




import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


_UpperCAmelCase	:						Any			=     [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]



def       UpperCAmelCase__  (     lowerCamelCase   ):
  for pegasus_name, hf_name in PATTERNS:
    lowercase    :Optional[Any]      	=						k.replace(lowerCamelCase,			lowerCamelCase   )
  return k



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  lowercase    :Optional[int]      	=						DEFAULTS.copy()
  cfg_kwargs.update(lowerCamelCase   )
  lowercase    :int      	=						PegasusConfig(**lowerCamelCase   )
  lowercase    :Union[str, Any]      	=						PegasusForConditionalGeneration(lowerCamelCase   )
  lowercase    :Optional[Any]      	=						torch_model.model.state_dict()
  lowercase    :str      	=						{}
  for k, v in tf_weights.items():
    lowercase    :str      	=						rename_state_dict_key(lowerCamelCase   )
    if new_k not in sd:
      raise ValueError(F"could not find new key {new_k} in state dict. (converted from {k})"   )

    if "dense" in k or "proj" in new_k:
      lowercase    :Optional[int]      	=						v.T
    lowercase    :Dict      	=						torch.tensor(lowerCamelCase,			dtype=sd[new_k].dtype   )
    assert v.shape == sd[new_k].shape, F"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
  # make sure embedding.padding_idx is respected
  lowercase    :Union[str, Any]      	=						torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]   )
  lowercase    :Union[str, Any]      	=						mapping["shared.weight"]
  lowercase    :Dict      	=						mapping["shared.weight"]
  lowercase    :str      	=						{k: torch.zeros_like(lowerCamelCase   ) for k, v in sd.items() if k.endswith("bias"   ) and k not in mapping}
  mapping.update(**lowerCamelCase   )
  lowercase	,    lowercase    :Dict      	=						torch_model.model.load_state_dict(lowerCamelCase,			strict=lowerCamelCase   )
  lowercase    :List[str]      	=						[
      k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
  ]
  assert unexpected_missing == [], F"no matches found for the following torch keys {unexpected_missing}"
  assert extra == [], F"no matches found for the following tf keys {extra}"
  return torch_model



def       UpperCAmelCase__  (     lowerCamelCase="./ckpt/aeslc/model.ckpt-32000"   ):
  lowercase    :int      	=						tf.train.list_variables(lowerCamelCase   )
  lowercase    :Optional[int]      	=						{}
  lowercase    :int      	=						["Adafactor", "global_step"]
  for name, shape in tqdm(lowerCamelCase,			desc="converting tf checkpoint to dict"   ):
    lowercase    :int      	=						any(pat in name for pat in ignore_name   )
    if skip_key:
      continue
    lowercase    :Optional[Any]      	=						tf.train.load_variable(lowerCamelCase,			lowerCamelCase   )
    lowercase    :Optional[Any]      	=						array
  return tf_weights



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  # save tokenizer first
  lowercase    :Optional[int]      	=						Path(lowerCamelCase   ).parent.name
  lowercase    :List[Any]      	=						task_specific_params[F"summarization_{dataset}"]["max_position_embeddings"]
  lowercase    :Dict      	=						PegasusTokenizer.from_pretrained("sshleifer/pegasus",			model_max_length=lowerCamelCase   )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(lowerCamelCase   )

  # convert model
  lowercase    :List[str]      	=						get_tf_weights_as_numpy(lowerCamelCase   )
  lowercase    :str      	=						task_specific_params[F"summarization_{dataset}"]
  if dataset == "large":
    lowercase    :Optional[int]      	=						task_specific_params
  lowercase    :str      	=						convert_pegasus(lowerCamelCase,			lowerCamelCase   )
  torch_model.save_pretrained(lowerCamelCase   )
  lowercase    :Optional[int]      	=						torch_model.state_dict()
  sd.pop("model.decoder.embed_positions.weight"   )
  sd.pop("model.encoder.embed_positions.weight"   )
  torch.save(lowerCamelCase,			Path(lowerCamelCase   ) / "pytorch_model.bin"   )


if __name__ == "__main__":
     _UpperCAmelCase	:						List[Any]			=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
     parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
     _UpperCAmelCase	:						Any			=     parser.parse_args()
     if args.save_dir is None:
          _UpperCAmelCase	:						Optional[Any]			=     Path(args.tf_ckpt_path).parent.name
          _UpperCAmelCase	:						str			=     os.path.join("pegasus", dataset)
     convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









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
     _UpperCAmelCase	:						Optional[Any]			=     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")
     _UpperCAmelCase			,			_UpperCAmelCase	:						Dict			=     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")
          _UpperCAmelCase	:						List[Any]			=     rh.cluster(
              name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
          )
     else:
          _UpperCAmelCase	:						Tuple			=     rh.cluster(
              name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
          )
     _UpperCAmelCase	:						Any			=     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)



