



'''simple docstring'''
# 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_bert/convert_megatron_bert_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 MegatronBertConfig






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_=0							):
						# Format the message.
						if name is None:
												_UpperCamelCase      :	int				       =     None
						else:
												_UpperCamelCase      :	Any				       =     '.' * max(0						,						spaces - 2							) + '# {:' + str(5_0 - spaces							) + 's}'
												_UpperCamelCase      :	List[Any]				       =     fmt.format(UpperCAmelCase_							)

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






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							):
						# 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 BERT.
						_UpperCamelCase      :	Union[str, Any]				       =     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      :	Union[str, Any]				       =     param.view(*UpperCAmelCase_							)
												_UpperCamelCase      :	Optional[Any]				       =     param.transpose(0						,						2							)
												_UpperCamelCase      :	Optional[int]				       =     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(*UpperCAmelCase_							)
												_UpperCamelCase      :	Optional[Any]				       =     param.transpose(0						,						1							).contiguous()
						_UpperCamelCase      :	int				       =     param.view(*UpperCAmelCase_							)
						return param






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							):
						# The converted output model.
						_UpperCamelCase      :	str				       =     {}

						# old versions did not store training args
						_UpperCamelCase      :	Any				       =     input_state_dict.get('args'						,						UpperCAmelCase_							)
						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      :	Any				       =     ds_args.tokenizer_type
												_UpperCamelCase      :	List[Any]				       =     ds_args.padded_vocab_size
												_UpperCamelCase      :	Optional[Any]				       =     ds_args.max_position_embeddings
												_UpperCamelCase      :	Dict				       =     ds_args.hidden_size
												_UpperCamelCase      :	List[Any]				       =     ds_args.num_layers
												_UpperCamelCase      :	List[Any]				       =     ds_args.num_attention_heads
												_UpperCamelCase      :	Any				       =     ds_args.ffn_hidden_size if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
												# pprint(config)

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

						# The model.
						_UpperCamelCase      :	List[Any]				       =     input_state_dict['model']
						# The language model.
						_UpperCamelCase      :	List[Any]				       =     model['language_model']
						# The embeddings.
						_UpperCamelCase      :	str				       =     lm['embedding']

						# The word embeddings.
						_UpperCamelCase      :	Any				       =     embeddings['word_embeddings']['weight']
						# Truncate the embedding table to vocab_size rows.
						_UpperCamelCase      :	str				       =     word_embeddings[: config.vocab_size, :]
						# Store the word embeddings.
						_UpperCamelCase      :	Optional[int]				       =     word_embeddings

						# The position embeddings.
						_UpperCamelCase      :	int				       =     embeddings['position_embeddings']['weight']
						assert pos_embeddings.size(0							) == config.max_position_embeddings and pos_embeddings.size(1							) == config.hidden_size
						# Store the position embeddings.
						_UpperCamelCase      :	List[Any]				       =     pos_embeddings

						# The token-type embeddings.
						_UpperCamelCase      :	Dict				       =     embeddings['tokentype_embeddings']['weight']
						# Store the position embeddings.
						_UpperCamelCase      :	int				       =     tokentype_embeddings

						# The transformer.
						_UpperCamelCase      :	Dict				       =     lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

						# The regex to extract layer names.
						_UpperCamelCase      :	Optional[Any]				       =     re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'							)

						# The simple map of names for "automated" rules.
						_UpperCamelCase      :	List[Any]				       =     {
						    'attention.dense': '.attention.output.dense.',
						    'self_attention.dense': '.attention.output.dense.',
						    'mlp.dense_h_to_4h': '.intermediate.dense.',
						    'mlp.dense_4h_to_h': '.output.dense.',
						}

						# Keep track of the attention/query/value tensor.
						_UpperCamelCase      :	Optional[Any]				       =     None

						# Extract the layers.
						for key, val in transformer.items():
												# Match the name.
												_UpperCamelCase      :	Optional[int]				       =     layer_re.match(UpperCAmelCase_							)

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

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

												# The name of the layer.
												_UpperCamelCase      :	List[Any]				       =     f'bert.encoder.layer.{layer_idx}'

												# For layernorm(s), simply store the layer norm.
												if op_name.endswith('layernorm'							):
																		_UpperCamelCase      :	Tuple				       =     'attention.ln' if op_name.startswith('input'							) else 'ln'
																		_UpperCamelCase      :	Optional[int]				       =     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":
																		# Make sure the QKV pointer is nil.
																		assert attention_qkv_weight is None, ""

