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      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					SCREAMING_SNAKE_CASE__     ,					SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']







  @register_to_config
  def __init__(							self       :				Any     ,   __magic_name__       :				int     ,   __magic_name__       :				int     ,   __magic_name__       :				Optional[int] = None     ,   __magic_name__       :				int = 50_257     ,   __magic_name__       :				int = 1_024     ,   __magic_name__       :				int = 768     ,   __magic_name__       :				int = 12     ,   __magic_name__       :				int = 12     ,   __magic_name__       :				Optional[int] = None     ,   __magic_name__       :				str = "gelu_new"     ,   __magic_name__       :				float = 0.1     ,   __magic_name__       :				float = 0.1     ,   __magic_name__       :				float = 0.1     ,   __magic_name__       :				float = 1e-5     ,   __magic_name__       :				float = 0.02     ,   __magic_name__       :				bool = True     ,   __magic_name__       :				bool = True     ,   __magic_name__       :				bool = False     ,   __magic_name__       :				bool = False     ,   )     ->		Any:
        super().__init__()

        SCREAMING_SNAKE_CASE_      =     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.'''      )

        SCREAMING_SNAKE_CASE_      =     prefix_inner_dim
        SCREAMING_SNAKE_CASE_      =     prefix_hidden_dim

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

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







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				torch.Tensor     ,   __magic_name__       :				torch.Tensor     ,   __magic_name__       :				Optional[torch.Tensor] = None     ,   __magic_name__       :				Optional[torch.Tensor] = None     ,   )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.transformer.transformer.wte(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.encode_prefix(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.decode_prefix(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     torch.cat((prefix_embeds, embedding_text)     ,   dim=1      )

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







  def       __A   (							self       :				Dict     ,   __magic_name__       :				int     ,   __magic_name__       :				torch.device      )     ->		torch.Tensor:
        return torch.zeros(__magic_name__     ,   self.prefix_length     ,   dtype=torch.intaa     ,   device=__magic_name__      )







  def       __A   (							self       :				Dict     ,   __magic_name__       :				int      )     ->		Any:
        return self.encode_prefix(__magic_name__      )







  @torch.no_grad()
  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				Dict     ,   __magic_name__       :				Any     ,   __magic_name__       :				Optional[int]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     torch.split(__magic_name__     ,   1     ,   dim=0      )
        SCREAMING_SNAKE_CASE_      =     []
        SCREAMING_SNAKE_CASE_      =     []
        for feature in features:
              SCREAMING_SNAKE_CASE_      =     self.decode_prefix(feature.to(__magic_name__      )      )  # back to the clip feature
              # Only support beam search for now
              SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self.generate_beam(
                  input_embeds=__magic_name__     ,   device=__magic_name__     ,   eos_token_id=__magic_name__      )
              generated_tokens.append(output_tokens[0]      )
              generated_seq_lengths.append(seq_lengths[0]      )
        SCREAMING_SNAKE_CASE_      =     torch.stack(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     torch.stack(__magic_name__      )
        return generated_tokens, generated_seq_lengths







  @torch.no_grad()
  def       __A   (							self       :				List[str]     ,   __magic_name__       :				Tuple=None     ,   __magic_name__       :				Dict=None     ,   __magic_name__       :				Optional[Any]=None     ,   __magic_name__       :				int = 5     ,   __magic_name__       :				int = 67     ,   __magic_name__       :				float = 1.0     ,   __magic_name__       :				Optional[int] = None     ,   )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     eos_token_id
        SCREAMING_SNAKE_CASE_      =     None
        SCREAMING_SNAKE_CASE_      =     None
        SCREAMING_SNAKE_CASE_      =     torch.ones(__magic_name__     ,   device=__magic_name__     ,   dtype=torch.int      )
        SCREAMING_SNAKE_CASE_      =     torch.zeros(__magic_name__     ,   device=__magic_name__     ,   dtype=torch.bool      )

        if input_embeds is not None:
              SCREAMING_SNAKE_CASE_      =     input_embeds
        else:
              SCREAMING_SNAKE_CASE_      =     self.transformer.transformer.wte(__magic_name__      )

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

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

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

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



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A   :     Any       =				logging.getLogger(__name__)


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      return (preds == labels).mean()


