
"""simple docstring"""




import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

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


if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
    raise Exception("""requires gluonnlp == 0.8.3""")

if version.parse(mx.__version__) != version.parse("""1.5.0"""):
    raise Exception("""requires mxnet == 1.5.0""")

logging.set_verbosity_info()
A__      :	Optional[int]          =    logging.get_logger(__name__)

A__      :	int          =    "The Nymphenburg Palace is a beautiful palace in Munich!"



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[Any] 							=						{
         "attention_cell": "multi_head",
         "num_layers": 4,
         "units": 1024,
         "hidden_size": 768,
         "max_length": 512,
         "num_heads": 8,
         "scaled": True,
         "dropout": 0.1,
         "use_residual": True,
         "embed_size": 1024,
         "embed_dropout": 0.1,
         "word_embed": None,
         "layer_norm_eps": 1E-5,
         "token_type_vocab_size": 2,
     }

     UpperCAmelCase__							: List[str] 							=						bort_4_8_768_1024_hparams

     # Let's construct the original Bort model here
     # Taken from official BERT implementation, see:
     # https://github.com/alexa/bort/blob/master/bort/bort.py
     UpperCAmelCase__							: List[str] 							=						BERTEncoder(
         attention_cell=predefined_args["attention_cell"]       ,    num_layers=predefined_args["num_layers"]       ,    units=predefined_args["units"]       ,    hidden_size=predefined_args["hidden_size"]       ,    max_length=predefined_args["max_length"]       ,    num_heads=predefined_args["num_heads"]       ,    scaled=predefined_args["scaled"]       ,    dropout=predefined_args["dropout"]       ,    output_attention=_A       ,    output_all_encodings=_A       ,    use_residual=predefined_args["use_residual"]       ,    activation=predefined_args.get("activation"       ,    "gelu"		)       ,    layer_norm_eps=predefined_args.get("layer_norm_eps"       ,    _A		)       ,    )

     # Vocab information needs to be fetched first
     # It's the same as RoBERTa, so RobertaTokenizer can be used later
     UpperCAmelCase__							: int 							=						"openwebtext_ccnews_stories_books_cased"

     # Specify download folder to Gluonnlp's vocab
     UpperCAmelCase__							: List[Any] 							=						os.path.join(get_home_dir()       ,    "models"		)
     UpperCAmelCase__							: List[str] 							=						_load_vocab(_A       ,    _A       ,    _A       ,    cls=_A		)

     UpperCAmelCase__							: str 							=						nlp.model.BERTModel(
         _A       ,    len(_A		)       ,    units=predefined_args["units"]       ,    embed_size=predefined_args["embed_size"]       ,    embed_dropout=predefined_args["embed_dropout"]       ,    word_embed=predefined_args["word_embed"]       ,    use_pooler=_A       ,    use_token_type_embed=_A       ,    token_type_vocab_size=predefined_args["token_type_vocab_size"]       ,    use_classifier=_A       ,    use_decoder=_A       ,    )

     original_bort.load_parameters(_A       ,    cast_dtype=_A       ,    ignore_extra=_A		)
     UpperCAmelCase__							: List[Any] 							=						original_bort._collect_params_with_prefix()

     # Build our config 🤗
     UpperCAmelCase__							: int 							=						{
         "architectures": ["BertForMaskedLM"],
         "attention_probs_dropout_prob": predefined_args["dropout"],
         "hidden_act": "gelu",
         "hidden_dropout_prob": predefined_args["dropout"],
         "hidden_size": predefined_args["embed_size"],
         "initializer_range": 0.02,
         "intermediate_size": predefined_args["hidden_size"],
         "layer_norm_eps": predefined_args["layer_norm_eps"],
         "max_position_embeddings": predefined_args["max_length"],
         "model_type": "bort",
         "num_attention_heads": predefined_args["num_heads"],
         "num_hidden_layers": predefined_args["num_layers"],
         "pad_token_id": 1,  # 2 = BERT, 1 = RoBERTa
         "type_vocab_size": 1,  # 2 = BERT, 1 = RoBERTa
         "vocab_size": len(_A		),
     }

     UpperCAmelCase__							: Optional[Any] 							=						BertConfig.from_dict(_A		)
     UpperCAmelCase__							: Optional[Any] 							=						BertForMaskedLM(_A		)
     hf_bort_model.eval()

