




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")

lowerCamelCase       :	Union[str, Any]									=     logging.getLogger(__name__)







@dataclass
class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		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.'''
         )
     }							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}     )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   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.'''
         )
     }							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     }							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
             '''value if set.'''
         )
     }							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
             '''value if set.'''
         )
     }							,   )







@dataclass
class 						A:



 '''simple docstring'''


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

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
      lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					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'			,							lowercase  )

      # 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()

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

      if training_args.do_eval:
            lowerCamelCase_	  =					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			,							)
            lowerCamelCase_	  =					eval_dataset.features['label'].names

      if training_args.do_predict:
            lowerCamelCase_	  =					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			,							)
            lowerCamelCase_	  =					predict_dataset.features['label'].names

      # Labels
      lowerCamelCase_	  =					len(lowercase  )

      # Load pretrained model and tokenizer
      # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      lowerCamelCase_	  =					AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path			,							num_labels=lowercase			,							idalabel={str(lowercase  ): label for i, label in enumerate(lowercase  )}			,							labelaid={label: i for i, label in enumerate(lowercase  )}			,							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			,							)
      lowerCamelCase_	  =					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			,							)
      lowerCamelCase_	  =					AutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path			,							from_tf=bool('.ckpt' in model_args.model_name_or_path  )			,							config=lowercase			,							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:
            lowerCamelCase_	  =					'max_length'
      else:
            # We will pad later, dynamically at batch creation, to the max sequence length in each batch
            lowerCamelCase_	  =					False

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

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

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

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

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

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

            trainer.log_metrics('train'			,							lowercase  )
            trainer.save_metrics('train'			,							lowercase  )
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            logger.info('*** Evaluate ***'  )
            lowerCamelCase_	  =					trainer.evaluate(eval_dataset=lowercase  )

            lowerCamelCase_	  =					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase  )
            lowerCamelCase_	  =					min(lowercase			,							len(lowercase  )  )

            trainer.log_metrics('eval'			,							lowercase  )
            trainer.save_metrics('eval'			,							lowercase  )

      # Prediction
      if training_args.do_predict:
            logger.info('*** Predict ***'  )
            lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					trainer.predict(lowercase			,							metric_key_prefix='predict'  )

            lowerCamelCase_	  =					(
                data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowercase  )
            )
            lowerCamelCase_	  =					min(lowercase			,							len(lowercase  )  )

            trainer.log_metrics('predict'			,							lowercase  )
            trainer.save_metrics('predict'			,							lowercase  )

            lowerCamelCase_	  =					np.argmax(lowercase			,							axis=1  )
            lowerCamelCase_	  =					os.path.join(training_args.output_dir			,							'predictions.txt'  )
            if trainer.is_world_process_zero():
                  with open(lowercase			,							'w'  ) as writer:
                        writer.write('index\tprediction\n'  )
                        for index, item in enumerate(lowercase  ):
                              lowerCamelCase_	  =					label_list[item]
                              writer.write(f"""{index}\t{item}\n"""  )


if __name__ == "__main__":
 main()









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.eager import context
 from tensorflow.python.framework import ops

 from transformers import GradientAccumulator, create_optimizer







@require_tf
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[int]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						int						)     ->			Any:

       """simple docstring"""






       self.assertEqual(len(A_						)		,							len(A_						)						)
       for a, b in zip(A_		,							A_						):
             self.assertAlmostEqual(A_		,							A_		,							delta=A_						)

 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(A_						):
             accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step		,							3						)
       self.assertEqual(len(accumulator.gradients						)		,							1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[-2.0, 5.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[0.0, 0.0]		,							tol=1E-2						)





 def        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					None
       ops.enable_eager_execution_internal()
       lowerCamelCase_	  =					tf.config.list_physical_devices('CPU'						)
       if len(A_						) == 1:
             tf.config.set_logical_device_configuration(
                 physical_devices[0]		,							[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       lowerCamelCase_	  =					tf.config.list_logical_devices(device_type='CPU'						)
       lowerCamelCase_	  =					tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
             lowerCamelCase_	  =					GradientAccumulator()
             lowerCamelCase_	  =					tf.Variable([4.0, 3.0]						)
             lowerCamelCase_  ,       lowerCamelCase_	  =					create_optimizer(5E-5		,							10		,							5						)
             lowerCamelCase_	  =					tf.Variable([0.0, 0.0]		,							trainable=A_						)

       def accumulate_on_replica(A_      :						Any						):
             accumulator([gradient]						)

       def apply_on_replica():
             optimizer.apply_gradients(list(zip(accumulator.gradients		,							[variable]						)						)						)

       @tf.function
       def accumulate(A_      :						List[Any]		,							A_      :						Tuple						):
             with strategy.scope():
                   lowerCamelCase_	  =					strategy.experimental_local_results(A_						)
                   local_variables[0].assign(A_						)
                   local_variables[1].assign(A_						)
                   strategy.run(A_		,							args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
             with strategy.scope():
                   strategy.run(A_						)

       def _check_local_values(A_      :						List[Any]		,							A_      :						str						):
             lowerCamelCase_	  =					strategy.experimental_local_results(accumulator._gradients[0]						)
             self.assertListAlmostEqual(values[0].value()		,							A_		,							tol=1E-2						)
             self.assertListAlmostEqual(values[1].value()		,							A_		,							tol=1E-2						)

       accumulate([1.0, 2.0]		,							[-1.0, 1.0]						)
       accumulate([3.0, -1.0]		,							[-1.0, -1.0]						)
       accumulate([-2.0, 2.0]		,							[3.0, -2.0]						)
       self.assertEqual(accumulator.step		,							3						)
       _check_local_values([2.0, 3.0]		,							[1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()		,							[4.0, 3.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       _check_local_values([0.0, 0.0]		,							[0.0, 0.0]						)