																		_UpperCamelCase      :	List[str]				       =     fix_query_key_value_ordering(UpperCAmelCase_						,						UpperCAmelCase_						,						3						,						UpperCAmelCase_						,						UpperCAmelCase_							)
																		# Store the tensor as we need the bias as well to interleave QKV and biases.
																		_UpperCamelCase      :	Union[str, Any]				       =     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":
																		# Make sure we read the weight tensor.
																		assert attention_qkv_weight is not None, ""

																		# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
																		_UpperCamelCase      :	Union[str, Any]				       =     attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																		_UpperCamelCase      :	Optional[int]				       =     attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																		_UpperCamelCase      :	Optional[int]				       =     attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

																		_UpperCamelCase      :	List[str]				       =     fix_query_key_value_ordering(UpperCAmelCase_						,						UpperCAmelCase_						,						3						,						UpperCAmelCase_						,						UpperCAmelCase_							)
																		# Split the bias.
																		_UpperCamelCase      :	Dict				       =     out_val[0 * config.hidden_size : 1 * config.hidden_size]
																		_UpperCamelCase      :	Any				       =     out_val[1 * config.hidden_size : 2 * config.hidden_size]
																		_UpperCamelCase      :	Any				       =     out_val[2 * config.hidden_size : 3 * config.hidden_size]

																		# Store.
																		_UpperCamelCase      :	int				       =     q
																		_UpperCamelCase      :	Optional[Any]				       =     q_bias
																		_UpperCamelCase      :	Optional[int]				       =     k
																		_UpperCamelCase      :	str				       =     k_bias
																		_UpperCamelCase      :	Optional[Any]				       =     v
																		_UpperCamelCase      :	Optional[Any]				       =     v_bias

																		# Clear the stored tensor.
																		_UpperCamelCase      :	Union[str, Any]				       =     None

												# Copy weights and biases as is.
												elif weight_or_bias in ["weight", "bias"]:
																		_UpperCamelCase      :	Union[str, Any]				       =     megatron_to_transformers[op_name]
																		_UpperCamelCase      :	Any				       =     val

    # The final layernorm.
						_UpperCamelCase      :	Dict				       =     transformer['final_layernorm.weight']
						_UpperCamelCase      :	str				       =     transformer['final_layernorm.bias']

						# The pooler.
						_UpperCamelCase      :	Tuple				       =     lm['pooler']

						# Store the matrix and the bias.
						_UpperCamelCase      :	Union[str, Any]				       =     pooler['dense.weight']
						_UpperCamelCase      :	List[Any]				       =     pooler['dense.bias']

						# The LM head from Megatron (for RACE).
						_UpperCamelCase      :	Dict				       =     model['lm_head']

						# The transform matrix.
						_UpperCamelCase      :	str				       =     lm_head['dense.weight']
						_UpperCamelCase      :	Any				       =     lm_head['dense.bias']

						# The transform LN.
						_UpperCamelCase      :	int				       =     lm_head['layernorm.weight']
						_UpperCamelCase      :	int				       =     lm_head['layernorm.bias']

						# For the decoder, we replicate the weights.
						_UpperCamelCase      :	Union[str, Any]				       =     word_embeddings
						_UpperCamelCase      :	Dict				       =     lm_head['bias']

						# The classifier from Megatron (for MLNI).
						_UpperCamelCase      :	Tuple				       =     model['binary_head']

						# Store the classifier.
						_UpperCamelCase      :	Optional[int]				       =     binary_head['weight']
						_UpperCamelCase      :	int				       =     binary_head['bias']

						# It should be done!
						return output_state_dict






def A__					(  ):
						# Create the argument parser.
						_UpperCamelCase      :	Optional[Any]				       =     argparse.ArgumentParser()
						parser.add_argument('--print-checkpoint-structure'						,						action='store_true'							)
						parser.add_argument('path_to_checkpoint'						,						type=UpperCAmelCase_						,						help='Path to the ZIP file containing the checkpoint'							)
						parser.add_argument(
						    '--config_file'						,						default=''						,						type=UpperCAmelCase_						,						help='An optional config json file describing the pre-trained model.'						,						)
						_UpperCamelCase      :	Union[str, Any]				       =     parser.parse_args()

