



'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      RemBertConfig,
      RemBertForCausalLM,
      RemBertForMaskedLM,
      RemBertForMultipleChoice,
      RemBertForQuestionAnswering,
      RemBertForSequenceClassification,
      RemBertForTokenClassification,
      RemBertModel,
  )
  from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class __A      :



 '''simple docstring'''







 def __init__(self			,       A			,       A=13			,       A=7			,       A=True			,       A=True			,       A=True			,       A=True			,       A=99			,       A=32			,       A=18			,       A=43			,       A=5			,       A=4			,       A=37			,       A="gelu"			,       A=0.1			,       A=0.1			,       A=512			,       A=16			,       A=2			,       A=0.02			,       A=3			,       A=4			,       A=None			,       )   ->					Optional[int]:


      """simple docstring"""



      _a     =  parent
      _a     =  batch_size
      _a     =  seq_length
      _a     =  is_training
      _a     =  use_input_mask
      _a     =  use_token_type_ids
      _a     =  use_labels
      _a     =  vocab_size
      _a     =  hidden_size
      _a     =  input_embedding_size
      _a     =  output_embedding_size
      _a     =  num_hidden_layers
      _a     =  num_attention_heads
      _a     =  intermediate_size
      _a     =  hidden_act
      _a     =  hidden_dropout_prob
      _a     =  attention_probs_dropout_prob
      _a     =  max_position_embeddings
      _a     =  type_vocab_size
      _a     =  type_sequence_label_size
      _a     =  initializer_range
      _a     =  num_labels
      _a     =  num_choices
      _a     =  scope



 def  a__  (self	)   ->					str:


      """simple docstring"""



      _a     =  ids_tensor([self.batch_size, self.seq_length]			,       self.vocab_size	)

      _a     =  None
      if self.use_input_mask:
           _a     =  random_attention_mask([self.batch_size, self.seq_length]	)

      _a     =  None
      if self.use_token_type_ids:
           _a     =  ids_tensor([self.batch_size, self.seq_length]			,       self.type_vocab_size	)

      _a     =  None
      _a     =  None
      _a     =  None
      if self.use_labels:
           _a     =  ids_tensor([self.batch_size]			,       self.type_sequence_label_size	)
           _a     =  ids_tensor([self.batch_size, self.seq_length]			,       self.num_labels	)
           _a     =  ids_tensor([self.batch_size]			,       self.num_choices	)

      _a     =  RemBertConfig(
          vocab_size=self.vocab_size			,       hidden_size=self.hidden_size			,       input_embedding_size=self.input_embedding_size			,       output_embedding_size=self.output_embedding_size			,       num_hidden_layers=self.num_hidden_layers			,       num_attention_heads=self.num_attention_heads			,       intermediate_size=self.intermediate_size			,       hidden_act=self.hidden_act			,       hidden_dropout_prob=self.hidden_dropout_prob			,       attention_probs_dropout_prob=self.attention_probs_dropout_prob			,       max_position_embeddings=self.max_position_embeddings			,       type_vocab_size=self.type_vocab_size			,       is_decoder=A			,       initializer_range=self.initializer_range			,       )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



 def  a__  (self	)   ->					Optional[int]:


      """simple docstring"""



      (
          (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   
      )     =  self.prepare_config_and_inputs()

      _a     =  True
      _a     =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
      _a     =  ids_tensor([self.batch_size, self.seq_length]			,       vocab_size=2	)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )



 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A			,       A	)   ->					Any:


      """simple docstring"""



      _a     =  RemBertModel(config=A	)
      model.to(A	)
      model.eval()
      _a     =  model(A			,       attention_mask=A			,       token_type_ids=A	)
      _a     =  model(A			,       token_type_ids=A	)
      _a     =  model(A	)
      self.parent.assertEqual(result.last_hidden_state.shape			,       (self.batch_size, self.seq_length, self.hidden_size)	)



 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       )   ->					Dict:


      """simple docstring"""



      _a     =  True
      _a     =  RemBertModel(A	)
      model.to(A	)
      model.eval()
      _a     =  model(
          A			,       attention_mask=A			,       token_type_ids=A			,       encoder_hidden_states=A			,       encoder_attention_mask=A			,       )
      _a     =  model(
          A			,       attention_mask=A			,       token_type_ids=A			,       encoder_hidden_states=A			,       )
      _a     =  model(A			,       attention_mask=A			,       token_type_ids=A	)
      self.parent.assertEqual(result.last_hidden_state.shape			,       (self.batch_size, self.seq_length, self.hidden_size)	)



