import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_SCREAMING_SNAKE_CASE     =     threading.Lock()
_SCREAMING_SNAKE_CASE     =     None

_SCREAMING_SNAKE_CASE     =     {
    """debug""": logging.DEBUG,
    """info""": logging.INFO,
    """warning""": logging.WARNING,
    """error""": logging.ERROR,
    """critical""": logging.CRITICAL,
}

_SCREAMING_SNAKE_CASE     =     logging.WARNING

_SCREAMING_SNAKE_CASE     =     True
def 					__lowercase						(    )					->int:

  '''simple docstring'''




  a__  		=  os.getenv("DIFFUSERS_VERBOSITY"			,					lowerCamelCase__       )
  if env_level_str:
    if env_level_str in log_levels:
      return log_levels[env_level_str]
    else:
      logging.getLogger().warning(
          f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, "
          f"has to be one of: { ', '.join(log_levels.keys()       ) }"       )
  return _default_log_level
def 					__lowercase						(    )					->str:

  '''simple docstring'''




  return __name__.split("."       )[0]
def 					__lowercase						(    )					->logging.Logger:

  '''simple docstring'''




  return logging.getLogger(_get_library_name()       )
def 					__lowercase						(    )					->None:

  '''simple docstring'''




  global _default_handler

  with _lock:
    if _default_handler:
      # This library has already configured the library root logger.
      return
    a__  		=  logging.StreamHandler()  # Set sys.stderr as stream.
    a__  		=  sys.stderr.flush

    # Apply our default configuration to the library root logger.
    a__  		=  _get_library_root_logger()
    library_root_logger.addHandler(_default_handler       )
    library_root_logger.setLevel(_get_default_logging_level()       )
    a__  		=  False
def 					__lowercase						(    )					->None:

  '''simple docstring'''




  global _default_handler

  with _lock:
    if not _default_handler:
      return

    a__  		=  _get_library_root_logger()
    library_root_logger.removeHandler(_default_handler       )
    library_root_logger.setLevel(logging.NOTSET       )
    a__  		=  None
def 					__lowercase						(    )					->Dict:

  '''simple docstring'''




  return log_levels
def 					__lowercase						(    lowerCamelCase__ = None       )					->logging.Logger:

  '''simple docstring'''




  if name is None:
    a__  		=  _get_library_name()

  _configure_library_root_logger()
  return logging.getLogger(lowerCamelCase__       )
def 					__lowercase						(    )					->int:

  '''simple docstring'''




  _configure_library_root_logger()
  return _get_library_root_logger().getEffectiveLevel()
def 					__lowercase						(    lowerCamelCase__       )					->None:

  '''simple docstring'''




  _configure_library_root_logger()
  _get_library_root_logger().setLevel(lowerCamelCase__       )
def 					__lowercase						(    )					->List[Any]:

  '''simple docstring'''




  return set_verbosity(lowerCamelCase__       )
def 					__lowercase						(    )					->List[Any]:

  '''simple docstring'''




  return set_verbosity(lowerCamelCase__       )
def 					__lowercase						(    )					->List[str]:

  '''simple docstring'''




  return set_verbosity(lowerCamelCase__       )
def 					__lowercase						(    )					->List[Any]:

  '''simple docstring'''




  return set_verbosity(lowerCamelCase__       )
def 					__lowercase						(    )					->None:

  '''simple docstring'''




  _configure_library_root_logger()

  assert _default_handler is not None
  _get_library_root_logger().removeHandler(_default_handler       )
def 					__lowercase						(    )					->None:

  '''simple docstring'''




  _configure_library_root_logger()

  assert _default_handler is not None
  _get_library_root_logger().addHandler(_default_handler       )
def 					__lowercase						(    lowerCamelCase__       )					->None:

  '''simple docstring'''




  _configure_library_root_logger()

  assert handler is not None
  _get_library_root_logger().addHandler(lowerCamelCase__       )
def 					__lowercase						(    lowerCamelCase__       )					->None:

  '''simple docstring'''




  _configure_library_root_logger()

  assert handler is not None and handler not in _get_library_root_logger().handlers
  _get_library_root_logger().removeHandler(lowerCamelCase__       )
def 					__lowercase						(    )					->None:

  '''simple docstring'''




  _configure_library_root_logger()
  a__  		=  False
def 					__lowercase						(    )					->None:

  '''simple docstring'''




  _configure_library_root_logger()
  a__  		=  True
def 					__lowercase						(    )					->None:

  '''simple docstring'''




  a__  		=  _get_library_root_logger().handlers

  for handler in handlers:
    a__  		=  logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"       )
    handler.setFormatter(lowerCamelCase__       )
def 					__lowercase						(    )					->None:

