

"""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


_a							=							['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
_a							=							{'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()
_a							=							logging.get_logger(__name__)

_a							=							' Hello world! cécé herlolip'

_a							=							[
    ('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  (  UpperCamelCase_					:	Any)     ->						Optional[Any]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	int,      UpperCamelCase_					:	Optional[int])     ->						Dict:
   '''simple docstring'''


   __lowercase								=						dct.pop(UpperCamelCase_)
   __lowercase								=						val




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						List[Any]:
   '''simple docstring'''


   __lowercase								=						torch.load(UpperCamelCase_,      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   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


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




@torch.no_grad()
def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Optional[int]=None)     ->						int:
   '''simple docstring'''


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

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







"""simple docstring"""





import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
   # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
   # setup instructions, if using on-demand hardware

   # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
   # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
   # Throw an error if user passes both BYO and on-demand cluster args
   # Otherwise, use default values
   _a							=							argparse.ArgumentParser()
   parser.add_argument('--user', type=str, default='ubuntu')
   parser.add_argument('--host', type=str, default='localhost')
   parser.add_argument('--key_path', type=str, default=None)
   parser.add_argument('--instance', type=str, default='V100:1')
   parser.add_argument('--provider', type=str, default='cheapest')
   parser.add_argument('--use_spot', type=bool, default=False)
   parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
   _a				,						_a							=							parser.parse_known_args()
   if args.host != "localhost":
      if args.instance != "V100:1" or args.provider != "cheapest":
         raise ValueError('Cannot specify both BYO and on-demand cluster args')
      _a							=							rh.cluster(
          name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
      )
   else:
      _a							=							rh.cluster(
          name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
      )
   _a							=							args.example.rsplit('/', 1)[0]

   # Set up remote environment
   cluster.install_packages(['pip:./'])  # Installs transformers from local source
   # Note transformers is copied into the home directory on the remote machine, so we can install from there
   cluster.run([F"pip install -r transformers/examples/{example_dir}/requirements.txt"])
   cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])

   # Run example. You can bypass the CLI wrapper and paste your own code here.
   cluster.run([F"python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"])

   # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
   # from my_script... import train
   # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
   # launch_train_gpu = rh.function(fn=train,
   #                                system=gpu,
   #                                reqs=reqs,
   #                                name='train_bert_glue')
   #
   # We can pass in arguments just like we would to a function:
   # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
   #                  stream_logs=True)




