



"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase = None ,  ) ->						Any:







  '''simple docstring'''


  lowercase_								= {}

  if train_file is not None:
    lowercase_								= [train_file]
  if eval_file is not None:
    lowercase_								= [eval_file]
  if test_file is not None:
    lowercase_								= [test_file]

  lowercase_								= datasets.load_dataset("""csv""" ,  data_files=__A							)
  lowercase_								= list(ds[list(files.keys()							)[0]].features.keys()							)
  lowercase_								= features_name.pop(__A							)
  lowercase_								= list(set(ds[list(files.keys()							)[0]][label_name]							)							)
  lowercase_								= {label: i for i, label in enumerate(__A							)}
  lowercase_								= tokenizer.model_input_names
  lowercase_								= {}

  if len(__A							) == 1:
    for k in files.keys():
      lowercase_								= ds[k].map(
          lambda __lowerCAmelCase							: tokenizer.batch_encode_plus(
              example[features_name[0]] ,  truncation=__A ,  max_length=__A ,  padding="""max_length"""							) ,  batched=__A ,  )
  elif len(__A							) == 2:
    for k in files.keys():
      lowercase_								= ds[k].map(
          lambda __lowerCAmelCase							: tokenizer.batch_encode_plus(
              (example[features_name[0]], example[features_name[1]]) ,  truncation=__A ,  max_length=__A ,  padding="""max_length""" ,  ) ,  batched=__A ,  )

  def gen_train():
    for ex in transformed_ds[datasets.Split.TRAIN]:
      lowercase_								= {k: v for k, v in ex.items() if k in input_names}
      lowercase_								= labelaid[ex[label_name]]
      yield (d, label)

  def gen_val():
    for ex in transformed_ds[datasets.Split.VALIDATION]:
      lowercase_								= {k: v for k, v in ex.items() if k in input_names}
      lowercase_								= labelaid[ex[label_name]]
      yield (d, label)

  def gen_test():
    for ex in transformed_ds[datasets.Split.TEST]:
      lowercase_								= {k: v for k, v in ex.items() if k in input_names}
      lowercase_								= labelaid[ex[label_name]]
      yield (d, label)

  lowercase_								= (
      tf.data.Dataset.from_generator(
          __A ,  ({k: tf.intaa for k in input_names}, tf.intaa) ,  ({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							)) ,  )
      if datasets.Split.TRAIN in transformed_ds
      else None
  )

  if train_ds is not None:
    lowercase_								= train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]							)							)							)

  lowercase_								= (
      tf.data.Dataset.from_generator(
          __A ,  ({k: tf.intaa for k in input_names}, tf.intaa) ,  ({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							)) ,  )
      if datasets.Split.VALIDATION in transformed_ds
      else None
  )

  if val_ds is not None:
    lowercase_								= val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]							)							)							)

  lowercase_								= (
      tf.data.Dataset.from_generator(
          __A ,  ({k: tf.intaa for k in input_names}, tf.intaa) ,  ({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							)) ,  )
      if datasets.Split.TEST in transformed_ds
      else None
  )

  if test_ds is not None:
    lowercase_								= test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]							)							)							)

  return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase						:		List[str]			= logging.getLogger(__name__)


@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(metadata={"help": "Which column contains the label"}   )
    lowercase__      =   field(default=__lowerCAmelCase      ,  metadata={"help": "The path of the training file"}   )
    lowercase__      =   field(default=__lowerCAmelCase      ,  metadata={"help": "The path of the development file"}   )
    lowercase__      =   field(default=__lowerCAmelCase      ,  metadata={"help": "The path of the test file"}   )
    lowercase__      =   field(
        default=128      ,  metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        }      ,  )
    lowercase__      =   field(
        default=__lowerCAmelCase      ,  metadata={"help": "Overwrite the cached training and evaluation sets"}   )







@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
    lowercase__      =   field(
        default=__lowerCAmelCase      ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
    lowercase__      =   field(
        default=__lowerCAmelCase      ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
    lowercase__      =   field(default=__lowerCAmelCase      ,  metadata={"help": "Set this flag to use fast tokenization."}   )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    lowercase__      =   field(
        default=__lowerCAmelCase      ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,  )







def 			_SCREAMING_SNAKE_CASE						() ->						List[Any]:







  '''simple docstring'''


  lowercase_								= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
  lowercase_								= parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir							)
      and os.listdir(training_args.output_dir							)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
        """ --overwrite_output_dir to overcome."""							)

