





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__           =			logging.getLogger(__name__)

@dataclass
class 							__SCREAMING_SNAKE_CASE       :
							snake_case  :   Optional[int]      =							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  :   bool      =							field(
							    default=_a ,		metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}							)
							snake_case  :   bool      =							field(
							    default=_a ,		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."""
							        )
							    } ,		)
							snake_case  :   Optional[int]      =							field(
							    default=_a ,		metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of training examples to this """
							            """value if set."""
							        )
							    } ,		)
							snake_case  :   Optional[int]      =							field(
							    default=_a ,		metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
							            """value if set."""
							        )
							    } ,		)
							snake_case  :   Optional[int]      =							field(
							    default=_a ,		metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of prediction examples to this """
							            """value if set."""
							        )
							    } ,		)






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






def      _UpperCamelCase  ():






							"""simple docstring"""





							UpperCamelCase__					  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
							UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      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""" ,      a__							)

							# 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()

							UpperCamelCase__					  =      training_args.get_process_log_level()
							logger.setLevel(a__							)
							datasets.utils.logging.set_verbosity(a__							)
							transformers.utils.logging.set_verbosity(a__							)
							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.
							UpperCamelCase__					  =      None
							if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
														UpperCamelCase__					  =      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:
																					UpperCamelCase__					  =      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:
																					UpperCamelCase__					  =      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 ,      )
														UpperCamelCase__					  =      train_dataset.features["""label"""].names

							if training_args.do_eval:
														UpperCamelCase__					  =      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 ,      )
														UpperCamelCase__					  =      eval_dataset.features["""label"""].names

							if training_args.do_predict:
														UpperCamelCase__					  =      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 ,      )
														UpperCamelCase__					  =      predict_dataset.features["""label"""].names

							# Labels
							UpperCamelCase__					  =      len(a__							)

							# Load pretrained model and tokenizer
							# In 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=a__ ,      idalabel={str(a__							): label for i, label in enumerate(a__							)} ,      labelaid={label: i for i, label in enumerate(a__							)} ,      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 ,      )
							UpperCamelCase__					  =      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 ,      )
							UpperCamelCase__					  =      AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path ,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path							) ,      config=a__ ,      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:
														UpperCamelCase__					  =      """max_length"""
							else:
														# We will pad later, dynamically at batch creation, to the max sequence length in each batch
														UpperCamelCase__					  =      False

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

							if training_args.do_train:
														if data_args.max_train_samples is not None:
																					UpperCamelCase__					  =      min(len(a__							) ,      data_args.max_train_samples							)
																					UpperCamelCase__					  =      train_dataset.select(range(a__							)							)
														with training_args.main_process_first(desc="""train dataset map pre-processing"""							):
																					UpperCamelCase__					  =      train_dataset.map(
																					    a__ ,      batched=a__ ,      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(a__							)							) ,      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:
																					UpperCamelCase__					  =      min(len(a__							) ,      data_args.max_eval_samples							)
																					UpperCamelCase__					  =      eval_dataset.select(range(a__							)							)
														with training_args.main_process_first(desc="""validation dataset map pre-processing"""							):
																					UpperCamelCase__					  =      eval_dataset.map(
																					    a__ ,      batched=a__ ,      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:
																					UpperCamelCase__					  =      min(len(a__							) ,      data_args.max_predict_samples							)
																					UpperCamelCase__					  =      predict_dataset.select(range(a__							)							)
														with training_args.main_process_first(desc="""prediction dataset map pre-processing"""							):
																					UpperCamelCase__					  =      predict_dataset.map(
																					    a__ ,      batched=a__ ,      load_from_cache_file=not data_args.overwrite_cache ,      desc="""Running tokenizer on prediction dataset""" ,      )

    # Get the metric function
							UpperCamelCase__					  =      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(a__ :EvalPrediction							):
														UpperCamelCase__					  =      p.predictions[0] if isinstance(p.predictions ,      a__							) else p.predictions
														UpperCamelCase__					  =      np.argmax(a__ ,      axis=1							)
														return metric.compute(predictions=a__ ,      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:
														UpperCamelCase__					  =      default_data_collator
							elif training_args.fpaa:
														UpperCamelCase__					  =      DataCollatorWithPadding(a__ ,      pad_to_multiple_of=8							)
							else:
														UpperCamelCase__					  =      None

