






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=0      )			->     int:
					# Format the message.
					if name is None:
										UpperCamelCase__      :       Union[str, Any]	    =  None
					else:
										UpperCamelCase__      :       Any	    =  "." * max(0 ,   spaces - 2      ) + "# {:" + str(50 - spaces      ) + "s}"
										UpperCamelCase__      :       str	    =  fmt.format(__lowerCAmelCase      )

					# Print and recurse (if needed).
					if isinstance(__lowerCAmelCase ,   __lowerCAmelCase      ):
										if msg is not None:
															print(__lowerCAmelCase      )
										for k in val.keys():
															recursive_print(__lowerCAmelCase ,   val[k] ,   spaces + 2      )
					elif isinstance(__lowerCAmelCase ,   torch.Tensor      ):
										print(__lowerCAmelCase ,   ":" ,   val.size()      )
					else:
										print(__lowerCAmelCase ,   ":" ,   __lowerCAmelCase      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     Optional[Any]:
					# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
					# for compatibility with later versions of NVIDIA Megatron-LM.
					# The inverse operation is performed inside Megatron-LM to read checkpoints:
					# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
					# If param is the weight tensor of the self-attention block, the returned tensor
					# will have to be transposed one more time to be read by HuggingFace GPT2.
					UpperCamelCase__      :       Dict	    =  param.size()
					if checkpoint_version == 1.0:
										# version 1.0 stores [num_heads * hidden_size * num_splits, :]
										UpperCamelCase__      :       Any	    =  (num_heads, hidden_size, num_splits) + input_shape[1:]
										UpperCamelCase__      :       Tuple	    =  param.view(*__lowerCAmelCase      )
										UpperCamelCase__      :       str	    =  param.transpose(0 ,   2      )
										UpperCamelCase__      :       List[str]	    =  param.transpose(1 ,   2      ).contiguous()
					elif checkpoint_version >= 2.0:
										# other versions store [num_heads * num_splits * hidden_size, :]
										UpperCamelCase__      :       int	    =  (num_heads, num_splits, hidden_size) + input_shape[1:]
										UpperCamelCase__      :       Dict	    =  param.view(*__lowerCAmelCase      )
										UpperCamelCase__      :       List[str]	    =  param.transpose(0 ,   1      ).contiguous()
					UpperCamelCase__      :       Optional[int]	    =  param.view(*__lowerCAmelCase      )
					return param







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     Optional[Any]:
					# The converted output model.
					UpperCamelCase__      :       str	    =  {}

					# old versions did not store training args
					UpperCamelCase__      :       List[str]	    =  input_state_dict.get("args" ,   __lowerCAmelCase      )
					if ds_args is not None:
										# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
										# from pprint import pprint
										# pprint(vars(ds_args))

										UpperCamelCase__      :       List[str]	    =  ds_args.padded_vocab_size
										UpperCamelCase__      :       Optional[Any]	    =  ds_args.max_position_embeddings
										UpperCamelCase__      :       str	    =  ds_args.hidden_size
										UpperCamelCase__      :       Any	    =  ds_args.num_layers
										UpperCamelCase__      :       Union[str, Any]	    =  ds_args.num_attention_heads
										UpperCamelCase__      :       Union[str, Any]	    =  ds_args.ffn_hidden_size
										# pprint(config)

					# The number of heads.
					UpperCamelCase__      :       Optional[Any]	    =  config.n_head
					# The hidden_size per head.
					UpperCamelCase__      :       Optional[int]	    =  config.n_embd // config.n_head
					# Megatron-LM checkpoint version
					if "checkpoint_version" in input_state_dict.keys():
										UpperCamelCase__      :       str	    =  input_state_dict["checkpoint_version"]
					else:
										UpperCamelCase__      :       List[Any]	    =  0.0

					# The model.
					UpperCamelCase__      :       Optional[int]	    =  input_state_dict["model"]
					# The language model.
					UpperCamelCase__      :       List[Any]	    =  model["language_model"]
					# The embeddings.
					UpperCamelCase__      :       List[str]	    =  lm["embedding"]

					# The word embeddings.
					UpperCamelCase__      :       Any	    =  embeddings["word_embeddings"]["weight"]
					# Truncate the embedding table to vocab_size rows.
					UpperCamelCase__      :       Any	    =  word_embeddings[: config.vocab_size, :]
					UpperCamelCase__      :       Tuple	    =  word_embeddings