						# Extract the basename.
						_UpperCamelCase      :	Tuple				       =     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      :	Dict				       =     torch.load(UpperCAmelCase_						,						map_location='cpu'							)
						else:
												_UpperCamelCase      :	List[Any]				       =     torch.load(args.path_to_checkpoint						,						map_location='cpu'							)

						if args.config_file == "":
												# Default config of megatron-bert 345m
												_UpperCamelCase      :	Any				       =     MegatronBertConfig()

												# different megatron-bert-*-345m models have different vocab sizes, so override the default
												# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
												_UpperCamelCase      :	Union[str, Any]				       =     input_state_dict['model']['lm_head']['bias'].numel()
						else:
												_UpperCamelCase      :	List[str]				       =     MegatronBertConfig.from_json_file(args.config_file							)

						# Convert.
						print('Converting'							)
						_UpperCamelCase      :	Union[str, Any]				       =     convert_megatron_checkpoint(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							)

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

						# Store the config to file.
						print('Saving config'							)
						config.save_pretrained(UpperCAmelCase_							)

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


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

if __name__ == "__main__":
					main()

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







'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_=0.999						,						UpperCAmelCase_="cosine"						,						):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(UpperCAmelCase_							):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(UpperCAmelCase_							):
																		return math.exp(t * -12.0							)

						else:
												raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'							)

						_UpperCamelCase      :	Union[str, Any]				       =     []
						for i in range(UpperCAmelCase_							):
												_UpperCamelCase      :	Any				       =     i / num_diffusion_timesteps
												_UpperCamelCase      :	Any				       =     (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_							) / alpha_bar_fn(UpperCAmelCase_							)						,						UpperCAmelCase_							)							)
						return torch.tensor(UpperCAmelCase_						,						dtype=torch.floataa							)






class 							lowercase__			(							lowercase		,      lowercase     ):
	lowercase__						       =    [e.name for e in KarrasDiffusionSchedulers]
	lowercase__						       =    2





	@register_to_config
	def __init__(				self      :       List[str]				,lowerCamelCase__      :       int = 1000				,lowerCamelCase__      :       float = 0.0_0_0_8_5				,lowerCamelCase__      :       float = 0.0_1_2				,lowerCamelCase__      :       str = "linear"				,lowerCamelCase__      :       Optional[Union[np.ndarray, List[float]]] = None				,lowerCamelCase__      :       str = "epsilon"				,lowerCamelCase__      :       str = "linspace"				,lowerCamelCase__      :       int = 0				,):







							'''simple docstring'''
							if trained_betas is not None:
													_UpperCamelCase      :	Tuple				       =     torch.tensor(lowerCamelCase__				,dtype=torch.floataa				)
							elif beta_schedule == "linear":
													_UpperCamelCase      :	int				       =     torch.linspace(lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,dtype=torch.floataa				)
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													_UpperCamelCase      :	Dict				       =     (
													    torch.linspace(beta_start**0.5				,beta_end**0.5				,lowerCamelCase__				,dtype=torch.floataa				) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													_UpperCamelCase      :	Dict				       =     betas_for_alpha_bar(lowerCamelCase__				)
							else:
													raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'				)

							_UpperCamelCase      :	Tuple				       =     1.0 - self.betas
							_UpperCamelCase      :	Any				       =     torch.cumprod(self.alphas				,dim=0				)

							#  set all values
							self.set_timesteps(lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       Any				,lowerCamelCase__      :       Optional[int]=None				):







							'''simple docstring'''
							if schedule_timesteps is None:
													_UpperCamelCase      :	Optional[Any]				       =     self.timesteps

							_UpperCamelCase      :	List[str]				       =     (schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter				) == 0:
													_UpperCamelCase      :	List[Any]				       =     1 if len(lowerCamelCase__				) > 1 else 0
							else:
													_UpperCamelCase      :	Optional[Any]				       =     timestep.cpu().item() if torch.is_tensor(lowerCamelCase__				) else timestep
													_UpperCamelCase      :	str				       =     self._index_counter[timestep_int]

							return indices[pos].item()





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







							'''simple docstring'''
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				,):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     self.index_for_timestep(lowerCamelCase__				)

							if self.state_in_first_order:
													_UpperCamelCase      :	str				       =     self.sigmas[step_index]
							else:
													_UpperCamelCase      :	List[Any]				       =     self.sigmas_interpol[step_index - 1]

							_UpperCamelCase      :	Optional[Any]				       =     sample / ((sigma**2 + 1) ** 0.5)
							return sample





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       int				,lowerCamelCase__      :       Union[str, torch.device] = None				,lowerCamelCase__      :       Optional[int] = None				,):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     num_inference_steps

