'''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():
 snake_case__	:     str      =    True
 snake_case__	:     List[Any]      =    '''p3dn.24xlarge'''
 import smdistributed.dataparallel.tensorflow as sdp
else:
 snake_case__	:     Union[str, Any]      =    False


def    _lowerCamelCase       ( lowerCamelCase_   :				int      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				Optional[int]=None  ):



     """simple docstring"""





     UpperCAmelCase_				:  Dict  = tqdm(lowercase_  )
     for i, batch in enumerate(lowercase_  ):
          with tf.GradientTape() as tape:
               UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = batch
               UpperCAmelCase_				:  Tuple  = model(lowercase_  )
               UpperCAmelCase_				:  Optional[int]  = loss(lowercase_      ,	outputs.logits  )

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

          UpperCAmelCase_				:  Dict  = 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_				:  Dict  = {'loss': loss_value.numpy()}
     return train_results


def    _lowerCamelCase       ( lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				str      ,	lowerCamelCase_   :				Tuple  ):



     """simple docstring"""





     UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[int]  = load_dataset('imdb'      ,	split=['train', 'test']  )

     # Preprocess train dataset
     UpperCAmelCase_				:  List[str]  = train_dataset.map(
         lambda lowerCamelCase_  : tokenizer(e['text']      ,	truncation=lowercase_      ,	padding='max_length'  )      ,	batched=lowercase_  )
     train_dataset.set_format(type='tensorflow'      ,	columns=['input_ids', 'attention_mask', 'label']  )

     UpperCAmelCase_				:  Union[str, Any]  = {
         x: train_dataset[x].to_tensor(default_value=0      ,	shape=[None, tokenizer.model_max_length]  )
         for x in ['input_ids', 'attention_mask']
     }
     UpperCAmelCase_				:  int  = tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])  )

     # Preprocess test dataset
     UpperCAmelCase_				:  Optional[Any]  = test_dataset.map(
         lambda lowerCamelCase_  : tokenizer(e['text']      ,	truncation=lowercase_      ,	padding='max_length'  )      ,	batched=lowercase_  )
     test_dataset.set_format(type='tensorflow'      ,	columns=['input_ids', 'attention_mask', 'label']  )

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

     if SDP_ENABLED:
          UpperCAmelCase_				:  List[Any]  = tf_train_dataset.shard(sdp.size()      ,	sdp.rank()  )
          UpperCAmelCase_				:  Dict  = tf_test_dataset.shard(sdp.size()      ,	sdp.rank()  )
     UpperCAmelCase_				:  int  = tf_train_dataset.batch(lowercase_      ,	drop_remainder=lowercase_  )
     UpperCAmelCase_				:  List[str]  = tf_test_dataset.batch(lowercase_      ,	drop_remainder=lowercase_  )

     return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
 snake_case__	:     List[str]      =    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'''])

 snake_case__     , snake_case__	:     int      =    parser.parse_known_args()

 # Set up logging
 snake_case__	:     List[Any]      =    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()

  snake_case__	:     Tuple      =    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
 snake_case__	:     str      =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 snake_case__	:     Any      =    AutoTokenizer.from_pretrained(args.model_name_or_path)

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

  snake_case__	:     int      =    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):
  snake_case__	:     Tuple      =    model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  logger.info('''*** Evaluate ***''')

  snake_case__	:     List[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)












from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)

@flax.struct.dataclass
class 			_lowerCamelCase    :

      """simple docstring"""

      lowerCAmelCase__      :			CommonSchedulerState

      # setable values
      lowerCAmelCase__      :			jnp.ndarray
      lowerCAmelCase__      :			jnp.ndarray
      lowerCAmelCase__      :			jnp.ndarray
      lowerCAmelCase__      :			Optional[int]     =       None

      # running values
      lowerCAmelCase__      :			Optional[jnp.ndarray]     =       None







      @classmethod
      def    snake_case						(  cls       :			str			,							snake_case       :			CommonSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray					):
          return cls(common=snake_case			,							init_noise_sigma=snake_case			,							timesteps=snake_case			,							sigmas=snake_case					)







@dataclass
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			LMSDiscreteSchedulerState







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			List[Any]     =       [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCAmelCase__      :			jnp.dtype







      @property
      def    snake_case						(  self       :			Any					):
          return True







      @register_to_config
      def __init__(  self       :			Union[str, Any]			,							snake_case       :			int = 1000			,							snake_case       :			float = 0.00_01			,							snake_case       :			float = 0.02			,							snake_case       :			str = "linear"			,							snake_case       :			Optional[jnp.ndarray] = None			,							snake_case       :			str = "epsilon"			,							snake_case       :			jnp.dtype = jnp.floataa			,							):
          __UpperCamelCase			=    dtype







      def    snake_case						(  self       :			int			,							snake_case       :			Optional[CommonSchedulerState] = None					):
          if common is None:
              __UpperCamelCase			=    CommonSchedulerState.create(self					)

