



'''simple docstring'''







import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class    lowerCamelCase_				(						unittest.TestCase	):






   '''simple docstring'''

   A			:  int					=						(ScoreSdeVeScheduler,)
   A			:  Optional[Any]					=						()


   @property
   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      4
       UpperCamelCase__	     =      3
       UpperCamelCase__	     =      8
       UpperCamelCase__	     =      8

       UpperCamelCase__	     =      torch.rand((batch_size, num_channels, height, width)			)

       return sample


   @property
   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      4
       UpperCamelCase__	     =      3
       UpperCamelCase__	     =      8
       UpperCamelCase__	     =      8

       UpperCamelCase__	     =      batch_size * num_channels * height * width
       UpperCamelCase__	     =      torch.arange(__UpperCamelCase			)
       UpperCamelCase__	     =      sample.reshape(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
       UpperCamelCase__	     =      sample / num_elems
       UpperCamelCase__	     =      sample.permute(3  ,    0  ,    1  ,    2			)

       return sample


   def 						_UpperCamelCase							(	self			):
       def model(__UpperCamelCase  ,    __UpperCamelCase  ,    *__UpperCamelCase			):
           return sample * t / (t + 1)

       return model


   def 						_UpperCamelCase							(	self  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      {
           "num_train_timesteps": 20_00,
           "snr": 0.15,
           "sigma_min": 0.01,
           "sigma_max": 13_48,
           "sampling_eps": 1e-5,
       }

       config.update(**__UpperCamelCase			)
       return config


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase=0  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      dict(self.forward_default_kwargs			)

       for scheduler_class in self.scheduler_classes:
           UpperCamelCase__	     =      self.dummy_sample
           UpperCamelCase__	     =      0.1 * sample

           UpperCamelCase__	     =      self.get_scheduler_config(**__UpperCamelCase			)
           UpperCamelCase__	     =      scheduler_class(**__UpperCamelCase			)

           with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(__UpperCamelCase			)
               UpperCamelCase__	     =      scheduler_class.from_pretrained(__UpperCamelCase			)

           UpperCamelCase__	     =      scheduler.step_pred(
               __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    generator=torch.manual_seed(0			)  ,    **__UpperCamelCase			).prev_sample
           UpperCamelCase__	     =      new_scheduler.step_pred(
               __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    generator=torch.manual_seed(0			)  ,    **__UpperCamelCase			).prev_sample

           assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"

           UpperCamelCase__	     =      scheduler.step_correct(__UpperCamelCase  ,    __UpperCamelCase  ,    generator=torch.manual_seed(0			)  ,    **__UpperCamelCase			).prev_sample
           UpperCamelCase__	     =      new_scheduler.step_correct(
               __UpperCamelCase  ,    __UpperCamelCase  ,    generator=torch.manual_seed(0			)  ,    **__UpperCamelCase			).prev_sample

           assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler correction are not identical"


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase=0  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      dict(self.forward_default_kwargs			)
       kwargs.update(__UpperCamelCase			)

       for scheduler_class in self.scheduler_classes:
           UpperCamelCase__	     =      self.dummy_sample
           UpperCamelCase__	     =      0.1 * sample

           UpperCamelCase__	     =      self.get_scheduler_config()
           UpperCamelCase__	     =      scheduler_class(**__UpperCamelCase			)

           with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(__UpperCamelCase			)
               UpperCamelCase__	     =      scheduler_class.from_pretrained(__UpperCamelCase			)

           UpperCamelCase__	     =      scheduler.step_pred(
               __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    generator=torch.manual_seed(0			)  ,    **__UpperCamelCase			).prev_sample
           UpperCamelCase__	     =      new_scheduler.step_pred(
               __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    generator=torch.manual_seed(0			)  ,    **__UpperCamelCase			).prev_sample

           assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"

