


'''simple docstring'''






import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
  from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
  from tensorflow.keras.optimizers import Adam






class        __snake_case	(       tf.keras.optimizers.schedules.LearningRateSchedule):







    """simple docstring"""



    def __init__(       self   :  List[str]   ,						lowerCamelCase   :  float   ,						lowerCamelCase   :  Callable   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  float = 1.0   ,						lowerCamelCase   :  str = None   ,						)       ->  Union[str, Any]:
      super().__init__()
      lowerCAmelCase_      :				Dict      =       initial_learning_rate
      lowerCAmelCase_      :				Tuple      =       warmup_steps
      lowerCAmelCase_      :				Tuple      =       power
      lowerCAmelCase_      :				Any      =       decay_schedule_fn
      lowerCAmelCase_      :				List[str]      =       name

    def __call__(       self   :  Optional[int]   ,						lowerCamelCase   :  Any		)       ->  Union[str, Any]:
      with tf.name_scope(self.name or """WarmUp"""		) as name:
        # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
        # learning rate will be `global_step/num_warmup_steps * init_lr`.
        lowerCAmelCase_      :				Tuple      =       tf.cast(lowerCamelCase   ,						tf.floataa		)
        lowerCAmelCase_      :				int      =       tf.cast(self.warmup_steps   ,						tf.floataa		)
        lowerCAmelCase_      :				Optional[Any]      =       global_step_float / warmup_steps_float
        lowerCAmelCase_      :				Optional[int]      =       self.initial_learning_rate * tf.math.pow(lowerCamelCase   ,						self.power		)
        return tf.cond(
            global_step_float < warmup_steps_float   ,						lambda: warmup_learning_rate   ,						lambda: self.decay_schedule_fn(step - self.warmup_steps		)   ,						name=lowerCamelCase   ,						)





    def __lowercase       (       self   :  List[str]		)       ->  str:
      return {
          "initial_learning_rate": self.initial_learning_rate,
          "decay_schedule_fn": self.decay_schedule_fn,
          "warmup_steps": self.warmup_steps,
          "power": self.power,
          "name": self.name,
      }







def     UpperCamelCase_      (   A__ :       float							,   A__ :       int							,   A__ :       int							,   A__ :       float = 0.0							,   A__ :       float = 0.9							,   A__ :       float = 0.999							,   A__ :       float = 1E-8							,   A__ :       Optional[float] = None							,   A__ :       Optional[float] = None							,   A__ :       float = 0.0							,   A__ :       float = 1.0							,   A__ :       Optional[List[str]] = None							,   ):
  '''simple docstring'''





  lowerCAmelCase_      :				List[Any]      =       tf.keras.optimizers.schedules.PolynomialDecay(
      initial_learning_rate=A__							,   decay_steps=num_train_steps - num_warmup_steps							,   end_learning_rate=init_lr * min_lr_ratio							,   power=A__							,   )
  if num_warmup_steps:
    lowerCAmelCase_      :				Union[str, Any]      =       WarmUp(
        initial_learning_rate=A__							,   decay_schedule_fn=A__							,   warmup_steps=A__							,   )
  if weight_decay_rate > 0.0:
    lowerCAmelCase_      :				List[str]      =       AdamWeightDecay(
        learning_rate=A__							,   weight_decay_rate=A__							,   beta_a=A__							,   beta_a=A__							,   epsilon=A__							,   clipnorm=A__							,   global_clipnorm=A__							,   exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]							,   include_in_weight_decay=A__							,   )
  else:
    lowerCAmelCase_      :				Optional[int]      =       tf.keras.optimizers.Adam(
        learning_rate=A__							,   beta_a=A__							,   beta_a=A__							,   epsilon=A__							,   clipnorm=A__							,   global_clipnorm=A__							,   )
  # We return the optimizer and the LR scheduler in order to better track the
  # evolution of the LR independently of the optimizer.
  return optimizer, lr_schedule