 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       )   ->					Any:


      """simple docstring"""



      _a     =  RemBertForCausalLM(config=A	)
      model.to(A	)
      model.eval()
      _a     =  model(A			,       attention_mask=A			,       token_type_ids=A			,       labels=A	)
      self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.seq_length, self.vocab_size)	)



 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A			,       A	)   ->					Any:


      """simple docstring"""



      _a     =  RemBertForMaskedLM(config=A	)
      model.to(A	)
      model.eval()
      _a     =  model(A			,       attention_mask=A			,       token_type_ids=A			,       labels=A	)
      self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.seq_length, self.vocab_size)	)



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


      """simple docstring"""



      _a     =  True
      _a     =  True
      _a     =  RemBertForCausalLM(config=A	)
      model.to(A	)
      model.eval()

      # first forward pass
      _a     =  model(
          A			,       attention_mask=A			,       encoder_hidden_states=A			,       encoder_attention_mask=A			,       use_cache=A			,       )
      _a     =  outputs.past_key_values

      # create hypothetical multiple next token and extent to next_input_ids
      _a     =  ids_tensor((self.batch_size, 3)			,       config.vocab_size	)
      _a     =  ids_tensor((self.batch_size, 3)			,       vocab_size=2	)

      # append to next input_ids and
      _a     =  torch.cat([input_ids, next_tokens]			,       dim=-1	)
      _a     =  torch.cat([input_mask, next_mask]			,       dim=-1	)

      _a     =  model(
          A			,       attention_mask=A			,       encoder_hidden_states=A			,       encoder_attention_mask=A			,       output_hidden_states=A			,       )['''hidden_states'''][0]
      _a     =  model(
          A			,       attention_mask=A			,       encoder_hidden_states=A			,       encoder_attention_mask=A			,       past_key_values=A			,       output_hidden_states=A			,       )['''hidden_states'''][0]

      # select random slice
      _a     =  ids_tensor((1,)			,       output_from_past.shape[-1]	).item()
      _a     =  output_from_no_past[:, -3:, random_slice_idx].detach()
      _a     =  output_from_past[:, :, random_slice_idx].detach()

      self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(A			,       A			,       atol=1E-3	)	)



 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A			,       A	)   ->					List[Any]:


      """simple docstring"""



      _a     =  RemBertForQuestionAnswering(config=A	)
      model.to(A	)
      model.eval()
      _a     =  model(
          A			,       attention_mask=A			,       token_type_ids=A			,       start_positions=A			,       end_positions=A			,       )
      self.parent.assertEqual(result.start_logits.shape			,       (self.batch_size, self.seq_length)	)
      self.parent.assertEqual(result.end_logits.shape			,       (self.batch_size, self.seq_length)	)



 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



      _a     =  self.num_labels
      _a     =  RemBertForSequenceClassification(A	)
      model.to(A	)
      model.eval()
      _a     =  model(A			,       attention_mask=A			,       token_type_ids=A			,       labels=A	)
      self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.num_labels)	)



 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A			,       A	)   ->					List[Any]:


      """simple docstring"""



      _a     =  self.num_labels
      _a     =  RemBertForTokenClassification(config=A	)
      model.to(A	)
      model.eval()
      _a     =  model(A			,       attention_mask=A			,       token_type_ids=A			,       labels=A	)
      self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.seq_length, self.num_labels)	)



 def  a__  (self			,       A			,       A			,       A			,       A			,       A			,       A			,       A	)   ->					int:


      """simple docstring"""



      _a     =  self.num_choices
      _a     =  RemBertForMultipleChoice(config=A	)
      model.to(A	)
      model.eval()
      _a     =  input_ids.unsqueeze(1	).expand(-1			,       self.num_choices			,       -1	).contiguous()
      _a     =  token_type_ids.unsqueeze(1	).expand(-1			,       self.num_choices			,       -1	).contiguous()
      _a     =  input_mask.unsqueeze(1	).expand(-1			,       self.num_choices			,       -1	).contiguous()
      _a     =  model(
          A			,       attention_mask=A			,       token_type_ids=A			,       labels=A			,       )
      self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.num_choices)	)