          __UpperCamelCase			=    jnp.arange(0			,							self.config.num_train_timesteps					).round()[::-1]
          __UpperCamelCase			=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

          # standard deviation of the initial noise distribution
          __UpperCamelCase			=    sigmas.max()

          return LMSDiscreteSchedulerState.create(
              common=snake_case			,							init_noise_sigma=snake_case			,							timesteps=snake_case			,							sigmas=snake_case			,							)







      def    snake_case						(  self       :			str			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			int					):
          ((__UpperCamelCase)		,   )			=    jnp.where(state.timesteps == timestep			,							size=1					)
          __UpperCamelCase			=    step_index[0]

          __UpperCamelCase			=    state.sigmas[step_index]
          __UpperCamelCase			=    sample / ((sigma**2 + 1) ** 0.5)
          return sample







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			str			,							snake_case       :			Tuple			,							snake_case       :			int					):

          def lms_derivative(snake_case       :			int					):
              __UpperCamelCase			=    1.0
              for k in range(snake_case					):
                  if current_order == k:
                      continue
                  prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
              return prod

          __UpperCamelCase			=    integrate.quad(snake_case			,							state.sigmas[t]			,							state.sigmas[t + 1]			,							epsrel=1E-4					)[0]

          return integrated_coeff







      def    snake_case						(  self       :			int			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			int			,							snake_case       :			Tuple = ()					):
          __UpperCamelCase			=    jnp.linspace(self.config.num_train_timesteps - 1			,							0			,							snake_case			,							dtype=self.dtype					)

          __UpperCamelCase			=    jnp.floor(snake_case					).astype(jnp.intaa					)
          __UpperCamelCase			=    jnp.ceil(snake_case					).astype(jnp.intaa					)

          __UpperCamelCase			=    jnp.mod(snake_case			,							1.0					)

          __UpperCamelCase			=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
          __UpperCamelCase			=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
          __UpperCamelCase			=    jnp.concatenate([sigmas, jnp.array([0.0]			,							dtype=self.dtype					)]					)

          __UpperCamelCase			=    timesteps.astype(jnp.intaa					)

          # initial running values
          __UpperCamelCase			=    jnp.zeros((0,) + shape			,							dtype=self.dtype					)

          return state.replace(
              timesteps=snake_case			,							sigmas=snake_case			,							num_inference_steps=snake_case			,							derivatives=snake_case			,							)







      def    snake_case						(  self       :			Tuple			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			int			,							snake_case       :			jnp.ndarray			,							snake_case       :			int = 4			,							snake_case       :			bool = True			,							):
          if state.num_inference_steps is None:
              raise ValueError(
                  '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''					)

          __UpperCamelCase			=    state.sigmas[timestep]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              __UpperCamelCase			=    sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              __UpperCamelCase			=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          else:
              raise ValueError(
                  F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"					)

          # 2. Convert to an ODE derivative
          __UpperCamelCase			=    (sample - pred_original_sample) / sigma
          __UpperCamelCase			=    state.replace(derivatives=jnp.append(state.derivatives			,							snake_case					)					)
          if len(state.derivatives					) > order:
              __UpperCamelCase			=    state.replace(derivatives=jnp.delete(state.derivatives			,							0					)					)

          # 3. Compute linear multistep coefficients
          __UpperCamelCase			=    min(timestep + 1			,							snake_case					)
          __UpperCamelCase			=    [self.get_lms_coefficient(snake_case			,							snake_case			,							snake_case			,							snake_case					) for curr_order in range(snake_case					)]

          # 4. Compute previous sample based on the derivatives path
          __UpperCamelCase			=    sample + sum(
              coeff * derivative for coeff, derivative in zip(snake_case			,							reversed(state.derivatives					)					)					)

          if not return_dict:
              return (prev_sample, state)

          return FlaxLMSSchedulerOutput(prev_sample=snake_case			,							state=snake_case					)







      def    snake_case						(  self       :			Dict			,							snake_case       :			LMSDiscreteSchedulerState			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray			,							snake_case       :			jnp.ndarray			,							):
          __UpperCamelCase			=    state.sigmas[timesteps].flatten()
          __UpperCamelCase			=    broadcast_to_shape_from_left(snake_case			,							noise.shape					)

          __UpperCamelCase			=    original_samples + noise * sigma

          return noisy_samples







      def __len__(  self       :			Optional[Any]					):
          return self.config.num_train_timesteps






