


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		Any:


       '''simple docstring'''





       _UpperCAmelCase					=      filter(lambda a__	: p.requires_grad      ,  model.parameters()	)
       _UpperCAmelCase					=      sum([np.prod(p.size()	) for p in model_parameters]	)
       return params


lowerCAmelCase__  :str             =							logging.getLogger(__name__)

def 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		int	)  ->		int:


       '''simple docstring'''





       if metric == "rouge2":
              _UpperCAmelCase					=      '{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              _UpperCAmelCase					=      '{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              _UpperCAmelCase					=      '{val_avg_em:.4f}-{step_count}'
       else:
              raise NotImplementedError(
                  F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
                  ' function.'	)

       _UpperCAmelCase					=      ModelCheckpoint(
           dirpath=_A      ,  filename=_A      ,  monitor=F'''val_{metric}'''      ,  mode='max'      ,  save_top_k=3      ,  every_n_epochs=1      ,  )
       return checkpoint_callback

def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		str	)  ->		Tuple:


       '''simple docstring'''





       return EarlyStopping(
           monitor=F'''val_{metric}'''      ,  mode='min' if 'loss' in metric else 'max'      ,  patience=_A      ,  verbose=_A      ,  )







class 		__a				(    pl.Callback      ):
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




         """simple docstring"""





         _UpperCAmelCase					=      {f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
         pl_module.logger.log_metrics(snake_case__						)
  @rank_zero_only
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=True						)			->	List[Any]:




         """simple docstring"""





         logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''						)
         _UpperCAmelCase					=      trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}						)
         # Log results
         _UpperCAmelCase					=      Path(pl_module.hparams.output_dir						)
         if type_path == "test":
                _UpperCAmelCase					=      od / 'test_results.txt'
                _UpperCAmelCase					=      od / 'test_generations.txt'
         else:
                # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
                # If people want this it will be easy enough to add back.
                _UpperCAmelCase					=      od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
                _UpperCAmelCase					=      od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
                results_file.parent.mkdir(exist_ok=snake_case__						)
                generations_file.parent.mkdir(exist_ok=snake_case__						)
         with open(snake_case__      ,   'a+'						) as writer:
                for key in sorted(snake_case__						):
                       if key in ["log", "progress_bar", "preds"]:
                              continue
                       _UpperCAmelCase					=      metrics[key]
                       if isinstance(snake_case__      ,   torch.Tensor						):
                              _UpperCAmelCase					=      val.item()
                       _UpperCAmelCase					=      f'''{key}: {val:.6f}\n'''
                       writer.write(snake_case__						)

         if not save_generations:
                return

         if "preds" in metrics:
                _UpperCAmelCase					=      '\n'.join(metrics['preds']						)
                generations_file.open('w+'						).write(snake_case__						)
  @rank_zero_only
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




         """simple docstring"""





         try:
                _UpperCAmelCase					=      pl_module.model.model.num_parameters()
         except AttributeError:
                _UpperCAmelCase					=      pl_module.model.num_parameters()

         _UpperCAmelCase					=      count_trainable_parameters(snake_case__						)
         # mp stands for million parameters
         trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}						)
  @rank_zero_only
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Tuple:




         """simple docstring"""





         save_json(pl_module.metrics      ,   pl_module.metrics_save_path						)
         return self._write_logs(snake_case__      ,   snake_case__      ,   'test'						)






  @rank_zero_only
  def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




         """simple docstring"""





         save_json(pl_module.metrics      ,   pl_module.metrics_save_path						)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")




# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__  :Dict             =							'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''

def 	lowerCAmelCase__			(      a__:		Optional[Any]=None	)  ->		List[Any]:


							'''simple docstring'''





							if subparsers is not None:
														_UpperCAmelCase					=      subparsers.add_parser('tpu-config'      ,  description=_description	)
							else:
														_UpperCAmelCase					=      argparse.ArgumentParser('Accelerate tpu-config command'      ,  description=_description	)
							# Core arguments
							_UpperCAmelCase					=      parser.add_argument_group(
							    'Config Arguments'      ,  'Arguments that can be configured through `accelerate config`.'	)
							config_args.add_argument(
							    '--config_file'      ,  type=a__      ,  default=a__      ,  help='Path to the config file to use for accelerate.'      ,  )
							config_args.add_argument(
							    '--tpu_name'      ,  default=a__      ,  help='The name of the TPU to use. If not specified, will use the TPU specified in the config file.'      ,  )
							config_args.add_argument(
							    '--tpu_zone'      ,  default=a__      ,  help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'      ,  )
							_UpperCAmelCase					=      parser.add_argument_group('TPU Arguments'      ,  'Arguments for options ran inside the TPU.'	)
							pod_args.add_argument(
							    '--use_alpha'      ,  action='store_true'      ,  help='Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'      ,  )
							pod_args.add_argument(
							    '--command_file'      ,  default=a__      ,  help='The path to the file containing the commands to run on the pod on startup.'      ,  )
							pod_args.add_argument(
							    '--command'      ,  action='append'      ,  nargs='+'      ,  help='A command to run on the pod. Can be passed multiple times.'      ,  )
							pod_args.add_argument(
							    '--install_accelerate'      ,  action='store_true'      ,  help='Whether to install accelerate on the pod. Defaults to False.'      ,  )
							pod_args.add_argument(
							    '--accelerate_version'      ,  default='latest'      ,  help='The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'      ,  )
							pod_args.add_argument(
							    '--debug'      ,  action='store_true'      ,  help='If set, will print the command that would be run instead of running it.'	)

							if subparsers is not None:
														parser.set_defaults(func=a__	)
							return parser

def 	lowerCAmelCase__			(      a__:		str	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(a__	):
														_UpperCAmelCase					=      load_config_from_file(args.config_file	)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					_UpperCAmelCase					=      defaults.command_file
														if not args.command and defaults.commands is not None:
																					_UpperCAmelCase					=      defaults.commands
														if not args.tpu_name:
																					_UpperCAmelCase					=      defaults.tpu_name
														if not args.tpu_zone:
																					_UpperCAmelCase					=      defaults.tpu_zone
							if args.accelerate_version == "dev":
														_UpperCAmelCase					=      'git+https://github.com/huggingface/accelerate.git'
							elif args.accelerate_version == "latest":
														_UpperCAmelCase					=      'accelerate -U'
							elif isinstance(parse(args.accelerate_version	)      ,  a__	):
														_UpperCAmelCase					=      F'''accelerate=={args.accelerate_version}'''

							if not args.command_file and not args.command:
														raise ValueError('You must specify either a command file or a command to run on the pod.'	)

							if args.command_file:
														with open(args.command_file      ,  'r'	) as f:
																					_UpperCAmelCase					=      [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]      ,  a__	):
														_UpperCAmelCase					=      [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							_UpperCAmelCase					=      ['cd /usr/share']
							if args.install_accelerate:
														new_cmd += [F'''pip install {args.accelerate_version}''']
							new_cmd += args.command
							_UpperCAmelCase					=      '; '.join(a__	)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							_UpperCAmelCase					=      ['gcloud']
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'''Running {" ".join(a__	)}'''	)
														return
							subprocess.run(a__	)
							print('Successfully setup pod.'	)







def 	lowerCAmelCase__			(      )  ->		Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      tpu_command_parser()
							_UpperCAmelCase					=      parser.parse_args()

							tpu_command_launcher(a__	)
