-large":
			__lowercase   							=	BartModel(lowerCamelCase       ).eval()
			model.load_state_dict(lowerCamelCase       )
			__lowercase   							=	model(lowerCamelCase       ).model[0]
		else:
			__lowercase   							=	BartForConditionalGeneration(lowerCamelCase       ).eval()  # an existing summarization ckpt
			model.model.load_state_dict(lowerCamelCase       )
			if hasattr(lowerCamelCase					,	"""lm_head"""       ):
				__lowercase   							=	make_linear_from_emb(model.model.shared       )
			__lowercase   							=	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__":
						__UpperCamelCase   :   Tuple      =    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"""
						)
						__UpperCamelCase   :   Any      =    parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)









"""simple docstring"""
from __future__ import annotations

from collections.abc import Generator





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		dict[int, int]       		=    {}
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    2
				while True:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    factor_map.pop(lowerCamelCase,						lowerCamelCase       )
								if factor:
												_SCREAMING_SNAKE_CASE   :		str       		=    factor + prime
												while x in factor_map:
																x += factor
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    factor
								else:
												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    prime
												yield prime
								prime += 1





def 				lowercase__		(					lowerCamelCase = 1E10       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    sieve()

				_SCREAMING_SNAKE_CASE   :		Dict       		=    1
				while True:
								_SCREAMING_SNAKE_CASE   :		int       		=    next(lowerCamelCase       )
								if (2 * prime * n) > limit:
												return n
								# Ignore the next prime as the reminder will be 2.
								next(lowerCamelCase       )
								n += 2


if __name__ == "__main__":
			print(solution())

