





import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
						import tensorflow as tf

						tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
						import torch

if is_datasets_available():
						from datasets import load_dataset


A_        =  5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors





def  __UpperCamelCase   (     a)			->Optional[int]:
					return PTtoTFCommand(
					    args.model_name,  args.local_dir,  args.max_error,  args.new_weights,  args.no_pr,  args.push,  args.extra_commit_description,  args.override_model_class,  )







class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					@staticmethod
					def        __magic_name__			(			_lowerCAmelCase						):
										lowerCamelCase__      =							parser.add_parser(
										    "pt-to-tf"						,	help=(
										        "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
										        " Can also be used to validate existing weights without opening PRs, with --no-pr."
										    )						,	)
										train_parser.add_argument(
										    "--model-name"						,	type=_lowerCAmelCase						,	required=_lowerCAmelCase						,	help="The model name, including owner/organization, as seen on the hub."						,	)
										train_parser.add_argument(
										    "--local-dir"						,	type=_lowerCAmelCase						,	default=""						,	help="Optional local directory of the model repository. Defaults to /tmp/{model_name}"						,	)
										train_parser.add_argument(
										    "--max-error"						,	type=_lowerCAmelCase						,	default=_lowerCAmelCase						,	help=(
										        F"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
										    )						,	)
										train_parser.add_argument(
										    "--new-weights"						,	action="store_true"						,	help="Optional flag to create new TensorFlow weights, even if they already exist."						,	)
										train_parser.add_argument(
										    "--no-pr"						,	action="store_true"						,	help="Optional flag to NOT open a PR with converted weights."						)
										train_parser.add_argument(
										    "--push"						,	action="store_true"						,	help="Optional flag to push the weights directly to `main` (requires permissions)"						,	)
										train_parser.add_argument(
										    "--extra-commit-description"						,	type=_lowerCAmelCase						,	default=""						,	help="Optional additional commit description to use when opening a PR (e.g. to tag the owner)."						,	)
										train_parser.add_argument(
										    "--override-model-class"						,	type=_lowerCAmelCase						,	default=_lowerCAmelCase						,	help="If you think you know better than the auto-detector, you can specify the model class here. "
										    "Can be either an AutoModel class or a specific model class like BertForSequenceClassification."						,	)
										train_parser.set_defaults(func=_lowerCAmelCase						)





					@staticmethod
					def        __magic_name__			(			_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							set(pt_outputs.keys()						)
										lowerCamelCase__      =							set(tf_outputs.keys()						)
										if pt_out_attrs != tf_out_attrs:
															raise ValueError(
															    F"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
															    F" {tf_out_attrs})"						)

										# 2. For each output attribute, computes the difference
										def _find_pt_tf_differences(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase=""						):
															# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
															# recursivelly, keeping the name of the attribute.
															if isinstance(_lowerCAmelCase						,	torch.Tensor						):
																				lowerCamelCase__      =							np.max(np.abs(pt_out.numpy() - tf_out.numpy()						)						)
																				lowerCamelCase__      =							tensor_difference
															else:
																				lowerCamelCase__      =							attr_name
																				for i, pt_item in enumerate(_lowerCAmelCase						):
																									# If it is a named attribute, we keep the name. Otherwise, just its index.
																									if isinstance(_lowerCAmelCase						,	_lowerCAmelCase						):
																														lowerCamelCase__      =							root_name + pt_item
																														lowerCamelCase__      =							tf_out[pt_item]
																														lowerCamelCase__      =							pt_out[pt_item]
																									else:
																														lowerCamelCase__      =							root_name + F"[{i}]"
																														lowerCamelCase__      =							tf_out[i]
																									lowerCamelCase__      =							_find_pt_tf_differences(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

															return differences

										return _find_pt_tf_differences(_lowerCAmelCase						,	_lowerCAmelCase						,	{}						)





