'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_A      :				Any 				=    logging.get_logger(__name__)

_A      :				List[str] 				=    {
    'google/bigbird-pegasus-large-arxiv': (
        'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-pubmed': (
        'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-bigpatent': (
        'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}







class     _lowercase					(    UpperCAmelCase_      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	List[str]				     = """bigbird_pegasus"""
 _SCREAMING_SNAKE_CASE					:	Optional[Any]				     = ["""past_key_values"""]
 _SCREAMING_SNAKE_CASE					:	Union[str, Any]				     = {
     """num_attention_heads""": """encoder_attention_heads""",
     """hidden_size""": """d_model""",
     """attention_probs_dropout_prob""": """attention_dropout""",
 }
 def __init__(				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     List[Any]=9_61_03				,	SCREAMING_SNAKE_CASE__       :     int=40_96				,	SCREAMING_SNAKE_CASE__       :     List[str]=16				,	SCREAMING_SNAKE_CASE__       :     Any=40_96				,	SCREAMING_SNAKE_CASE__       :     Any=16				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=16				,	SCREAMING_SNAKE_CASE__       :     List[Any]=40_96				,	SCREAMING_SNAKE_CASE__       :     int=16				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=0.0				,	SCREAMING_SNAKE_CASE__       :     Any=0.0				,	SCREAMING_SNAKE_CASE__       :     str=True				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=True				,	SCREAMING_SNAKE_CASE__       :     Optional[int]="gelu_new"				,	SCREAMING_SNAKE_CASE__       :     List[str]=10_24				,	SCREAMING_SNAKE_CASE__       :     Dict=0.1				,	SCREAMING_SNAKE_CASE__       :     Any=0.0				,	SCREAMING_SNAKE_CASE__       :     str=0.0				,	SCREAMING_SNAKE_CASE__       :     Optional[int]=0.0_2				,	SCREAMING_SNAKE_CASE__       :     str=2				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=0.0				,	SCREAMING_SNAKE_CASE__       :     int=True				,	SCREAMING_SNAKE_CASE__       :     str=0				,	SCREAMING_SNAKE_CASE__       :     List[Any]=2				,	SCREAMING_SNAKE_CASE__       :     int=1				,	SCREAMING_SNAKE_CASE__       :     Optional[int]="block_sparse"				,	SCREAMING_SNAKE_CASE__       :     int=64				,	SCREAMING_SNAKE_CASE__       :     int=3				,	SCREAMING_SNAKE_CASE__       :     Dict=False				,	**SCREAMING_SNAKE_CASE__       :     Any				,	)  ->   Any:
  __lowerCAmelCase    							=						vocab_size
  __lowerCAmelCase    							=						max_position_embeddings
  __lowerCAmelCase    							=						d_model
  __lowerCAmelCase    							=						encoder_ffn_dim
  __lowerCAmelCase    							=						encoder_layers
  __lowerCAmelCase    							=						encoder_attention_heads
  __lowerCAmelCase    							=						decoder_ffn_dim
  __lowerCAmelCase    							=						decoder_layers
  __lowerCAmelCase    							=						decoder_attention_heads
  __lowerCAmelCase    							=						dropout
  __lowerCAmelCase    							=						attention_dropout
  __lowerCAmelCase    							=						activation_dropout
  __lowerCAmelCase    							=						activation_function
  __lowerCAmelCase    							=						init_std
  __lowerCAmelCase    							=						encoder_layerdrop
  __lowerCAmelCase    							=						decoder_layerdrop
  __lowerCAmelCase    							=						classifier_dropout
  __lowerCAmelCase    							=						use_cache
  __lowerCAmelCase    							=						encoder_layers
  __lowerCAmelCase    							=						scale_embedding  # scale factor will be sqrt(d_model) if True

  # extra config
  __lowerCAmelCase    							=						attention_type
  __lowerCAmelCase    							=						block_size
  __lowerCAmelCase    							=						num_random_blocks
  __lowerCAmelCase    							=						use_bias

  super().__init__(
      pad_token_id=__UpperCamelCase				,	bos_token_id=__UpperCamelCase				,	eos_token_id=__UpperCamelCase				,	is_encoder_decoder=__UpperCamelCase				,	decoder_start_token_id=__UpperCamelCase				,	**__UpperCamelCase				,	)







class     _lowercase					(    UpperCAmelCase_      ):


