'''simple docstring'''





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def      __lowercase  (		lowerCamelCase      :  List[Any]						,						lowerCamelCase      :  Any=0.9_9_9						,						lowerCamelCase      :  Any="cosine"						,						):
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(lowerCamelCase      :  List[str]			):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2			) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(lowerCamelCase      :  Dict			):
   return math.exp(t * -1_2.0			)

 else:
  raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"			)

 UpperCamelCase_      :		Tuple				=      []
 for i in range(__snake_case			):
  UpperCamelCase_      :		Dict				=      i / num_diffusion_timesteps
  UpperCamelCase_      :		Dict				=      (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(__snake_case			) / alpha_bar_fn(__snake_case			)						,						__snake_case			)			)
 return torch.tensor(__snake_case						,						dtype=torch.floataa			)






class 	_lowercase		(      snake_case_		,   snake_case_ ):
 lowercase         =     [e.name for e in KarrasDiffusionSchedulers]
 lowercase         =     2

 @register_to_config
 def __init__(				self			:							str     ,   snake_case			:							str = 1_0_0_0     ,   snake_case			:							Optional[Any] = 0.00085     ,   snake_case			:							int = 0.012     ,   snake_case			:							Optional[int] = "linear"     ,   snake_case			:							Optional[Any] = None     ,   snake_case			:							List[str] = "epsilon"     ,   snake_case			:							Optional[Any] = "linspace"     ,   snake_case			:							List[Any] = 0     ,   )     ->		Dict:

  """simple docstring"""





  if trained_betas is not None:
   UpperCamelCase_      :		Union[str, Any]				=      torch.tensor(snake_case     ,   dtype=torch.floataa    )
  elif beta_schedule == "linear":
   UpperCamelCase_      :		str				=      torch.linspace(snake_case     ,   snake_case     ,   snake_case     ,   dtype=torch.floataa    )
  elif beta_schedule == "scaled_linear":
   # this schedule is very specific to the latent diffusion model.
   UpperCamelCase_      :		Optional[Any]				=      (
       torch.linspace(beta_start**0.5     ,   beta_end**0.5     ,   snake_case     ,   dtype=torch.floataa    ) ** 2
   )
  elif beta_schedule == "squaredcos_cap_v2":
   # Glide cosine schedule
   UpperCamelCase_      :		Optional[int]				=      betas_for_alpha_bar(snake_case    )
  else:
   raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"    )

  UpperCamelCase_      :		Any				=      1.0 - self.betas
  UpperCamelCase_      :		List[str]				=      torch.cumprod(self.alphas     ,   dim=0    )

  #  set all values
  self.set_timesteps(snake_case     ,   snake_case     ,   snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   snake_case			:							Dict     ,   snake_case			:							List[Any]=None    )     ->		Optional[Any]:

  """simple docstring"""





  if schedule_timesteps is None:
   UpperCamelCase_      :		Union[str, Any]				=      self.timesteps

  UpperCamelCase_      :		Any				=      (schedule_timesteps == timestep).nonzero()

  # The sigma index that is taken for the **very** first `step`
  # is always the second index (or the last index if there is only 1)
  # This way we can ensure we don't accidentally skip a sigma in
  # case we start in the middle of the denoising schedule (e.g. for image-to-image)
  if len(self._index_counter    ) == 0:
   UpperCamelCase_      :		List[str]				=      1 if len(snake_case    ) > 1 else 0
  else:
   UpperCamelCase_      :		Dict				=      timestep.cpu().item() if torch.is_tensor(snake_case    ) else timestep
   UpperCamelCase_      :		Union[str, Any]				=      self._index_counter[timestep_int]

  return indices[pos].item()

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		List[str]:

  """simple docstring"""





  if self.config.timestep_spacing in ["linspace", "trailing"]:
   return self.sigmas.max()

  return (self.sigmas.max() ** 2 + 1) ** 0.5

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							Dict     ,   snake_case			:							List[Any]     ,   )     ->		torch.FloatTensor:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      self.index_for_timestep(snake_case    )

  if self.state_in_first_order:
   UpperCamelCase_      :		List[str]				=      self.sigmas[step_index]
  else:
   UpperCamelCase_      :		List[Any]				=      self.sigmas_interpol[step_index - 1]