					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	*_lowerCAmelCase						,	):
										lowerCamelCase__      =							logging.get_logger("transformers-cli/pt_to_tf"						)
										lowerCamelCase__      =							model_name
										lowerCamelCase__      =							local_dir if local_dir else os.path.join("/tmp"						,	_lowerCAmelCase						)
										lowerCamelCase__      =							max_error
										lowerCamelCase__      =							new_weights
										lowerCamelCase__      =							no_pr
										lowerCamelCase__      =							push
										lowerCamelCase__      =							extra_commit_description
										lowerCamelCase__      =							override_model_class





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):

										def _get_audio_input():
															lowerCamelCase__      =							load_dataset("hf-internal-testing/librispeech_asr_dummy"						,	"clean"						,	split="validation"						)
															lowerCamelCase__      =							ds.sort("id"						).select(range(2						)						)[:2]["audio"]
															lowerCamelCase__      =							[x["array"] for x in speech_samples]
															return raw_samples

										lowerCamelCase__      =							type(pt_model.config						)
										if model_config_class in PROCESSOR_MAPPING:
															lowerCamelCase__      =							AutoProcessor.from_pretrained(self._local_dir						)
															if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
																				lowerCamelCase__      =							processor.tokenizer.eos_token
										elif model_config_class in IMAGE_PROCESSOR_MAPPING:
															lowerCamelCase__      =							AutoImageProcessor.from_pretrained(self._local_dir						)
										elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
															lowerCamelCase__      =							AutoFeatureExtractor.from_pretrained(self._local_dir						)
										elif model_config_class in TOKENIZER_MAPPING:
															lowerCamelCase__      =							AutoTokenizer.from_pretrained(self._local_dir						)
															if processor.pad_token is None:
																				lowerCamelCase__      =							processor.eos_token
										else:
															raise ValueError(F"Unknown data processing type (model config type: {model_config_class})"						)

										lowerCamelCase__      =							set(inspect.signature(pt_model.forward						).parameters.keys()						)
										lowerCamelCase__      =							{}
										if "input_ids" in model_forward_signature:
															processor_inputs.update(
															    {
															        "text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
															        "padding": True,
															        "truncation": True,
															    }						)
										if "pixel_values" in model_forward_signature:
															lowerCamelCase__      =							load_dataset("cifar10"						,	"plain_text"						,	split="test"						)[:2]["img"]
															processor_inputs.update({"images": sample_images}						)
										if "input_features" in model_forward_signature:
															lowerCamelCase__      =							inspect.signature(processor.feature_extractor						).parameters
															# Pad to the largest input length by default but take feature extractor default
															# padding value if it exists e.g. "max_length" and is not False or None
															if "padding" in feature_extractor_signature:
																				lowerCamelCase__      =							feature_extractor_signature["padding"].default
																				if default_strategy is not False and default_strategy is not None:
																									lowerCamelCase__      =							default_strategy
																				else:
																									lowerCamelCase__      =							True
															else:
																				lowerCamelCase__      =							True
															processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy}						)
										if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
															processor_inputs.update({"audio": _get_audio_input(), "padding": True}						)
										lowerCamelCase__      =							processor(**_lowerCAmelCase						,	return_tensors="pt"						)
										lowerCamelCase__      =							processor(**_lowerCAmelCase						,	return_tensors="tf"						)

										# Extra input requirements, in addition to the input modality
										if (
										    config.is_encoder_decoder
										    or (hasattr(_lowerCAmelCase						,	"encoder"						) and hasattr(_lowerCAmelCase						,	"decoder"						))
										    or "decoder_input_ids" in tf_dummy_inputs
										):
															lowerCamelCase__      =							np.asarray([[1], [1]]						,	dtype=_lowerCAmelCase						) * (pt_model.config.decoder_start_token_id or 0)
															pt_input.update({"decoder_input_ids": torch.tensor(_lowerCAmelCase						)}						)
															tf_input.update({"decoder_input_ids": tf.convert_to_tensor(_lowerCAmelCase						)}						)

										return pt_input, tf_input






					def        __magic_name__			(			self						):
										# hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
										if version.parse(huggingface_hub.__version__						) < version.parse("0.9.0"						):
															raise ImportError(
															    "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
															    " installation."						)
										else:
															from huggingface_hub import Repository, create_commit
															from huggingface_hub._commit_api import CommitOperationAdd

