'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	torch.FloatTensor







class     _lowercase					(    UpperCAmelCase__  ,							UpperCAmelCase__      ):


 '''simple docstring'''






 @register_to_config
 def __init__(				self       :     str				,	SCREAMING_SNAKE_CASE__       :     Optional[Union[int, Tuple[int, int]]] = None				,	SCREAMING_SNAKE_CASE__       :     int = 3				,	SCREAMING_SNAKE_CASE__       :     int = 3				,	SCREAMING_SNAKE_CASE__       :     bool = False				,	SCREAMING_SNAKE_CASE__       :     str = "positional"				,	SCREAMING_SNAKE_CASE__       :     int = 0				,	SCREAMING_SNAKE_CASE__       :     bool = True				,	SCREAMING_SNAKE_CASE__       :     Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")				,	SCREAMING_SNAKE_CASE__       :     Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")				,	SCREAMING_SNAKE_CASE__       :     Tuple[int] = (2_24, 4_48, 6_72, 8_96)				,	SCREAMING_SNAKE_CASE__       :     int = 2				,	SCREAMING_SNAKE_CASE__       :     float = 1				,	SCREAMING_SNAKE_CASE__       :     int = 1				,	SCREAMING_SNAKE_CASE__       :     str = "conv"				,	SCREAMING_SNAKE_CASE__       :     str = "conv"				,	SCREAMING_SNAKE_CASE__       :     str = "silu"				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 8				,	SCREAMING_SNAKE_CASE__       :     int = 32				,	SCREAMING_SNAKE_CASE__       :     float = 1e-5				,	SCREAMING_SNAKE_CASE__       :     str = "default"				,	SCREAMING_SNAKE_CASE__       :     bool = True				,	SCREAMING_SNAKE_CASE__       :     Optional[str] = None				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = None				,	)  ->   Any:
  super().__init__()

  __lowerCAmelCase    							=						sample_size
  __lowerCAmelCase    							=						block_out_channels[0] * 4

  # Check inputs
  if len(SCREAMING_SNAKE_CASE__							) != len(SCREAMING_SNAKE_CASE__							):
   raise ValueError(
       f"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""							)

  if len(SCREAMING_SNAKE_CASE__							) != len(SCREAMING_SNAKE_CASE__							):
   raise ValueError(
       f"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""							)

  # input
  __lowerCAmelCase    							=						nn.Convad(SCREAMING_SNAKE_CASE__				,	block_out_channels[0]				,	kernel_size=3				,	padding=(1, 1)							)

  # time
  if time_embedding_type == "fourier":
   __lowerCAmelCase    							=						GaussianFourierProjection(embedding_size=block_out_channels[0]				,	scale=16							)
   __lowerCAmelCase    							=						2 * block_out_channels[0]
  elif time_embedding_type == "positional":
   __lowerCAmelCase    							=						Timesteps(block_out_channels[0]				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						block_out_channels[0]

  __lowerCAmelCase    							=						TimestepEmbedding(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

  # class embedding
  if class_embed_type is None and num_class_embeds is not None:
   __lowerCAmelCase    							=						nn.Embedding(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  elif class_embed_type == "timestep":
   __lowerCAmelCase    							=						TimestepEmbedding(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  elif class_embed_type == "identity":
   __lowerCAmelCase    							=						nn.Identity(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  else:
   __lowerCAmelCase    							=						None

  __lowerCAmelCase    							=						nn.ModuleList([]							)
  __lowerCAmelCase    							=						None
  __lowerCAmelCase    							=						nn.ModuleList([]							)

  # down
  __lowerCAmelCase    							=						block_out_channels[0]
  for i, down_block_type in enumerate(SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase    							=						output_channel
   __lowerCAmelCase    							=						block_out_channels[i]
   __lowerCAmelCase    							=						i == len(SCREAMING_SNAKE_CASE__							) - 1