  UpperCamelCase_      :		Optional[int]				=      sample / ((sigma**2 + 1) ** 0.5)
  return sample

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							Any = None     ,   snake_case			:							Dict = None     ,   )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      num_inference_steps

  UpperCamelCase_      :		List[Any]				=      num_train_timesteps or self.config.num_train_timesteps

  # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
  if self.config.timestep_spacing == "linspace":
   UpperCamelCase_      :		Any				=      np.linspace(0     ,   num_train_timesteps - 1     ,   snake_case     ,   dtype=snake_case    )[::-1].copy()
  elif self.config.timestep_spacing == "leading":
   UpperCamelCase_      :		Union[str, Any]				=      num_train_timesteps // self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   UpperCamelCase_      :		Optional[Any]				=      (np.arange(0     ,   snake_case    ) * step_ratio).round()[::-1].copy().astype(snake_case    )
   timesteps += self.config.steps_offset
  elif self.config.timestep_spacing == "trailing":
   UpperCamelCase_      :		str				=      num_train_timesteps / self.num_inference_steps
   # creates integer timesteps by multiplying by ratio
   # casting to int to avoid issues when num_inference_step is power of 3
   UpperCamelCase_      :		List[str]				=      (np.arange(snake_case     ,   0     ,   -step_ratio    )).round().copy().astype(snake_case    )
   timesteps -= 1
  else:
   raise ValueError(
       f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."    )

  UpperCamelCase_      :		Optional[Any]				=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
  UpperCamelCase_      :		Optional[Any]				=      torch.from_numpy(np.log(snake_case    )    ).to(snake_case    )

  UpperCamelCase_      :		Optional[Any]				=      np.interp(snake_case     ,   np.arange(0     ,   len(snake_case    )    )     ,   snake_case    )
  UpperCamelCase_      :		int				=      np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
  UpperCamelCase_      :		int				=      torch.from_numpy(snake_case    ).to(device=snake_case    )

  # compute up and down sigmas
  UpperCamelCase_      :		Optional[Any]				=      sigmas.roll(-1    )
  UpperCamelCase_      :		Optional[Any]				=      0.0
  UpperCamelCase_      :		Optional[int]				=      (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
  UpperCamelCase_      :		Optional[Any]				=      (sigmas_next**2 - sigmas_up**2) ** 0.5
  UpperCamelCase_      :		List[str]				=      0.0

  # compute interpolated sigmas
  UpperCamelCase_      :		Union[str, Any]				=      sigmas.log().lerp(sigmas_down.log()     ,   0.5    ).exp()
  UpperCamelCase_      :		int				=      0.0

  # set sigmas
  UpperCamelCase_      :		List[str]				=      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
  UpperCamelCase_      :		List[str]				=      torch.cat(
      [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )
  UpperCamelCase_      :		List[str]				=      torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2    ), sigmas_up[-1:]]    )
  UpperCamelCase_      :		Any				=      torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2    ), sigmas_down[-1:]]    )

  if str(snake_case    ).startswith('mps'    ):
   # mps does not support float64
   UpperCamelCase_      :		Optional[int]				=      torch.from_numpy(snake_case    ).to(snake_case     ,   dtype=torch.floataa    )
  else:
   UpperCamelCase_      :		Tuple				=      torch.from_numpy(snake_case    ).to(snake_case    )

  UpperCamelCase_      :		List[Any]				=      self.sigma_to_t(snake_case    ).to(snake_case     ,   dtype=timesteps.dtype    )
  UpperCamelCase_      :		Dict				=      torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])     ,   dim=-1    ).flatten()

  UpperCamelCase_      :		str				=      torch.cat([timesteps[:1], interleaved_timesteps]    )

  UpperCamelCase_      :		Dict				=      None

  # for exp beta schedules, such as the one for `pipeline_shap_e.py`
  # we need an index counter
  UpperCamelCase_      :		Optional[int]				=      defaultdict(snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							Dict    )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      sigma.log()