										# Fetch remote data
										lowerCamelCase__      =							Repository(local_dir=self._local_dir						,	clone_from=self._model_name						)

										# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
										lowerCamelCase__      =							AutoConfig.from_pretrained(self._local_dir						)
										lowerCamelCase__      =							config.architectures
										if self._override_model_class is not None:
															if self._override_model_class.startswith("TF"						):
																				lowerCamelCase__      =							[self._override_model_class[2:]]
															else:
																				lowerCamelCase__      =							[self._override_model_class]
															try:
																				lowerCamelCase__      =							getattr(import_module("transformers"						)						,	architectures[0]						)
															except AttributeError:
																				raise ValueError(F"Model class {self._override_model_class} not found in transformers."						)
															try:
																				lowerCamelCase__      =							getattr(import_module("transformers"						)						,	"TF" + architectures[0]						)
															except AttributeError:
																				raise ValueError(F"TF model class TF{self._override_model_class} not found in transformers."						)
										elif architectures is None:  # No architecture defined -- use auto classes
															lowerCamelCase__      =							getattr(import_module("transformers"						)						,	"AutoModel"						)
															lowerCamelCase__      =							getattr(import_module("transformers"						)						,	"TFAutoModel"						)
															self._logger.warning("No detected architecture, using AutoModel/TFAutoModel"						)
										else:  # Architecture defined -- use it
															if len(_lowerCAmelCase						) > 1:
																				raise ValueError(F"More than one architecture was found, aborting. (architectures = {architectures})"						)
															self._logger.warning(F"Detected architecture: {architectures[0]}"						)
															lowerCamelCase__      =							getattr(import_module("transformers"						)						,	architectures[0]						)
															try:
																				lowerCamelCase__      =							getattr(import_module("transformers"						)						,	"TF" + architectures[0]						)
															except AttributeError:
																				raise AttributeError(F"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."						)

        # Check the TF dummy inputs to see what keys we need in the forward pass
										lowerCamelCase__      =							tf_class.from_config(_lowerCAmelCase						)
										lowerCamelCase__      =							tf_from_pt_model.dummy_inputs

										del tf_from_pt_model  # Try to keep only one model in memory at a time

										# Load the model and get some basic inputs
										lowerCamelCase__      =							pt_class.from_pretrained(self._local_dir						)
										pt_model.eval()

										lowerCamelCase__     ,		lowerCamelCase__      =							self.get_inputs(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						)

										with torch.no_grad():
															lowerCamelCase__      =							pt_model(**_lowerCAmelCase						,	output_hidden_states=_lowerCAmelCase						)
										del pt_model  # will no longer be used, and may have a large memory footprint

										lowerCamelCase__      =							tf_class.from_pretrained(self._local_dir						,	from_pt=_lowerCAmelCase						)
										lowerCamelCase__      =							tf_from_pt_model(**_lowerCAmelCase						,	output_hidden_states=_lowerCAmelCase						,	training=_lowerCAmelCase						)

										# Confirms that cross loading PT weights into TF worked.
										lowerCamelCase__      =							self.find_pt_tf_differences(_lowerCAmelCase						,	_lowerCAmelCase						)
										lowerCamelCase__      =							{k: v for k, v in crossload_differences.items() if "hidden" not in k}
										lowerCamelCase__      =							{k: v for k, v in crossload_differences.items() if "hidden" in k}
										if len(_lowerCAmelCase						) == 0 and architectures is not None:
															raise ValueError(
															    F"Something went wrong -- the config file has architectures ({architectures}), but no model head"
															    " output was found. All outputs start with 'hidden'"						)
										lowerCamelCase__      =							max(output_differences.values()						) if output_differences else 0.0
										lowerCamelCase__      =							max(hidden_differences.values()						)
										if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
															raise ValueError(
															    "The cross-loaded TensorFlow model has different outputs, something went wrong!\n"
															    + F"\nList of maximum output differences above the threshold ({self._max_error}):\n"
															    + "\n".join([F"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]						)
															    + F"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
															    + "\n".join([F"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]						)						)

