






'''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 				lowercase_					(     __A			:							List[Any]      )							->   Tuple:



 """simple docstring"""




 lowercase       : List[str]						=[
     '''encoder.version''',
     '''decoder.version''',
     '''model.encoder.version''',
     '''model.decoder.version''',
     '''_float_tensor''',
 ]
 for k in ignore_keys:
  state_dict.pop(__A       ,   __A      )


def 				lowercase_					(     __A			:							Any       ,   __A			:							Tuple       ,   __A			:							Union[str, Any]      )							->   Union[str, Any]:



 """simple docstring"""




 lowercase       : Dict						=dct.pop(__A      )
 lowercase       : str						=val


def 				lowercase_					(     __A			:							Tuple      )							->   List[Any]:



 """simple docstring"""




 lowercase       : Dict						=torch.load(__A       ,   map_location='''cpu'''      )
 lowercase       : List[str]						=torch.hub.load('''pytorch/fairseq'''       ,   '''bart.large.cnn'''      ).eval()
 hub_interface.model.load_state_dict(sd['''model''']      )
 return hub_interface


def 				lowercase_					(     __A			:							str      )							->   Union[str, Any]:



 """simple docstring"""




 lowercase	,		lowercase       : Union[str, Any]						=emb.weight.shape
 lowercase       : Any						=nn.Linear(__A       ,   __A       ,   bias=__A      )
 lowercase       : Any						=emb.weight.data
 return lin_layer


@torch.no_grad()
def 				lowercase_					(     __A			:							int       ,   __A			:							Dict       ,   __A			:							Dict=None      )							->   Tuple:



 """simple docstring"""




 if not os.path.exists(__A      ):
  lowercase       : Any						=torch.hub.load('''pytorch/fairseq'''       ,   __A      ).eval()
 else:
  lowercase       : Optional[Any]						=load_xsum_checkpoint(__A      )

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

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


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'''




from __future__ import annotations


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int | float | str							, SCREAMING_SNAKE_CASE   : int | float | str    ):
  if nth_term == "":
    return [""]
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						int(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						[]
  for temp in range(int(SCREAMING_SNAKE_CASE    )    ):
    series.append(f'''1 / {pow(temp + 1							, int(SCREAMING_SNAKE_CASE    )    )}''' if series else '1'    )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  _a					:			List[Any]							=    int(input('Enter the last number (nth term) of the P-Series'))
  _a					:			Tuple							=    int(input('Enter the power for  P-Series'))
  print('Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p')
  print(p_series(nth_term, power))


