

"""simple docstring"""




import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          AlbertForMaskedLM,
          AlbertForMultipleChoice,
          AlbertForPreTraining,
          AlbertForQuestionAnswering,
          AlbertForSequenceClassification,
          AlbertForTokenClassification,
          AlbertModel,
      )
      from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class      __lowercase			:

  def __init__(  self		:			Tuple					,A		:			List[str]					,A		:			Tuple=13					,A		:			Union[str, Any]=7					,A		:			List[str]=True					,A		:			Optional[int]=True					,A		:			Optional[int]=True					,A		:			Tuple=True					,A		:			Tuple=99					,A		:			Dict=16					,A		:			Optional[Any]=36					,A		:			Optional[Any]=6					,A		:			Optional[Any]=6					,A		:			List[Any]=6					,A		:			int=37					,A		:			Any="gelu"					,A		:			Any=0.1					,A		:			int=0.1					,A		:			Union[str, Any]=512					,A		:			Dict=16					,A		:			Optional[int]=2					,A		:			Tuple=0.0_2					,A		:			Any=3					,A		:			Optional[Any]=4					,A		:			Tuple=None					,):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					parent
   UpperCAmelCase__					:  List[Any]					   =					batch_size
   UpperCAmelCase__					:  Tuple					   =					seq_length
   UpperCAmelCase__					:  List[str]					   =					is_training
   UpperCAmelCase__					:  Optional[Any]					   =					use_input_mask
   UpperCAmelCase__					:  Optional[Any]					   =					use_token_type_ids
   UpperCAmelCase__					:  Any					   =					use_labels
   UpperCAmelCase__					:  List[Any]					   =					vocab_size
   UpperCAmelCase__					:  Dict					   =					embedding_size
   UpperCAmelCase__					:  Optional[Any]					   =					hidden_size
   UpperCAmelCase__					:  List[Any]					   =					num_hidden_layers
   UpperCAmelCase__					:  Any					   =					num_hidden_groups
   UpperCAmelCase__					:  Dict					   =					num_attention_heads
   UpperCAmelCase__					:  Dict					   =					intermediate_size
   UpperCAmelCase__					:  Tuple					   =					hidden_act
   UpperCAmelCase__					:  List[str]					   =					hidden_dropout_prob
   UpperCAmelCase__					:  Optional[Any]					   =					attention_probs_dropout_prob
   UpperCAmelCase__					:  List[Any]					   =					max_position_embeddings
   UpperCAmelCase__					:  str					   =					type_vocab_size
   UpperCAmelCase__					:  Optional[Any]					   =					type_sequence_label_size
   UpperCAmelCase__					:  int					   =					initializer_range
   UpperCAmelCase__					:  Tuple					   =					num_labels
   UpperCAmelCase__					:  Optional[Any]					   =					num_choices
   UpperCAmelCase__					:  List[str]					   =					scope

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size    )

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

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

   UpperCAmelCase__					:  Tuple					   =					None
   UpperCAmelCase__					:  List[str]					   =					None
   UpperCAmelCase__					:  List[Any]					   =					None
   if self.use_labels:
    UpperCAmelCase__					:  List[Any]					   =					ids_tensor([self.batch_size]					,self.type_sequence_label_size    )
    UpperCAmelCase__					:  Union[str, Any]					   =					ids_tensor([self.batch_size, self.seq_length]					,self.num_labels    )
    UpperCAmelCase__					:  List[str]					   =					ids_tensor([self.batch_size]					,self.num_choices    )

   UpperCAmelCase__					:  Dict					   =					self.get_config()

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

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   return AlbertConfig(
       vocab_size=self.vocab_size					,hidden_size=self.hidden_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					,initializer_range=self.initializer_range					,num_hidden_groups=self.num_hidden_groups					,)

  def    __lowercase       (  self		:			int					,A		:			Optional[int]					,A		:			Tuple					,A		:			Any					,A		:			Any					,A		:			Dict					,A		:			str					,A		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					AlbertModel(config=A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  List[str]					   =					model(A					,attention_mask=A					,token_type_ids=A    )
   UpperCAmelCase__					:  List[Any]					   =					model(A					,token_type_ids=A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					model(A    )
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)    )
   self.parent.assertEqual(result.pooler_output.shape					,(self.batch_size, self.hidden_size)    )