					# The position embeddings.
					UpperCamelCase__      :       Union[str, Any]	    =  embeddings["position_embeddings"]["weight"]
					# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
					UpperCamelCase__      :       Optional[int]	    =  pos_embeddings.size(0      )
					if n_positions != config.n_positions:
										raise ValueError(
										    f'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'      )
					# Store the position embeddings.
					UpperCamelCase__      :       Tuple	    =  pos_embeddings

					# The transformer.
					UpperCamelCase__      :       str	    =  lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

					# The regex to extract layer names.
					UpperCamelCase__      :       Dict	    =  re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"      )

					# The simple map of names for "automated" rules.
					UpperCamelCase__      :       List[Any]	    =  {
					    "attention.dense": ".attn.c_proj.",
					    "self_attention.dense": ".attn.c_proj.",
					    "mlp.dense_h_to_4h": ".mlp.c_fc.",
					    "mlp.dense_4h_to_h": ".mlp.c_proj.",
					}

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										UpperCamelCase__      :       Optional[Any]	    =  layer_re.match(__lowerCAmelCase      )

										# Stop if that's not a layer
										if m is None:
															break

										# The index of the layer.
										UpperCamelCase__      :       Optional[int]	    =  int(m.group(1      )      )
										# The name of the operation.
										UpperCamelCase__      :       List[Any]	    =  m.group(2      )
										# Is it a weight or a bias?
										UpperCamelCase__      :       Optional[Any]	    =  m.group(3      )

										# The name of the layer.
										UpperCamelCase__      :       int	    =  f'transformer.h.{layer_idx}'

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith("layernorm"      ):
															UpperCamelCase__      :       List[str]	    =  "ln_1" if op_name.startswith("input"      ) else "ln_2"
															UpperCamelCase__      :       str	    =  val

										# Transpose the QKV matrix.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "weight":
															# Insert a tensor of 1x1xDxD bias.
															UpperCamelCase__      :       Tuple	    =  torch.tril(torch.ones((n_positions, n_positions) ,   dtype=torch.floataa      )      ).view(
															    1 ,   1 ,   __lowerCAmelCase ,   __lowerCAmelCase      )
															UpperCamelCase__      :       Optional[int]	    =  causal_mask

															# Insert a "dummy" tensor for masked_bias.
															UpperCamelCase__      :       Any	    =  torch.tensor(-1E4 ,   dtype=torch.floataa      )
															UpperCamelCase__      :       int	    =  masked_bias

															UpperCamelCase__      :       Dict	    =  fix_query_key_value_ordering(__lowerCAmelCase ,   __lowerCAmelCase ,   3 ,   __lowerCAmelCase ,   __lowerCAmelCase      )
															# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
															UpperCamelCase__      :       int	    =  out_val.transpose(0 ,   1      ).contiguous()
															# Store.
															UpperCamelCase__      :       Dict	    =  out_val

										# Transpose the bias.
										elif (
										    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
										) and weight_or_bias == "bias":
															UpperCamelCase__      :       List[Any]	    =  fix_query_key_value_ordering(__lowerCAmelCase ,   __lowerCAmelCase ,   3 ,   __lowerCAmelCase ,   __lowerCAmelCase      )
															# Store. No change of shape.
															UpperCamelCase__      :       int	    =  out_val

										# Transpose the weights.
										elif weight_or_bias == "weight":
															UpperCamelCase__      :       List[Any]	    =  megatron_to_transformers[op_name]
															UpperCamelCase__      :       Dict	    =  val.transpose(0 ,   1      )

										# Copy the bias.
										elif weight_or_bias == "bias":
															UpperCamelCase__      :       Any	    =  megatron_to_transformers[op_name]
															UpperCamelCase__      :       Any	    =  val

    # DEBUG.
					assert config.n_layer == layer_idx + 1

					# The final layernorm.
					UpperCamelCase__      :       Any	    =  transformer["final_layernorm.weight"]
					UpperCamelCase__      :       Tuple	    =  transformer["final_layernorm.bias"]

					# For LM head, transformers' wants the matrix to weight embeddings.
					UpperCamelCase__      :       Dict	    =  word_embeddings

					# It should be done!
					return output_state_dict







def 		SCREAMING_SNAKE_CASE							( )			->     Optional[Any]:
					# Create the argument parser.
					UpperCamelCase__      :       int	    =  argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure" ,   action="store_true"      )
					parser.add_argument(
					    "path_to_checkpoint" ,   type=__lowerCAmelCase ,   help="Path to the checkpoint file (.zip archive or direct .pt file)" ,   )
					parser.add_argument(
					    "--config_file" ,   default="" ,   type=__lowerCAmelCase ,   help="An optional config json file describing the pre-trained model." ,   )
					UpperCamelCase__      :       Optional[int]	    =  parser.parse_args()