							_UpperCamelCase      :	List[str]				       =     num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
													_UpperCamelCase      :	Optional[int]				       =     np.linspace(0				,num_train_timesteps - 1				,lowerCamelCase__				,dtype=lowerCamelCase__				)[::-1].copy()
							elif self.config.timestep_spacing == "leading":
													_UpperCamelCase      :	Optional[Any]				       =     num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase      :	Tuple				       =     (np.arange(0				,lowerCamelCase__				) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__				)
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													_UpperCamelCase      :	Dict				       =     num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase      :	int				       =     (np.arange(lowerCamelCase__				,0				,-step_ratio				)).round().copy().astype(lowerCamelCase__				)
													timesteps -= 1
							else:
													raise ValueError(
													    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'				)

							_UpperCamelCase      :	List[str]				       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
							_UpperCamelCase      :	str				       =     torch.from_numpy(np.log(lowerCamelCase__				)				).to(lowerCamelCase__				)

							_UpperCamelCase      :	int				       =     np.interp(lowerCamelCase__				,np.arange(0				,len(lowerCamelCase__				)				)				,lowerCamelCase__				)
							_UpperCamelCase      :	Any				       =     np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
							_UpperCamelCase      :	int				       =     torch.from_numpy(lowerCamelCase__				).to(device=lowerCamelCase__				)

							# compute up and down sigmas
							_UpperCamelCase      :	Dict				       =     sigmas.roll(-1				)
							_UpperCamelCase      :	Tuple				       =     0.0
							_UpperCamelCase      :	Optional[Any]				       =     (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
							_UpperCamelCase      :	List[str]				       =     (sigmas_next**2 - sigmas_up**2) ** 0.5
							_UpperCamelCase      :	Tuple				       =     0.0

							# compute interpolated sigmas
							_UpperCamelCase      :	Any				       =     sigmas.log().lerp(sigmas_down.log()				,0.5				).exp()
							_UpperCamelCase      :	Tuple				       =     0.0

							# set sigmas
							_UpperCamelCase      :	Tuple				       =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
							_UpperCamelCase      :	Tuple				       =     torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)
							_UpperCamelCase      :	Optional[int]				       =     torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2				), sigmas_up[-1:]]				)
							_UpperCamelCase      :	List[str]				       =     torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2				), sigmas_down[-1:]]				)

							if str(lowerCamelCase__				).startswith('mps'				):
													# mps does not support float64
													_UpperCamelCase      :	int				       =     torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	Dict				       =     torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				)

							_UpperCamelCase      :	List[str]				       =     self.sigma_to_t(lowerCamelCase__				).to(lowerCamelCase__				,dtype=timesteps.dtype				)
							_UpperCamelCase      :	List[Any]				       =     torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])				,dim=-1				).flatten()

							_UpperCamelCase      :	int				       =     torch.cat([timesteps[:1], interleaved_timesteps]				)

							_UpperCamelCase      :	Optional[int]				       =     None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							_UpperCamelCase      :	Optional[Any]				       =     defaultdict(lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       List[Any]				,lowerCamelCase__      :       Optional[Any]				):







							'''simple docstring'''
							# get log sigma
							_UpperCamelCase      :	Union[str, Any]				       =     sigma.log()

							# get distribution
							_UpperCamelCase      :	str				       =     log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							_UpperCamelCase      :	List[str]				       =     dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
							_UpperCamelCase      :	Any				       =     low_idx + 1

							_UpperCamelCase      :	Optional[int]				       =     self.log_sigmas[low_idx]
							_UpperCamelCase      :	Optional[int]				       =     self.log_sigmas[high_idx]

							# interpolate sigmas
							_UpperCamelCase      :	Optional[int]				       =     (low - log_sigma) / (low - high)
							_UpperCamelCase      :	Any				       =     w.clamp(0				,1				)

							# transform interpolation to time range
							_UpperCamelCase      :	int				       =     (1 - w) * low_idx + w * high_idx
							_UpperCamelCase      :	Dict				       =     t.view(sigma.shape				)
							return t





	@property
	def 			UpperCamelCase_						(				self      :       Any				):







							'''simple docstring'''
							return self.sample is None





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       Union[torch.FloatTensor, np.ndarray]				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				,lowerCamelCase__      :       Union[torch.FloatTensor, np.ndarray]				,lowerCamelCase__      :       Optional[torch.Generator] = None				,lowerCamelCase__      :       bool = True				,):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[Any]				       =     self.index_for_timestep(lowerCamelCase__				)