  def    __lowercase       (  self		:			Optional[int]					,A		:			List[str]					,A		:			int					,A		:			Optional[Any]					,A		:			List[Any]					,A		:			Tuple					,A		:			List[str]					,A		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					AlbertForPreTraining(config=A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  Any					   =					model(
       A					,attention_mask=A					,token_type_ids=A					,labels=A					,sentence_order_label=A					,)
   self.parent.assertEqual(result.prediction_logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)    )
   self.parent.assertEqual(result.sop_logits.shape					,(self.batch_size, config.num_labels)    )

  def    __lowercase       (  self		:			int					,A		:			List[str]					,A		:			Dict					,A		:			Optional[int]					,A		:			Dict					,A		:			int					,A		:			Dict					,A		:			Dict    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					AlbertForMaskedLM(config=A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  Optional[int]					   =					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    __lowercase       (  self		:			Tuple					,A		:			List[str]					,A		:			Any					,A		:			List[str]					,A		:			Union[str, Any]					,A		:			Any					,A		:			Union[str, Any]					,A		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					AlbertForQuestionAnswering(config=A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  List[Any]					   =					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    __lowercase       (  self		:			str					,A		:			int					,A		:			Tuple					,A		:			Dict					,A		:			Dict					,A		:			str					,A		:			Dict					,A		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self.num_labels
   UpperCAmelCase__					:  Dict					   =					AlbertForSequenceClassification(A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  Optional[Any]					   =					model(A					,attention_mask=A					,token_type_ids=A					,labels=A    )
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)    )

  def    __lowercase       (  self		:			str					,A		:			Tuple					,A		:			List[Any]					,A		:			List[Any]					,A		:			List[str]					,A		:			Dict					,A		:			int					,A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self.num_labels
   UpperCAmelCase__					:  str					   =					AlbertForTokenClassification(config=A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  Optional[Any]					   =					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    __lowercase       (  self		:			Union[str, Any]					,A		:			List[str]					,A		:			str					,A		:			Any					,A		:			Tuple					,A		:			int					,A		:			Optional[Any]					,A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					self.num_choices
   UpperCAmelCase__					:  Dict					   =					AlbertForMultipleChoice(config=A    )
   model.to(A    )
   model.eval()
   UpperCAmelCase__					:  Union[str, Any]					   =					input_ids.unsqueeze(1    ).expand(-1					,self.num_choices					,-1    ).contiguous()
   UpperCAmelCase__					:  Union[str, Any]					   =					token_type_ids.unsqueeze(1    ).expand(-1					,self.num_choices					,-1    ).contiguous()
   UpperCAmelCase__					:  Dict					   =					input_mask.unsqueeze(1    ).expand(-1					,self.num_choices					,-1    ).contiguous()
   UpperCAmelCase__					:  int					   =					model(
       A					,attention_mask=A					,token_type_ids=A					,labels=A					,)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_choices)    )




  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					self.prepare_config_and_inputs()
   (
       (
       UpperCAmelCase__
   )   ,      (
       UpperCAmelCase__
   )   ,      (
       UpperCAmelCase__
   )   ,      (
       UpperCAmelCase__
   )   ,      (
       UpperCAmelCase__
   )   ,      (
       UpperCAmelCase__
   )   ,      (
       UpperCAmelCase__
   )   ,      
   )					:  Union[str, Any]					   =					config_and_inputs
   UpperCAmelCase__					:  List[str]					   =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
   return config, inputs_dict







@require_torch
class      __lowercase			(    __lowerCamelCase	,			__lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       (
      (
          AlbertModel,
          AlbertForPreTraining,
          AlbertForMaskedLM,
          AlbertForMultipleChoice,
          AlbertForSequenceClassification,
          AlbertForTokenClassification,
          AlbertForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  snake_case_				       =       (
      {
          """feature-extraction""": AlbertModel,
          """fill-mask""": AlbertForMaskedLM,
          """question-answering""": AlbertForQuestionAnswering,
          """text-classification""": AlbertForSequenceClassification,
          """token-classification""": AlbertForTokenClassification,
          """zero-shot""": AlbertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  snake_case_				       =       True

  def    __lowercase       (  self		:			Optional[Any]					,A		:			Optional[Any]					,A		:			Any					,A		:			int=False    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					super()._prepare_for_class(A					,A					,return_labels=A    )

   if return_labels:
    if model_class in get_values(A    ):
     UpperCAmelCase__					:  str					   =					torch.zeros(
         (self.model_tester.batch_size, self.model_tester.seq_length)					,dtype=torch.long					,device=A    )
     UpperCAmelCase__					:  Optional[Any]					   =					torch.zeros(
         self.model_tester.batch_size					,dtype=torch.long					,device=A    )
   return inputs_dict

