'''simple docstring'''





from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel





@require_tf
class 	_lowercase		:
 lowercase         =     MarianConfig
 lowercase         =     {}
 lowercase         =     'gelu'

 def __init__(				self			:							Optional[Any]     ,   snake_case			:							Tuple     ,   snake_case			:							List[Any]=1_3     ,   snake_case			:							int=7     ,   snake_case			:							Tuple=True     ,   snake_case			:							List[str]=False     ,   snake_case			:							Tuple=9_9     ,   snake_case			:							int=3_2     ,   snake_case			:							Union[str, Any]=2     ,   snake_case			:							Optional[int]=4     ,   snake_case			:							Dict=3_7     ,   snake_case			:							Optional[int]=0.1     ,   snake_case			:							Dict=0.1     ,   snake_case			:							Optional[int]=2_0     ,   snake_case			:							List[str]=2     ,   snake_case			:							Dict=1     ,   snake_case			:							int=0     ,   )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		int				=      parent
  UpperCamelCase_      :		int				=      batch_size
  UpperCamelCase_      :		str				=      seq_length
  UpperCamelCase_      :		Tuple				=      is_training
  UpperCamelCase_      :		List[Any]				=      use_labels
  UpperCamelCase_      :		Tuple				=      vocab_size
  UpperCamelCase_      :		int				=      hidden_size
  UpperCamelCase_      :		List[str]				=      num_hidden_layers
  UpperCamelCase_      :		Tuple				=      num_attention_heads
  UpperCamelCase_      :		Any				=      intermediate_size

  UpperCamelCase_      :		List[str]				=      hidden_dropout_prob
  UpperCamelCase_      :		Optional[int]				=      attention_probs_dropout_prob
  UpperCamelCase_      :		int				=      max_position_embeddings
  UpperCamelCase_      :		List[str]				=      eos_token_id
  UpperCamelCase_      :		Optional[int]				=      pad_token_id
  UpperCamelCase_      :		Any				=      bos_token_id

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      ids_tensor([self.batch_size, self.seq_length - 1]     ,   self.vocab_size    )
  UpperCamelCase_      :		List[Any]				=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )     ,   1    )
  UpperCamelCase_      :		Any				=      tf.concat([input_ids, eos_tensor]     ,   axis=1    )

  UpperCamelCase_      :		Dict				=      ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size    )

  UpperCamelCase_      :		List[Any]				=      self.config_cls(
      vocab_size=self.vocab_size     ,   d_model=self.hidden_size     ,   encoder_layers=self.num_hidden_layers     ,   decoder_layers=self.num_hidden_layers     ,   encoder_attention_heads=self.num_attention_heads     ,   decoder_attention_heads=self.num_attention_heads     ,   encoder_ffn_dim=self.intermediate_size     ,   decoder_ffn_dim=self.intermediate_size     ,   dropout=self.hidden_dropout_prob     ,   attention_dropout=self.attention_probs_dropout_prob     ,   max_position_embeddings=self.max_position_embeddings     ,   eos_token_ids=[2]     ,   bos_token_id=self.bos_token_id     ,   pad_token_id=self.pad_token_id     ,   decoder_start_token_id=self.pad_token_id     ,   **self.config_updates     ,   )
  UpperCamelCase_      :		Optional[Any]				=      prepare_marian_inputs_dict(snake_case     ,   snake_case     ,   snake_case    )
  return config, inputs_dict


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

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      TFMarianModel(config=snake_case    ).get_decoder()
  UpperCamelCase_      :		Any				=      inputs_dict['input_ids']

  UpperCamelCase_      :		Tuple				=      input_ids[:1, :]
  UpperCamelCase_      :		Any				=      inputs_dict['attention_mask'][:1, :]
  UpperCamelCase_      :		Optional[Any]				=      inputs_dict['head_mask']
  UpperCamelCase_      :		List[Any]				=      1

  # first forward pass
  UpperCamelCase_      :		Optional[int]				=      model(snake_case     ,   attention_mask=snake_case     ,   head_mask=snake_case     ,   use_cache=snake_case    )