           UpperCamelCase__	     =      scheduler.step_correct(__UpperCamelCase  ,    __UpperCamelCase  ,    generator=torch.manual_seed(0			)  ,    **__UpperCamelCase			).prev_sample
           UpperCamelCase__	     =      new_scheduler.step_correct(
               __UpperCamelCase  ,    __UpperCamelCase  ,    generator=torch.manual_seed(0			)  ,    **__UpperCamelCase			).prev_sample

           assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler correction are not identical"


   def 						_UpperCamelCase							(	self			):
       for timesteps in [10, 1_00, 10_00]:
           self.check_over_configs(num_train_timesteps=__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]  ,    [1, 1_00, 10_00]			):
           self.check_over_configs(sigma_min=__UpperCamelCase  ,    sigma_max=__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       for t in [0.1, 0.5, 0.75]:
           self.check_over_forward(time_step=__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      dict(self.forward_default_kwargs			)

       UpperCamelCase__	     =      self.scheduler_classes[0]
       UpperCamelCase__	     =      self.get_scheduler_config()
       UpperCamelCase__	     =      scheduler_class(**__UpperCamelCase			)

       UpperCamelCase__	     =      3

       UpperCamelCase__	     =      self.dummy_model()
       UpperCamelCase__	     =      self.dummy_sample_deter

       scheduler.set_sigmas(__UpperCamelCase			)
       scheduler.set_timesteps(__UpperCamelCase			)
       UpperCamelCase__	     =      torch.manual_seed(0			)

       for i, t in enumerate(scheduler.timesteps			):
           UpperCamelCase__	     =      scheduler.sigmas[i]

           for _ in range(scheduler.config.correct_steps			):
               with torch.no_grad():
                   UpperCamelCase__	     =      model(__UpperCamelCase  ,    __UpperCamelCase			)
               UpperCamelCase__	     =      scheduler.step_correct(__UpperCamelCase  ,    __UpperCamelCase  ,    generator=__UpperCamelCase  ,    **__UpperCamelCase			).prev_sample

           with torch.no_grad():
               UpperCamelCase__	     =      model(__UpperCamelCase  ,    __UpperCamelCase			)

           UpperCamelCase__	     =      scheduler.step_pred(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    generator=__UpperCamelCase  ,    **__UpperCamelCase			)
           UpperCamelCase__     ,	UpperCamelCase__	     =      output.prev_sample, output.prev_sample_mean

       UpperCamelCase__	     =      torch.sum(torch.abs(__UpperCamelCase			)			)
       UpperCamelCase__	     =      torch.mean(torch.abs(__UpperCamelCase			)			)

       assert np.isclose(result_sum.item()  ,    14_372_758_528.0			)
       assert np.isclose(result_mean.item()  ,    18_714_530.0			)







   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      dict(self.forward_default_kwargs			)

       UpperCamelCase__	     =      kwargs.pop("num_inference_steps"  ,    __UpperCamelCase			)

       for scheduler_class in self.scheduler_classes:
           UpperCamelCase__	     =      self.get_scheduler_config()
           UpperCamelCase__	     =      scheduler_class(**__UpperCamelCase			)

           UpperCamelCase__	     =      self.dummy_sample
           UpperCamelCase__	     =      0.1 * sample

           if num_inference_steps is not None and hasattr(__UpperCamelCase  ,    "set_timesteps"			):
               scheduler.set_timesteps(__UpperCamelCase			)
           elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,    "set_timesteps"			):
               UpperCamelCase__	     =      num_inference_steps

           UpperCamelCase__	     =      scheduler.step_pred(__UpperCamelCase  ,    0  ,    __UpperCamelCase  ,    generator=torch.manual_seed(0			)  ,    **__UpperCamelCase			).prev_sample
           UpperCamelCase__	     =      scheduler.step_pred(__UpperCamelCase  ,    1  ,    __UpperCamelCase  ,    generator=torch.manual_seed(0			)  ,    **__UpperCamelCase			).prev_sample

           self.assertEqual(output_a.shape  ,    sample.shape			)
           self.assertEqual(output_a.shape  ,    output_a.shape			)









