


import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
      _snake_case   = argparse.ArgumentParser()

      # Hyperparameters sent by the client are passed as command-line arguments to the script.
      parser.add_argument("--epochs", type=int, default=1)
      parser.add_argument("--per_device_train_batch_size", type=int, default=1_6)
      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)

      _snake_case ,						_snake_case   = parser.parse_known_args()

      # overwrite batch size until we have tf_glue.py
      _snake_case   = 1_6
      _snake_case   = 1_6

      # Set up logging
      _snake_case   = logging.getLogger(__name__)

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

      # Load model and tokenizer
      _snake_case   = TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      _snake_case   = AutoTokenizer.from_pretrained(args.model_name_or_path)

      # Load dataset
      _snake_case ,						_snake_case   = load_dataset("imdb", split=["train", "test"])
      _snake_case   = train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
      _snake_case   = test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

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

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

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

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

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

      _snake_case   = time.time()
      _snake_case   = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
      _snake_case   = time.time() - start_train_time

      logger.info("*** Train ***")
      logger.info(f'''train_runtime = {end_train_time}''')
      for key, value in train_results.history.items():
            logger.info(f'''  {key} = {value}''')







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class    A_   :







 """simple docstring"""





 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 @classmethod
 def   _UpperCamelCase    (			cls					: Union[str, Any]				)    ->			int:
      return cls()




@dataclass
class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: ScoreSdeVeSchedulerState
 _UpperCamelCase				: jnp.ndarray
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None




class    A_   ( a_      ,						a_     ):







 """simple docstring"""





 @property
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			List[str]:
      return True
 @register_to_config
 def __init__(			self					: List[str]       ,    _a					: int = 2_0_0_0       ,    _a					: float = 0.1_5       ,    _a					: float = 0.0_1       ,    _a					: float = 1_3_4_8.0       ,    _a					: float = 1E-5       ,    _a					: int = 1       ,    )    ->			List[Any]:
      pass
 def   _UpperCamelCase    (			self					: int				)    ->			List[Any]:
      UpperCAmelCase						:				str		  =			ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          _a       ,    self.config.num_train_timesteps       ,    self.config.sigma_min       ,    self.config.sigma_max       ,    self.config.sampling_eps       ,    )
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: Tuple = ()       ,    _a					: float = None				)    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				Tuple		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

      UpperCAmelCase						:				Dict		  =			jnp.linspace(1       ,    _a       ,    _a				)
      return state.replace(timesteps=_a				)
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: float = None       ,    _a					: float = None       ,    _a					: float = None       ,    )    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				str		  =			sigma_min if sigma_min is not None else self.config.sigma_min
      UpperCAmelCase						:				Any		  =			sigma_max if sigma_max is not None else self.config.sigma_max
      UpperCAmelCase						:				Any		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
           UpperCAmelCase						:				List[Any]		  =			self.set_timesteps(_a       ,    _a       ,    _a				)

      UpperCAmelCase						:				List[Any]		  =			jnp.exp(jnp.linspace(jnp.log(_a				)       ,    jnp.log(_a				)       ,    _a				)				)
      UpperCAmelCase						:				List[str]		  =			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]				)

      return state.replace(discrete_sigmas=_a       ,    sigmas=_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: Optional[int]       ,    _a					: Optional[int]       ,    _a					: Union[str, Any]				)    ->			List[str]:
      return jnp.where(timesteps == 0       ,    jnp.zeros_like(_a				)       ,    state.discrete_sigmas[timesteps - 1]				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: int       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

      UpperCAmelCase						:				Union[str, Any]		  =			timestep * jnp.ones(
          sample.shape[0]       ,    )
      UpperCAmelCase						:				int		  =			(timestep * (len(state.timesteps				) - 1)).long()

      UpperCAmelCase						:				List[str]		  =			state.discrete_sigmas[timesteps]
      UpperCAmelCase						:				Optional[int]		  =			self.get_adjacent_sigma(_a       ,    _a       ,    _a				)
      UpperCAmelCase						:				Tuple		  =			jnp.zeros_like(_a				)
      UpperCAmelCase						:				List[Any]		  =			(sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      UpperCAmelCase						:				Union[str, Any]		  =			diffusion.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				Tuple		  =			drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      UpperCAmelCase						:				List[str]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)
      UpperCAmelCase						:				Any		  =			sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      UpperCAmelCase						:				str		  =			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
           return (prev_sample, prev_sample_mean, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    prev_sample_mean=_a       ,    state=_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      UpperCAmelCase						:				Optional[int]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)

      # compute step size from the model_output, the noise, and the snr
      UpperCAmelCase						:				str		  =			jnp.linalg.norm(_a				)
      UpperCAmelCase						:				Optional[int]		  =			jnp.linalg.norm(_a				)
      UpperCAmelCase						:				int		  =			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      UpperCAmelCase						:				List[Any]		  =			step_size * jnp.ones(sample.shape[0]				)

      # compute corrected sample: model_output term and noise term
      UpperCAmelCase						:				Optional[Any]		  =			step_size.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				int		  =			sample + step_size * model_output
      UpperCAmelCase						:				int		  =			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
           return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    state=_a				)

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



