






"""simple docstring"""


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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


lowercase_								=	logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      torch.FloatTensor





class    snake_case						(				_lowerCAmelCase  ,      _lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[Any]							=					1


   @register_to_config
   def __init__(      self					:	Tuple,			_lowerCamelCase					:	int = 40,			_lowerCamelCase					:	float = 0.0_02,			_lowerCamelCase					:	float = 80.0,			_lowerCamelCase					:	float = 0.5,			_lowerCamelCase					:	float = 1.0,			_lowerCamelCase					:	float = 7.0,			_lowerCamelCase					:	bool = True,			):

     '''simple docstring'''
     # standard deviation of the initial noise distribution
     __A		     =					sigma_max

     __A		     =					np.linspace(0,			1,			_lowerCamelCase    )
     __A		     =					self._convert_to_karras(_lowerCamelCase    )
     __A		     =					self.sigma_to_t(_lowerCamelCase    )

     # setable values
     __A		     =					None
     __A		     =					torch.from_numpy(_lowerCamelCase    )
     __A		     =					torch.from_numpy(_lowerCamelCase    )
     __A		     =					False
     __A		     =					False


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	int,			_lowerCamelCase					:	Optional[Any]=None    ):

     '''simple docstring'''
     if schedule_timesteps is None:
       __A		     =					self.timesteps

     __A		     =					(schedule_timesteps == timestep).nonzero()
     return indices.item()


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int],			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	Union[float, torch.FloatTensor]    ):

     '''simple docstring'''
     # Get sigma corresponding to timestep
     if isinstance(_lowerCamelCase,			torch.Tensor    ):
       __A		     =					timestep.to(self.timesteps.device    )
     __A		     =					self.index_for_timestep(_lowerCamelCase    )
     __A		     =					self.sigmas[step_idx]

     __A		     =					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

     __A		     =					True
     return sample


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Union[float, np.ndarray]    ):

     '''simple docstring'''
     if not isinstance(_lowerCamelCase,			np.ndarray    ):
       __A		     =					np.array(_lowerCamelCase,			dtype=np.floataa    )

     __A		     =					10_00 * 0.25 * np.log(sigmas + 1e-44    )

     return timesteps


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Optional[int] = None,			_lowerCamelCase					:	Union[str, torch.device] = None,			_lowerCamelCase					:	Optional[List[int]] = None,			):

     '''simple docstring'''
     if num_inference_steps is None and timesteps is None:
       raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''    )

     if num_inference_steps is not None and timesteps is not None:
       raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''    )

     # Follow DDPMScheduler custom timesteps logic
     if timesteps is not None:
       for i in range(1,			len(_lowerCamelCase    )    ):
         if timesteps[i] >= timesteps[i - 1]:
           raise ValueError('''`timesteps` must be in descending order.'''    )

       if timesteps[0] >= self.config.num_train_timesteps:
         raise ValueError(
             f'`timesteps` must start before `self.config.train_timesteps`:'
             f' {self.config.num_train_timesteps}.'    )

       __A		     =					np.array(_lowerCamelCase,			dtype=np.intaa    )
       __A		     =					True
     else:
       if num_inference_steps > self.config.num_train_timesteps:
         raise ValueError(
             f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
             f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
             f' maximal {self.config.num_train_timesteps} timesteps.'    )

       __A		     =					num_inference_steps

       __A		     =					self.config.num_train_timesteps // self.num_inference_steps
       __A		     =					(np.arange(0,			_lowerCamelCase    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
       __A		     =					False

     # Map timesteps to Karras sigmas directly for multistep sampling
     # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
     __A		     =					self.config.num_train_timesteps
     __A		     =					timesteps[::-1].copy()
     __A		     =					ramp / (num_train_timesteps - 1)
     __A		     =					self._convert_to_karras(_lowerCamelCase    )
     __A		     =					self.sigma_to_t(_lowerCamelCase    )

     __A		     =					np.concatenate([sigmas, [self.sigma_min]]    ).astype(np.floataa    )
     __A		     =					torch.from_numpy(_lowerCamelCase    ).to(device=_lowerCamelCase    )

     if str(_lowerCamelCase    ).startswith('''mps'''    ):
       # mps does not support float64
       __A		     =					torch.from_numpy(_lowerCamelCase    ).to(_lowerCamelCase,			dtype=torch.floataa    )
     else:
       __A		     =					torch.from_numpy(_lowerCamelCase    ).to(device=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	Any    ):

