





import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ....modeling_utils import ModuleUtilsMixin
from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings


lowerCAmelCase_   =   logging.get_logger(__name__)

lowerCAmelCase_   =   "MMBTConfig"

class 				A    (nn.Module				):



      def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   List[Any]:



             '''simple docstring'''

             super().__init__()
             _snake_case      :							List[Any]     				=						config
             _snake_case      :							Optional[Any]     				=						encoder
             _snake_case      :							Optional[int]     				=						nn.Linear(config.modal_hidden_size     ,  config.hidden_size					)
             _snake_case      :							List[Any]     				=						embeddings.position_embeddings
             _snake_case      :							Optional[Any]     				=						embeddings.token_type_embeddings
             _snake_case      :							Tuple     				=						embeddings.word_embeddings
             _snake_case      :							List[Any]     				=						embeddings.LayerNorm
             _snake_case      :							int     				=						nn.Dropout(p=config.hidden_dropout_prob					)



      def    __a (					self     ,  lowercase_     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						self.proj_embeddings(self.encoder(lowercase_					)					)
             _snake_case      :							List[str]     				=						token_embeddings.size(1					)

             if start_token is not None:
                    _snake_case      :							Any     				=						self.word_embeddings(lowercase_					)
                    seq_length += 1
                    _snake_case      :							Optional[Any]     				=						torch.cat([start_token_embeds.unsqueeze(1					), token_embeddings]     ,  dim=1					)

             if end_token is not None:
                    _snake_case      :							Optional[int]     				=						self.word_embeddings(lowercase_					)
                    seq_length += 1
                    _snake_case      :							int     				=						torch.cat([token_embeddings, end_token_embeds.unsqueeze(1					)]     ,  dim=1					)

             if position_ids is None:
                    _snake_case      :							Optional[int]     				=						torch.arange(lowercase_     ,  dtype=torch.long     ,  device=input_modal.device					)
                    _snake_case      :							Union[str, Any]     				=						position_ids.unsqueeze(0					).expand(input_modal.size(0					)     ,  lowercase_					)

             if token_type_ids is None:
                    _snake_case      :							Union[str, Any]     				=						torch.zeros(
                        (input_modal.size(0					), seq_length)     ,  dtype=torch.long     ,  device=input_modal.device					)

             _snake_case      :							Dict     				=						self.position_embeddings(lowercase_					)
             _snake_case      :							List[str]     				=						self.token_type_embeddings(lowercase_					)
             _snake_case      :							Dict     				=						token_embeddings + position_embeddings + token_type_embeddings
             _snake_case      :							List[Any]     				=						self.LayerNorm(lowercase_					)
             _snake_case      :							str     				=						self.dropout(lowercase_					)
             return embeddings


lowerCAmelCase_   =   R"\n    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and\n    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.\n    It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and\n    obtain state-of-the-art performance on various multimodal classification benchmark tasks.\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration.\n        transformer (`nn.Module`): A text transformer that is used by MMBT.\n            It should have embeddings, encoder, and pooler attributes.\n        encoder (`nn.Module`): Encoder for the second modality.\n            It should take in a batch of modal inputs and return k, n dimension embeddings.\n"

lowerCAmelCase_   =   R"\n    Args:\n        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):\n            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image\n            Encoder, the shape would be (batch_size, channels, height, width)\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's\n            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See\n            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification\n            tasks.\n        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.\n        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:\n            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n            [What are attention masks?](../glossary#attention-mask)\n        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:\n            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n            1]`:\n\n            - 0 corresponds to a *sentence A* token,\n            - 1 corresponds to a *sentence B* token.\n\n            [What are token type IDs?](../glossary#token-type-ids)\n        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:\n            Segment token indices to indicate different portions of the non-text modality. The embeddings from these\n            tokens will be summed with the respective token embeddings for the non-text modality.\n        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n            config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.\n            Selected in the range `[0, config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n\n            - 1 indicates the head is **not masked**,\n            - 0 indicates the head is **masked**.\n\n        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):\n            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n            model's internal embedding lookup matrix.\n        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n            tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"

@add_start_docstrings(
    """The bare MMBT Model outputting raw hidden-states without any specific head on top."""       ,__UpperCAmelCase       ,)
class 				A    (nn.Module       ,__UpperCAmelCase				):



      def __init__(					self     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   List[Any]:



             '''simple docstring'''

             super().__init__()
             _snake_case      :							List[Any]     				=						config
             _snake_case      :							List[Any]     				=						transformer
             _snake_case      :							int     				=						ModalEmbeddings(lowercase_     ,  lowercase_     ,  transformer.embeddings					)



      @add_start_docstrings_to_model_forward(lowercase_					)
      @replace_return_docstrings(output_type=lowercase_     ,  config_class=_CONFIG_FOR_DOC					)
      def    __a (					self     ,  lowercase_     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  )      ->   Union[str, Any]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						output_attentions if output_attentions is not None else self.config.output_attentions
             _snake_case      :							Optional[int]     				=						(
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             _snake_case      :							Union[str, Any]     				=						return_dict if return_dict is not None else self.config.use_return_dict

             if input_ids is not None and inputs_embeds is not None:
                    raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''					)
             elif input_ids is not None:
                    _snake_case      :							Any     				=						input_ids.size()
             elif inputs_embeds is not None:
                    _snake_case      :							Union[str, Any]     				=						inputs_embeds.size()[:-1]
             else:
                    raise ValueError('''You have to specify either input_ids or inputs_embeds'''					)

             _snake_case      :							Optional[int]     				=						input_ids.device if input_ids is not None else inputs_embeds.device

             _snake_case      :							Dict     				=						self.modal_encoder(
                 lowercase_     ,  start_token=lowercase_     ,  end_token=lowercase_     ,  position_ids=lowercase_     ,  token_type_ids=lowercase_     ,  )

             _snake_case      :							Any     				=						modal_embeddings.size()[:-1]

             if token_type_ids is None:
                    _snake_case      :							List[str]     				=						torch.ones(lowercase_     ,  dtype=torch.long     ,  device=lowercase_					)

             _snake_case      :							int     				=						self.transformer.embeddings(
                 input_ids=lowercase_     ,  position_ids=lowercase_     ,  token_type_ids=lowercase_     ,  inputs_embeds=lowercase_					)

             _snake_case      :							List[Any]     				=						torch.cat([modal_embeddings, txt_embeddings]     ,  1					)

             _snake_case      :							str     				=						embedding_output.size()[:-1]

             if attention_mask is None:
                    _snake_case      :							Dict     				=						torch.ones(lowercase_     ,  device=lowercase_					)
             else:
                    _snake_case      :							Tuple     				=						torch.cat(
                        [torch.ones(lowercase_     ,  device=lowercase_     ,  dtype=torch.long					), attention_mask]     ,  dim=1					)
             if encoder_attention_mask is None:
                    _snake_case      :							Dict     				=						torch.ones(lowercase_     ,  device=lowercase_					)
             else:
                    _snake_case      :							Optional[int]     				=						torch.cat(
                        [torch.ones(lowercase_     ,  device=lowercase_					), encoder_attention_mask]     ,  dim=1					)

             _snake_case      :							List[str]     				=						self.get_extended_attention_mask(lowercase_     ,  lowercase_					)
             _snake_case      :							List[Any]     				=						self.invert_attention_mask(lowercase_					)
             _snake_case      :							List[str]     				=						self.get_head_mask(lowercase_     ,  self.config.num_hidden_layers					)

             _snake_case      :							Dict     				=						self.transformer.encoder(
                 lowercase_     ,  attention_mask=lowercase_     ,  head_mask=lowercase_     ,  encoder_hidden_states=lowercase_     ,  encoder_attention_mask=lowercase_     ,  output_attentions=lowercase_     ,  output_hidden_states=lowercase_     ,  return_dict=lowercase_     ,  )

             _snake_case      :							Tuple     				=						encoder_outputs[0]
             _snake_case      :							Tuple     				=						self.transformer.pooler(lowercase_					)

             if not return_dict:
                    return (sequence_output, pooled_output) + encoder_outputs[1:]

             return BaseModelOutputWithPooling(
                 last_hidden_state=lowercase_     ,  pooler_output=lowercase_     ,  hidden_states=encoder_outputs.hidden_states     ,  attentions=encoder_outputs.attentions     ,  )



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



             '''simple docstring'''

             return self.embeddings.word_embeddings



      def    __a (					self     ,  lowercase_					)      ->   List[str]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						value