'''simple docstring'''







import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('SDP_ENABLED') or is_sagemaker_dp_enabled():
      lowercase__  :	Any            =      True
      lowercase__  :	Tuple            =      'p3dn.24xlarge'
      import smdistributed.dataparallel.tensorflow as sdp
else:
      lowercase__  :	Dict            =      False





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_=None						):
    UpperCamelCase__	     =      tqdm(lowerCAmelCase_						)
    for i, batch in enumerate(lowerCAmelCase_						):
        with tf.GradientTape() as tape:
            UpperCamelCase__     ,	UpperCamelCase__	     =      batch
            UpperCamelCase__	     =      model(lowerCAmelCase_						)
            UpperCamelCase__	     =      loss(lowerCAmelCase_       ,	outputs.logits						)

        if SDP_ENABLED:
            UpperCamelCase__	     =      sdp.DistributedGradientTape(lowerCAmelCase_       ,	sparse_as_dense=lowerCAmelCase_						)

        UpperCamelCase__	     =      tape.gradient(lowerCAmelCase_       ,	model.trainable_variables						)
        opt.apply_gradients(zip(lowerCAmelCase_       ,	model.trainable_variables						)						)

        pbar.set_description(F'Loss: {loss_value:.4f}'						)

        if SDP_ENABLED and i == 0:
            sdp.broadcast_variables(model.variables       ,	root_rank=0						)
            sdp.broadcast_variables(opt.variables()       ,	root_rank=0						)

        if max_steps and i >= max_steps:
            break

    UpperCamelCase__	     =      {"loss": loss_value.numpy()}
    return train_results





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						):
    # Load dataset
    UpperCamelCase__     ,	UpperCamelCase__	     =      load_dataset("imdb"       ,	split=["train", "test"]						)

    # Preprocess train dataset
    UpperCamelCase__	     =      train_dataset.map(
        lambda lowerCAmelCase_						: tokenizer(e["text"]       ,	truncation=lowerCAmelCase_       ,	padding="max_length"						)       ,	batched=lowerCAmelCase_						)
    train_dataset.set_format(type="tensorflow"       ,	columns=["input_ids", "attention_mask", "label"]						)

    UpperCamelCase__	     =      {
        x: train_dataset[x].to_tensor(default_value=0       ,	shape=[None, tokenizer.model_max_length]						)
        for x in ["input_ids", "attention_mask"]
    }
    UpperCamelCase__	     =      tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])						)

    # Preprocess test dataset
    UpperCamelCase__	     =      test_dataset.map(
        lambda lowerCAmelCase_						: tokenizer(e["text"]       ,	truncation=lowerCAmelCase_       ,	padding="max_length"						)       ,	batched=lowerCAmelCase_						)
    test_dataset.set_format(type="tensorflow"       ,	columns=["input_ids", "attention_mask", "label"]						)

    UpperCamelCase__	     =      {
        x: test_dataset[x].to_tensor(default_value=0       ,	shape=[None, tokenizer.model_max_length]						)
        for x in ["input_ids", "attention_mask"]
    }
    UpperCamelCase__	     =      tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])						)

    if SDP_ENABLED:
        UpperCamelCase__	     =      tf_train_dataset.shard(sdp.size()       ,	sdp.rank()						)
        UpperCamelCase__	     =      tf_test_dataset.shard(sdp.size()       ,	sdp.rank()						)
    UpperCamelCase__	     =      tf_train_dataset.batch(lowerCAmelCase_       ,	drop_remainder=lowerCAmelCase_						)
    UpperCamelCase__	     =      tf_test_dataset.batch(lowerCAmelCase_       ,	drop_remainder=lowerCAmelCase_						)

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
      lowercase__  :	Any            =      argparse.ArgumentParser()