     '''simple docstring'''
     __A		     =					self.config.sigma_min
     __A		     =					self.config.sigma_max

     __A		     =					self.config.rho
     __A		     =					sigma_min ** (1 / rho)
     __A		     =					sigma_max ** (1 / rho)
     __A		     =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.config.sigma_data

     __A		     =					sigma_data**2 / (sigma**2 + sigma_data**2)
     __A		     =					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	Dict    ):

     '''simple docstring'''
     __A		     =					self.config.sigma_min
     __A		     =					self.config.sigma_data

     __A		     =					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
     __A		     =					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
     return c_skip, c_out


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	Union[float, torch.FloatTensor],			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	Optional[torch.Generator] = None,			_lowerCamelCase					:	bool = True,			):

     '''simple docstring'''

     if (
         isinstance(_lowerCamelCase,			_lowerCamelCase    )
         or isinstance(_lowerCamelCase,			torch.IntTensor    )
         or isinstance(_lowerCamelCase,			torch.LongTensor    )
     ):
       raise ValueError(
           (
               '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
               f' `{self.__class__}.step()` is not supported. Make sure to pass'
               ''' one of the `scheduler.timesteps` as a timestep.'''
           ),			)

     if not self.is_scale_input_called:
       logger.warning(
           '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
           '''See `StableDiffusionPipeline` for a usage example.'''    )

     if isinstance(_lowerCamelCase,			torch.Tensor    ):
       __A		     =					timestep.to(self.timesteps.device    )

     __A		     =					self.config.sigma_min
     __A		     =					self.config.sigma_max

     __A		     =					self.index_for_timestep(_lowerCamelCase    )

     # sigma_next corresponds to next_t in original implementation
     __A		     =					self.sigmas[step_index]
     if step_index + 1 < self.config.num_train_timesteps:
       __A		     =					self.sigmas[step_index + 1]
     else:
       # Set sigma_next to sigma_min
       __A		     =					self.sigmas[-1]

     # Get scalings for boundary conditions
     __A     ,       __A		     =					self.get_scalings_for_boundary_condition(_lowerCamelCase    )

     # 1. Denoise model output using boundary conditions
     __A		     =					c_out * model_output + c_skip * sample
     if self.config.clip_denoised:
       __A		     =					denoised.clamp(-1,			1    )

     # 2. Sample z ~ N(0, s_noise^2 * I)
     # Noise is not used for onestep sampling.
     if len(self.timesteps    ) > 1:
       __A		     =					randn_tensor(
           model_output.shape,			dtype=model_output.dtype,			device=model_output.device,			generator=_lowerCamelCase    )
     else:
       __A		     =					torch.zeros_like(_lowerCamelCase    )
     __A		     =					noise * self.config.s_noise

     __A		     =					sigma_next.clamp(min=_lowerCamelCase,			max=_lowerCamelCase    )

     # 3. Return noisy sample
     # tau = sigma_hat, eps = sigma_min
     __A		     =					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

     if not return_dict:
       return (prev_sample,)

     return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any,			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	torch.FloatTensor,			_lowerCamelCase					:	torch.FloatTensor,			):

     '''simple docstring'''
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     __A		     =					self.sigmas.to(device=original_samples.device,			dtype=original_samples.dtype    )
     if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase    ):
       # mps does not support float64
       __A		     =					self.timesteps.to(original_samples.device,			dtype=torch.floataa    )
       __A		     =					timesteps.to(original_samples.device,			dtype=torch.floataa    )
     else:
       __A		     =					self.timesteps.to(original_samples.device    )
       __A		     =					timesteps.to(original_samples.device    )

     __A		     =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

     __A		     =					sigmas[step_indices].flatten()
     while len(sigma.shape    ) < len(original_samples.shape    ):
       __A		     =					sigma.unsqueeze(-1    )

     __A		     =					original_samples + noise * sigma
     return noisy_samples






   def __len__(      self					:	Any    ):

     '''simple docstring'''
     return self.config.num_train_timesteps









'''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)