  UpperCamelCase_      :		int				=      outputs.to_tuple()

  # create hypothetical next token and extent to next_input_ids
  UpperCamelCase_      :		List[Any]				=      ids_tensor((self.batch_size, 3)     ,   config.vocab_size    )
  UpperCamelCase_      :		Any				=      tf.cast(ids_tensor((self.batch_size, 3)     ,   2    )     ,   tf.inta    )

  # append to next input_ids and
  UpperCamelCase_      :		str				=      tf.concat([input_ids, next_tokens]     ,   axis=-1    )
  UpperCamelCase_      :		Union[str, Any]				=      tf.concat([attention_mask, next_attn_mask]     ,   axis=-1    )

  UpperCamelCase_      :		Optional[Any]				=      model(snake_case     ,   attention_mask=snake_case    )[0]
  UpperCamelCase_      :		Union[str, Any]				=      model(snake_case     ,   attention_mask=snake_case     ,   past_key_values=snake_case    )[0]

  self.parent.assertEqual(next_tokens.shape[1]     ,   output_from_past.shape[1]    )

  # select random slice
  UpperCamelCase_      :		List[str]				=      int(ids_tensor((1,)     ,   output_from_past.shape[-1]    )    )
  UpperCamelCase_      :		Any				=      output_from_no_past[:, -3:, random_slice_idx]
  UpperCamelCase_      :		Any				=      output_from_past[:, :, random_slice_idx]

  # test that outputs are equal for slice
  tf.debugging.assert_near(snake_case     ,   snake_case     ,   rtol=1e-3    )





def      __lowercase  (		lowerCamelCase      :  int						,						lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  Dict=None						,						lowerCamelCase      :  int=None						,						lowerCamelCase      :  Tuple=None						,						lowerCamelCase      :  Dict=None						,						lowerCamelCase      :  List[str]=None						,						):
 if attention_mask is None:
  UpperCamelCase_      :		Any				=      tf.cast(tf.math.not_equal(lowerCAmelCase__						,						config.pad_token_id			)						,						tf.inta			)
 if decoder_attention_mask is None:
  UpperCamelCase_      :		Union[str, Any]				=      tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape						,						dtype=tf.inta			),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,						config.pad_token_id			)						,						tf.inta			),
      ]						,						axis=-1						,						)
 if head_mask is None:
  UpperCamelCase_      :		Optional[int]				=      tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
 if decoder_head_mask is None:
  UpperCamelCase_      :		List[Any]				=      tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
 if cross_attn_head_mask is None:
  UpperCamelCase_      :		List[str]				=      tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }





@require_tf
class 	_lowercase		(      snake_case__		,   snake_case__		,   unittest.TestCase ):
 lowercase         =     (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
 lowercase         =     (TFMarianMTModel,) if is_tf_available() else ()
 lowercase         =     (
     {
         'conversational': TFMarianMTModel,
         'feature-extraction': TFMarianModel,
         'summarization': TFMarianMTModel,
         'text2text-generation': TFMarianMTModel,
         'translation': TFMarianMTModel,
     }
     if is_tf_available()
     else {}
 )
 lowercase         =     True
 lowercase         =     False
 lowercase         =     False

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

  """simple docstring"""





  UpperCamelCase_      :		str				=      TFMarianModelTester(self    )
  UpperCamelCase_      :		Tuple				=      ConfigTester(self     ,   config_class=snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple    )     ->		Dict:

  """simple docstring"""





  self.config_tester.run_common_tests()


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

  """simple docstring"""





  UpperCamelCase_      :		int				=      self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.check_decoder_model_past_large_inputs(*snake_case    )





@require_tf
class 	_lowercase		(      unittest.TestCase ):
 lowercase         =     1_0_0_0  # show more chars for failing integration tests

 @classmethod
 def 		SCREAMING_SNAKE_CASE__     (				cls			:							Dict    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
  return cls

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

  """simple docstring"""





  return AutoTokenizer.from_pretrained(self.model_name    )