  # Setup logging
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,  datefmt="""%m/%d/%Y %H:%M:%S""" ,  level=logging.INFO ,  )
  logger.info(
      F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1							)}, '''
      F'''16-bits training: {training_args.fpaa}'''							)
  logger.info(F'''Training/evaluation parameters {training_args}'''							)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  lowercase_								= AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,  cache_dir=model_args.cache_dir ,  )

  lowercase_								= get_tfds(
      train_file=data_args.train_file ,  eval_file=data_args.dev_file ,  test_file=data_args.test_file ,  tokenizer=__A ,  label_column_id=data_args.label_column_id ,  max_seq_length=data_args.max_seq_length ,  )

  lowercase_								= AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path ,  num_labels=len(__A							) ,  labelaid=__A ,  idalabel={id: label for label, id in labelaid.items()} ,  finetuning_task="""text-classification""" ,  cache_dir=model_args.cache_dir ,  )

  with training_args.strategy.scope():
    lowercase_								= TFAutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path ,  from_pt=bool(""".bin""" in model_args.model_name_or_path							) ,  config=__A ,  cache_dir=model_args.cache_dir ,  )

  def compute_metrics(__lowerCAmelCase							) -> Dict:
    lowercase_								= np.argmax(p.predictions ,  axis=1							)

    return {"acc": (preds == p.label_ids).mean()}

  # Initialize our Trainer
  lowercase_								= TFTrainer(
      model=__A ,  args=__A ,  train_dataset=__A ,  eval_dataset=__A ,  compute_metrics=__A ,  )

  # Training
  if training_args.do_train:
    trainer.train()
    trainer.save_model()
    tokenizer.save_pretrained(training_args.output_dir							)

  # Evaluation
  lowercase_								= {}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""							)
    lowercase_								= trainer.evaluate()
    lowercase_								= os.path.join(training_args.output_dir ,  """eval_results.txt"""							)

    with open(__A ,  """w"""							) as writer:
      logger.info("""***** Eval results *****"""							)

      for key, value in result.items():
        logger.info(F'''  {key} = {value}'''							)
        writer.write(F'''{key} = {value}\n'''							)

      results.update(__A							)

  return results


if __name__ == "__main__":
       main()






"""simple docstring"""
from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase      ,  __UpperCAmelCase   ):
    lowercase__      =   [r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]

    @register_to_config
    def __init__(				self			:				int ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[int] = None ,  lowerCAmelCase_			:				int = 5_0_2_5_7 ,  lowerCAmelCase_			:				int = 1_0_2_4 ,  lowerCAmelCase_			:				int = 7_6_8 ,  lowerCAmelCase_			:				int = 1_2 ,  lowerCAmelCase_			:				int = 1_2 ,  lowerCAmelCase_			:				Optional[int] = None ,  lowerCAmelCase_			:				str = "gelu_new" ,  lowerCAmelCase_			:				float = 0.1 ,  lowerCAmelCase_			:				float = 0.1 ,  lowerCAmelCase_			:				float = 0.1 ,  lowerCAmelCase_			:				float = 1E-5 ,  lowerCAmelCase_			:				float = 0.02 ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				bool = False ,  lowerCAmelCase_			:				bool = False ,  ):







      """simple docstring"""







      super().__init__()

      lowercase_								= prefix_length

      if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
        raise ValueError(
            F'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
            F''' `n_embd`: {n_embd} are not equal.''')

      lowercase_								= prefix_inner_dim
      lowercase_								= prefix_hidden_dim

      lowercase_								= (
          nn.Linear(self.prefix_inner_dim ,  self.prefix_hidden_dim)
          if self.prefix_hidden_dim is not None
          else nn.Identity()
      )
      lowercase_								= (
          nn.Linear(self.prefix_hidden_dim ,  lowerCAmelCase_) if self.prefix_hidden_dim is not None else nn.Identity()
      )

      lowercase_								= GPTaConfig(
          vocab_size=lowerCAmelCase_ ,  n_positions=lowerCAmelCase_ ,  n_embd=lowerCAmelCase_ ,  n_layer=lowerCAmelCase_ ,  n_head=lowerCAmelCase_ ,  n_inner=lowerCAmelCase_ ,  activation_function=lowerCAmelCase_ ,  resid_pdrop=lowerCAmelCase_ ,  embd_pdrop=lowerCAmelCase_ ,  attn_pdrop=lowerCAmelCase_ ,  layer_norm_epsilon=lowerCAmelCase_ ,  initializer_range=lowerCAmelCase_ ,  scale_attn_weights=lowerCAmelCase_ ,  use_cache=lowerCAmelCase_ ,  scale_attn_by_inverse_layer_idx=lowerCAmelCase_ ,  reorder_and_upcast_attn=lowerCAmelCase_ ,  )
      lowercase_								= GPTaLMHeadModel(lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				torch.Tensor ,  lowerCAmelCase_			:				torch.Tensor ,  lowerCAmelCase_			:				Optional[torch.Tensor] = None ,  lowerCAmelCase_			:				Optional[torch.Tensor] = None ,  ):







      """simple docstring"""







      lowercase_								= self.transformer.transformer.wte(lowerCAmelCase_)
      lowercase_								= self.encode_prefix(lowerCAmelCase_)
      lowercase_								= self.decode_prefix(lowerCAmelCase_)
      lowercase_								= torch.cat((prefix_embeds, embedding_text) ,  dim=1)

      if labels is not None:
        lowercase_								= self.get_dummy_token(input_ids.shape[0] ,  input_ids.device)
        lowercase_								= torch.cat((dummy_token, input_ids) ,  dim=1)
      lowercase_								= self.transformer(inputs_embeds=lowerCAmelCase_ ,  labels=lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_)
      if self.prefix_hidden_dim is not None:
        return out, hidden
      else:
        return out