     # Parameter mapping table (Gluonnlp to Transformers)
     # * denotes layer index
     #
     # | Gluon Parameter                                                | Transformers Parameter
     # | -------------------------------------------------------------- | ----------------------
     # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
     # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
     # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
     # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
     # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
     # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
     # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
     # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
     # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
     # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
     # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
     # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
     # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
     # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
     # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
     # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
     # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
     # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
     # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
     # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

     # Helper function to convert MXNET Arrays to PyTorch
     def to_torch(lowerCAmelCase	:       List[Any]		) -> nn.Parameter:
          return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()		)		)

     # Check param shapes and map new HF param back
     def check_and_map_params(lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       str		):
          UpperCAmelCase__							: List[Any] 							=						hf_param.shape

          UpperCAmelCase__							: int 							=						to_torch(params[gluon_param]		)
          UpperCAmelCase__							: Optional[int] 							=						gluon_param.shape

          assert (
              shape_hf == shape_gluon
          ), F"The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers"

          return gluon_param

     UpperCAmelCase__							: List[Any] 							=						check_and_map_params(
         hf_bort_model.bert.embeddings.word_embeddings.weight       ,    "word_embed.0.weight"		)
     UpperCAmelCase__							: Optional[int] 							=						check_and_map_params(
         hf_bort_model.bert.embeddings.position_embeddings.weight       ,    "encoder.position_weight"		)
     UpperCAmelCase__							: List[str] 							=						check_and_map_params(
         hf_bort_model.bert.embeddings.LayerNorm.bias       ,    "encoder.layer_norm.beta"		)
     UpperCAmelCase__							: Any 							=						check_and_map_params(
         hf_bort_model.bert.embeddings.LayerNorm.weight       ,    "encoder.layer_norm.gamma"		)

     # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
     UpperCAmelCase__							: List[Any] 							=						torch.zeros_like(
         hf_bort_model.bert.embeddings.token_type_embeddings.weight.data		)

     for i in range(hf_bort_config.num_hidden_layers		):
          UpperCAmelCase__							: BertLayer 							=						hf_bort_model.bert.encoder.layer[i]

          # self attention
          UpperCAmelCase__							: BertSelfAttention 							=						layer.attention.self

          UpperCAmelCase__							: Tuple 							=						check_and_map_params(
              self_attn.key.bias.data       ,    F"encoder.transformer_cells.{i}.attention_cell.proj_key.bias"		)

          UpperCAmelCase__							: int 							=						check_and_map_params(
              self_attn.key.weight.data       ,    F"encoder.transformer_cells.{i}.attention_cell.proj_key.weight"		)
          UpperCAmelCase__							: Union[str, Any] 							=						check_and_map_params(
              self_attn.query.bias.data       ,    F"encoder.transformer_cells.{i}.attention_cell.proj_query.bias"		)
          UpperCAmelCase__							: Optional[int] 							=						check_and_map_params(
              self_attn.query.weight.data       ,    F"encoder.transformer_cells.{i}.attention_cell.proj_query.weight"		)
          UpperCAmelCase__							: List[Any] 							=						check_and_map_params(
              self_attn.value.bias.data       ,    F"encoder.transformer_cells.{i}.attention_cell.proj_value.bias"		)
          UpperCAmelCase__							: List[Any] 							=						check_and_map_params(
              self_attn.value.weight.data       ,    F"encoder.transformer_cells.{i}.attention_cell.proj_value.weight"		)

          # self attention output
          UpperCAmelCase__							: BertSelfOutput 							=						layer.attention.output

          UpperCAmelCase__							: Dict 							=						check_and_map_params(
              self_output.dense.bias       ,    F"encoder.transformer_cells.{i}.proj.bias"		)
          UpperCAmelCase__							: str 							=						check_and_map_params(
              self_output.dense.weight       ,    F"encoder.transformer_cells.{i}.proj.weight"		)
          UpperCAmelCase__							: Tuple 							=						check_and_map_params(
              self_output.LayerNorm.bias       ,    F"encoder.transformer_cells.{i}.layer_norm.beta"		)
          UpperCAmelCase__							: Optional[int] 							=						check_and_map_params(
              self_output.LayerNorm.weight       ,    F"encoder.transformer_cells.{i}.layer_norm.gamma"		)