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(       self   :  str   ,						lowerCamelCase   :  Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001   ,						lowerCamelCase   :  float = 0.9   ,						lowerCamelCase   :  float = 0.999   ,						lowerCamelCase   :  float = 1E-7   ,						lowerCamelCase   :  bool = False   ,						lowerCamelCase   :  float = 0.0   ,						lowerCamelCase   :  Optional[List[str]] = None   ,						lowerCamelCase   :  Optional[List[str]] = None   ,						lowerCamelCase   :  str = "AdamWeightDecay"   ,						**lowerCamelCase   :  Union[str, Any]   ,						)       ->  Optional[Any]:
      super().__init__(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase   ,						**lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       weight_decay_rate
      lowerCAmelCase_      :				Union[str, Any]      =       include_in_weight_decay
      lowerCAmelCase_      :				str      =       exclude_from_weight_decay

    @classmethod
    def __lowercase       (       cls   :  Optional[Any]   ,						lowerCamelCase   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				List[Any]      =       {"""WarmUp""": WarmUp}
      return super(lowerCamelCase   ,						cls		).from_config(lowerCamelCase   ,						custom_objects=lowerCamelCase		)

    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  int		)       ->  List[str]:
      super(lowerCamelCase   ,						self		)._prepare_local(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       tf.constant(
          self.weight_decay_rate   ,						name="""adam_weight_decay_rate"""		)

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  Tuple:
      lowerCAmelCase_      :				Union[str, Any]      =       self._do_use_weight_decay(var.name		)
      if do_decay:
        return var.assign_sub(
            learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["""weight_decay_rate"""]   ,						use_locking=self._use_locking   ,						)
      return tf.no_op()

    def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Tuple=None   ,						**lowerCamelCase   :  List[Any]		)       ->  List[str]:
      lowerCAmelCase_,							lowerCAmelCase_      :				Dict      =       list(zip(*lowerCamelCase		)		)
      return super(lowerCamelCase   ,						self		).apply_gradients(zip(lowerCamelCase   ,						lowerCamelCase		)   ,						name=lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Dict		)       ->  List[Any]:
      if apply_state is None:
        return self._decayed_lr_t[var_dtype], {}

      lowerCAmelCase_      :				int      =       apply_state or {}
      lowerCAmelCase_      :				Any      =       apply_state.get((var_device, var_dtype)		)
      if coefficients is None:
        lowerCAmelCase_      :				Dict      =       self._fallback_apply_state(lowerCamelCase   ,						lowerCamelCase		)
        lowerCAmelCase_      :				Union[str, Any]      =       coefficients

      return coefficients["lr_t"], {"apply_state": apply_state}

    def __lowercase       (       self   :  List[Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  Optional[int]=None		)       ->  str:
      lowerCAmelCase_,							lowerCAmelCase_      :				Tuple      =       self._get_lr(var.device   ,						var.dtype.base_dtype   ,						lowerCamelCase		)
      lowerCAmelCase_      :				str      =       self._decay_weights_op(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)
      with tf.control_dependencies([decay]		):
        return super(lowerCamelCase   ,						self		)._resource_apply_dense(lowerCamelCase   ,						lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  int   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Any=None		)       ->  List[Any]:
      lowerCAmelCase_,							lowerCAmelCase_      :				Tuple      =       self._get_lr(var.device   ,						var.dtype.base_dtype   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Optional[Any]      =       self._decay_weights_op(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)
      with tf.control_dependencies([decay]		):
        return super(lowerCamelCase   ,						self		)._resource_apply_sparse(lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase   ,						**lowerCamelCase		)

    def __lowercase       (       self   :  Tuple		)       ->  int:
      lowerCAmelCase_      :				Any      =       super().get_config()
      config.update({"""weight_decay_rate""": self.weight_decay_rate}		)
      return config