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				torch.device):







      """simple docstring"""







      return torch.zeros(lowerCAmelCase_ ,  self.prefix_length ,  dtype=torch.intaa ,  device=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				List[str] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      return self.encode_prefix(lowerCAmelCase_)

    @torch.no_grad()
    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				str ,  lowerCAmelCase_			:				Dict ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      lowercase_								= torch.split(lowerCAmelCase_ ,  1 ,  dim=0)
      lowercase_								= []
      lowercase_								= []
      for feature in features:
        lowercase_								= self.decode_prefix(feature.to(lowerCAmelCase_))  # back to the clip feature
        # Only support beam search for now
        lowercase_					,    lowercase_								= self.generate_beam(
            input_embeds=lowerCAmelCase_ ,  device=lowerCAmelCase_ ,  eos_token_id=lowerCAmelCase_)
        generated_tokens.append(output_tokens[0])
        generated_seq_lengths.append(seq_lengths[0])
      lowercase_								= torch.stack(lowerCAmelCase_)
      lowercase_								= torch.stack(lowerCAmelCase_)
      return generated_tokens, generated_seq_lengths




    @torch.no_grad()
    def 					_UpperCAmelCase	(				self			:				int ,  lowerCAmelCase_			:				List[str]=None ,  lowerCAmelCase_			:				Tuple=None ,  lowerCAmelCase_			:				str=None ,  lowerCAmelCase_			:				int = 5 ,  lowerCAmelCase_			:				int = 6_7 ,  lowerCAmelCase_			:				float = 1.0 ,  lowerCAmelCase_			:				Optional[int] = None ,  ):







      """simple docstring"""







      lowercase_								= eos_token_id
      lowercase_								= None
      lowercase_								= None
      lowercase_								= torch.ones(lowerCAmelCase_ ,  device=lowerCAmelCase_ ,  dtype=torch.int)
      lowercase_								= torch.zeros(lowerCAmelCase_ ,  device=lowerCAmelCase_ ,  dtype=torch.bool)

      if input_embeds is not None:
        lowercase_								= input_embeds
      else:
        lowercase_								= self.transformer.transformer.wte(lowerCAmelCase_)

      for i in range(lowerCAmelCase_):
        lowercase_								= self.transformer(inputs_embeds=lowerCAmelCase_)
        lowercase_								= outputs.logits
        lowercase_								= logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
        lowercase_								= logits.softmax(-1).log()

        if scores is None:
          lowercase_					,    lowercase_								= logits.topk(lowerCAmelCase_ ,  -1)
          lowercase_								= generated.expand(lowerCAmelCase_ ,  *generated.shape[1:])
          lowercase_					,    lowercase_								= next_tokens.permute(1 ,  0), scores.squeeze(0)
          if tokens is None:
            lowercase_								= next_tokens
          else:
            lowercase_								= tokens.expand(lowerCAmelCase_ ,  *tokens.shape[1:])
            lowercase_								= torch.cat((tokens, next_tokens) ,  dim=1)
        else:
          lowercase_								= -float(np.inf)
          lowercase_								= 0
          lowercase_								= scores[:, None] + logits
          seq_lengths[~is_stopped] += 1
          lowercase_								= scores_sum / seq_lengths[:, None]
          lowercase_					,    lowercase_								= scores_sum_average.view(-1).topk(lowerCAmelCase_ ,  -1)
          lowercase_								= next_tokens // scores_sum.shape[1]
          lowercase_								= seq_lengths[next_tokens_source]
          lowercase_								= next_tokens % scores_sum.shape[1]
          lowercase_								= next_tokens.unsqueeze(1)
          lowercase_								= tokens[next_tokens_source]
          lowercase_								= torch.cat((tokens, next_tokens) ,  dim=1)
          lowercase_								= generated[next_tokens_source]
          lowercase_								= scores_sum_average * seq_lengths
          lowercase_								= is_stopped[next_tokens_source]

        lowercase_								= self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0] ,  1 ,  -1)
        lowercase_								= torch.cat((generated, next_token_embed) ,  dim=1)
        lowercase_								= is_stopped + next_tokens.eq(lowerCAmelCase_).squeeze()
        if is_stopped.all():
          break

      lowercase_								= scores / seq_lengths
      lowercase_								= scores.argsort(descending=lowerCAmelCase_)
      # tokens tensors are already padded to max_seq_length
      lowercase_								= [tokens[i] for i in order]
      lowercase_								= torch.stack(lowerCAmelCase_ ,  dim=0)
      lowercase_								= torch.tensor([seq_lengths[i] for i in order] ,  dtype=seq_lengths.dtype)
      return output_texts, seq_lengths