   __lowerCAmelCase    							=						get_down_block(
       SCREAMING_SNAKE_CASE__				,	num_layers=SCREAMING_SNAKE_CASE__				,	in_channels=SCREAMING_SNAKE_CASE__				,	out_channels=SCREAMING_SNAKE_CASE__				,	temb_channels=SCREAMING_SNAKE_CASE__				,	add_downsample=not is_final_block				,	resnet_eps=SCREAMING_SNAKE_CASE__				,	resnet_act_fn=SCREAMING_SNAKE_CASE__				,	resnet_groups=SCREAMING_SNAKE_CASE__				,	attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel				,	downsample_padding=SCREAMING_SNAKE_CASE__				,	resnet_time_scale_shift=SCREAMING_SNAKE_CASE__				,	downsample_type=SCREAMING_SNAKE_CASE__				,	)
   self.down_blocks.append(SCREAMING_SNAKE_CASE__							)

  # mid
  __lowerCAmelCase    							=						UNetMidBlockaD(
      in_channels=block_out_channels[-1]				,	temb_channels=SCREAMING_SNAKE_CASE__				,	resnet_eps=SCREAMING_SNAKE_CASE__				,	resnet_act_fn=SCREAMING_SNAKE_CASE__				,	output_scale_factor=SCREAMING_SNAKE_CASE__				,	resnet_time_scale_shift=SCREAMING_SNAKE_CASE__				,	attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]				,	resnet_groups=SCREAMING_SNAKE_CASE__				,	add_attention=SCREAMING_SNAKE_CASE__				,	)

  # up
  __lowerCAmelCase    							=						list(reversed(SCREAMING_SNAKE_CASE__							)							)
  __lowerCAmelCase    							=						reversed_block_out_channels[0]
  for i, up_block_type in enumerate(SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase    							=						output_channel
   __lowerCAmelCase    							=						reversed_block_out_channels[i]
   __lowerCAmelCase    							=						reversed_block_out_channels[min(i + 1				,	len(SCREAMING_SNAKE_CASE__							) - 1							)]

   __lowerCAmelCase    							=						i == len(SCREAMING_SNAKE_CASE__							) - 1

   __lowerCAmelCase    							=						get_up_block(
       SCREAMING_SNAKE_CASE__				,	num_layers=layers_per_block + 1				,	in_channels=SCREAMING_SNAKE_CASE__				,	out_channels=SCREAMING_SNAKE_CASE__				,	prev_output_channel=SCREAMING_SNAKE_CASE__				,	temb_channels=SCREAMING_SNAKE_CASE__				,	add_upsample=not is_final_block				,	resnet_eps=SCREAMING_SNAKE_CASE__				,	resnet_act_fn=SCREAMING_SNAKE_CASE__				,	resnet_groups=SCREAMING_SNAKE_CASE__				,	attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel				,	resnet_time_scale_shift=SCREAMING_SNAKE_CASE__				,	upsample_type=SCREAMING_SNAKE_CASE__				,	)
   self.up_blocks.append(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						output_channel

  # out
  __lowerCAmelCase    							=						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,	32							)
  __lowerCAmelCase    							=						nn.GroupNorm(num_channels=block_out_channels[0]				,	num_groups=SCREAMING_SNAKE_CASE__				,	eps=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						nn.SiLU()
  __lowerCAmelCase    							=						nn.Convad(block_out_channels[0]				,	SCREAMING_SNAKE_CASE__				,	kernel_size=3				,	padding=1							)





 def a (				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     torch.FloatTensor				,	SCREAMING_SNAKE_CASE__       :     Union[torch.Tensor, float, int]				,	SCREAMING_SNAKE_CASE__       :     Optional[torch.Tensor] = None				,	SCREAMING_SNAKE_CASE__       :     bool = True				,	)  ->   Union[UNetaDOutput, Tuple]:
  # 0. center input if necessary
  if self.config.center_input_sample:
   __lowerCAmelCase    							=						2 * sample - 1.0

  # 1. time
  __lowerCAmelCase    							=						timestep
  if not torch.is_tensor(SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase    							=						torch.tensor([timesteps]				,	dtype=torch.long				,	device=sample.device							)
  elif torch.is_tensor(SCREAMING_SNAKE_CASE__							) and len(timesteps.shape							) == 0:
   __lowerCAmelCase    							=						timesteps[None].to(sample.device							)

  # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
  __lowerCAmelCase    							=						timesteps * torch.ones(sample.shape[0]				,	dtype=timesteps.dtype				,	device=timesteps.device							)

  __lowerCAmelCase    							=						self.time_proj(SCREAMING_SNAKE_CASE__							)

  # timesteps does not contain any weights and will always return f32 tensors
  # but time_embedding might actually be running in fp16. so we need to cast here.
  # there might be better ways to encapsulate this.
  __lowerCAmelCase    							=						t_emb.to(dtype=self.dtype							)
  __lowerCAmelCase    							=						self.time_embedding(SCREAMING_SNAKE_CASE__							)

  if self.class_embedding is not None:
   if class_labels is None:
    raise ValueError("""class_labels should be provided when doing class conditioning"""							)

   if self.config.class_embed_type == "timestep":
    __lowerCAmelCase    							=						self.time_proj(SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase    							=						self.class_embedding(SCREAMING_SNAKE_CASE__							).to(dtype=self.dtype							)
   __lowerCAmelCase    							=						emb + class_emb

  # 2. pre-process
  __lowerCAmelCase    							=						sample
  __lowerCAmelCase    							=						self.conv_in(SCREAMING_SNAKE_CASE__							)

  # 3. down
  __lowerCAmelCase    							=						(sample,)
  for downsample_block in self.down_blocks:
   if hasattr(SCREAMING_SNAKE_CASE__				,	"""skip_conv"""							):
    __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						downsample_block(
        hidden_states=SCREAMING_SNAKE_CASE__				,	temb=SCREAMING_SNAKE_CASE__				,	skip_sample=SCREAMING_SNAKE_CASE__							)
   else:
    __lowerCAmelCase							,	__lowerCAmelCase    							=						downsample_block(hidden_states=SCREAMING_SNAKE_CASE__				,	temb=SCREAMING_SNAKE_CASE__							)

   down_block_res_samples += res_samples

  # 4. mid
  __lowerCAmelCase    							=						self.mid_block(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

  # 5. up
  __lowerCAmelCase    							=						None
  for upsample_block in self.up_blocks:
   __lowerCAmelCase    							=						down_block_res_samples[-len(upsample_block.resnets							) :]
   __lowerCAmelCase    							=						down_block_res_samples[: -len(upsample_block.resnets							)]

   if hasattr(SCREAMING_SNAKE_CASE__				,	"""skip_conv"""							):
    __lowerCAmelCase							,	__lowerCAmelCase    							=						upsample_block(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
   else:
    __lowerCAmelCase    							=						upsample_block(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

        # 6. post-process
  __lowerCAmelCase    							=						self.conv_norm_out(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						self.conv_act(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						self.conv_out(SCREAMING_SNAKE_CASE__							)

  if skip_sample is not None:
   sample += skip_sample

  if self.config.time_embedding_type == "fourier":
   __lowerCAmelCase    							=						timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]							)))							)
   __lowerCAmelCase    							=						sample / timesteps

  if not return_dict:
   return (sample,)

  return UNetaDOutput(sample=SCREAMING_SNAKE_CASE__							)







'''simple docstring'''

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      :				str 				=    logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_A      :				str 				=    {
    '''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      :				List[str] 				=    {
    '''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      :				Optional[Any] 				=    sorted(arg_to_scheduler.keys())
_A      :				Any 				=    '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class     _lowercase					(    pl.LightningModule      ):


 '''simple docstring'''






 def __init__(				self       :     int				,	SCREAMING_SNAKE_CASE__       :     argparse.Namespace				,	SCREAMING_SNAKE_CASE__       :     Dict=None				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]="base"				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=None				,	SCREAMING_SNAKE_CASE__       :     str=None				,	SCREAMING_SNAKE_CASE__       :     str=None				,	**SCREAMING_SNAKE_CASE__       :     Any				,	)  ->   Dict:
  super().__init__()
  # TODO: move to self.save_hyperparameters()
  # self.save_hyperparameters()
  # can also expand arguments into trainer signature for easier reading

  self.save_hyperparameters(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						0
  __lowerCAmelCase    							=						Path(self.hparams.output_dir							)
  __lowerCAmelCase    							=						self.hparams.cache_dir if self.hparams.cache_dir else None
  if config is None:
   __lowerCAmelCase    							=						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=SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__				,	)
  else:
   __lowerCAmelCase    							=						config