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					AlbertModelTester(self    )
   UpperCAmelCase__					:  List[str]					   =					ConfigTester(self					,config_class=A					,hidden_size=37    )

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   self.config_tester.run_common_tests()

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*A    )

  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*A    )

  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*A    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*A    )

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*A    )

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*A    )

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  int					   =					self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
    UpperCAmelCase__					:  List[str]					   =					type
    self.model_tester.create_and_check_model(*A    )




  @slow
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    UpperCAmelCase__					:  List[str]					   =					AlbertModel.from_pretrained(A    )
    self.assertIsNotNone(A    )







@require_torch
class      __lowercase			(    unittest.TestCase					):

  @slow
  def    __lowercase       (  self		:			List[str]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					AlbertModel.from_pretrained("""albert-base-v2"""    )
   UpperCAmelCase__					:  List[Any]					   =					torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]    )
   UpperCAmelCase__					:  Optional[Any]					   =					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
   with torch.no_grad():
    UpperCAmelCase__					:  str					   =					model(A					,attention_mask=A    )[0]
   UpperCAmelCase__					:  int					   =					torch.Size((1, 11, 768)    )
   self.assertEqual(output.shape					,A    )
   UpperCAmelCase__					:  Optional[int]					   =					torch.tensor(
       [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]    )

   self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,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


__UpperCAmelCase =				logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__UpperCAmelCase =				{
    '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
__UpperCAmelCase =				{
    '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
}
__UpperCAmelCase =				sorted(arg_to_scheduler.keys())
__UpperCAmelCase =				'{' + ', '.join(arg_to_scheduler_choices) + '}'







class      __lowercase			(    pl.LightningModule					):

  def __init__(  self		:			List[str]					,A		:			argparse.Namespace					,A		:			List[Any]=None					,A		:			Any="base"					,A		:			List[str]=None					,A		:			Optional[Any]=None					,A		:			int=None					,**A		:			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(A    )
   UpperCAmelCase__					:  Union[str, Any]					   =					0
   UpperCAmelCase__					:  List[Any]					   =					Path(self.hparams.output_dir    )
   UpperCAmelCase__					:  str					   =					self.hparams.cache_dir if self.hparams.cache_dir else None
   if config is None:
    UpperCAmelCase__					:  Any					   =					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:
    UpperCAmelCase__					:  PretrainedConfig					   =					config

   UpperCAmelCase__					:  Optional[int]					   =					("""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:
    UpperCAmelCase__					:  Dict					   =					AutoTokenizer.from_pretrained(
        self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,cache_dir=A					,)
   else:
    UpperCAmelCase__					:  PreTrainedTokenizer					   =					tokenizer
   UpperCAmelCase__					:  Optional[int]					   =					MODEL_MODES[mode]
   if model is None:
    UpperCAmelCase__					:  Optional[Any]					   =					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:
    UpperCAmelCase__					:  List[Any]					   =					model

  def    __lowercase       (  self		:			Optional[int]					,*A		:			int					,**A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					self.model_type.from_pretrained(*A					,**A    )

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[int]					   =					arg_to_scheduler[self.hparams.lr_scheduler]
   UpperCAmelCase__					:  Optional[int]					   =					get_schedule_func(
       self.opt					,num_warmup_steps=self.hparams.warmup_steps					,num_training_steps=self.total_steps()    )
   UpperCAmelCase__					:  Any					   =					{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
   return scheduler

  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					self.model
   UpperCAmelCase__					:  Any					   =					["""bias""", """LayerNorm.weight"""]
   UpperCAmelCase__					:  str					   =					[
       {
           """params""": [
               p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )
           ],  # check this named paramters
           """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__					:  List[str]					   =					Adafactor(
        A					,lr=self.hparams.learning_rate					,scale_parameter=A					,relative_step=A    )

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

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

   return [optimizer], [scheduler]

  def    __lowercase       (  self		:			Optional[int]					,A		:			List[str]					,A		:			int    ):






   '''simple docstring'''
   return self.validation_step(A					,A    )

  def    __lowercase       (  self		:			Any					,A		:			Tuple    ):






   '''simple docstring'''
   return self.validation_end(A    )