@add_start_docstrings(
    """
    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
    """       ,__UpperCAmelCase       ,__UpperCAmelCase       ,)
class 				A    (nn.Module				):



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



             '''simple docstring'''

             super().__init__()
             _snake_case      :							Optional[Any]     				=						config.num_labels

             _snake_case      :							Optional[int]     				=						MMBTModel(lowercase_     ,  lowercase_     ,  lowercase_					)
             _snake_case      :							Tuple     				=						nn.Dropout(config.hidden_dropout_prob					)
             _snake_case      :							Tuple     				=						nn.Linear(config.hidden_size     ,  config.num_labels					)



      def    __a (					self     ,  lowercase_     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  )      ->   str:



             '''simple docstring'''

             _snake_case      :							Dict     				=						return_dict if return_dict is not None else self.config.use_return_dict

             _snake_case      :							Tuple     				=						self.mmbt(
                 input_modal=lowercase_     ,  input_ids=lowercase_     ,  modal_start_tokens=lowercase_     ,  modal_end_tokens=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  modal_token_type_ids=lowercase_     ,  position_ids=lowercase_     ,  modal_position_ids=lowercase_     ,  head_mask=lowercase_     ,  inputs_embeds=lowercase_     ,  return_dict=lowercase_     ,  )

             _snake_case      :							str     				=						outputs[1]

             _snake_case      :							int     				=						self.dropout(lowercase_					)
             _snake_case      :							str     				=						self.classifier(lowercase_					)

             _snake_case      :							str     				=						None
             if labels is not None:
                    if self.num_labels == 1:
                           #  We are doing regression
                           _snake_case      :							List[Any]     				=						MSELoss()
                           _snake_case      :							List[str]     				=						loss_fct(logits.view(-1					)     ,  labels.view(-1					)					)
                    else:
                           _snake_case      :							str     				=						CrossEntropyLoss()
                           _snake_case      :							Union[str, Any]     				=						loss_fct(logits.view(-1     ,  self.num_labels					)     ,  labels.view(-1					)					)

             if not return_dict:
                    _snake_case      :							Optional[Any]     				=						(logits,) + outputs[2:]
                    return ((loss,) + output) if loss is not None else output

             return SequenceClassifierOutput(
                 loss=lowercase_     ,  logits=lowercase_     ,  hidden_states=outputs.hidden_states     ,  attentions=outputs.attentions     ,  )













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


lowerCAmelCase_   =   logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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

class 				A    (pl.LightningModule				):



      def __init__(					self     ,  lowercase_     ,  lowercase_=None     ,  lowercase_="base"     ,  lowercase_=None     ,  lowercase_=None     ,  lowercase_=None     ,  **lowercase_     ,  )      ->   Optional[int]:



             '''simple docstring'''

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

             self.save_hyperparameters(lowercase_					)
             _snake_case      :							Union[str, Any]     				=						0
             _snake_case      :							Any     				=						Path(self.hparams.output_dir					)
             _snake_case      :							Optional[Any]     				=						self.hparams.cache_dir if self.hparams.cache_dir else None
             if config is None:
                    _snake_case      :							List[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=lowercase_     ,  **lowercase_     ,  )
             else:
                    _snake_case      :							PretrainedConfig     				=						config

             _snake_case      :							Optional[Any]     				=						('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
             for p in extra_model_params:
                    if getattr(self.hparams     ,  lowercase_     ,  lowercase_					):
                           assert hasattr(self.config     ,  lowercase_					), F'''model config doesn\'t have a `{p}` attribute'''
                           setattr(self.config     ,  lowercase_     ,  getattr(self.hparams     ,  lowercase_					)					)

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



      def    __a (					self     ,  *lowercase_     ,  **lowercase_					)      ->   Any:



             '''simple docstring'''

             _snake_case      :							Optional[Any]     				=						self.model_type.from_pretrained(*lowercase_     ,  **lowercase_					)



      def    __a (					self					)      ->   Any:



             '''simple docstring'''

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



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



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.model
             _snake_case      :							List[Any]     				=						['''bias''', '''LayerNorm.weight''']
             _snake_case      :							Optional[Any]     				=						[
                 {
                     '''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:
                    _snake_case      :							Optional[int]     				=						Adafactor(
                        lowercase_     ,  lr=self.hparams.learning_rate     ,  scale_parameter=lowercase_     ,  relative_step=lowercase_					)

             else:
                    _snake_case      :							Optional[Any]     				=						AdamW(
                        lowercase_     ,  lr=self.hparams.learning_rate     ,  eps=self.hparams.adam_epsilon					)
             _snake_case      :							Optional[int]     				=						optimizer