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


      """simple docstring"""



      _a     =  self.prepare_config_and_inputs()
      (
          (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   
      )     =  config_and_inputs
      _a     =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
      return config, inputs_dict






@require_torch
class __A      (  A       ,       A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	Optional[int]										=	(
     (
         RemBertModel,
         RemBertForMaskedLM,
         RemBertForCausalLM,
         RemBertForMultipleChoice,
         RemBertForQuestionAnswering,
         RemBertForSequenceClassification,
         RemBertForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowerCamelCase   :	Optional[Any]										=	(RemBertForCausalLM,) if is_torch_available() else ()
 __lowerCamelCase   :	Optional[Any]										=	(
     {
         'feature-extraction': RemBertModel,
         'fill-mask': RemBertForMaskedLM,
         'question-answering': RemBertForQuestionAnswering,
         'text-classification': RemBertForSequenceClassification,
         'text-generation': RemBertForCausalLM,
         'token-classification': RemBertForTokenClassification,
         'zero-shot': RemBertForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )



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


      """simple docstring"""



      _a     =  RemBertModelTester(self	)
      _a     =  ConfigTester(self			,       config_class=A			,       hidden_size=37	)



 def  a__  (self	)   ->					Union[str, Any]:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def  a__  (self	)   ->					Optional[int]:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A	)



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


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           _a     =  type
           self.model_tester.create_and_check_model(*A	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*A	)



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*A	)



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*A	)



 def  a__  (self	)   ->					str:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*A	)



 def  a__  (self	)   ->					str:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*A	)



 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*A	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      _a     =  self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*A	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      (
          (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   (
          _a
      )  ,   
      )     =  self.model_tester.prepare_config_and_inputs_for_decoder()

      _a     =  None

      self.model_tester.create_and_check_model_as_decoder(
          A			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       A			,       )



 @slow
 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a     =  RemBertModel.from_pretrained(A	)
           self.assertIsNotNone(A	)






@require_torch
class __A      (  unittest.TestCase						):



 '''simple docstring'''







 @slow
 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      _a     =  RemBertModel.from_pretrained('''google/rembert'''	)
      _a     =  torch.tensor([[312, 56_498, 313, 2_125, 313]]	)
      _a     =  torch.tensor([[0, 0, 0, 1, 1]]	)
      with torch.no_grad():
           _a     =  model(A			,       token_type_ids=A			,       output_hidden_states=A	)

      _a     =  1_152

      _a     =  torch.Size((1, 5, hidden_size)	)
      self.assertEqual(output['''last_hidden_state'''].shape			,       A	)

      _a     =  torch.tensor(
          [
              [
                  [0.0754, -0.2022, 0.1904],
                  [-0.3354, -0.3692, -0.4791],
                  [-0.2314, -0.6729, -0.0749],
                  [-0.0396, -0.3105, -0.4234],
                  [-0.1571, -0.0525, 0.5353],
              ]
          ]	)

      # Running on the original tf implementation gives slightly different results here.
      # Not clear why this variations is present
      # TODO: Find reason for discrepancy
      # expected_original_implementation = [[
      #     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
      #     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
      #     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
      #     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
      #     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
      # ]]

      self.assertTrue(torch.allclose(output['''last_hidden_state'''][:, :, :3]			,       A			,       atol=1E-4	)	)








'''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


lowercase_			=  logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

lowercase_			=  {
    "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
lowercase_			=  {
    "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
}
lowercase_			=  sorted(arg_to_scheduler.keys())
lowercase_			=  "{" + ", ".join(arg_to_scheduler_choices) + "}"






class __A      (  pl.LightningModule						):



 '''simple docstring'''







 def __init__(self			,       A			,       A=None			,       A="base"			,       A=None			,       A=None			,       A=None			,       **A			,       )   ->					int:


      """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(A	)
      _a     =  0
      _a     =  Path(self.hparams.output_dir	)
      _a     =  self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
           _a     =  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=A			,       **A			,       )
      else:
           _a     =  config

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

      if tokenizer is None:
           _a     =  AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,       cache_dir=A			,       )
      else:
           _a     =  tokenizer
      _a     =  MODEL_MODES[mode]
      if model is None:
           _a     =  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=A			,       )
      else:
           _a     =  model



 def  a__  (self			,       *A			,       **A	)   ->					List[str]:


      """simple docstring"""



      _a     =  self.model_type.from_pretrained(*A			,       **A	)



 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  arg_to_scheduler[self.hparams.lr_scheduler]
      _a     =  get_schedule_func(
          self.opt			,       num_warmup_steps=self.hparams.warmup_steps			,       num_training_steps=self.total_steps()	)
      _a     =  {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
      return scheduler



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


      """simple docstring"""



      _a     =  self.model
      _a     =  ['''bias''', '''LayerNorm.weight''']
      _a     =  [
          {
              '''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:
           _a     =  Adafactor(
               A			,       lr=self.hparams.learning_rate			,       scale_parameter=A			,       relative_step=A	)

      else:
           _a     =  AdamW(
               A			,       lr=self.hparams.learning_rate			,       eps=self.hparams.adam_epsilon	)
      _a     =  optimizer

      _a     =  self.get_lr_scheduler()

      return [optimizer], [scheduler]



 def  a__  (self			,       A			,       A	)   ->					Tuple:


      """simple docstring"""



      return self.validation_step(A			,       A	)



 def  a__  (self			,       A	)   ->					Any:


      """simple docstring"""



      return self.validation_end(A	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      _a     =  max(1			,       self.hparams.gpus	)  # TODO: consider num_tpu_cores
      _a     =  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			,       A	)   ->					int:


      """simple docstring"""



      if stage == "test":
           _a     =  len(self.test_dataloader().dataset	)
      else:
           _a     =  self.get_dataloader('''train'''			,       self.hparams.train_batch_size			,       shuffle=A	)
           _a     =  len(self.train_dataloader().dataset	)



 def  a__  (self			,       A			,       A			,       A = False	)   ->					Optional[int]:


      """simple docstring"""



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



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


      """simple docstring"""



      return self.train_loader



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



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



 def  a__  (self	)   ->					Any:


      """simple docstring"""



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



 def  a__  (self			,       A	)   ->					Union[str, Any]:


      """simple docstring"""



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



 @pl.utilities.rank_zero_only
 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.output_dir.joinpath('''best_tfmr'''	)
      _a     =  self.step_count
      self.model.save_pretrained(A	)
      self.tokenizer.save_pretrained(A	)



 @staticmethod
 def  a__  (A			,       A	)   ->					str:


      """simple docstring"""



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






class __A      (  pl.Callback						):



 '''simple docstring'''







 def  a__  (self			,       A			,       A	)   ->					str:


      """simple docstring"""



      if (
          trainer.is_global_zero and trainer.global_rank == 0
      ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
           pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class __A      (  pl.Callback						):



 '''simple docstring'''







 def  a__  (self			,       A			,       A	)   ->					List[Any]:


      """simple docstring"""



      _a     =  trainer.lr_schedulers[0]['''scheduler''']
      _a     =  {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
      pl_module.logger.log_metrics(A	)



 def  a__  (self			,       A			,       A	)   ->					Tuple:


      """simple docstring"""



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



 def  a__  (self			,       A			,       A	)   ->					Dict:


      """simple docstring"""



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





def lowerCAmelCase  (__A  ,     __A):



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,     __A  ,     __A=None  ,     __A=True  ,     __A=None  ,     __A=[]  ,     __A=None  ,     __A=None  ,     **__A  ,     ):



     """simple docstring"""



     pl.seed_everything(args.seed)

     # init model
     _a     =  Path(model.hparams.output_dir)
     odir.mkdir(exist_ok=__A)

     # add custom checkpoints
     if checkpoint_callback is None:
          _a     =  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(__A)
     if logging_callback is None:
          _a     =  LoggingCallback()

     _a     =  {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          _a     =  16
          # train_params["amp_level"] = args.fp16_opt_level

     if args.gpus > 1:
          _a     =  '''auto'''  # "ddp"
          _a     =  '''ddp'''

     _a     =  args.accumulate_grad_batches
     _a     =  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
     _a     =  '''auto'''

     _a     =  pl.Trainer.from_argparse_args(
         __A  ,     weights_summary=__A  ,     callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]  ,     logger=__A  ,     **__A  ,     )

     if args.do_train:
          trainer.fit(__A)

     return trainer