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Dict					   =					max(1					,self.hparams.gpus    )  # TODO: consider num_tpu_cores
   UpperCAmelCase__					:  Union[str, Any]					   =					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
   return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

  def    __lowercase       (  self		:			List[Any]					,A		:			Optional[int]    ):






   '''simple docstring'''
   if stage == "test":
    UpperCAmelCase__					:  Tuple					   =					len(self.test_dataloader().dataset    )
   else:
    UpperCAmelCase__					:  Any					   =					self.get_dataloader("""train"""					,self.hparams.train_batch_size					,shuffle=A    )
    UpperCAmelCase__					:  int					   =					len(self.train_dataloader().dataset    )

  def    __lowercase       (  self		:			Dict					,A		:			str					,A		:			int					,A		:			bool = False    ):






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

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   return self.train_loader

  def    __lowercase       (  self		:			List[Any]    ):






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

  def    __lowercase       (  self		:			Optional[Any]    ):






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

  def    __lowercase       (  self		:			Union[str, Any]					,A		:			Optional[int]    ):






   '''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    __lowercase       (  self		:			Tuple					,A		:			Dict[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					self.output_dir.joinpath("""best_tfmr"""    )
   UpperCAmelCase__					:  int					   =					self.step_count
   self.model.save_pretrained(A    )
   self.tokenizer.save_pretrained(A    )




  @staticmethod
  def    __lowercase       (  A		:			Any					,A		:			Optional[int]    ):






   '''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=str(Path(A    ).parent / """test_run""" / """cache"""    )					,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      __lowercase			(    pl.Callback					):

  def    __lowercase       (  self		:			List[Any]					,A		:			Any					,A		:			Dict    ):






   '''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      __lowercase			(    pl.Callback					):

  def    __lowercase       (  self		:			Optional[int]					,A		:			Union[str, Any]					,A		:			int    ):






   '''simple docstring'''
   # print(pl_module.model.rag)
   for name, param in pl_module.model.rag.named_parameters():
    if param.grad is None:
     print(A    )







class      __lowercase			(    pl.Callback					):

  def    __lowercase       (  self		:			Tuple					,A		:			Dict					,A		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					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(A    )

  def    __lowercase       (  self		:			List[str]					,A		:			pl.Trainer					,A		:			pl.LightningModule    ):






   '''simple docstring'''
   rank_zero_info("""***** Validation results *****"""    )
   UpperCAmelCase__					:  List[Any]					   =					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    __lowercase       (  self		:			Tuple					,A		:			pl.Trainer					,A		:			pl.LightningModule    ):






   '''simple docstring'''
   rank_zero_info("""***** Test results *****"""    )
   UpperCAmelCase__					:  Union[str, Any]					   =					trainer.callback_metrics
   # Log and save results to file
   UpperCAmelCase__					:  Any					   =					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						(					__UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





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




def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase=None							,    __UpperCamelCase=True							,    __UpperCamelCase=[]							,    __UpperCamelCase=None							,    __UpperCamelCase=None							,    **__UpperCamelCase							,    ):
 '''simple docstring'''





 pl.seed_everything(args.seed      )

 # init model
 UpperCAmelCase__					:  Optional[int]					   =					Path(model.hparams.output_dir      )
 odir.mkdir(exist_ok=__UpperCamelCase      )

 # add custom checkpoints
 if checkpoint_callback is None:
  UpperCAmelCase__					:  List[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(__UpperCamelCase      )
 if logging_callback is None:
  UpperCAmelCase__					:  Tuple					   =					LoggingCallback()

 UpperCAmelCase__					:  Optional[int]					   =					{}

 if args.fpaa:
  UpperCAmelCase__					:  Any					   =					16

 if args.gpus > 1:
  UpperCAmelCase__					:  Any					   =					"""auto"""
  UpperCAmelCase__					:  str					   =					"""ddp"""

 UpperCAmelCase__					:  Dict					   =					args.accumulate_grad_batches
 UpperCAmelCase__					:  Tuple					   =					None
 UpperCAmelCase__					:  Tuple					   =					"""auto"""

 UpperCAmelCase__					:  Any					   =					pl.Trainer.from_argparse_args(
     __UpperCamelCase							,    weights_summary=__UpperCamelCase							,    callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]							,    logger=__UpperCamelCase							,    val_check_interval=1							,    num_sanity_val_steps=2							,    **__UpperCamelCase							,    )

 if args.do_train:
  trainer.fit(__UpperCamelCase      )

 else:
  print("""RAG modeling tests with new set functions successfuly executed!"""      )
 return trainer





