




import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

_UpperCAmelCase	:						Optional[Any]			=     logging.getLogger(__name__)




@dataclass
class    __lowerCAmelCase       :
       _a 					=						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.'''
               )
           }      , )
       _a 					=						field(
           default=lowerCAmelCase      , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''})
       _a 					=						field(
           default=lowerCAmelCase      , metadata={
               '''help''': (
                   '''Whether to pad all samples to `max_seq_length`. '''
                   '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
               )
           }      , )
       _a 					=						field(
           default=lowerCAmelCase      , metadata={
               '''help''': (
                   '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                   '''value if set.'''
               )
           }      , )
       _a 					=						field(
           default=lowerCAmelCase      , metadata={
               '''help''': (
                   '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                   '''value if set.'''
               )
           }      , )
       _a 					=						field(
           default=lowerCAmelCase      , metadata={
               '''help''': (
                   '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
                   '''value if set.'''
               )
           }      , )







@dataclass
class    __lowerCAmelCase       :
       _a 					=						field(
           default=lowerCAmelCase      , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
       _a 					=						field(
           default=lowerCAmelCase      , metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''})
       _a 					=						field(
           default=lowerCAmelCase      , metadata={'''help''': '''Train language if it is different from the evaluation language.'''})
       _a 					=						field(
           default=lowerCAmelCase      , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
       _a 					=						field(
           default=lowerCAmelCase      , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
       _a 					=						field(
           default=lowerCAmelCase      , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}      , )
       _a 					=						field(
           default=lowerCAmelCase      , metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}      , )
       _a 					=						field(
           default=lowerCAmelCase      , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}      , )
       _a 					=						field(
           default='''main'''      , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}      , )
       _a 					=						field(
           default=lowerCAmelCase      , metadata={
               '''help''': (
                   '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                   '''with private models).'''
               )
           }      , )
       _a 					=						field(
           default=lowerCAmelCase      , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}      , )







def       UpperCAmelCase__  (     ):
  # 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.

  lowercase    :str      	=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
  lowercase    :Dict      	=						parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("run_xnli",			lowerCamelCase   )

  # Setup logging
  logging.basicConfig(
      format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",			datefmt="%m/%d/%Y %H:%M:%S",			handlers=[logging.StreamHandler(sys.stdout   )],			)

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  lowercase    :List[Any]      	=						training_args.get_process_log_level()
  logger.setLevel(lowerCamelCase   )
  datasets.utils.logging.set_verbosity(lowerCamelCase   )
  transformers.utils.logging.set_verbosity(lowerCamelCase   )
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # Log on each process the small summary:
  logger.warning(
      F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
      + F"distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}"   )
  logger.info(F"Training/evaluation parameters {training_args}"   )

  # Detecting last checkpoint.
  lowercase    :List[str]      	=						None
  if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
    lowercase    :Optional[int]      	=						get_last_checkpoint(training_args.output_dir   )
    if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
      raise ValueError(
          F"Output directory ({training_args.output_dir}) already exists and is not empty. "
          "Use --overwrite_output_dir to overcome."   )
    elif last_checkpoint is not None:
      logger.info(
          F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
          "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."   )

    # Set seed before initializing model.
  set_seed(training_args.seed   )

  # In distributed training, the load_dataset function guarantees that only one local process can concurrently
  # download the dataset.
  # Downloading and loading xnli dataset from the hub.
  if training_args.do_train:
    if model_args.train_language is None:
      lowercase    :Union[str, Any]      	=						load_dataset(
          "xnli",			model_args.language,			split="train",			cache_dir=model_args.cache_dir,			use_auth_token=True if model_args.use_auth_token else None,			)
    else:
      lowercase    :Optional[int]      	=						load_dataset(
          "xnli",			model_args.train_language,			split="train",			cache_dir=model_args.cache_dir,			use_auth_token=True if model_args.use_auth_token else None,			)
    lowercase    :Dict      	=						train_dataset.features["label"].names

  if training_args.do_eval:
    lowercase    :int      	=						load_dataset(
        "xnli",			model_args.language,			split="validation",			cache_dir=model_args.cache_dir,			use_auth_token=True if model_args.use_auth_token else None,			)
    lowercase    :Tuple      	=						eval_dataset.features["label"].names

  if training_args.do_predict:
    lowercase    :Any      	=						load_dataset(
        "xnli",			model_args.language,			split="test",			cache_dir=model_args.cache_dir,			use_auth_token=True if model_args.use_auth_token else None,			)
    lowercase    :str      	=						predict_dataset.features["label"].names

