


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


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

_lowercase       :      Union[str, Any]	=	" Hello world! cécé herlolip"

_lowercase       :      List[str]	=	[
    ("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				(   UpperCamelCase__:     Any						) ->   str:





 """simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     int						) ->   int:





 """simple docstring"""







 A								=				dct.pop(UpperCamelCase__						)
 A								=				val





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   Any:





 """simple docstring"""







 A								=				torch.load(UpperCamelCase__							,					map_location="""cpu"""						)
 A								=				torch.hub.load("""pytorch/fairseq"""							,					"""bart.large.cnn"""						).eval()
 hub_interface.model.load_state_dict(sd["""model"""]						)
 return hub_interface





def   _lowerCAmelCase				(   UpperCamelCase__:     List[Any]						) ->   Optional[int]:





 """simple docstring"""







 A					,			A								=				emb.weight.shape
 A								=				nn.Linear(UpperCamelCase__							,					UpperCamelCase__							,					bias=UpperCamelCase__						)
 A								=				emb.weight.data
 return lin_layer





@torch.no_grad()
def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     int							,					UpperCamelCase__:     Optional[Any]=None						) ->   Union[str, Any]:





 """simple docstring"""







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

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

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


if __name__ == "__main__":
    _lowercase       :      str	=	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"
    )
    _lowercase       :      Optional[int]	=	parser.parse_args()
    convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   list[int]:





	"""simple docstring"""







	if num <= 0:
		raise ValueError("""Input must be a positive integer"""						)

	A								=				[True] * (num + 1)

	A								=				2
	while p * p <= num:
		if primes[p]:
			for i in range(p * p							,					num + 1							,					UpperCamelCase__						):
				A								=				False
		p += 1

	return [prime for prime in range(2							,					num + 1						) if primes[prime]]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowercase       :      Tuple	=	int(input("Enter a positive integer: ").strip())
				print(prime_sieve_eratosthenes(user_num))