  # get distribution
  UpperCamelCase_      :		Dict				=      log_sigma - self.log_sigmas[:, None]

  # get sigmas range
  UpperCamelCase_      :		Union[str, Any]				=      dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
  UpperCamelCase_      :		int				=      low_idx + 1

  UpperCamelCase_      :		Any				=      self.log_sigmas[low_idx]
  UpperCamelCase_      :		Any				=      self.log_sigmas[high_idx]

  # interpolate sigmas
  UpperCamelCase_      :		Optional[int]				=      (low - log_sigma) / (low - high)
  UpperCamelCase_      :		str				=      w.clamp(0     ,   1    )

  # transform interpolation to time range
  UpperCamelCase_      :		Union[str, Any]				=      (1 - w) * low_idx + w * high_idx
  UpperCamelCase_      :		Optional[int]				=      t.view(sigma.shape    )
  return t

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Union[str, Any]:

  """simple docstring"""





  return self.sample is None

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							Tuple     ,   snake_case			:							int     ,   snake_case			:							Tuple     ,   snake_case			:							Optional[Any] = None     ,   snake_case			:							int = True     ,   )     ->		Union[SchedulerOutput, Tuple]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      self.index_for_timestep(snake_case    )

  # advance index counter by 1
  UpperCamelCase_      :		List[str]				=      timestep.cpu().item() if torch.is_tensor(snake_case    ) else timestep
  self._index_counter[timestep_int] += 1

  if self.state_in_first_order:
   UpperCamelCase_      :		List[str]				=      self.sigmas[step_index]
   UpperCamelCase_      :		Tuple				=      self.sigmas_interpol[step_index]
   UpperCamelCase_      :		Tuple				=      self.sigmas_up[step_index]
   UpperCamelCase_      :		List[str]				=      self.sigmas_down[step_index - 1]
  else:
   # 2nd order / KPDM2's method
   UpperCamelCase_      :		List[str]				=      self.sigmas[step_index - 1]
   UpperCamelCase_      :		Tuple				=      self.sigmas_interpol[step_index - 1]
   UpperCamelCase_      :		Optional[Any]				=      self.sigmas_up[step_index - 1]
   UpperCamelCase_      :		str				=      self.sigmas_down[step_index - 1]

  # currently only gamma=0 is supported. This usually works best anyways.
  # We can support gamma in the future but then need to scale the timestep before
  # passing it to the model which requires a change in API
  UpperCamelCase_      :		int				=      0
  UpperCamelCase_      :		List[Any]				=      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

  UpperCamelCase_      :		Tuple				=      model_output.device
  UpperCamelCase_      :		int				=      randn_tensor(model_output.shape     ,   dtype=model_output.dtype     ,   device=snake_case     ,   generator=snake_case    )

  # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  if self.config.prediction_type == "epsilon":
   UpperCamelCase_      :		Optional[int]				=      sigma_hat if self.state_in_first_order else sigma_interpol
   UpperCamelCase_      :		Dict				=      sample - sigma_input * model_output
  elif self.config.prediction_type == "v_prediction":
   UpperCamelCase_      :		Union[str, Any]				=      sigma_hat if self.state_in_first_order else sigma_interpol
   UpperCamelCase_      :		Optional[int]				=      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
       sample / (sigma_input**2 + 1)
   )
  elif self.config.prediction_type == "sample":
   raise NotImplementedError('prediction_type not implemented yet: sample'    )
  else:
   raise ValueError(
       f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"    )

  if self.state_in_first_order:
   # 2. Convert to an ODE derivative for 1st order
   UpperCamelCase_      :		Optional[int]				=      (sample - pred_original_sample) / sigma_hat
   # 3. delta timestep
   UpperCamelCase_      :		Tuple				=      sigma_interpol - sigma_hat

   # store for 2nd order step
   UpperCamelCase_      :		List[Any]				=      sample
   UpperCamelCase_      :		Union[str, Any]				=      dt
   UpperCamelCase_      :		str				=      sample + derivative * dt
  else:
   # DPM-Solver-2
   # 2. Convert to an ODE derivative for 2nd order
   UpperCamelCase_      :		str				=      (sample - pred_original_sample) / sigma_interpol
   # 3. delta timestep
   UpperCamelCase_      :		Union[str, Any]				=      sigma_down - sigma_hat