  # Labels
  lowercase    :Tuple      	=						len(lowerCamelCase   )

  # Load pretrained model and tokenizer
  # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  lowercase    :List[Any]      	=						AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path,			num_labels=lowerCamelCase,			idalabel={str(lowerCamelCase   ): label for i, label in enumerate(lowerCamelCase   )},			labelaid={label: i for i, label in enumerate(lowerCamelCase   )},			finetuning_task="xnli",			cache_dir=model_args.cache_dir,			revision=model_args.model_revision,			use_auth_token=True if model_args.use_auth_token else None,			)
  lowercase    :Optional[int]      	=						AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,			do_lower_case=model_args.do_lower_case,			cache_dir=model_args.cache_dir,			use_fast=model_args.use_fast_tokenizer,			revision=model_args.model_revision,			use_auth_token=True if model_args.use_auth_token else None,			)
  lowercase    :Dict      	=						AutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path,			from_tf=bool(".ckpt" in model_args.model_name_or_path   ),			config=lowerCamelCase,			cache_dir=model_args.cache_dir,			revision=model_args.model_revision,			use_auth_token=True if model_args.use_auth_token else None,			ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,			)

  # Preprocessing the datasets
  # Padding strategy
  if data_args.pad_to_max_length:
    lowercase    :List[Any]      	=						"max_length"
  else:
    # We will pad later, dynamically at batch creation, to the max sequence length in each batch
    lowercase    :Union[str, Any]      	=						False

  def preprocess_function(lowerCamelCase   ):
    # Tokenize the texts
    return tokenizer(
        examples["premise"],			examples["hypothesis"],			padding=lowerCamelCase,			max_length=data_args.max_seq_length,			truncation=lowerCamelCase,			)

  if training_args.do_train:
    if data_args.max_train_samples is not None:
      lowercase    :Dict      	=						min(len(lowerCamelCase   ),			data_args.max_train_samples   )
      lowercase    :Tuple      	=						train_dataset.select(range(lowerCamelCase   )   )
    with training_args.main_process_first(desc="train dataset map pre-processing"   ):
      lowercase    :Union[str, Any]      	=						train_dataset.map(
          lowerCamelCase,			batched=lowerCamelCase,			load_from_cache_file=not data_args.overwrite_cache,			desc="Running tokenizer on train dataset",			)
    # Log a few random samples from the training set:
    for index in random.sample(range(len(lowerCamelCase   )   ),			3   ):
      logger.info(F"Sample {index} of the training set: {train_dataset[index]}."   )

  if training_args.do_eval:
    if data_args.max_eval_samples is not None:
      lowercase    :str      	=						min(len(lowerCamelCase   ),			data_args.max_eval_samples   )
      lowercase    :str      	=						eval_dataset.select(range(lowerCamelCase   )   )
    with training_args.main_process_first(desc="validation dataset map pre-processing"   ):
      lowercase    :str      	=						eval_dataset.map(
          lowerCamelCase,			batched=lowerCamelCase,			load_from_cache_file=not data_args.overwrite_cache,			desc="Running tokenizer on validation dataset",			)

  if training_args.do_predict:
    if data_args.max_predict_samples is not None:
      lowercase    :Dict      	=						min(len(lowerCamelCase   ),			data_args.max_predict_samples   )
      lowercase    :Optional[Any]      	=						predict_dataset.select(range(lowerCamelCase   )   )
    with training_args.main_process_first(desc="prediction dataset map pre-processing"   ):
      lowercase    :Any      	=						predict_dataset.map(
          lowerCamelCase,			batched=lowerCamelCase,			load_from_cache_file=not data_args.overwrite_cache,			desc="Running tokenizer on prediction dataset",			)

    # Get the metric function
  lowercase    :Any      	=						evaluate.load("xnli"   )

  # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
  # predictions and label_ids field) and has to return a dictionary string to float.
  def compute_metrics(lowerCamelCase   ):
    lowercase    :int      	=						p.predictions[0] if isinstance(p.predictions,			lowerCamelCase   ) else p.predictions
    lowercase    :List[Any]      	=						np.argmax(lowerCamelCase,			axis=1   )
    return metric.compute(predictions=lowerCamelCase,			references=p.label_ids   )

  # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
  if data_args.pad_to_max_length:
    lowercase    :str      	=						default_data_collator
  elif training_args.fpaa:
    lowercase    :Optional[Any]      	=						DataCollatorWithPadding(lowerCamelCase,			pad_to_multiple_of=8   )
  else:
    lowercase    :str      	=						None

  # Initialize our Trainer
  lowercase    :str      	=						Trainer(
      model=lowerCamelCase,			args=lowerCamelCase,			train_dataset=train_dataset if training_args.do_train else None,			eval_dataset=eval_dataset if training_args.do_eval else None,			compute_metrics=lowerCamelCase,			tokenizer=lowerCamelCase,			data_collator=lowerCamelCase,			)

  # Training
  if training_args.do_train:
    lowercase    :List[Any]      	=						None
    if training_args.resume_from_checkpoint is not None:
      lowercase    :Any      	=						training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowercase    :int      	=						last_checkpoint
    lowercase    :str      	=						trainer.train(resume_from_checkpoint=lowerCamelCase   )
    lowercase    :Union[str, Any]      	=						train_result.metrics
    lowercase    :int      	=						(
        data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase   )
    )
    lowercase    :Optional[int]      	=						min(lowerCamelCase,			len(lowerCamelCase   )   )

    trainer.save_model()  # Saves the tokenizer too for easy upload

    trainer.log_metrics("train",			lowerCamelCase   )
    trainer.save_metrics("train",			lowerCamelCase   )
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    logger.info("*** Evaluate ***"   )
    lowercase    :Dict      	=						trainer.evaluate(eval_dataset=lowerCamelCase   )

    lowercase    :Union[str, Any]      	=						data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase   )
    lowercase    :str      	=						min(lowerCamelCase,			len(lowerCamelCase   )   )

    trainer.log_metrics("eval",			lowerCamelCase   )
    trainer.save_metrics("eval",			lowerCamelCase   )

  # Prediction
  if training_args.do_predict:
    logger.info("*** Predict ***"   )
    lowercase    :Tuple      	=						trainer.predict(lowerCamelCase,			metric_key_prefix="predict"   )

    lowercase    :Dict      	=						(
        data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCamelCase   )
    )
    lowercase    :Optional[int]      	=						min(lowerCamelCase,			len(lowerCamelCase   )   )

    trainer.log_metrics("predict",			lowerCamelCase   )
    trainer.save_metrics("predict",			lowerCamelCase   )

    lowercase    :Any      	=						np.argmax(lowerCamelCase,			axis=1   )
    lowercase    :int      	=						os.path.join(training_args.output_dir,			"predictions.txt"   )
    if trainer.is_world_process_zero():
      with open(lowerCamelCase,			"w"   ) as writer:
        writer.write("index\tprediction\n"   )
        for index, item in enumerate(lowerCamelCase   ):
          lowercase    :List[str]      	=						label_list[item]
          writer.write(F"{index}\t{item}\n"   )


if __name__ == "__main__":
     main()






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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase	:						Optional[int]			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[Any]			=     {
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						'''gptj'''
       _a 					=						{
           '''max_position_embeddings''': '''n_positions''',
           '''hidden_size''': '''n_embd''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }






       def __init__(   self:					List[str]       ,					_lowerCAmelCase:					Union[str, Any]=5_04_00       ,					_lowerCAmelCase:					Optional[int]=20_48       ,					_lowerCAmelCase:					Dict=40_96       ,					_lowerCAmelCase:					str=28       ,					_lowerCAmelCase:					Dict=16       ,					_lowerCAmelCase:					str=64       ,					_lowerCAmelCase:					Dict=None       ,					_lowerCAmelCase:					Optional[int]="gelu_new"       ,					_lowerCAmelCase:					Optional[int]=0.0       ,					_lowerCAmelCase:					List[str]=0.0       ,					_lowerCAmelCase:					str=0.0       ,					_lowerCAmelCase:					Any=1e-5       ,					_lowerCAmelCase:					str=0.02       ,					_lowerCAmelCase:					str=True       ,					_lowerCAmelCase:					Any=5_02_56       ,					_lowerCAmelCase:					Union[str, Any]=5_02_56       ,					_lowerCAmelCase:					Optional[int]=False       ,					**_lowerCAmelCase:					int       ,					):
         lowercase    :str      	=						vocab_size
         lowercase    :Union[str, Any]      	=						n_positions
         lowercase    :Optional[int]      	=						n_embd
         lowercase    :int      	=						n_layer
         lowercase    :Tuple      	=						n_head
         lowercase    :Optional[Any]      	=						n_inner
         lowercase    :List[Any]      	=						rotary_dim
         lowercase    :List[Any]      	=						activation_function
         lowercase    :Tuple      	=						resid_pdrop
         lowercase    :Optional[int]      	=						embd_pdrop
         lowercase    :Any      	=						attn_pdrop
         lowercase    :Optional[int]      	=						layer_norm_epsilon
         lowercase    :str      	=						initializer_range
         lowercase    :Optional[int]      	=						use_cache

         lowercase    :Dict      	=						bos_token_id
         lowercase    :List[Any]      	=						eos_token_id

         super().__init__(
             bos_token_id=_lowerCAmelCase       ,					eos_token_id=_lowerCAmelCase       ,					tie_word_embeddings=_lowerCAmelCase       ,					**_lowerCAmelCase				)







class    __lowerCAmelCase       (							lowerCAmelCase):






       def __init__(   self:					Tuple       ,					_lowerCAmelCase:					PretrainedConfig       ,					_lowerCAmelCase:					str = "default"       ,					_lowerCAmelCase:					List[PatchingSpec] = None       ,					_lowerCAmelCase:					bool = False       ,					):
         super().__init__(_lowerCAmelCase       ,					task=_lowerCAmelCase       ,					patching_specs=_lowerCAmelCase       ,					use_past=_lowerCAmelCase				)
         if not getattr(self._config       ,					"pad_token_id"       ,					_lowerCAmelCase				):
           # TODO: how to do that better?
           lowercase    :int      	=						0






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Optional[int]      	=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}				)
         if self.use_past:
           self.fill_with_past_key_values_(_lowerCAmelCase       ,					direction="inputs"				)
           lowercase    :Any      	=						{0: "batch", 1: "past_sequence + sequence"}
         else:
           lowercase    :Optional[int]      	=						{0: "batch", 1: "sequence"}

         return common_inputs






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         return self._config.n_layer






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         return self._config.n_head






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]       ,					_lowerCAmelCase:					PreTrainedTokenizer       ,					_lowerCAmelCase:					int = -1       ,					_lowerCAmelCase:					int = -1       ,					_lowerCAmelCase:					bool = False       ,					_lowerCAmelCase:					Optional[TensorType] = None       ,					):
         lowercase    :Union[str, Any]      	=						super(_lowerCAmelCase       ,					self				).generate_dummy_inputs(
             _lowerCAmelCase       ,					batch_size=_lowerCAmelCase       ,					seq_length=_lowerCAmelCase       ,					is_pair=_lowerCAmelCase       ,					framework=_lowerCAmelCase				)

         # We need to order the input in the way they appears in the forward()
         lowercase    :Optional[Any]      	=						OrderedDict({"input_ids": common_inputs["input_ids"]}				)

         # Need to add the past_keys
         if self.use_past:
           if not is_torch_available():
             raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
           else:
             import torch

             lowercase	,    lowercase    :Optional[int]      	=						common_inputs["input_ids"].shape
             # Not using the same length for past_key_values
             lowercase    :Union[str, Any]      	=						seqlen + 2
             lowercase    :Optional[Any]      	=						(
                 batch,
                 self.num_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // self.num_attention_heads,
             )
             lowercase    :Optional[int]      	=						[
                 (torch.zeros(_lowerCAmelCase				), torch.zeros(_lowerCAmelCase				)) for _ in range(self.num_layers				)
             ]

         lowercase    :List[Any]      	=						common_inputs["attention_mask"]
         if self.use_past:
           lowercase    :Tuple      	=						ordered_inputs["attention_mask"].dtype
           lowercase    :List[Any]      	=						torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(_lowerCAmelCase       ,					_lowerCAmelCase       ,					dtype=_lowerCAmelCase				)]       ,					dim=1				)

         return ordered_inputs






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         return 13



