

import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      def get_masked_lm_array(a  ):
            SCREAMING_SNAKE_CASE_				:  Dict           =     f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tf.train.load_variable(a					,   a  )

            if "kernel" in name:
                  SCREAMING_SNAKE_CASE_				:  Tuple           =     array.transpose()

            return torch.from_numpy(a  )

      def get_encoder_array(a  ):
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tf.train.load_variable(a					,   a  )

            if "kernel" in name:
                  SCREAMING_SNAKE_CASE_				:  List[Any]           =     array.transpose()

            return torch.from_numpy(a  )

      def get_encoder_layer_array(a					,   a  ):
            SCREAMING_SNAKE_CASE_				:  Any           =     f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     tf.train.load_variable(a					,   a  )

            if "kernel" in name:
                  SCREAMING_SNAKE_CASE_				:  List[Any]           =     array.transpose()

            return torch.from_numpy(a  )

      def get_encoder_attention_layer_array(a					,   a					,   a  ):
            SCREAMING_SNAKE_CASE_				:  int           =     f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tf.train.load_variable(a					,   a  )
            SCREAMING_SNAKE_CASE_				:  str           =     array.reshape(a  )

            if "kernel" in name:
                  SCREAMING_SNAKE_CASE_				:  Optional[int]           =     array.transpose()

            return torch.from_numpy(a  )

      print(f"Loading model based on config from {config_path}..."  )
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     BertConfig.from_json_file(a  )
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BertForMaskedLM(a  )

      # Layers
      for layer_index in range(0					,   config.num_hidden_layers  ):
            SCREAMING_SNAKE_CASE_				:  BertLayer           =     model.bert.encoder.layer[layer_index]

            # Self-attention
            SCREAMING_SNAKE_CASE_				:  BertSelfAttention           =     layer.attention.self

            SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     get_encoder_attention_layer_array(
                a					,   '_query_dense/kernel'					,   self_attn.query.weight.data.shape  )
            SCREAMING_SNAKE_CASE_				:  List[str]           =     get_encoder_attention_layer_array(
                a					,   '_query_dense/bias'					,   self_attn.query.bias.data.shape  )
            SCREAMING_SNAKE_CASE_				:  int           =     get_encoder_attention_layer_array(
                a					,   '_key_dense/kernel'					,   self_attn.key.weight.data.shape  )
            SCREAMING_SNAKE_CASE_				:  List[Any]           =     get_encoder_attention_layer_array(
                a					,   '_key_dense/bias'					,   self_attn.key.bias.data.shape  )
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     get_encoder_attention_layer_array(
                a					,   '_value_dense/kernel'					,   self_attn.value.weight.data.shape  )
            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     get_encoder_attention_layer_array(
                a					,   '_value_dense/bias'					,   self_attn.value.bias.data.shape  )

            # Self-attention Output
            SCREAMING_SNAKE_CASE_				:  BertSelfOutput           =     layer.attention.output

            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     get_encoder_attention_layer_array(
                a					,   '_output_dense/kernel'					,   self_output.dense.weight.data.shape  )
            SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     get_encoder_attention_layer_array(
                a					,   '_output_dense/bias'					,   self_output.dense.bias.data.shape  )

            SCREAMING_SNAKE_CASE_				:  List[str]           =     get_encoder_layer_array(a					,   '_attention_layer_norm/gamma'  )
            SCREAMING_SNAKE_CASE_				:  str           =     get_encoder_layer_array(a					,   '_attention_layer_norm/beta'  )

            # Intermediate
            SCREAMING_SNAKE_CASE_				:  BertIntermediate           =     layer.intermediate

            SCREAMING_SNAKE_CASE_				:  Dict           =     get_encoder_layer_array(a					,   '_intermediate_dense/kernel'  )
            SCREAMING_SNAKE_CASE_				:  Any           =     get_encoder_layer_array(a					,   '_intermediate_dense/bias'  )

            # Output
            SCREAMING_SNAKE_CASE_				:  BertOutput           =     layer.output

            SCREAMING_SNAKE_CASE_				:  Any           =     get_encoder_layer_array(a					,   '_output_dense/kernel'  )
            SCREAMING_SNAKE_CASE_				:  Any           =     get_encoder_layer_array(a					,   '_output_dense/bias'  )

            SCREAMING_SNAKE_CASE_				:  Optional[int]           =     get_encoder_layer_array(a					,   '_output_layer_norm/gamma'  )
            SCREAMING_SNAKE_CASE_				:  Tuple           =     get_encoder_layer_array(a					,   '_output_layer_norm/beta'  )

