




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       lowerCAmelCase_      (	lowerCamelCase:							List[str]    )       ->      Dict:


      '''simple docstring'''




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


__SCREAMING_SNAKE_CASE     : str      =logging.getLogger(__name__)






class       A      (		pl.Callback		):
     """simple docstring"""

     def 			__snake_case (     self	: Union[str, Any]						,	A__	: Optional[int]						,	A__	: Dict      )					->							Tuple:
           lowerCamelCase__   	=					{F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
           pl_module.logger.log_metrics(A__      )




     @rank_zero_only
     def 			__snake_case (     self	: int						,	A__	: pl.Trainer						,	A__	: pl.LightningModule						,	A__	: str						,	A__	: Dict=True      )					->							Tuple:
           logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""      )
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					Path(pl_module.hparams.output_dir      )
           if type_path == "test":
                 lowerCamelCase__   	=					od / 'test_results.txt'
                 lowerCamelCase__   	=					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.
                 lowerCamelCase__   	=					od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
                 lowerCamelCase__   	=					od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
                 results_file.parent.mkdir(exist_ok=A__      )
                 generations_file.parent.mkdir(exist_ok=A__      )
           with open(A__						,	'a+'      ) as writer:
                 for key in sorted(A__      ):
                       if key in ["log", "progress_bar", "preds"]:
                             continue
                       lowerCamelCase__   	=					metrics[key]
                       if isinstance(A__						,	torch.Tensor      ):
                             lowerCamelCase__   	=					val.item()
                       lowerCamelCase__   	=					F"""{key}: {val:.6f}\n"""
                       writer.write(A__      )

           if not save_generations:
                 return

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




     @rank_zero_only
     def 			__snake_case (     self	: Tuple						,	A__	: Tuple						,	A__	: Optional[Any]      )					->							Optional[int]:





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

           lowerCamelCase__   	=					count_trainable_parameters(A__      )
           # 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 			__snake_case (     self	: Optional[int]						,	A__	: pl.Trainer						,	A__	: pl.LightningModule      )					->							Tuple:
           save_json(pl_module.metrics						,	pl_module.metrics_save_path      )
           return self._write_logs(A__						,	A__						,	'test'      )




     @rank_zero_only
     def 			__snake_case (     self	: int						,	A__	: pl.Trainer						,	A__	: Optional[Any]      )					->							Tuple:
           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       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							Any  ,					lowerCamelCase:							Union[str, Any]=1  ,					lowerCamelCase:							Dict=False    )       ->      Any:


      '''simple docstring'''




      if metric == "rouge2":
            lowerCamelCase__   	=					'{val_avg_rouge2:.4f}-{step_count}'
      elif metric == "bleu":
            lowerCamelCase__   	=					'{val_avg_bleu:.4f}-{step_count}'
      elif metric == "loss":
            lowerCamelCase__   	=					'{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.'    )

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


def       lowerCAmelCase_      (	lowerCamelCase:							List[Any]  ,					lowerCamelCase:							List[str]    )       ->      Optional[int]:


      '''simple docstring'''




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






import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


__lowercase				    =      logging.get_logger(__name__)

if is_vision_available():
      import PIL.Image
      from PIL import Image
      from PIL.Image import Image as ImageType
else:
      __lowercase				    =      object

if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

class 	__snake_case					:






    '''simple docstring'''





    def __init__( self    :		Optional[int]      ,    snake_case    :		int				):

      """simple docstring"""





      __lowerCAmelCase								=					value





    def __str__( self    :		List[Any]				):

      """simple docstring"""





      return self.to_string()





    def 						A		( self    :		int				):

      """simple docstring"""





      logger.error(
          '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''				)
      return self._value





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      logger.error(
          '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''				)
      return str(self._value				)


class 	__snake_case					(      _a							,					_a ):






    '''simple docstring'''





    def 						A		( self    :		int				):

      """simple docstring"""





      return self._value





    def 						A		( self    :		List[str]				):

      """simple docstring"""





      return self._value


class 	__snake_case					(      _a							,					_a ):






    '''simple docstring'''





    def __init__( self    :		int      ,    snake_case    :		Optional[int]				):

      """simple docstring"""





      super().__init__(snake_case				)

      if not is_vision_available():
        raise ImportError('''PIL must be installed in order to handle images.'''				)

      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					None

      if isinstance(snake_case      ,    snake_case				):
        __lowerCAmelCase								=					value
      elif isinstance(snake_case      ,    (str, pathlib.Path)				):
        __lowerCAmelCase								=					value
      elif isinstance(snake_case      ,    torch.Tensor				):
        __lowerCAmelCase								=					value
      else:
        raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(snake_case				)}'''				)





    def 						A		( self    :		Tuple      ,    snake_case    :		Union[str, Any]=None      ,    snake_case    :		int=None				):

      """simple docstring"""





      from IPython.display import Image, display

      display(Image(self.to_string()				)				)





