




import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


a_ 		=  logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf

@dataclass
class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			List[str]     =       "tf"
      lowerCAmelCase__      :			Optional[str]     =       field(
          default=_SCREAMING_SNAKE_CASE				,      metadata={"help": "Name of TPU"}				,      )

      lowerCAmelCase__      :			Optional[str]     =       field(
          default=_SCREAMING_SNAKE_CASE				,      metadata={"help": "Zone of TPU"}				,      )

      lowerCAmelCase__      :			Optional[str]     =       field(
          default=_SCREAMING_SNAKE_CASE				,      metadata={"help": "Name of Cloud TPU-enabled project"}				,      )

      lowerCAmelCase__      :			float     =       field(
          default=1.0				,      metadata={"help": "Power for the Polynomial decay LR scheduler."}				,      )

      lowerCAmelCase__      :			bool     =       field(default=_SCREAMING_SNAKE_CASE				,      metadata={"help": "Whether to activate the XLA compilation or not"} )







      @cached_property
      def    snake_case						(  self       :			int					):
          requires_backends(self			,							['''tf''']					)
          logger.info('''Tensorflow: setting up strategy'''					)

          __UpperCamelCase			=    tf.config.list_physical_devices('''GPU'''					)

          # Set to float16 at first
          if self.fpaa:
              tf.keras.mixed_precision.set_global_policy('''mixed_float16'''					)

          if self.no_cuda:
              __UpperCamelCase			=    tf.distribute.OneDeviceStrategy(device='''/cpu:0'''					)
          else:
              try:
                  if self.tpu_name:
                      __UpperCamelCase			=    tf.distribute.cluster_resolver.TPUClusterResolver(
                          self.tpu_name			,							zone=self.tpu_zone			,							project=self.gcp_project					)
                  else:
                      __UpperCamelCase			=    tf.distribute.cluster_resolver.TPUClusterResolver()
              except ValueError:
                  if self.tpu_name:
                      raise RuntimeError(F"Couldn't connect to TPU {self.tpu_name}!"					)
                  else:
                      __UpperCamelCase			=    None

              if tpu:
                  # Set to bfloat16 in case of TPU
                  if self.fpaa:
                      tf.keras.mixed_precision.set_global_policy('''mixed_bfloat16'''					)

                  tf.config.experimental_connect_to_cluster(snake_case					)
                  tf.tpu.experimental.initialize_tpu_system(snake_case					)

                  __UpperCamelCase			=    tf.distribute.TPUStrategy(snake_case					)

              elif len(snake_case					) == 0:
                  __UpperCamelCase			=    tf.distribute.OneDeviceStrategy(device='''/cpu:0'''					)
              elif len(snake_case					) == 1:
                  __UpperCamelCase			=    tf.distribute.OneDeviceStrategy(device='''/gpu:0'''					)
              elif len(snake_case					) > 1:
                  # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                  __UpperCamelCase			=    tf.distribute.MirroredStrategy()
              else:
                  raise ValueError('''Cannot find the proper strategy, please check your environment properties.'''					)

          return strategy







      @property
      def    snake_case						(  self       :			Any					):
          requires_backends(self			,							['''tf''']					)
          return self._setup_strategy







      @property
      def    snake_case						(  self       :			Union[str, Any]					):
          requires_backends(self			,							['''tf''']					)
          return self._setup_strategy.num_replicas_in_sync







      @property
      def    snake_case						(  self       :			Tuple					):
          return False  # TF Logging is handled by Keras not the Trainer







      @property
      def    snake_case						(  self       :			List[Any]					):
          if self.per_gpu_train_batch_size:
              logger.warning(
                  '''Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '''
                  '''version. Using `--per_device_train_batch_size` is preferred.'''					)
          __UpperCamelCase			=    self.per_gpu_train_batch_size or self.per_device_train_batch_size
          return per_device_batch_size * self.n_replicas







      @property
      def    snake_case						(  self       :			str					):
          if self.per_gpu_eval_batch_size:
              logger.warning(
                  '''Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '''
                  '''version. Using `--per_device_eval_batch_size` is preferred.'''					)
          __UpperCamelCase			=    self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
          return per_device_batch_size * self.n_replicas







      @property
      def    snake_case						(  self       :			List[Any]					):
          requires_backends(self			,							['''tf''']					)
          warnings.warn(
              '''The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'''			,							snake_case			,							)
          return self._setup_strategy.num_replicas_in_sync












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