   UpperCamelCase_      :		Optional[int]				=      self.sample
   UpperCamelCase_      :		List[str]				=      None

   UpperCamelCase_      :		int				=      sample + derivative * dt
   UpperCamelCase_      :		Optional[int]				=      prev_sample + noise * sigma_up

  if not return_dict:
   return (prev_sample,)

  return SchedulerOutput(prev_sample=snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]     ,   snake_case			:							Dict     ,   snake_case			:							Optional[Any]     ,   snake_case			:							Any     ,   )     ->		torch.FloatTensor:

  """simple docstring"""





  UpperCamelCase_      :		str				=      self.sigmas.to(device=original_samples.device     ,   dtype=original_samples.dtype    )
  if original_samples.device.type == "mps" and torch.is_floating_point(snake_case    ):
   # mps does not support float64
   UpperCamelCase_      :		Any				=      self.timesteps.to(original_samples.device     ,   dtype=torch.floataa    )
   UpperCamelCase_      :		Union[str, Any]				=      timesteps.to(original_samples.device     ,   dtype=torch.floataa    )
  else:
   UpperCamelCase_      :		Any				=      self.timesteps.to(original_samples.device    )
   UpperCamelCase_      :		List[Any]				=      timesteps.to(original_samples.device    )

  UpperCamelCase_      :		Union[str, Any]				=      [self.index_for_timestep(snake_case     ,   snake_case    ) for t in timesteps]

  UpperCamelCase_      :		Any				=      sigmas[step_indices].flatten()
  while len(sigma.shape    ) < len(original_samples.shape    ):
   UpperCamelCase_      :		List[str]				=      sigma.unsqueeze(-1    )

  UpperCamelCase_      :		Tuple				=      original_samples + noise * sigma
  return noisy_samples


 def __len__(				self			:							List[str]    )     ->		Optional[int]:

  """simple docstring"""





  return self.config.num_train_timesteps




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a_					      = logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

a_					      = {
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a_					      = {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a_					      = sorted(arg_to_scheduler.keys())
a_					      = '{' + ', '.join(arg_to_scheduler_choices) + '}'





class 	_lowercase		(      pl.LightningModule ):

 def __init__(				self			:							List[str]     ,   snake_case			:							argparse.Namespace     ,   snake_case			:							Any=None     ,   snake_case			:							str="base"     ,   snake_case			:							Union[str, Any]=None     ,   snake_case			:							Any=None     ,   snake_case			:							str=None     ,   **snake_case			:							str     ,   )     ->		Union[str, Any]:

  """simple docstring"""





  super().__init__()
  # TODO: move to self.save_hyperparameters()
  # self.save_hyperparameters()
  # can also expand arguments into trainer signature for easier reading

  self.save_hyperparameters(snake_case    )
  UpperCamelCase_      :		Tuple				=      0
  UpperCamelCase_      :		Optional[int]				=      Path(self.hparams.output_dir    )
  UpperCamelCase_      :		Any				=      self.hparams.cache_dir if self.hparams.cache_dir else None
  if config is None:
   UpperCamelCase_      :		Optional[int]				=      AutoConfig.from_pretrained(
       self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,   **({'num_labels': num_labels} if num_labels is not None else {})     ,   cache_dir=snake_case     ,   **snake_case     ,   )
  else:
   UpperCamelCase_      :		PretrainedConfig				=      config

  UpperCamelCase_      :		Any				=      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
  for p in extra_model_params:
   if getattr(self.hparams     ,   snake_case     ,   snake_case    ):
    assert hasattr(self.config     ,   snake_case    ), f"model config doesn't have a `{p}` attribute"
    setattr(self.config     ,   snake_case     ,   getattr(self.hparams     ,   snake_case    )    )

  if tokenizer is None:
   UpperCamelCase_      :		Union[str, Any]				=      AutoTokenizer.from_pretrained(
       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,   cache_dir=snake_case     ,   )
  else:
   UpperCamelCase_      :		PreTrainedTokenizer				=      tokenizer
  UpperCamelCase_      :		Optional[int]				=      MODEL_MODES[mode]
  if model is None:
   UpperCamelCase_      :		Tuple				=      self.model_type.from_pretrained(
       self.hparams.model_name_or_path     ,   from_tf=bool('.ckpt' in self.hparams.model_name_or_path    )     ,   config=self.config     ,   cache_dir=snake_case     ,   )
  else:
   UpperCamelCase_      :		Optional[int]				=      model