          # intermediate
          UpperCAmelCase__							: BertIntermediate 							=						layer.intermediate

          UpperCAmelCase__							: Union[str, Any] 							=						check_and_map_params(
              intermediate.dense.bias       ,    F"encoder.transformer_cells.{i}.ffn.ffn_1.bias"		)
          UpperCAmelCase__							: Optional[int] 							=						check_and_map_params(
              intermediate.dense.weight       ,    F"encoder.transformer_cells.{i}.ffn.ffn_1.weight"		)

          # output
          UpperCAmelCase__							: BertOutput 							=						layer.output

          UpperCAmelCase__							: str 							=						check_and_map_params(
              bert_output.dense.bias       ,    F"encoder.transformer_cells.{i}.ffn.ffn_2.bias"		)
          UpperCAmelCase__							: Tuple 							=						check_and_map_params(
              bert_output.dense.weight       ,    F"encoder.transformer_cells.{i}.ffn.ffn_2.weight"		)
          UpperCAmelCase__							: Tuple 							=						check_and_map_params(
              bert_output.LayerNorm.bias       ,    F"encoder.transformer_cells.{i}.ffn.layer_norm.beta"		)
          UpperCAmelCase__							: int 							=						check_and_map_params(
              bert_output.LayerNorm.weight       ,    F"encoder.transformer_cells.{i}.ffn.layer_norm.gamma"		)

     # Save space and energy 🎄
     hf_bort_model.half()

     # Compare output of both models
     UpperCAmelCase__							: List[str] 							=						RobertaTokenizer.from_pretrained("roberta-base"		)

     UpperCAmelCase__							: Tuple 							=						tokenizer.encode_plus(_A		)["input_ids"]

     # Get gluon output
     UpperCAmelCase__							: Union[str, Any] 							=						mx.nd.array([input_ids]		)
     UpperCAmelCase__							: Optional[Any] 							=						original_bort(inputs=_A       ,    token_types=[]		)

     # Get Transformer output (save and reload model again)
     hf_bort_model.save_pretrained(_A		)
     UpperCAmelCase__							: List[Any] 							=						BertModel.from_pretrained(_A		)
     hf_bort_model.eval()

     UpperCAmelCase__							: int 							=						tokenizer.encode_plus(_A       ,    return_tensors="pt"		)
     UpperCAmelCase__							: Any 							=						hf_bort_model(**_A		)[0]

     UpperCAmelCase__							: Dict 							=						output_gluon[0].asnumpy()
     UpperCAmelCase__							: Optional[Any] 							=						output_hf[0].detach().numpy()

     UpperCAmelCase__							: Union[str, Any] 							=						np.max(np.abs(hf_layer - gluon_layer		)		).item()
     UpperCAmelCase__							: List[Any] 							=						np.allclose(_A       ,    _A       ,    atol=1E-3		)

     if success:
          print("✔️ Both model do output the same tensors"		)
     else:
          print("❌ Both model do **NOT** output the same tensors"		)
          print("Absolute difference is:"       ,    _A		)


if __name__ == "__main__":
    A__      :	int          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    A__      :	str          =    parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""




# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class   _lowercase						:


     '''simple docstring'''





     _A								=							42

     # setable values
     _A								=							42
     _A								=							42
     _A								=							None



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          return cls(common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase  )







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							[e.name for e in FlaxKarrasDiffusionSchedulers]

     _A								=							42



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return True



     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 10_00 ,			__UpperCamelCase = 0.0001 ,			__UpperCamelCase = 0.02 ,			__UpperCamelCase = "linear" ,			__UpperCamelCase = None ,			__UpperCamelCase = "fixed_small" ,			__UpperCamelCase = True ,			__UpperCamelCase = "epsilon" ,			__UpperCamelCase = jnp.floataa ,			)->					List[str]:
          UpperCAmelCase__							: int 							=						dtype



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None  )->					DDPMSchedulerState:
          if common is None:
               UpperCAmelCase__							: int 							=						CommonSchedulerState.create(self  )

          # standard deviation of the initial noise distribution
          UpperCAmelCase__							: Tuple 							=						jnp.array(1.0 ,			dtype=self.dtype  )

          UpperCAmelCase__							: Tuple 							=						jnp.arange(0 ,			self.config.num_train_timesteps  ).round()[::-1]

          return DDPMSchedulerState.create(
              common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					jnp.ndarray:
          return sample