      # Embeddings
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     get_encoder_array('_position_embedding_layer/embeddings'  )
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     get_encoder_array('_type_embedding_layer/embeddings'  )
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     get_encoder_array('_embedding_norm_layer/gamma'  )
      SCREAMING_SNAKE_CASE_				:  Tuple           =     get_encoder_array('_embedding_norm_layer/beta'  )

      # LM Head
      SCREAMING_SNAKE_CASE_				:  Dict           =     model.cls.predictions.transform

      SCREAMING_SNAKE_CASE_				:  str           =     get_masked_lm_array('dense/kernel'  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     get_masked_lm_array('dense/bias'  )

      SCREAMING_SNAKE_CASE_				:  Tuple           =     get_masked_lm_array('layer_norm/gamma'  )
      SCREAMING_SNAKE_CASE_				:  Optional[int]           =     get_masked_lm_array('layer_norm/beta'  )

      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     get_masked_lm_array('embedding_table'  )

      # Pooling
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     BertPooler(config=a  )
      SCREAMING_SNAKE_CASE_				:  BertPooler           =     get_encoder_array('_pooler_layer/kernel'  )
      SCREAMING_SNAKE_CASE_				:  BertPooler           =     get_encoder_array('_pooler_layer/bias'  )

      # Export final model
      model.save_pretrained(a  )

      # Integration test - should load without any errors ;)
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     BertForMaskedLM.from_pretrained(a  )
      print(new_model.eval()  )

      print('Model conversion was done sucessfully!'  )


if __name__ == "__main__":
    lowerCAmelCase     :   str						     =  argparse.ArgumentParser()
    parser.add_argument(
        '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
    )
    parser.add_argument(
        '--bert_config_file',
        type=str,
        required=True,
        help='The config json file corresponding to the BERT model. This specifies the model architecture.',
    )
    parser.add_argument(
        '--pytorch_dump_path',
        type=str,
        required=True,
        help='Path to the output PyTorch model.',
    )
    lowerCAmelCase     :   List[str]						     =  parser.parse_args()
    convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[str]       =  (PNDMScheduler,)
  SCREAMING_SNAKE_CASE    :      Dict       =  (('''num_inference_steps''', 50),)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     {
            'num_train_timesteps': 1000,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**_SCREAMING_SNAKE_CASE					)
        return config

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Any           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              # copy over dummy past residuals
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Any           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  int           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  int           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

              # copy over dummy past residuals (must be after setting timesteps)
              SCREAMING_SNAKE_CASE_				:  Tuple           =     dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Dict           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

                    # copy over dummy past residual (must be after setting timesteps)
                    SCREAMING_SNAKE_CASE_				:  str           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  List[str]           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     10
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_sample_deter
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

        for i, t in enumerate(scheduler.prk_timesteps					):
              SCREAMING_SNAKE_CASE_				:  Any           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps					):
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        return sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     dict(self.forward_default_kwargs					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  int           =     0.1 * sample

              if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              SCREAMING_SNAKE_CASE_				:  Dict           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              SCREAMING_SNAKE_CASE_				:  str           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              self.assertEqual(output_a.shape ,					sample.shape					)
              self.assertEqual(output_a.shape ,					output_a.shape					)

              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              self.assertEqual(output_a.shape ,					sample.shape					)
              self.assertEqual(output_a.shape ,					output_a.shape					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for timesteps in [100, 1000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.get_scheduler_config(steps_offset=1					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
        scheduler.set_timesteps(10					)
        assert torch.equal(
            scheduler.timesteps ,					torch.LongTensor(
                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					) ,					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for beta_start, beta_end in zip([0.0001, 0.001] ,					[0.002, 0.02]					):
              self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE ,					beta_end=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for t in [1, 5, 10]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for t, num_inference_steps in zip([1, 5, 10] ,					[10, 50, 100]					):
              self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     27

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  int           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample

              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

              # before power of 3 fix, would error on first step, so we only need to do two
              for i, t in enumerate(scheduler.prk_timesteps[:2]					):
                    SCREAMING_SNAKE_CASE_				:  int           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        with self.assertRaises(_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  str           =     self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.step_plms(self.dummy_sample ,					1 ,					self.dummy_sample					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.full_loop()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 198.1318					) < 1e-2
        assert abs(result_mean.item() - 0.2580					) < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.full_loop(prediction_type='v_prediction'					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 67.3986					) < 1e-2
        assert abs(result_mean.item() - 0.0878					) < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					beta_start=0.01					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 230.0399					) < 1e-2
        assert abs(result_mean.item() - 0.2995					) < 1e-3



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					beta_start=0.01					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 186.9482					) < 1e-2
        assert abs(result_mean.item() - 0.2434					) < 1e-3