					# Extract the basename.
					UpperCamelCase__      :       Optional[int]	    =  os.path.dirname(args.path_to_checkpoint      )

					# Load the model.
					# the .zip is very optional, let's keep it for backward compatibility
					print(f'Extracting PyTorch state dictionary from {args.path_to_checkpoint}'      )
					if args.path_to_checkpoint.endswith(".zip"      ):
										with zipfile.ZipFile(args.path_to_checkpoint ,   "r"      ) as checkpoint:
															with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"      ) as pytorch_dict:
																				UpperCamelCase__      :       Union[str, Any]	    =  torch.load(__lowerCAmelCase ,   map_location="cpu"      )
					else:
										UpperCamelCase__      :       Optional[int]	    =  torch.load(args.path_to_checkpoint ,   map_location="cpu"      )

					UpperCamelCase__      :       Union[str, Any]	    =  input_state_dict.get("args" ,   __lowerCAmelCase      )

					# Read the config, or default to the model released by NVIDIA.
					if args.config_file == "":
										if ds_args is not None:
															if ds_args.bias_gelu_fusion:
																				UpperCamelCase__      :       Optional[int]	    =  "gelu_fast"
															elif ds_args.openai_gelu:
																				UpperCamelCase__      :       Union[str, Any]	    =  "gelu_new"
															else:
																				UpperCamelCase__      :       Optional[Any]	    =  "gelu"
										else:
															# in the very early days this used to be "gelu_new"
															UpperCamelCase__      :       Union[str, Any]	    =  "gelu_new"

										# Spell out all parameters in case the defaults change.
										UpperCamelCase__      :       Tuple	    =  GPTaConfig(
										    vocab_size=5_0257 ,   n_positions=1024 ,   n_embd=1024 ,   n_layer=24 ,   n_head=16 ,   n_inner=4096 ,   activation_function=__lowerCAmelCase ,   resid_pdrop=0.1 ,   embd_pdrop=0.1 ,   attn_pdrop=0.1 ,   layer_norm_epsilon=1E-5 ,   initializer_range=0.0_2 ,   summary_type="cls_index" ,   summary_use_proj=__lowerCAmelCase ,   summary_activation=__lowerCAmelCase ,   summary_proj_to_labels=__lowerCAmelCase ,   summary_first_dropout=0.1 ,   scale_attn_weights=__lowerCAmelCase ,   use_cache=__lowerCAmelCase ,   bos_token_id=5_0256 ,   eos_token_id=5_0256 ,   )
					else:
										UpperCamelCase__      :       Any	    =  GPTaConfig.from_json_file(args.config_file      )

					UpperCamelCase__      :       Any	    =  ["GPT2LMHeadModel"]

					# Convert.
					print("Converting"      )
					UpperCamelCase__      :       Optional[int]	    =  convert_megatron_checkpoint(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(__lowerCAmelCase ,   __lowerCAmelCase      )

					# Add tokenizer class info to config
					# see https://github.com/huggingface/transformers/issues/13906)
					if ds_args is not None:
										UpperCamelCase__      :       List[str]	    =  ds_args.tokenizer_type
										if tokenizer_type == "GPT2BPETokenizer":
															UpperCamelCase__      :       int	    =  "gpt2"
										elif tokenizer_type == "PretrainedFromHF":
															UpperCamelCase__      :       Any	    =  ds_args.tokenizer_name_or_path
										else:
															raise ValueError(f'Unrecognized tokenizer_type {tokenizer_type}'      )
					else:
										UpperCamelCase__      :       Optional[Any]	    =  "gpt2"

					UpperCamelCase__      :       List[Any]	    =  AutoTokenizer.from_pretrained(__lowerCAmelCase      )
					UpperCamelCase__      :       Optional[Any]	    =  type(__lowerCAmelCase      ).__name__
					UpperCamelCase__      :       Union[str, Any]	    =  tokenizer_class

					# Store the config to file.
					print("Saving config"      )
					config.save_pretrained(__lowerCAmelCase      )

					# Save tokenizer based on args
					print(f'Adding {tokenizer_class} tokenizer files'      )
					tokenizer.save_pretrained(__lowerCAmelCase      )

					# Store the state_dict to file.
					UpperCamelCase__      :       List[str]	    =  os.path.join(__lowerCAmelCase ,   "pytorch_model.bin"      )
					print(f'Saving checkpoint to "{output_checkpoint_file}"'      )
					torch.save(__lowerCAmelCase ,   __lowerCAmelCase      )