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

  if tokenizer is None:
   __lowerCAmelCase    							=						AutoTokenizer.from_pretrained(
       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,	cache_dir=SCREAMING_SNAKE_CASE__				,	)
  else:
   __lowerCAmelCase    							=						tokenizer
  __lowerCAmelCase    							=						MODEL_MODES[mode]
  if model is None:
   __lowerCAmelCase    							=						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=SCREAMING_SNAKE_CASE__				,	)
  else:
   __lowerCAmelCase    							=						model
 def a (				self       :     Dict				,	*SCREAMING_SNAKE_CASE__       :     List[Any]				,	**SCREAMING_SNAKE_CASE__       :     int							)  ->   Optional[int]:
  __lowerCAmelCase    							=						self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__							)
 def a (				self       :     str							)  ->   Tuple:
  __lowerCAmelCase    							=						arg_to_scheduler[self.hparams.lr_scheduler]
  __lowerCAmelCase    							=						get_schedule_func(
      self.opt				,	num_warmup_steps=self.hparams.warmup_steps				,	num_training_steps=self.total_steps()							)
  __lowerCAmelCase    							=						{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
  return scheduler
 def a (				self       :     Union[str, Any]							)  ->   Optional[int]:
  __lowerCAmelCase    							=						self.model
  __lowerCAmelCase    							=						["""bias""", """LayerNorm.weight"""]
  __lowerCAmelCase    							=						[
      {
          """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:
   __lowerCAmelCase    							=						Adafactor(
       SCREAMING_SNAKE_CASE__				,	lr=self.hparams.learning_rate				,	scale_parameter=SCREAMING_SNAKE_CASE__				,	relative_step=SCREAMING_SNAKE_CASE__							)

  else:
   __lowerCAmelCase    							=						AdamW(
       SCREAMING_SNAKE_CASE__				,	lr=self.hparams.learning_rate				,	eps=self.hparams.adam_epsilon							)
  __lowerCAmelCase    							=						optimizer

  __lowerCAmelCase    							=						self.get_lr_scheduler()

  return [optimizer], [scheduler]
 def a (				self       :     str				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     List[Any]							)  ->   Dict:
  return self.validation_step(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]							)  ->   Any:
  return self.validation_end(SCREAMING_SNAKE_CASE__							)
 def a (				self       :     List[str]							)  ->   int:
  __lowerCAmelCase    							=						max(1				,	self.hparams.gpus							)  # TODO: consider num_tpu_cores
  __lowerCAmelCase    							=						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
  return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     Tuple							)  ->   Tuple:
  if mode == "test":
   __lowerCAmelCase    							=						len(self.test_dataloader().dataset							)
  else:
   __lowerCAmelCase    							=						self.get_dataloader("""train"""				,	self.hparams.train_batch_size				,	shuffle=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						len(self.train_dataloader().dataset							)
 def a (				self       :     str				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     bool = False							)  ->   str:
  raise NotImplementedError("""You must implement this for your task"""							)
 def a (				self       :     Union[str, Any]							)  ->   Tuple:
  return self.train_loader
 def a (				self       :     Tuple							)  ->   Optional[int]:
  return self.get_dataloader("""dev"""				,	self.hparams.eval_batch_size				,	shuffle=SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Tuple							)  ->   int:
  return self.get_dataloader("""test"""				,	self.hparams.eval_batch_size				,	shuffle=SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   int:
  return os.path.join(
      self.hparams.data_dir				,	"""cached_{}_{}_{}""".format(
          SCREAMING_SNAKE_CASE__				,	list(filter(SCREAMING_SNAKE_CASE__				,	self.hparams.model_name_or_path.split("""/"""							)							)							).pop()				,	str(self.hparams.max_seq_length							)				,	)				,	)
 @pl.utilities.rank_zero_only
 def a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     Dict[str, Any]							)  ->   None:
  __lowerCAmelCase    							=						self.output_dir.joinpath("""best_tfmr"""							)
  __lowerCAmelCase    							=						self.step_count
  self.model.save_pretrained(SCREAMING_SNAKE_CASE__							)
  self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)