      # Hyperparameters sent by the client are passed as command-line arguments to the script.
      parser.add_argument('--epochs', type=int, default=3)
      parser.add_argument('--per_device_train_batch_size', type=int, default=16)
      parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
      parser.add_argument('--model_name_or_path', type=str)
      parser.add_argument('--learning_rate', type=str, default=5e-5)
      parser.add_argument('--do_train', type=bool, default=True)
      parser.add_argument('--do_eval', type=bool, default=True)
      parser.add_argument('--output_dir', type=str)
      parser.add_argument('--max_steps', type=int, default=None)

      # Data, model, and output directories
      parser.add_argument('--output_data_dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
      parser.add_argument('--model_dir', type=str, default=os.environ['SM_MODEL_DIR'])
      parser.add_argument('--n_gpus', type=str, default=os.environ['SM_NUM_GPUS'])

      lowercase__       ,	lowercase__  :	Union[str, Any]            =      parser.parse_known_args()

      # Set up logging
      lowercase__  :	int            =      logging.getLogger(__name__)

      logging.basicConfig(
          level=logging.getLevelName('INFO'),
          handlers=[logging.StreamHandler(sys.stdout)],
          format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
      )

      if SDP_ENABLED:
            sdp.init()

            lowercase__  :	Union[str, Any]            =      tf.config.experimental.list_physical_devices('GPU')
            for gpu in gpus:
                  tf.config.experimental.set_memory_growth(gpu, True)
            if gpus:
                  tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], 'GPU')

    # Load model and tokenizer
      lowercase__  :	int            =      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      lowercase__  :	Any            =      AutoTokenizer.from_pretrained(args.model_name_or_path)

      # get datasets
      lowercase__       ,	lowercase__  :	Dict            =      get_datasets(
          tokenizer=tokenizer,
          train_batch_size=args.per_device_train_batch_size,
          eval_batch_size=args.per_device_eval_batch_size,
      )

      # fine optimizer and loss
      lowercase__  :	Any            =      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
      lowercase__  :	Tuple            =      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
      lowercase__  :	Tuple            =      [tf.keras.metrics.SparseCategoricalAccuracy()]
      model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

      # Training
      if args.do_train:
            # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
            lowercase__  :	List[str]            =      time.time()
            lowercase__  :	Union[str, Any]            =      fit(
                model,
                loss,
                optimizer,
                tf_train_dataset,
                args.epochs,
                args.per_device_train_batch_size,
                max_steps=args.max_steps,
            )
            lowercase__  :	Dict            =      time.time() - start_train_time
            logger.info('*** Train ***')
            logger.info(F'''train_runtime = {end_train_time}''')

            lowercase__  :	List[str]            =      os.path.join(args.output_dir, 'train_results.txt')

            if not SDP_ENABLED or sdp.rank() == 0:
                  with open(output_eval_file, 'w') as writer:
                        logger.info('***** Train results *****')
                        logger.info(train_results)
                        for key, value in train_results.items():
                              logger.info(F'''  {key} = {value}''')
                              writer.write(F'''{key} = {value}\n''')

    # Evaluation
      if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
            lowercase__  :	Tuple            =      model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
            logger.info('*** Evaluate ***')

            lowercase__  :	Any            =      os.path.join(args.output_dir, 'eval_results.txt')

            with open(output_eval_file, 'w') as writer:
                  logger.info('***** Eval results *****')
                  logger.info(result)
                  for key, value in result.items():
                        logger.info(F'''  {key} = {value}''')
                        writer.write(F'''{key} = {value}\n''')

    # Save result
      if SDP_ENABLED:
            if sdp.rank() == 0:
                  model.save_pretrained(args.output_dir)
                  tokenizer.save_pretrained(args.output_dir)
      else:
            model.save_pretrained(args.output_dir)
            tokenizer.save_pretrained(args.output_dir)