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      if self._raw is not None:
        return self._raw

      if self._path is not None:
        __lowerCAmelCase								=					Image.open(self._path				)
        return self._raw





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      if self._path is not None:
        return self._path

      if self._raw is not None:
        __lowerCAmelCase								=					tempfile.mkdtemp()
        __lowerCAmelCase								=					os.path.join(snake_case      ,    str(uuid.uuida()				) + '''.png'''				)
        self._raw.save(self._path				)

        return self._path

      if self._tensor is not None:
        __lowerCAmelCase								=					self._tensor.cpu().detach().numpy()

        # There is likely simpler than load into image into save
        __lowerCAmelCase								=					Image.fromarray((array * 255).astype(np.uinta				)				)

        __lowerCAmelCase								=					tempfile.mkdtemp()
        __lowerCAmelCase								=					os.path.join(snake_case      ,    str(uuid.uuida()				) + '''.png'''				)

        img.save(self._path				)

        return self._path


class 	__snake_case					(      _a ):






    '''simple docstring'''





    def __init__( self    :		List[Any]      ,    snake_case    :		int      ,    snake_case    :		Any=16_000				):

      """simple docstring"""





      super().__init__(snake_case				)

      if not is_soundfile_availble():
        raise ImportError('''soundfile must be installed in order to handle audio.'''				)

      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					None

      __lowerCAmelCase								=					samplerate

      if isinstance(snake_case      ,    (str, pathlib.Path)				):
        __lowerCAmelCase								=					value
      elif isinstance(snake_case      ,    torch.Tensor				):
        __lowerCAmelCase								=					value
      else:
        raise ValueError(f'''Unsupported audio type: {type(snake_case				)}'''				)





    def 						A		( self    :		Optional[int]      ,    snake_case    :		Tuple=None      ,    snake_case    :		Tuple=None				):

      """simple docstring"""





      from IPython.display import Audio, display

      display(Audio(self.to_string()      ,    rate=self.samplerate				)				)





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      if self._tensor is not None:
        return self._tensor

      if self._path is not None:
        __lowerCAmelCase						,    __lowerCAmelCase								=					sf.read(self._path				)
        __lowerCAmelCase								=					torch.tensor(snake_case				)
        return self._tensor





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      if self._path is not None:
        return self._path

      if self._tensor is not None:
        __lowerCAmelCase								=					tempfile.mkdtemp()
        __lowerCAmelCase								=					os.path.join(snake_case      ,    str(uuid.uuida()				) + '''.wav'''				)
        sf.write(self._path      ,    self._tensor      ,    samplerate=self.samplerate				)
        return self._path


__lowercase				    =      {"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
__lowercase				    =      {str: AgentText}

if is_vision_available():
      __lowercase				    =      AgentImage


def        __SCREAMING_SNAKE_CASE	(				*__lowercase:		List[Any]  ,				**__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					[(arg.to_raw() if isinstance(__lowercase  ,				__lowercase) else arg) for arg in args]
  __lowerCAmelCase								=					{k: (v.to_raw() if isinstance(__lowercase  ,				__lowercase) else v) for k, v in kwargs.items()}
  return args, kwargs




def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		List[Any]=None):



  '''simple docstring'''






  if isinstance(__lowercase  ,				__lowercase):
    __lowerCAmelCase								=					{}
    for i, (k, v) in enumerate(outputs.items()):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          __lowerCAmelCase								=					AGENT_TYPE_MAPPING[output_types[i]](__lowercase)
        else:
          __lowerCAmelCase								=					AgentType(__lowercase)

      else:
        # If the class does not have defined output, then we map according to the type
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(__lowercase  ,				_k):
            __lowerCAmelCase								=					_v(__lowercase)
        if k not in decoded_outputs:
          __lowerCAmelCase								=					AgentType[v]

  elif isinstance(__lowercase  ,				(list, tuple)):
    __lowerCAmelCase								=					type(__lowercase)()
    for i, v in enumerate(__lowercase):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__lowercase))
        else:
          decoded_outputs.append(AgentType(__lowercase))
      else:
        # If the class does not have defined output, then we map according to the type
        __lowerCAmelCase								=					False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(__lowercase  ,				_k):
            decoded_outputs.append(_v(__lowercase))
            __lowerCAmelCase								=					True

        if not found:
          decoded_outputs.append(AgentType(__lowercase))

  else:
    if output_types[0] in AGENT_TYPE_MAPPING:
      # If the class has defined outputs, we can map directly according to the class definition
      __lowerCAmelCase								=					AGENT_TYPE_MAPPING[output_types[0]](__lowercase)

    else:
      # If the class does not have defined output, then we map according to the type
      for _k, _v in INSTANCE_TYPE_MAPPING.items():
        if isinstance(__lowercase  ,				_k):
          return _v(__lowercase)
      return AgentType(__lowercase)

  return decoded_outputs