 @staticmethod
 def a (				SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]							)  ->   Any:
  parser.add_argument(
      """--model_name_or_path"""				,	default=SCREAMING_SNAKE_CASE__				,	type=SCREAMING_SNAKE_CASE__				,	required=SCREAMING_SNAKE_CASE__				,	help="""Path to pretrained model or model identifier from huggingface.co/models"""				,	)
  parser.add_argument(
      """--config_name"""				,	default=""""""				,	type=SCREAMING_SNAKE_CASE__				,	help="""Pretrained config name or path if not the same as model_name"""							)
  parser.add_argument(
      """--tokenizer_name"""				,	default=SCREAMING_SNAKE_CASE__				,	type=SCREAMING_SNAKE_CASE__				,	help="""Pretrained tokenizer name or path if not the same as model_name"""				,	)
  parser.add_argument(
      """--cache_dir"""				,	default=""""""				,	type=SCREAMING_SNAKE_CASE__				,	help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""				,	)
  parser.add_argument(
      """--encoder_layerdrop"""				,	type=SCREAMING_SNAKE_CASE__				,	help="""Encoder layer dropout probability (Optional). Goes into model.config"""				,	)
  parser.add_argument(
      """--decoder_layerdrop"""				,	type=SCREAMING_SNAKE_CASE__				,	help="""Decoder layer dropout probability (Optional). Goes into model.config"""				,	)
  parser.add_argument(
      """--dropout"""				,	type=SCREAMING_SNAKE_CASE__				,	help="""Dropout probability (Optional). Goes into model.config"""				,	)
  parser.add_argument(
      """--attention_dropout"""				,	type=SCREAMING_SNAKE_CASE__				,	help="""Attention dropout probability (Optional). Goes into model.config"""				,	)
  parser.add_argument("""--learning_rate"""				,	default=5e-5				,	type=SCREAMING_SNAKE_CASE__				,	help="""The initial learning rate for Adam."""							)
  parser.add_argument(
      """--lr_scheduler"""				,	default="""linear"""				,	choices=SCREAMING_SNAKE_CASE__				,	metavar=SCREAMING_SNAKE_CASE__				,	type=SCREAMING_SNAKE_CASE__				,	help="""Learning rate scheduler"""				,	)
  parser.add_argument("""--weight_decay"""				,	default=0.0				,	type=SCREAMING_SNAKE_CASE__				,	help="""Weight decay if we apply some."""							)
  parser.add_argument("""--adam_epsilon"""				,	default=1e-8				,	type=SCREAMING_SNAKE_CASE__				,	help="""Epsilon for Adam optimizer."""							)
  parser.add_argument("""--warmup_steps"""				,	default=0				,	type=SCREAMING_SNAKE_CASE__				,	help="""Linear warmup over warmup_steps."""							)
  parser.add_argument("""--num_workers"""				,	default=4				,	type=SCREAMING_SNAKE_CASE__				,	help="""kwarg passed to DataLoader"""							)
  parser.add_argument("""--num_train_epochs"""				,	dest="""max_epochs"""				,	default=3				,	type=SCREAMING_SNAKE_CASE__							)
  parser.add_argument("""--train_batch_size"""				,	default=32				,	type=SCREAMING_SNAKE_CASE__							)
  parser.add_argument("""--eval_batch_size"""				,	default=32				,	type=SCREAMING_SNAKE_CASE__							)
  parser.add_argument("""--adafactor"""				,	action="""store_true"""							)







class     _lowercase					(    pl.Callback      ):


 '''simple docstring'''