@dataclass
class      lowerCamelCase    :
  """simple docstring"""







  lowerCamelCase__   =  field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,					)



@dataclass
class      lowerCamelCase    :
  """simple docstring"""







  lowerCamelCase__   =  field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()    )}    )
  lowerCamelCase__   =  field(metadata={'''help''': '''Should contain the data files for the task.'''}    )
  lowerCamelCase__   =  field(
      default=1_2_8     ,					metadata={
          '''help''': (
              '''The maximum total input sequence length after tokenization. Sequences longer '''
              '''than this will be truncated, sequences shorter will be padded.'''
          )
      }     ,					)
  lowerCamelCase__   =  field(
      default=SCREAMING_SNAKE_CASE__     ,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )



def    a__       (						):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.

      SCREAMING_SNAKE_CASE_      =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     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 if training_args.local_rank in [-1, 0] else logging.WARN   ,					)
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"   ,					training_args.local_rank   ,					training_args.device   ,					training_args.n_gpu   ,					bool(training_args.local_rank != -1 )   ,					training_args.fpaa   ,					)
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank ):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s"   ,					__UpperCamelCase )

      # Set seed
      set_seed(training_args.seed )

      try:
            SCREAMING_SNAKE_CASE_      =     processors[data_args.task_name]()
            SCREAMING_SNAKE_CASE_      =     processor.get_labels()
            SCREAMING_SNAKE_CASE_      =     len(__UpperCamelCase )
      except KeyError:
            raise ValueError("Task not found: %s" % (data_args.task_name) )

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

      SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,					num_labels=__UpperCamelCase   ,					finetuning_task=data_args.task_name   ,					cache_dir=model_args.cache_dir   ,					)
      SCREAMING_SNAKE_CASE_      =     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   ,					)
      SCREAMING_SNAKE_CASE_      =     AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path   ,					from_tf=bool(".ckpt" in model_args.model_name_or_path )   ,					config=__UpperCamelCase   ,					cache_dir=model_args.cache_dir   ,					)

      # Get datasets
      SCREAMING_SNAKE_CASE_      =     (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir   ,					tokenizer=__UpperCamelCase   ,					task=data_args.task_name   ,					max_seq_length=data_args.max_seq_length   ,					overwrite_cache=data_args.overwrite_cache   ,					mode=Split.train   ,					)
          if training_args.do_train
          else None
      )
      SCREAMING_SNAKE_CASE_      =     (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir   ,					tokenizer=__UpperCamelCase   ,					task=data_args.task_name   ,					max_seq_length=data_args.max_seq_length   ,					overwrite_cache=data_args.overwrite_cache   ,					mode=Split.dev   ,					)
          if training_args.do_eval
          else None
      )

      def compute_metrics(__UpperCamelCase ) -> Dict:
            SCREAMING_SNAKE_CASE_      =     np.argmax(p.predictions   ,					axis=1 )
            return {"acc": simple_accuracy(__UpperCamelCase   ,					p.label_ids )}

      # Data collator
      SCREAMING_SNAKE_CASE_      =     DataCollatorWithPadding(__UpperCamelCase   ,					pad_to_multiple_of=8 ) if training_args.fpaa else None

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE_      =     Trainer(
          model=__UpperCamelCase   ,					args=__UpperCamelCase   ,					train_dataset=__UpperCamelCase   ,					eval_dataset=__UpperCamelCase   ,					compute_metrics=__UpperCamelCase   ,					data_collator=__UpperCamelCase   ,					)

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
      SCREAMING_SNAKE_CASE_      =     {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***" )

            SCREAMING_SNAKE_CASE_      =     trainer.evaluate()

            SCREAMING_SNAKE_CASE_      =     os.path.join(training_args.output_dir   ,					"eval_results.txt" )
            if trainer.is_world_master():
                  with open(__UpperCamelCase   ,					"w" ) as writer:
                        logger.info("***** Eval results *****" )
                        for key, value in result.items():
                              logger.info("  %s = %s"   ,					__UpperCamelCase   ,					__UpperCamelCase )
                              writer.write("%s = %s\n" % (key, value) )

                        results.update(__UpperCamelCase )

      return results


def    a__       (						__UpperCamelCase ):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
      main()