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   *snake_case			:							Dict     ,   **snake_case			:							List[str]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      self.model_type.from_pretrained(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      arg_to_scheduler[self.hparams.lr_scheduler]
  UpperCamelCase_      :		List[str]				=      get_schedule_func(
      self.opt     ,   num_warmup_steps=self.hparams.warmup_steps     ,   num_training_steps=self.total_steps()    )
  UpperCamelCase_      :		Optional[Any]				=      {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
  return scheduler

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      self.model
  UpperCamelCase_      :		int				=      ['bias', 'LayerNorm.weight']
  UpperCamelCase_      :		Optional[int]				=      [
      {
          'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )],
          'weight_decay': self.hparams.weight_decay,
      },
      {
          'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay    )],
          'weight_decay': 0.0,
      },
  ]
  if self.hparams.adafactor:
   UpperCamelCase_      :		Union[str, Any]				=      Adafactor(
       snake_case     ,   lr=self.hparams.learning_rate     ,   scale_parameter=snake_case     ,   relative_step=snake_case    )

  else:
   UpperCamelCase_      :		List[Any]				=      AdamW(
       snake_case     ,   lr=self.hparams.learning_rate     ,   eps=self.hparams.adam_epsilon    )
  UpperCamelCase_      :		List[str]				=      optimizer

  UpperCamelCase_      :		Optional[int]				=      self.get_lr_scheduler()

  return [optimizer], [scheduler]

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							Tuple     ,   snake_case			:							Union[str, Any]    )     ->		Optional[int]:

  """simple docstring"""





  return self.validation_step(snake_case     ,   snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   snake_case			:							str    )     ->		Any:

  """simple docstring"""





  return self.validation_end(snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      max(1     ,   self.hparams.gpus    )  # TODO: consider num_tpu_cores
  UpperCamelCase_      :		List[str]				=      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
  return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   snake_case			:							List[str]    )     ->		Union[str, Any]:

  """simple docstring"""





  if mode == "test":
   UpperCamelCase_      :		List[str]				=      len(self.test_dataloader().dataset    )
  else:
   UpperCamelCase_      :		Optional[Any]				=      self.get_dataloader('train'     ,   self.hparams.train_batch_size     ,   shuffle=snake_case    )
   UpperCamelCase_      :		int				=      len(self.train_dataloader().dataset    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   snake_case			:							str     ,   snake_case			:							int     ,   snake_case			:							bool = False    )     ->		Optional[int]:

  """simple docstring"""





  raise NotImplementedError('You must implement this for your task'    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Optional[Any]:

  """simple docstring"""





  return self.train_loader

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		Optional[Any]:

  """simple docstring"""





  return self.get_dataloader('dev'     ,   self.hparams.eval_batch_size     ,   shuffle=snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Any:

  """simple docstring"""





  return self.get_dataloader('test'     ,   self.hparams.eval_batch_size     ,   shuffle=snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]     ,   snake_case			:							Dict    )     ->		str:

  """simple docstring"""





  return os.path.join(
      self.hparams.data_dir     ,   'cached_{}_{}_{}'.format(
          snake_case     ,   list(filter(snake_case     ,   self.hparams.model_name_or_path.split('/'    )    )    ).pop()     ,   str(self.hparams.max_seq_length    )     ,   )     ,   )

 @pl.utilities.rank_zero_only
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							Dict[str, Any]    )     ->		None:

  """simple docstring"""





  UpperCamelCase_      :		str				=      self.output_dir.joinpath('best_tfmr'    )
  UpperCamelCase_      :		Dict				=      self.step_count
  self.model.save_pretrained(snake_case    )
  self.tokenizer.save_pretrained(snake_case    )