 '''simple docstring'''






 @property
 def a (				self       :     Optional[Any]							)  ->   Optional[int]:
  if self.task in ["default", "seq2seq-lm"]:
   __lowerCAmelCase    							=						OrderedDict(
       [
           ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
           ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
       ]							)

   if self.use_past:
    __lowerCAmelCase    							=						{0: """batch"""}
    __lowerCAmelCase    							=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
   else:
    __lowerCAmelCase    							=						{0: """batch""", 1: """decoder_sequence"""}
    __lowerCAmelCase    							=						{0: """batch""", 1: """decoder_sequence"""}

   if self.use_past:
    self.fill_with_past_key_values_(__UpperCamelCase				,	direction="""inputs"""							)
  elif self.task == "causal-lm":
   # TODO: figure this case out.
   __lowerCAmelCase    							=						OrderedDict(
       [
           ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
           ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
       ]							)
   if self.use_past:
    __lowerCAmelCase							,	__lowerCAmelCase    							=						self.num_layers
    for i in range(__UpperCamelCase							):
     __lowerCAmelCase    							=						{0: """batch""", 2: """past_sequence + sequence"""}
     __lowerCAmelCase    							=						{0: """batch""", 2: """past_sequence + sequence"""}
  else:
   __lowerCAmelCase    							=						OrderedDict(
       [
           ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
           ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
           ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
           ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
       ]							)

  return common_inputs
 @property
 def a (				self       :     int							)  ->   str:
  if self.task in ["default", "seq2seq-lm"]:
   __lowerCAmelCase    							=						super().outputs
  else:
   __lowerCAmelCase    							=						super(__UpperCamelCase				,	self							).outputs
   if self.use_past:
    __lowerCAmelCase							,	__lowerCAmelCase    							=						self.num_layers
    for i in range(__UpperCamelCase							):
     __lowerCAmelCase    							=						{0: """batch""", 2: """past_sequence + sequence"""}
     __lowerCAmelCase    							=						{0: """batch""", 2: """past_sequence + sequence"""}
  return common_outputs
 def a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     Any = -1				,	SCREAMING_SNAKE_CASE__       :     Tuple = -1				,	SCREAMING_SNAKE_CASE__       :     Any = False				,	SCREAMING_SNAKE_CASE__       :     List[Any] = None				,	)  ->   Dict:
  __lowerCAmelCase    							=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
      __UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase							)

  # Generate decoder inputs
  __lowerCAmelCase    							=						seq_length if not self.use_past else 1
  __lowerCAmelCase    							=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
      __UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase							)
  __lowerCAmelCase    							=						{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
  __lowerCAmelCase    							=						dict(**__UpperCamelCase				,	**__UpperCamelCase							)

  if self.use_past:
   if not is_torch_available():
    raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
   else:
    import torch
   __lowerCAmelCase							,	__lowerCAmelCase    							=						common_inputs["""input_ids"""].shape
   __lowerCAmelCase    							=						common_inputs["""decoder_input_ids"""].shape[1]
   __lowerCAmelCase							,	__lowerCAmelCase    							=						self.num_attention_heads
   __lowerCAmelCase    							=						(
       batch,
       num_encoder_attention_heads,
       encoder_seq_length,
       self._config.hidden_size // num_encoder_attention_heads,
   )
   __lowerCAmelCase    							=						decoder_seq_length + 3
   __lowerCAmelCase    							=						(
       batch,
       num_decoder_attention_heads,
       decoder_past_length,
       self._config.hidden_size // num_decoder_attention_heads,
   )

   __lowerCAmelCase    							=						torch.cat(
       [common_inputs["""decoder_attention_mask"""], torch.ones(__UpperCamelCase				,	__UpperCamelCase							)]				,	dim=1							)