####################################################################################################

if __name__ == "__main__":
	main()

####################################################################################################







# 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       __a  :
 _lowerCAmelCase	:	CommonSchedulerState

 # setable values
 _lowerCAmelCase	:	jnp.ndarray
 _lowerCAmelCase	:	jnp.ndarray
 _lowerCAmelCase	:	Optional[int]				=			None







 @classmethod
 def  __lowercase (		cls :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       CommonSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray						):


      '''simple docstring'''






      return cls(common=SCREAMING_SNAKE_CASE							,       init_noise_sigma=SCREAMING_SNAKE_CASE							,       timesteps=SCREAMING_SNAKE_CASE						)







@dataclass
class       __a  (   A__	):
 _lowerCAmelCase	:	DDPMSchedulerState







class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Tuple				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCAmelCase	:	jnp.dtype







 @property
 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      return True







 @register_to_config
 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       int = 10_00							,       SCREAMING_SNAKE_CASE :       float = 0.0_0_0_1							,       SCREAMING_SNAKE_CASE :       float = 0.0_2							,       SCREAMING_SNAKE_CASE :       str = "linear"							,       SCREAMING_SNAKE_CASE :       Optional[jnp.ndarray] = None							,       SCREAMING_SNAKE_CASE :       str = "fixed_small"							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       str = "epsilon"							,       SCREAMING_SNAKE_CASE :       jnp.dtype = jnp.floataa							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  dtype







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[CommonSchedulerState] = None						):


      '''simple docstring'''






      if common is None:
           UpperCamelCase__      :       Tuple	    =  CommonSchedulerState.create(self						)

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

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

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







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[int] = None						):


      '''simple docstring'''






      return sample







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple = ()						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  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							,       SCREAMING_SNAKE_CASE						) * step_ratio).round()[::-1]

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







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       Optional[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Tuple	    =  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__      :       int	    =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
           UpperCamelCase__      :       List[Any]	    =  self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           UpperCamelCase__      :       Union[str, Any]	    =  jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
           UpperCamelCase__      :       Optional[Any]	    =  jnp.log(jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)						)
      elif variance_type == "fixed_large":
           UpperCamelCase__      :       Dict	    =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           UpperCamelCase__      :       List[str]	    =  jnp.log(state.common.betas[t]						)
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           UpperCamelCase__      :       Tuple	    =  variance
           UpperCamelCase__      :       int	    =  state.common.betas[t]
           UpperCamelCase__      :       Union[str, Any]	    =  (predicted_variance + 1) / 2
           UpperCamelCase__      :       List[str]	    =  frac * max_log + (1 - frac) * min_log

      return variance







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[jax.random.KeyArray] = None							,       SCREAMING_SNAKE_CASE :       bool = True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  timestep

      if key is None:
           UpperCamelCase__      :       str	    =  jax.random.PRNGKey(0						)

      if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
           UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  jnp.split(SCREAMING_SNAKE_CASE							,       sample.shape[1]							,       axis=1						)
      else:
           UpperCamelCase__      :       Optional[int]	    =  None

      # 1. compute alphas, betas
      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Dict	    =  jnp.where(t > 0							,       state.common.alphas_cumprod[t - 1]							,       jnp.array(1.0							,       dtype=self.dtype						)						)
      UpperCamelCase__      :       Any	    =  1 - alpha_prod_t
      UpperCamelCase__      :       List[str]	    =  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__      :       Optional[Any]	    =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
           UpperCamelCase__      :       Dict	    =  model_output
      elif self.config.prediction_type == "v_prediction":
           UpperCamelCase__      :       str	    =  (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[str]	    =  jnp.clip(SCREAMING_SNAKE_CASE							,       -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[Any]	    =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      UpperCamelCase__      :       str	    =  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__      :       Any	    =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      def random_variance():
           UpperCamelCase__      :       str	    =  jax.random.split(SCREAMING_SNAKE_CASE							,       num=1						)
           UpperCamelCase__      :       Tuple	    =  jax.random.normal(SCREAMING_SNAKE_CASE							,       shape=model_output.shape							,       dtype=self.dtype						)
           return (self._get_variance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       predicted_variance=SCREAMING_SNAKE_CASE						) ** 0.5) * noise

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

      UpperCamelCase__      :       List[str]	    =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE							,       state=SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






      return add_noise_common(state.common							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






      return get_velocity_common(state.common							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def __len__(		self :       int						):


      '''simple docstring'''






      return self.config.num_train_timesteps