             _snake_case      :							Tuple     				=						self.get_lr_scheduler()

             return [optimizer], [scheduler]



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Any:



             '''simple docstring'''

             return self.validation_step(lowercase_     ,  lowercase_					)



      def    __a (					self     ,  lowercase_					)      ->   Optional[Any]:



             '''simple docstring'''

             return self.validation_end(lowercase_					)



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							List[str]     				=						max(1     ,  self.hparams.gpus					)  # TODO: consider num_tpu_cores
             _snake_case      :							Dict     				=						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     ,  lowercase_					)      ->   int:



             '''simple docstring'''

             if stage == "test":
                    _snake_case      :							Optional[Any]     				=						len(self.test_dataloader().dataset					)
             else:
                    _snake_case      :							Any     				=						self.get_dataloader('''train'''     ,  self.hparams.train_batch_size     ,  shuffle=lowercase_					)
                    _snake_case      :							Union[str, Any]     				=						len(self.train_dataloader().dataset					)



      def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_ = False					)      ->   Union[str, Any]:



             '''simple docstring'''

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



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             return self.train_loader



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

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



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



             '''simple docstring'''

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



      def    __a (					self     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

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



      @pl.utilities.rank_zero_only
      def    __a (					self     ,  lowercase_					)      ->   None:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.output_dir.joinpath('''best_tfmr'''					)
             _snake_case      :							str     				=						self.step_count
             self.model.save_pretrained(lowercase_					)
             self.tokenizer.save_pretrained(lowercase_					)



      @staticmethod
      def    __a (					lowercase_     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

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


class 				A    (pl.Callback				):



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   List[Any]:



             '''simple docstring'''

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


class 				A    (pl.Callback				):



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						trainer.lr_schedulers[0]['''scheduler''']
             _snake_case      :							Any     				=						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
             pl_module.logger.log_metrics(lowercase_					)



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[int]:



             '''simple docstring'''

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



      def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Optional[Any]:



             '''simple docstring'''

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




def 						A_			(  lowercase_      ,		lowercase_  )			->						None:
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           '''--output_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		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=lowercase_      ,		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=lowercase_  )
       parser.add_argument('''--max_grad_norm'''      ,		dest='''gradient_clip_val'''      ,		default=1.0      ,		type=lowercase_      ,		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=lowercase_      ,		default=1      ,		help='''Number of updates steps to accumulate before performing a backward/update pass.'''      ,		)
       parser.add_argument('''--seed'''      ,		type=lowercase_      ,		default=42      ,		help='''random seed for initialization'''  )
       parser.add_argument(
           '''--data_dir'''      ,		default=lowercase_      ,		type=lowercase_      ,		required=lowercase_      ,		help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''      ,		)




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=True      ,		lowercase_=None      ,		lowercase_=[]      ,		lowercase_=None      ,		lowercase_=None      ,		**lowercase_      ,		)			->						Optional[Any]:
       pl.seed_everything(args.seed  )

       # init model
       _snake_case      :							int     				=						Path(model.hparams.output_dir  )
       odir.mkdir(exist_ok=lowercase_  )

       # add custom checkpoints
       if checkpoint_callback is None:
              _snake_case      :							Union[str, 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(lowercase_  )
       if logging_callback is None:
              _snake_case      :							Optional[int]     				=						LoggingCallback()

       _snake_case      :							Union[str, Any]     				=						{}

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

       if args.gpus > 1:
              _snake_case      :							List[str]     				=						'''auto'''  # "ddp"
              _snake_case      :							Dict     				=						'''ddp'''

       _snake_case      :							Optional[int]     				=						args.accumulate_grad_batches
       _snake_case      :							Optional[Any]     				=						None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
       _snake_case      :							Tuple     				=						'''auto'''

       _snake_case      :							int     				=						pl.Trainer.from_argparse_args(
           lowercase_      ,		weights_summary=lowercase_      ,		callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]      ,		logger=lowercase_      ,		**lowercase_      ,		)

       if args.do_train:
              trainer.fit(lowercase_  )

       return trainer