    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  Dict		)       ->  int:
      if self.weight_decay_rate == 0:
        return False

      if self._include_in_weight_decay:
        for r in self._include_in_weight_decay:
          if re.search(lowerCamelCase   ,						lowerCamelCase		) is not None:
            return True

      if self._exclude_from_weight_decay:
        for r in self._exclude_from_weight_decay:
          if re.search(lowerCamelCase   ,						lowerCamelCase		) is not None:
            return False
      return True







class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(       self   :  Any		)       ->  Tuple:
      lowerCAmelCase_      :				List[str]      =       []
      lowerCAmelCase_      :				Tuple      =       None

    @property
    def __lowercase       (       self   :  int		)       ->  List[Any]:
      if self._accum_steps is None:
        lowerCAmelCase_      :				Optional[Any]      =       tf.Variable(
            tf.constant(0   ,						dtype=tf.intaa		)   ,						trainable=lowerCamelCase   ,						synchronization=tf.VariableSynchronization.ON_READ   ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA   ,						)

      return self._accum_steps.value()

    @property
    def __lowercase       (       self   :  List[str]		)       ->  Optional[int]:
      if not self._gradients:
        raise ValueError("""The accumulator should be called first to initialize the gradients"""		)
      return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]

    def __call__(       self   :  Optional[Any]   ,						lowerCamelCase   :  List[Any]		)       ->  str:
      if not self._gradients:
        lowerCAmelCase_      :				Union[str, Any]      =       self.step  # Create the step variable.
        self._gradients.extend(
            [
                tf.Variable(
                    tf.zeros_like(lowerCamelCase		)   ,						trainable=lowerCamelCase   ,						synchronization=tf.VariableSynchronization.ON_READ   ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA   ,						)
                if gradient is not None
                else gradient
                for gradient in gradients
            ]		)
      if len(lowerCamelCase		) != len(self._gradients		):
        raise ValueError(F'Expected {len(self._gradients		)} gradients, but got {len(lowerCamelCase		)}'		)

      for accum_gradient, gradient in zip(self._gradients   ,						lowerCamelCase		):
        if accum_gradient is not None and gradient is not None:
          accum_gradient.assign_add(lowerCamelCase		)

      self._accum_steps.assign_add(1		)





    def __lowercase       (       self   :  Dict		)       ->  List[str]:
      if not self._gradients:
        return
      self._accum_steps.assign(0		)
      for gradient in self._gradients:
        if gradient is not None:
          gradient.assign(tf.zeros_like(lowerCamelCase		)		)







'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @property
    def __lowercase       (       self   :  str		)       ->  List[Any]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[Any]      =       UNetaDModel(
          block_out_channels=(32, 64)   ,						layers_per_block=2   ,						sample_size=32   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")   ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")   ,						)
      return model





    def __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       self.dummy_uncond_unet
      lowerCAmelCase_      :				Tuple      =       PNDMScheduler()

      lowerCAmelCase_      :				List[Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Dict      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				List[Any]      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				str      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				int      =       pndm(generator=lowerCamelCase   ,						num_inference_steps=20   ,						output_type="""numpy"""   ,						return_dict=lowerCamelCase		)[0]

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]
      lowerCAmelCase_      :				Optional[Any]      =       image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







@slow
@require_torch
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				str      =       """google/ddpm-cifar10-32"""

      lowerCAmelCase_      :				Dict      =       UNetaDModel.from_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       PNDMScheduler()

      lowerCAmelCase_      :				Union[str, Any]      =       PNDMPipeline(unet=lowerCamelCase   ,						scheduler=lowerCamelCase		)
      pndm.to(lowerCamelCase		)
      pndm.set_progress_bar_config(disable=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, Any]      =       pndm(generator=lowerCamelCase   ,						output_type="""numpy"""		).images

      lowerCAmelCase_      :				Optional[int]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				List[Any]      =       np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]		)

      assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2