 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				snake_case			:							Dict     ,   snake_case			:							Dict    )     ->		Any:

  """simple docstring"""





  parser.add_argument(
      '--model_name_or_path'     ,   default=snake_case     ,   type=snake_case     ,   required=snake_case     ,   help='Path to pretrained model or model identifier from huggingface.co/models'     ,   )
  parser.add_argument(
      '--config_name'     ,   default=''     ,   type=snake_case     ,   help='Pretrained config name or path if not the same as model_name'    )
  parser.add_argument(
      '--tokenizer_name'     ,   default=snake_case     ,   type=snake_case     ,   help='Pretrained tokenizer name or path if not the same as model_name'     ,   )
  parser.add_argument(
      '--cache_dir'     ,   default=''     ,   type=snake_case     ,   help='Where do you want to store the pre-trained models downloaded from huggingface.co'     ,   )
  parser.add_argument(
      '--encoder_layerdrop'     ,   type=snake_case     ,   help='Encoder layer dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument(
      '--decoder_layerdrop'     ,   type=snake_case     ,   help='Decoder layer dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument(
      '--dropout'     ,   type=snake_case     ,   help='Dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument(
      '--attention_dropout'     ,   type=snake_case     ,   help='Attention dropout probability (Optional). Goes into model.config'     ,   )
  parser.add_argument('--learning_rate'     ,   default=5e-5     ,   type=snake_case     ,   help='The initial learning rate for Adam.'    )
  parser.add_argument(
      '--lr_scheduler'     ,   default='linear'     ,   choices=snake_case     ,   metavar=snake_case     ,   type=snake_case     ,   help='Learning rate scheduler'     ,   )
  parser.add_argument('--weight_decay'     ,   default=0.0     ,   type=snake_case     ,   help='Weight decay if we apply some.'    )
  parser.add_argument('--adam_epsilon'     ,   default=1e-8     ,   type=snake_case     ,   help='Epsilon for Adam optimizer.'    )
  parser.add_argument('--warmup_steps'     ,   default=0     ,   type=snake_case     ,   help='Linear warmup over warmup_steps.'    )
  parser.add_argument('--num_workers'     ,   default=4     ,   type=snake_case     ,   help='kwarg passed to DataLoader'    )
  parser.add_argument('--num_train_epochs'     ,   dest='max_epochs'     ,   default=3     ,   type=snake_case    )
  parser.add_argument('--train_batch_size'     ,   default=3_2     ,   type=snake_case    )
  parser.add_argument('--eval_batch_size'     ,   default=3_2     ,   type=snake_case    )
  parser.add_argument('--adafactor'     ,   action='store_true'    )





class 	_lowercase		(      pl.Callback ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							Any    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      trainer.lr_schedulers[0]['scheduler']
  UpperCamelCase_      :		int				=      {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
  pl_module.logger.log_metrics(snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							pl.Trainer     ,   snake_case			:							pl.LightningModule    )     ->		List[Any]:

  """simple docstring"""





  rank_zero_info('***** Validation results *****'    )
  UpperCamelCase_      :		Any				=      trainer.callback_metrics
  # Log results
  for key in sorted(snake_case    ):
   if key not in ["log", "progress_bar"]:
    rank_zero_info('{} = {}\n'.format(snake_case     ,   str(metrics[key]    )    )    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							pl.Trainer     ,   snake_case			:							pl.LightningModule    )     ->		int:

  """simple docstring"""





  rank_zero_info('***** Test results *****'    )
  UpperCamelCase_      :		Optional[int]				=      trainer.callback_metrics
  # Log and save results to file
  UpperCamelCase_      :		Union[str, Any]				=      os.path.join(pl_module.hparams.output_dir     ,   'test_results.txt'    )
  with open(snake_case     ,   'w'    ) as writer:
   for key in sorted(snake_case    ):
    if key not in ["log", "progress_bar"]:
     rank_zero_info('{} = {}\n'.format(snake_case     ,   str(metrics[key]    )    )    )
     writer.write('{} = {}\n'.format(snake_case     ,   str(metrics[key]    )    )    )





def      __lowercase  (		lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  Any			):
 #  To allow all pl args uncomment the following line
 #  parser = pl.Trainer.add_argparse_args(parser)
 parser.add_argument(
     '--output_dir'						,						default=lowerCamelCase						,						type=lowerCamelCase						,						required=lowerCamelCase						,						help='The output directory where the model predictions and checkpoints will be written.'						,						)
 parser.add_argument(
     '--fp16'						,						action='store_true'						,						help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'						,						)

 parser.add_argument(
     '--fp16_opt_level'						,						type=lowerCamelCase						,						default='O2'						,						help=(
         'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
         'See details at https://nvidia.github.io/apex/amp.html'
     )						,						)
 parser.add_argument('--n_tpu_cores'						,						dest='tpu_cores'						,						type=lowerCamelCase			)
 parser.add_argument('--max_grad_norm'						,						dest='gradient_clip_val'						,						default=1.0						,						type=lowerCamelCase						,						help='Max gradient norm'			)
 parser.add_argument('--do_train'						,						action='store_true'						,						help='Whether to run training.'			)
 parser.add_argument('--do_predict'						,						action='store_true'						,						help='Whether to run predictions on the test set.'			)
 parser.add_argument(
     '--gradient_accumulation_steps'						,						dest='accumulate_grad_batches'						,						type=lowerCamelCase						,						default=1						,						help='Number of updates steps to accumulate before performing a backward/update pass.'						,						)
 parser.add_argument('--seed'						,						type=lowerCamelCase						,						default=42						,						help='random seed for initialization'			)
 parser.add_argument(
     '--data_dir'						,						default=lowerCamelCase						,						type=lowerCamelCase						,						required=lowerCamelCase						,						help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'						,						)






def      __lowercase  (		lowerCamelCase      :  BaseTransformer						,						lowerCamelCase      :  argparse.Namespace						,						lowerCamelCase      :  int=None						,						lowerCamelCase      :  Tuple=True						,						lowerCamelCase      :  Union[str, Any]=[]						,						lowerCamelCase      :  Any=None						,						lowerCamelCase      :  Optional[Any]=None						,						**lowerCamelCase      :  int						,						):
 pl.seed_everything(args.seed			)

 # init model
 UpperCamelCase_      :		int				=      Path(model.hparams.output_dir			)
 odir.mkdir(exist_ok=lowerCamelCase			)

 # add custom checkpoints
 if checkpoint_callback is None:
  UpperCamelCase_      :		Any				=      pl.callbacks.ModelCheckpoint(
      filepath=args.output_dir						,						prefix='checkpoint'						,						monitor='val_loss'						,						mode='min'						,						save_top_k=1			)
 if early_stopping_callback:
  extra_callbacks.append(lowerCamelCase			)
 if logging_callback is None:
  UpperCamelCase_      :		str				=      LoggingCallback()

 UpperCamelCase_      :		Union[str, Any]				=      {}

 # TODO: remove with PyTorch 1.6 since pl uses native amp
 if args.fpaa:
  UpperCamelCase_      :		Optional[Any]				=      16
  UpperCamelCase_      :		Optional[int]				=      args.fpaa_opt_level

 if args.gpus > 1:
  UpperCamelCase_      :		Optional[int]				=      'ddp'

 UpperCamelCase_      :		Optional[int]				=      args.accumulate_grad_batches
 UpperCamelCase_      :		str				=      extra_train_kwargs.get('accelerator'						,						lowerCamelCase			)
 UpperCamelCase_      :		List[Any]				=      extra_train_kwargs.get('profiler'						,						lowerCamelCase			)

 UpperCamelCase_      :		Optional[Any]				=      pl.Trainer.from_argparse_args(
     lowerCamelCase						,						weights_summary=lowerCamelCase						,						callbacks=[logging_callback] + extra_callbacks						,						logger=lowerCamelCase						,						checkpoint_callback=lowerCamelCase						,						**lowerCamelCase						,						)

 if args.do_train:
  trainer.fit(lowerCamelCase			)

 return trainer