 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     Dict							)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						trainer.lr_schedulers[0]["""scheduler"""]
  __lowerCAmelCase    							=						{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
  pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__							)
 def a (				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     pl.Trainer				,	SCREAMING_SNAKE_CASE__       :     pl.LightningModule							)  ->   Dict:
  rank_zero_info("""***** Validation results *****"""							)
  __lowerCAmelCase    							=						trainer.callback_metrics
  # Log results
  for key in sorted(SCREAMING_SNAKE_CASE__							):
   if key not in ["log", "progress_bar"]:
    rank_zero_info("""{} = {}\n""".format(SCREAMING_SNAKE_CASE__				,	str(metrics[key]							)							)							)





 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     pl.Trainer				,	SCREAMING_SNAKE_CASE__       :     pl.LightningModule							)  ->   List[str]:
  rank_zero_info("""***** Test results *****"""							)
  __lowerCAmelCase    							=						trainer.callback_metrics
  # Log and save results to file
  __lowerCAmelCase    							=						os.path.join(pl_module.hparams.output_dir				,	"""test_results.txt"""							)
  with open(SCREAMING_SNAKE_CASE__				,	"""w"""							) as writer:
   for key in sorted(SCREAMING_SNAKE_CASE__							):
    if key not in ["log", "progress_bar"]:
     rank_zero_info("""{} = {}\n""".format(SCREAMING_SNAKE_CASE__				,	str(metrics[key]							)							)							)
     writer.write("""{} = {}\n""".format(SCREAMING_SNAKE_CASE__				,	str(metrics[key]							)							)							)







def 					UpperCamelCase_						( snake_case_	:	Tuple						,		snake_case_	:	Optional[int]				) ->					None:






 '''simple docstring'''

 parser.add_argument(
     """--output_dir"""						,		default=snake_case_						,		type=snake_case_						,		required=snake_case_						,		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=snake_case_						,		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=snake_case_				)
 parser.add_argument("""--max_grad_norm"""						,		dest="""gradient_clip_val"""						,		default=1.0						,		type=snake_case_						,		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=snake_case_						,		default=1						,		help="""Number of updates steps to accumulate before performing a backward/update pass."""						,		)
 parser.add_argument("""--seed"""						,		type=snake_case_						,		default=42						,		help="""random seed for initialization"""				)
 parser.add_argument(
     """--data_dir"""						,		default=snake_case_						,		type=snake_case_						,		required=snake_case_						,		help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""						,		)







def 					UpperCamelCase_						( snake_case_	:	BaseTransformer						,		snake_case_	:	argparse.Namespace						,		snake_case_	:	Optional[int]=None						,		snake_case_	:	List[Any]=True						,		snake_case_	:	Optional[Any]=[]						,		snake_case_	:	List[str]=None						,		snake_case_	:	int=None						,		**snake_case_	:	Optional[int]						,		) ->					Dict:






 '''simple docstring'''

 pl.seed_everything(args.seed				)

 # init model
 __lowerCAmelCase    							=						Path(model.hparams.output_dir				)
 odir.mkdir(exist_ok=snake_case_				)

 # add custom checkpoints
 if checkpoint_callback is None:
  __lowerCAmelCase    							=						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(snake_case_				)
 if logging_callback is None:
  __lowerCAmelCase    							=						LoggingCallback()

 __lowerCAmelCase    							=						{}

 # TODO: remove with PyTorch 1.6 since pl uses native amp
 if args.fpaa:
  __lowerCAmelCase    							=						16
  __lowerCAmelCase    							=						args.fpaa_opt_level

 if args.gpus > 1:
  __lowerCAmelCase    							=						"""ddp"""

 __lowerCAmelCase    							=						args.accumulate_grad_batches
 __lowerCAmelCase    							=						extra_train_kwargs.get("""accelerator"""						,		snake_case_				)
 __lowerCAmelCase    							=						extra_train_kwargs.get("""profiler"""						,		snake_case_				)

 __lowerCAmelCase    							=						pl.Trainer.from_argparse_args(
     snake_case_						,		weights_summary=snake_case_						,		callbacks=[logging_callback] + extra_callbacks						,		logger=snake_case_						,		checkpoint_callback=snake_case_						,		**snake_case_						,		)

 if args.do_train:
  trainer.fit(snake_case_				)

 return trainer