							# advance index counter by 1
							_UpperCamelCase      :	int				       =     timestep.cpu().item() if torch.is_tensor(lowerCamelCase__				) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
													_UpperCamelCase      :	Optional[int]				       =     self.sigmas[step_index]
													_UpperCamelCase      :	str				       =     self.sigmas_interpol[step_index]
													_UpperCamelCase      :	Optional[int]				       =     self.sigmas_up[step_index]
													_UpperCamelCase      :	List[Any]				       =     self.sigmas_down[step_index - 1]
							else:
													# 2nd order / KPDM2's method
													_UpperCamelCase      :	List[Any]				       =     self.sigmas[step_index - 1]
													_UpperCamelCase      :	Dict				       =     self.sigmas_interpol[step_index - 1]
													_UpperCamelCase      :	List[str]				       =     self.sigmas_up[step_index - 1]
													_UpperCamelCase      :	Optional[Any]				       =     self.sigmas_down[step_index - 1]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							_UpperCamelCase      :	int				       =     0
							_UpperCamelCase      :	Union[str, Any]				       =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							_UpperCamelCase      :	Union[str, Any]				       =     model_output.device
							_UpperCamelCase      :	Dict				       =     randn_tensor(model_output.shape				,dtype=model_output.dtype				,device=lowerCamelCase__				,generator=lowerCamelCase__				)

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
													_UpperCamelCase      :	Union[str, Any]				       =     sigma_hat if self.state_in_first_order else sigma_interpol
													_UpperCamelCase      :	str				       =     sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
													_UpperCamelCase      :	Optional[Any]				       =     sigma_hat if self.state_in_first_order else sigma_interpol
													_UpperCamelCase      :	Optional[Any]				       =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
													    sample / (sigma_input**2 + 1)
													)
							elif self.config.prediction_type == "sample":
													raise NotImplementedError('prediction_type not implemented yet: sample'				)
							else:
													raise ValueError(
													    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'				)

							if self.state_in_first_order:
													# 2. Convert to an ODE derivative for 1st order
													_UpperCamelCase      :	Dict				       =     (sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													_UpperCamelCase      :	Optional[Any]				       =     sigma_interpol - sigma_hat

													# store for 2nd order step
													_UpperCamelCase      :	Union[str, Any]				       =     sample
													_UpperCamelCase      :	Any				       =     dt
													_UpperCamelCase      :	Optional[Any]				       =     sample + derivative * dt
							else:
													# DPM-Solver-2
													# 2. Convert to an ODE derivative for 2nd order
													_UpperCamelCase      :	Tuple				       =     (sample - pred_original_sample) / sigma_interpol
													# 3. delta timestep
													_UpperCamelCase      :	int				       =     sigma_down - sigma_hat

													_UpperCamelCase      :	Dict				       =     self.sample
													_UpperCamelCase      :	Tuple				       =     None

													_UpperCamelCase      :	Dict				       =     sample + derivative * dt
													_UpperCamelCase      :	int				       =     prev_sample + noise * sigma_up

							if not return_dict:
													return (prev_sample,)

							return SchedulerOutput(prev_sample=lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Dict				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       torch.FloatTensor				,):







							'''simple docstring'''
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							_UpperCamelCase      :	Optional[int]				       =     self.sigmas.to(device=original_samples.device				,dtype=original_samples.dtype				)
							if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__				):
													# mps does not support float64
													_UpperCamelCase      :	Any				       =     self.timesteps.to(original_samples.device				,dtype=torch.floataa				)
													_UpperCamelCase      :	Dict				       =     timesteps.to(original_samples.device				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	List[str]				       =     self.timesteps.to(original_samples.device				)
													_UpperCamelCase      :	str				       =     timesteps.to(original_samples.device				)

							_UpperCamelCase      :	Optional[int]				       =     [self.index_for_timestep(lowerCamelCase__				,lowerCamelCase__				) for t in timesteps]

							_UpperCamelCase      :	Any				       =     sigmas[step_indices].flatten()
							while len(sigma.shape				) < len(original_samples.shape				):
													_UpperCamelCase      :	Optional[Any]				       =     sigma.unsqueeze(-1				)

							_UpperCamelCase      :	Dict				       =     original_samples + noise * sigma
							return noisy_samples





	def __len__(				self      :       List[str]				):







							'''simple docstring'''
							return self.config.num_train_timesteps


