"""simple docstring"""






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


lowerCAmelCase__						:						Optional[int]		  =			logging.getLogger(__name__)



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return (preds == labels).mean()
@dataclass
class        snake_case :




					"""simple docstring"""

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






@dataclass
class        snake_case :




					"""simple docstring"""

					snake_case__       					=				field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()       )}       )
					snake_case__       					=				field(metadata={"help": "Should contain the data files for the task."}       )
					snake_case__       					=				field(
					    default=1_28   ,					metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }   ,					)
					snake_case__       					=				field(
					    default=_UpperCAmelCase   ,					metadata={"help": "Overwrite the cached training and evaluation sets"}       )






def 						a_		(      ):
				UpperCAmelCase__    =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				UpperCAmelCase__    =							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'      ,    SCREAMING_SNAKE_CASE_ )

				# Set seed
				set_seed(training_args.seed )

				try:
								UpperCAmelCase__    =							processors[data_args.task_name]()
								UpperCAmelCase__    =							processor.get_labels()
								UpperCAmelCase__    =							len(SCREAMING_SNAKE_CASE_ )
				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.

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

				# Get datasets
				UpperCAmelCase__    =							(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir      ,    tokenizer=SCREAMING_SNAKE_CASE_      ,    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
				)
				UpperCAmelCase__    =							(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir      ,    tokenizer=SCREAMING_SNAKE_CASE_      ,    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(lowerCamelCase ) -> Dict:
								UpperCAmelCase__    =							np.argmax(p.predictions      ,    axis=1 )
								return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE_      ,    p.label_ids )}

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

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

				# 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
				UpperCAmelCase__    =							{}
				if training_args.do_eval:
								logger.info('*** Evaluate ***' )

								UpperCAmelCase__    =							trainer.evaluate()

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

																results.update(SCREAMING_SNAKE_CASE_ )

				return results



def 						a_		(      lowerCamelCase ):
				main()


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        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]







					@register_to_config
					def __init__(       self     :			str					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			int = 50_257					,lowerCamelCase__     :			int = 1_024					,lowerCamelCase__     :			int = 768					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			int = 12					,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 = 0.0_2					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = False					,lowerCamelCase__     :			bool = False					,):
									super().__init__()

									UpperCAmelCase__    =							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.'''    )

									UpperCAmelCase__    =							prefix_inner_dim
									UpperCAmelCase__    =							prefix_hidden_dim

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

									UpperCAmelCase__    =							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__					,)
									UpperCAmelCase__    =							GPTaLMHeadModel(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,):
									UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )
									UpperCAmelCase__    =							self.encode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							self.decode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.cat((prefix_embeds, embedding_text)					,dim=1    )

									if labels is not None:
													UpperCAmelCase__    =							self.get_dummy_token(input_ids.shape[0]					,input_ids.device    )
													UpperCAmelCase__    =							torch.cat((dummy_token, input_ids)					,dim=1    )
									UpperCAmelCase__    =							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 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			torch.device    ):
									return torch.zeros(lowerCamelCase__					,self.prefix_length					,dtype=torch.intaa					,device=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[str]    ):
									return self.encode_prefix(lowerCamelCase__    )







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							torch.split(lowerCamelCase__					,1					,dim=0    )
									UpperCAmelCase__    =							[]
									UpperCAmelCase__    =							[]
									for feature in features:
													UpperCAmelCase__    =							self.decode_prefix(feature.to(lowerCamelCase__    )    )  # back to the clip feature
													# Only support beam search for now
													UpperCAmelCase__   ,			UpperCAmelCase__    =							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]    )
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )
									return generated_tokens, generated_seq_lengths







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			List[Any]=None					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			int = 5					,lowerCamelCase__     :			int = 67					,lowerCamelCase__     :			float = 1.0					,lowerCamelCase__     :			Optional[int] = None					,):
									UpperCAmelCase__    =							eos_token_id
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							torch.ones(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.int    )
									UpperCAmelCase__    =							torch.zeros(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.bool    )

									if input_embeds is not None:
													UpperCAmelCase__    =							input_embeds
									else:
													UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )

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

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

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

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