   __lowerCAmelCase    							=						[]
   # If the number of encoder and decoder layers are present in the model configuration, both are considered
   __lowerCAmelCase							,	__lowerCAmelCase    							=						self.num_layers
   __lowerCAmelCase    							=						min(__UpperCamelCase				,	__UpperCamelCase							)
   __lowerCAmelCase    							=						max(__UpperCamelCase				,	__UpperCamelCase							) - min_num_layers
   __lowerCAmelCase    							=						"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

   for _ in range(__UpperCamelCase							):
    common_inputs["past_key_values"].append(
        (
            torch.zeros(__UpperCamelCase							),
            torch.zeros(__UpperCamelCase							),
            torch.zeros(__UpperCamelCase							),
            torch.zeros(__UpperCamelCase							),
        )							)
   # TODO: test this.
   __lowerCAmelCase    							=						encoder_shape if remaining_side_name == """encoder""" else decoder_shape
   for _ in range(__UpperCamelCase				,	__UpperCamelCase							):
    common_inputs["past_key_values"].append((torch.zeros(__UpperCamelCase							), torch.zeros(__UpperCamelCase							))							)
  return common_inputs
 def a (				self       :     str				,	SCREAMING_SNAKE_CASE__       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     Optional[Any] = -1				,	SCREAMING_SNAKE_CASE__       :     Dict = -1				,	SCREAMING_SNAKE_CASE__       :     str = False				,	SCREAMING_SNAKE_CASE__       :     Optional[Any] = None				,	)  ->   Dict:
  __lowerCAmelCase    							=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
      __UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase							)

  if self.use_past:
   if not is_torch_available():
    raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
   else:
    import torch
   __lowerCAmelCase							,	__lowerCAmelCase    							=						common_inputs["""input_ids"""].shape
   # Not using the same length for past_key_values
   __lowerCAmelCase    							=						seqlen + 2
   __lowerCAmelCase							,	__lowerCAmelCase    							=						self.num_layers
   __lowerCAmelCase							,	__lowerCAmelCase    							=						self.num_attention_heads
   __lowerCAmelCase    							=						(
       batch,
       num_encoder_attention_heads,
       past_key_values_length,
       self._config.hidden_size // num_encoder_attention_heads,
   )

   __lowerCAmelCase    							=						common_inputs["""attention_mask"""].dtype
   __lowerCAmelCase    							=						torch.cat(
       [common_inputs["""attention_mask"""], torch.ones(__UpperCamelCase				,	__UpperCamelCase				,	dtype=__UpperCamelCase							)]				,	dim=1							)
   __lowerCAmelCase    							=						[
       (torch.zeros(__UpperCamelCase							), torch.zeros(__UpperCamelCase							)) for _ in range(__UpperCamelCase							)
   ]
  return common_inputs
 def a (				self       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any] = -1				,	SCREAMING_SNAKE_CASE__       :     List[Any] = -1				,	SCREAMING_SNAKE_CASE__       :     Tuple = False				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = None				,	)  ->   Optional[Any]:
  # Copied from OnnxConfig.generate_dummy_inputs
  # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
  # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
  __lowerCAmelCase    							=						compute_effective_axis_dimension(
      __UpperCamelCase				,	fixed_dimension=OnnxConfig.default_fixed_batch				,	num_token_to_add=0							)

  # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
  __lowerCAmelCase    							=						tokenizer.num_special_tokens_to_add(__UpperCamelCase							)
  __lowerCAmelCase    							=						compute_effective_axis_dimension(
      __UpperCamelCase				,	fixed_dimension=OnnxConfig.default_fixed_sequence				,	num_token_to_add=__UpperCamelCase							)

  # Generate dummy inputs according to compute batch and sequence
  __lowerCAmelCase    							=						[""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
  __lowerCAmelCase    							=						dict(tokenizer(__UpperCamelCase				,	return_tensors=__UpperCamelCase							)							)
  return common_inputs
 def a (				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     str = -1				,	SCREAMING_SNAKE_CASE__       :     Any = -1				,	SCREAMING_SNAKE_CASE__       :     int = False				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = None				,	)  ->   Dict:
  if self.task in ["default", "seq2seq-lm"]:
   __lowerCAmelCase    							=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
       __UpperCamelCase				,	batch_size=__UpperCamelCase				,	seq_length=__UpperCamelCase				,	is_pair=__UpperCamelCase				,	framework=__UpperCamelCase							)