  '''simple docstring'''




  a__  		=  _get_library_root_logger().handlers

  for handler in handlers:
    handler.setFormatter(lowerCamelCase__       )
def 					__lowercase						(    self			,					*lowerCamelCase__			,					**lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS"			,					lowerCamelCase__       )
  if no_advisory_warnings:
    return
  self.warning(*lowerCamelCase__			,					**lowerCamelCase__       )


_SCREAMING_SNAKE_CASE     =     warning_advice







class        UpperCAmelCase_					:




    def __init__(       self	: Optional[int]   ,   *lowerCAmelCase	: Optional[int]   ,   **lowerCAmelCase	: str )						->			Tuple:  # pylint: disable=unused-argument
      a__  		=  args[0] if args else None




    def __iter__(       self	: Any )						->			Optional[int]:
      return iter(self._iterator )




    def __getattr__(       self	: Optional[Any]   ,   lowerCAmelCase	: Optional[int] )						->			Tuple:


      def empty_fn(*lowerCAmelCase	: Optional[int]   ,   **lowerCAmelCase	: Dict ):  # pylint: disable=unused-argument
        return

      return empty_fn




    def __enter__(       self	: List[Any] )						->			Tuple:
      return self




    def __exit__(       self	: Optional[int]   ,   lowerCAmelCase	: str   ,   lowerCAmelCase	: str   ,   lowerCAmelCase	: Dict )						->			Optional[int]:
      return







class        UpperCAmelCase_					:




    def __call__(       self	: Optional[Any]   ,   *lowerCAmelCase	: Any   ,   **lowerCAmelCase	: str )						->			List[str]:


      if _tqdm_active:
        return tqdm_lib.tqdm(*_UpperCAmelCase   ,   **_UpperCAmelCase )
      else:
        return EmptyTqdm(*_UpperCAmelCase   ,   **_UpperCAmelCase )




    def     __lowerCamelCase			(       self	: Any   ,   *lowerCAmelCase	: Union[str, Any]   ,   **lowerCAmelCase	: Any )						->			List[str]:
      a__  		=  None
      if _tqdm_active:
        return tqdm_lib.tqdm.set_lock(*_UpperCAmelCase   ,   **_UpperCAmelCase )




    def     __lowerCamelCase			(       self	: Dict )						->			List[Any]:


      if _tqdm_active:
        return tqdm_lib.tqdm.get_lock()

_SCREAMING_SNAKE_CASE     =     _tqdm_cls()
def 					__lowercase						(    )					->bool:

  '''simple docstring'''




  global _tqdm_active
  return bool(_tqdm_active       )
def 					__lowercase						(    )					->Union[str, Any]:

  '''simple docstring'''




  global _tqdm_active
  a__  		=  True







def 					__lowercase						(    )					->str:

  '''simple docstring'''




  global _tqdm_active
  a__  		=  False



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 import save_json




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
											pl_module.logger.log_metrics(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
											__lowercase								=   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
											__lowercase								=   Path(pl_module.hparams.output_dir							)
											if type_path == "test":
															__lowercase								=   od / 'test_results.txt'
															__lowercase								=   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.
															__lowercase								=   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
															__lowercase								=   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
															results_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
															generations_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
											with open(_UpperCAmelCase	,					'a+'							) as writer:
															for key in sorted(_UpperCAmelCase							):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__lowercase								=   metrics[key]
																			if isinstance(_UpperCAmelCase	,					torch.Tensor							):
																							__lowercase								=   val.item()
																			__lowercase								=   f"""{key}: {val:.6f}\n"""
																			writer.write(_UpperCAmelCase							)

											if not save_generations:
															return

											if "preds" in metrics:
															__lowercase								=   '\n'.join(metrics['preds']							)
															generations_file.open('w+'							).write(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


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

											__lowercase								=   count_trainable_parameters(_UpperCAmelCase							)
											# 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      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


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





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						str:



											"""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")






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: List[str]       ,				SCREAMING_SNAKE_CASE			: Union[str, Any]=1       ,				SCREAMING_SNAKE_CASE			: str=False     )	->		List[str]:
				if metric == "rouge2":
								__lowercase								=   '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								__lowercase								=   '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "loss":
								__lowercase								=   '{val_avg_loss:.4f}-{step_count}'
				else:
								raise NotImplementedError(
								    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
								    ' this function.'     )

				__lowercase								=   ModelCheckpoint(
				    dirpath=SCREAMING_SNAKE_CASE       ,				filename=SCREAMING_SNAKE_CASE       ,				monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				save_top_k=SCREAMING_SNAKE_CASE       ,				)
				return checkpoint_callback




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: int     )	->		Union[str, Any]:
				return EarlyStopping(
				    monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				patience=SCREAMING_SNAKE_CASE       ,				verbose=SCREAMING_SNAKE_CASE       ,				)