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = ()  )->					DDPMSchedulerState:
          UpperCAmelCase__							: Dict 							=						self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          UpperCAmelCase__							: Optional[int] 							=						(jnp.arange(0 ,			__UpperCamelCase  ) * step_ratio).round()[::-1]

          return state.replace(
              num_inference_steps=__UpperCamelCase ,			timesteps=__UpperCamelCase ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase=None  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: int 							=						jnp.where(t > 0 ,			state.common.alphas_cumprod[t - 1] ,			jnp.array(1.0 ,			dtype=self.dtype  )  )

          # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
          # and sample from it to get previous sample
          # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
          UpperCAmelCase__							: Any 							=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
               UpperCAmelCase__							: Union[str, Any] 							=						self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small":
               UpperCAmelCase__							: Dict 							=						jnp.clip(__UpperCamelCase ,			a_min=1E-20  )
          # for rl-diffuser https://arxiv.org/abs/2205.09991
          elif variance_type == "fixed_small_log":
               UpperCAmelCase__							: Tuple 							=						jnp.log(jnp.clip(__UpperCamelCase ,			a_min=1E-20  )  )
          elif variance_type == "fixed_large":
               UpperCAmelCase__							: Union[str, Any] 							=						state.common.betas[t]
          elif variance_type == "fixed_large_log":
               # Glide max_log
               UpperCAmelCase__							: Optional[int] 							=						jnp.log(state.common.betas[t]  )
          elif variance_type == "learned":
               return predicted_variance
          elif variance_type == "learned_range":
               UpperCAmelCase__							: List[str] 							=						variance
               UpperCAmelCase__							: Union[str, Any] 							=						state.common.betas[t]
               UpperCAmelCase__							: Optional[int] 							=						(predicted_variance + 1) / 2
               UpperCAmelCase__							: Any 							=						frac * max_log + (1 - frac) * min_log

          return variance



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			)->					Union[FlaxDDPMSchedulerOutput, Tuple]:
          UpperCAmelCase__							: List[str] 							=						timestep

          if key is None:
               UpperCAmelCase__							: int 							=						jax.random.PRNGKey(0  )

          if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
               UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						jnp.split(__UpperCamelCase ,			sample.shape[1] ,			axis=1  )
          else:
               UpperCAmelCase__							: Optional[Any] 							=						None

          # 1. compute alphas, betas
          UpperCAmelCase__							: Union[str, Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: Tuple 							=						jnp.where(t > 0 ,			state.common.alphas_cumprod[t - 1] ,			jnp.array(1.0 ,			dtype=self.dtype  )  )
          UpperCAmelCase__							: Union[str, Any] 							=						1 - alpha_prod_t
          UpperCAmelCase__							: Tuple 							=						1 - alpha_prod_t_prev

          # 2. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
          if self.config.prediction_type == "epsilon":
               UpperCAmelCase__							: List[Any] 							=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
               UpperCAmelCase__							: Any 							=						model_output
          elif self.config.prediction_type == "v_prediction":
               UpperCAmelCase__							: Union[str, Any] 							=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
                   " for the FlaxDDPMScheduler."  )

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
               UpperCAmelCase__							: List[Any] 							=						jnp.clip(__UpperCamelCase ,			-1 ,			1  )

          # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          UpperCAmelCase__							: List[str] 							=						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
          UpperCAmelCase__							: List[Any] 							=						state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

          # 5. Compute predicted previous sample µ_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          UpperCAmelCase__							: Tuple 							=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          def random_variance():
               UpperCAmelCase__							: Any 							=						jax.random.split(__UpperCamelCase ,			num=1  )
               UpperCAmelCase__							: int 							=						jax.random.normal(__UpperCamelCase ,			shape=model_output.shape ,			dtype=self.dtype  )
               return (self._get_variance(__UpperCamelCase ,			__UpperCamelCase ,			predicted_variance=__UpperCamelCase  ) ** 0.5) * noise

          UpperCAmelCase__							: Dict 							=						jnp.where(t > 0 ,			random_variance() ,			jnp.zeros(model_output.shape ,			dtype=self.dtype  )  )

          UpperCAmelCase__							: Any 							=						pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase ,			state=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return add_noise_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return get_velocity_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def __len__(      self  )->					Tuple:
          return self.config.num_train_timesteps