  elif self.task == "causal-lm":
   __lowerCAmelCase    							=						self._generate_dummy_inputs_for_causal_lm(
       __UpperCamelCase				,	batch_size=__UpperCamelCase				,	seq_length=__UpperCamelCase				,	is_pair=__UpperCamelCase				,	framework=__UpperCamelCase							)
  else:
   __lowerCAmelCase    							=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
       __UpperCamelCase				,	batch_size=__UpperCamelCase				,	seq_length=__UpperCamelCase				,	is_pair=__UpperCamelCase				,	framework=__UpperCamelCase							)

  return common_inputs





 def a (				self       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     Any				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     str							)  ->   List[str]:
  if self.task in ["default", "seq2seq-lm"]:
   __lowerCAmelCase    							=						super()._flatten_past_key_values_(__UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase							)
  else:
   __lowerCAmelCase    							=						super(__UpperCamelCase				,	self							)._flatten_past_key_values_(
       __UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase				,	__UpperCamelCase							)







'''simple docstring'''

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

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

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


_A      :				str 				=    logging.getLogger(__name__)

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

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


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







class     _lowercase					(    pl.LightningModule      ):


 '''simple docstring'''






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

  self.save_hyperparameters(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						0
  __lowerCAmelCase    							=						Path(self.hparams.output_dir							)
  __lowerCAmelCase    							=						self.hparams.cache_dir if self.hparams.cache_dir else None
  if config is None:
   __lowerCAmelCase    							=						AutoConfig.from_pretrained(
       self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path				,	**({"""num_labels""": num_labels} if num_labels is not None else {})				,	cache_dir=SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__				,	)
  else:
   __lowerCAmelCase    							=						config

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

  if tokenizer is None:
   __lowerCAmelCase    							=						AutoTokenizer.from_pretrained(
       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,	cache_dir=SCREAMING_SNAKE_CASE__				,	)
  else:
   __lowerCAmelCase    							=						tokenizer
  __lowerCAmelCase    							=						MODEL_MODES[mode]
  if model is None:
   __lowerCAmelCase    							=						self.model_type.from_pretrained(
       self.hparams.model_name_or_path				,	from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path							)				,	config=self.config				,	cache_dir=SCREAMING_SNAKE_CASE__				,	)
  else:
   __lowerCAmelCase    							=						model
 def a (				self       :     Dict				,	*SCREAMING_SNAKE_CASE__       :     List[Any]				,	**SCREAMING_SNAKE_CASE__       :     int							)  ->   Optional[int]:
  __lowerCAmelCase    							=						self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__							)
 def a (				self       :     str							)  ->   Tuple:
  __lowerCAmelCase    							=						arg_to_scheduler[self.hparams.lr_scheduler]
  __lowerCAmelCase    							=						get_schedule_func(
      self.opt				,	num_warmup_steps=self.hparams.warmup_steps				,	num_training_steps=self.total_steps()							)
  __lowerCAmelCase    							=						{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
  return scheduler
 def a (				self       :     Union[str, Any]							)  ->   Optional[int]:
  __lowerCAmelCase    							=						self.model
  __lowerCAmelCase    							=						["""bias""", """LayerNorm.weight"""]
  __lowerCAmelCase    							=						[
      {
          """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay							)],
          """weight_decay""": self.hparams.weight_decay,
      },
      {
          """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay							)],
          """weight_decay""": 0.0,
      },
  ]
  if self.hparams.adafactor:
   __lowerCAmelCase    							=						Adafactor(
       SCREAMING_SNAKE_CASE__				,	lr=self.hparams.learning_rate				,	scale_parameter=SCREAMING_SNAKE_CASE__				,	relative_step=SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase    							=						self.get_lr_scheduler()

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





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







class     _lowercase					(    pl.Callback      ):


 '''simple docstring'''






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





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







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






 '''simple docstring'''

 parser.add_argument(
     """--output_dir"""						,		default=snake_case_						,		type=snake_case_						,		required=snake_case_						,		help="""The output directory where the model predictions and checkpoints will be written."""						,		)
 parser.add_argument(
     """--fp16"""						,		action="""store_true"""						,		help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""						,		)

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







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






 '''simple docstring'''

 pl.seed_everything(args.seed				)

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

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

 __lowerCAmelCase    							=						{}

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

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

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

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

 if args.do_train:
  trainer.fit(snake_case_				)

 return trainer