							# Initialize our Trainer
							UpperCamelCase__					  =      Trainer(
							    model=a__ ,      args=a__ ,      train_dataset=train_dataset if training_args.do_train else None ,      eval_dataset=eval_dataset if training_args.do_eval else None ,      compute_metrics=a__ ,      tokenizer=a__ ,      data_collator=a__ ,      )

							# Training
							if training_args.do_train:
														UpperCamelCase__					  =      None
														if training_args.resume_from_checkpoint is not None:
																					UpperCamelCase__					  =      training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					UpperCamelCase__					  =      last_checkpoint
														UpperCamelCase__					  =      trainer.train(resume_from_checkpoint=a__							)
														UpperCamelCase__					  =      train_result.metrics
														UpperCamelCase__					  =      (
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(a__							)
														)
														UpperCamelCase__					  =      min(a__ ,      len(a__							)							)

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

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

							# Evaluation
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""							)
														UpperCamelCase__					  =      trainer.evaluate(eval_dataset=a__							)

														UpperCamelCase__					  =      data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a__							)
														UpperCamelCase__					  =      min(a__ ,      len(a__							)							)

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

							# Prediction
							if training_args.do_predict:
														logger.info("""*** Predict ***"""							)
														UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      trainer.predict(a__ ,      metric_key_prefix="""predict"""							)

														UpperCamelCase__					  =      (
														    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(a__							)
														)
														UpperCamelCase__					  =      min(a__ ,      len(a__							)							)

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

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


if __name__ == "__main__":
					main()











from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[torch.FloatTensor]      =							None
							snake_case  :   torch.FloatTensor      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None






class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def __init__(    self	,			__lowerCAmelCase=1	,			__lowerCAmelCase=0	,			__lowerCAmelCase=2	,			__lowerCAmelCase=512	,			__lowerCAmelCase="cls"	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			**__lowerCAmelCase	,			):
														super().__init__(pad_token_id=__lowerCAmelCase	,			bos_token_id=__lowerCAmelCase	,			eos_token_id=__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      project_dim
														UpperCamelCase__					  =      pooler_fn
														UpperCamelCase__					  =      learn_encoder
														UpperCamelCase__					  =      use_attention_mask






class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   int      =							[r"""pooler""", r"""logit_scale"""]
							snake_case  :   Tuple      =							[r"""position_ids""", r"""predictions.decoder.bias"""]
							snake_case  :   str      =							"""roberta"""
							snake_case  :   Dict      =							RobertaSeriesConfig






							def __init__(    self	,			__lowerCAmelCase	):
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      XLMRobertaModel(__lowerCAmelCase	)
														UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""has_pre_transformation"""	,			__lowerCAmelCase	)
														if self.has_pre_transformation:
																					UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
																					UpperCamelCase__					  =      nn.LayerNorm(config.hidden_size	,			eps=config.layer_norm_eps	)
														self.post_init()







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict

														UpperCamelCase__					  =      self.base_model(
														    input_ids=__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			token_type_ids=__lowerCAmelCase	,			position_ids=__lowerCAmelCase	,			head_mask=__lowerCAmelCase	,			inputs_embeds=__lowerCAmelCase	,			encoder_hidden_states=__lowerCAmelCase	,			encoder_attention_mask=__lowerCAmelCase	,			output_attentions=__lowerCAmelCase	,			output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,			return_dict=__lowerCAmelCase	,			)

														if self.has_pre_transformation:
																					UpperCamelCase__					  =      outputs["""hidden_states"""][-2]
																					UpperCamelCase__					  =      self.pre_LN(__lowerCAmelCase	)
																					UpperCamelCase__					  =      self.transformation_pre(__lowerCAmelCase	)

																					return TransformationModelOutput(
																					    projection_state=__lowerCAmelCase	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)
														else:
																					UpperCamelCase__					  =      self.transformation(outputs.last_hidden_state	)
																					return TransformationModelOutput(
																					    projection_state=__lowerCAmelCase	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)




