

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():
 SCREAMING_SNAKE_CASE						:List[Any]      =			True
 SCREAMING_SNAKE_CASE						:Optional[Any]      =			"""p3dn.24xlarge"""
 import smdistributed.dataparallel.tensorflow as sdp
else:
 SCREAMING_SNAKE_CASE						:str      =			False




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_=None       )->		List[Any]:



    """simple docstring"""






    UpperCamelCase_          =		tqdm(lowercase_       )
    for i, batch in enumerate(lowercase_       ):
        with tf.GradientTape() as tape:
            UpperCamelCase_   ,    UpperCamelCase_          =		batch
            UpperCamelCase_          =		model(lowercase_       )
            UpperCamelCase_          =		loss(lowercase_       ,		outputs.logits       )

        if SDP_ENABLED:
            UpperCamelCase_          =		sdp.DistributedGradientTape(lowercase_       ,		sparse_as_dense=lowercase_       )

        UpperCamelCase_          =		tape.gradient(lowercase_       ,		model.trainable_variables       )
        opt.apply_gradients(zip(lowercase_       ,		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        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		str:



    """simple docstring"""






    UpperCamelCase_   ,    UpperCamelCase_          =		load_dataset("imdb"       ,		split=["train", "test"]       )

    # Preprocess train dataset
    UpperCamelCase_          =		train_dataset.map(
        lambda SCREAMING_SNAKE_CASE_       : tokenizer(e["text"]       ,		truncation=lowercase_       ,		padding="max_length"       )       ,		batched=lowercase_       )
    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 SCREAMING_SNAKE_CASE_       : tokenizer(e["text"]       ,		truncation=lowercase_       ,		padding="max_length"       )       ,		batched=lowercase_       )
    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(lowercase_       ,		drop_remainder=lowercase_       )
    UpperCamelCase_          =		tf_test_dataset.batch(lowercase_       ,		drop_remainder=lowercase_       )

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE						:int      =			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"""])

 SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE						:int      =			parser.parse_known_args()

 # Set up logging
 SCREAMING_SNAKE_CASE						:Tuple      =			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()

  SCREAMING_SNAKE_CASE						:List[str]      =			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
 SCREAMING_SNAKE_CASE						:Tuple      =			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 SCREAMING_SNAKE_CASE						:Union[str, Any]      =			AutoTokenizer.from_pretrained(args.model_name_or_path)

 # get datasets
 SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE						:List[Any]      =			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
 SCREAMING_SNAKE_CASE						:int      =			tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
 SCREAMING_SNAKE_CASE						:List[str]      =			tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 SCREAMING_SNAKE_CASE						:Optional[int]      =			[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)
  SCREAMING_SNAKE_CASE						:int      =			time.time()
  SCREAMING_SNAKE_CASE						:Union[str, Any]      =			fit(
      model,
      loss,
      optimizer,
      tf_train_dataset,
      args.epochs,
      args.per_device_train_batch_size,
      max_steps=args.max_steps,
  )
  SCREAMING_SNAKE_CASE						:Optional[Any]      =			time.time() - start_train_time
  logger.info("""*** Train ***""")
  logger.info(F'''train_runtime = {end_train_time}''')

  SCREAMING_SNAKE_CASE						:List[Any]      =			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):
  SCREAMING_SNAKE_CASE						:Union[str, Any]      =			model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  logger.info("""*** Evaluate ***""")

  SCREAMING_SNAKE_CASE						:Optional[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)



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					(DDIMScheduler,)
      lowerCamelCase_	:      Union[str, Any]          =					(('eta', 0.0), ('num_inference_steps', 50))







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       Dict:
        snake_case_       						=      {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase   )
        return config







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       List[str]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(**lowerCamelCase   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        snake_case_						,				snake_case_       						=      10, 0.0

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase   )

        for t in scheduler.timesteps:
          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   ).prev_sample

        return sample







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowerCamelCase   )

        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(steps_offset=1   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )
        scheduler.set_timesteps(5   )
        assert torch.equal(scheduler.timesteps							,  torch.LongTensor([801, 601, 401, 201, 1]   )   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,  [0.002, 0.02, 0.2, 2]   ):
          self.check_over_configs(beta_start=lowerCamelCase							,  beta_end=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        self.check_over_configs(thresholding=lowerCamelCase   )
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=lowerCamelCase							,  prediction_type=lowerCamelCase							,  sample_max_value=lowerCamelCase							,  )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for t, num_inference_steps in zip([1, 10, 50]							,  [10, 50, 500]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  num_inference_steps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for t, eta in zip([1, 10, 49]							,  [0.0, 0.5, 1.0]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  eta=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420							,  400   ) - 0.1_4771   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980							,  960   ) - 0.3_2460   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487							,  486   ) - 0.0_0979   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999							,  998   ) - 0.02   )   ) < 1e-5







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.full_loop()

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 172.0067   ) < 1e-2
        assert abs(result_mean.item() - 0.22_3967   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.full_loop(prediction_type="""v_prediction"""   )

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 52.5302   ) < 1e-2
        assert abs(result_mean.item() - 0.0684   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.8295   ) < 1e-2
        assert abs(result_mean.item() - 0.1951   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.0784   ) < 1e-2
        assert abs(result_mean.item() - 0.1941   ) < 1e-3