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

  """simple docstring"""





  return self.tokenizer.eos_token_id

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

  """simple docstring"""





  warnings.simplefilter('error'    )
  UpperCamelCase_      :		TFMarianMTModel				=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
  assert isinstance(snake_case     ,   snake_case    )
  UpperCamelCase_      :		List[Any]				=      model.config
  self.assertListEqual(c.bad_words_ids     ,   [[c.pad_token_id]]    )
  self.assertEqual(c.max_length     ,   5_1_2    )
  self.assertEqual(c.decoder_start_token_id     ,   c.pad_token_id    )
  return model

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   **snake_case			:							int    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      self.translate_src_text(**snake_case    )
  self.assertListEqual(self.expected_text     ,   snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   **snake_case			:							int    )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		str				=      self.tokenizer(self.src_text     ,   **snake_case     ,   padding=snake_case     ,   return_tensors='tf'    )
  UpperCamelCase_      :		Dict				=      self.model.generate(
      model_inputs.input_ids     ,   attention_mask=model_inputs.attention_mask     ,   num_beams=2     ,   max_length=1_2_8    )
  UpperCamelCase_      :		Dict				=      self.tokenizer.batch_decode(generated_ids.numpy()     ,   skip_special_tokens=snake_case    )
  return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class 	_lowercase		(      snake_case__ ):
 lowercase         =     'mt'
 lowercase         =     'en'
 lowercase         =     ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
 lowercase         =     ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']

 @unittest.skip('Skipping until #12647 is resolved.'    )
 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Union[str, Any]:

  """simple docstring"""





  self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 	_lowercase		(      snake_case__ ):
 lowercase         =     'en'
 lowercase         =     'zh'
 lowercase         =     ['My name is Wolfgang and I live in Berlin']
 lowercase         =     ['我叫沃尔夫冈 我住在柏林']

 @unittest.skip('Skipping until #12647 is resolved.'    )
 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple    )     ->		Tuple:

  """simple docstring"""





  self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 	_lowercase		(      snake_case__ ):
 lowercase         =     'en'
 lowercase         =     'ROMANCE'
 lowercase         =     [
     '>>fr<< Don\'t spend so much time watching TV.',
     '>>pt<< Your message has been sent.',
     '>>es<< He\'s two years older than me.',
 ]
 lowercase         =     [
     'Ne passez pas autant de temps à regarder la télé.',
     'A sua mensagem foi enviada.',
     'Es dos años más viejo que yo.',
 ]

 @unittest.skip('Skipping until #12647 is resolved.'    )
 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		int:

  """simple docstring"""





  self._assert_generated_batch_equal_expected()


 @unittest.skip('Skipping until #12647 is resolved.'    )
 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      TranslationPipeline(self.model     ,   self.tokenizer     ,   framework='tf'    )
  UpperCamelCase_      :		List[Any]				=      pipeline(self.src_text    )
  self.assertEqual(self.expected_text     ,   [x['translation_text'] for x in output]    )




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

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

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


a_					      = logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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


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





class 	_lowercase		(      pl.LightningModule ):

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

  """simple docstring"""





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

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

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

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

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

  """simple docstring"""





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

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Dict:

  """simple docstring"""





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

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

  """simple docstring"""





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

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

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

  return [optimizer], [scheduler]

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

  """simple docstring"""





  return self.validation_step(snake_case     ,   snake_case    )

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

  """simple docstring"""





  return self.validation_end(snake_case    )

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

  """simple docstring"""





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

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

  """simple docstring"""





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

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

  """simple docstring"""





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

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

  """simple docstring"""





  return self.train_loader

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

  """simple docstring"""





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

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Any:

  """simple docstring"""





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

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

  """simple docstring"""





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

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

  """simple docstring"""





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


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

  """simple docstring"""





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





class 	_lowercase		(      pl.Callback ):

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

  """simple docstring"""





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

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

  """simple docstring"""





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


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

  """simple docstring"""





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





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

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






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

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

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

 UpperCamelCase_      :		Union[str, Any]				=      {}

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

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

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

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

 if args.do_train:
  trainer.fit(lowerCamelCase			)

 return trainer