										# Save the weights in a TF format (if needed) and confirms that the results are still good
										lowerCamelCase__      =							os.path.join(self._local_dir						,	_lowerCAmelCase						)
										lowerCamelCase__      =							os.path.join(self._local_dir						,	_lowerCAmelCase						)
										if (not os.path.exists(_lowerCAmelCase						) and not os.path.exists(_lowerCAmelCase						)) or self._new_weights:
															tf_from_pt_model.save_pretrained(self._local_dir						)
										del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

										lowerCamelCase__      =							tf_class.from_pretrained(self._local_dir						)
										lowerCamelCase__      =							tf_model(**_lowerCAmelCase						,	output_hidden_states=_lowerCAmelCase						)

										lowerCamelCase__      =							self.find_pt_tf_differences(_lowerCAmelCase						,	_lowerCAmelCase						)
										lowerCamelCase__      =							{k: v for k, v in conversion_differences.items() if "hidden" not in k}
										lowerCamelCase__      =							{k: v for k, v in conversion_differences.items() if "hidden" in k}
										if len(_lowerCAmelCase						) == 0 and architectures is not None:
															raise ValueError(
															    F"Something went wrong -- the config file has architectures ({architectures}), but no model head"
															    " output was found. All outputs start with 'hidden'"						)
										lowerCamelCase__      =							max(output_differences.values()						) if output_differences else 0.0
										lowerCamelCase__      =							max(hidden_differences.values()						)
										if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
															raise ValueError(
															    "The converted TensorFlow model has different outputs, something went wrong!\n"
															    + F"\nList of maximum output differences above the threshold ({self._max_error}):\n"
															    + "\n".join([F"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]						)
															    + F"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
															    + "\n".join([F"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]						)						)

										lowerCamelCase__      =							"Update TF weights" if self._new_weights else "Add TF weights"
										if self._push:
															repo.git_add(auto_lfs_track=_lowerCAmelCase						)
															repo.git_commit(_lowerCAmelCase						)
															repo.git_push(blocking=_lowerCAmelCase						)  # this prints a progress bar with the upload
															self._logger.warning(F"TF weights pushed into {self._model_name}"						)
										elif not self._no_pr:
															self._logger.warning("Uploading the weights into a new PR..."						)
															lowerCamelCase__      =							(
															    "Model converted by the [`transformers`' `pt_to_tf`"
															    " CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). "
															    "All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"
															    F"Maximum crossload output difference={max_crossload_output_diff:.3e}; "
															    F"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"
															    F"Maximum conversion output difference={max_conversion_output_diff:.3e}; "
															    F"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"
															)
															if self._max_error > MAX_ERROR:
																				commit_descrition += (
																				    F"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"
																				)
															if self._extra_commit_description:
																				commit_descrition += "\n\n" + self._extra_commit_description

															# sharded model -> adds all related files (index and .h5 shards)
															if os.path.exists(_lowerCAmelCase						):
																				lowerCamelCase__      =							[
																				    CommitOperationAdd(path_in_repo=_lowerCAmelCase						,	path_or_fileobj=_lowerCAmelCase						)
																				]
																				for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"						):
																									operations += [
																									    CommitOperationAdd(path_in_repo=os.path.basename(_lowerCAmelCase						)						,	path_or_fileobj=_lowerCAmelCase						)
																									]
															else:
																				lowerCamelCase__      =							[CommitOperationAdd(path_in_repo=_lowerCAmelCase						,	path_or_fileobj=_lowerCAmelCase						)]

															lowerCamelCase__      =							create_commit(
															    repo_id=self._model_name						,	operations=_lowerCAmelCase						,	commit_message=_lowerCAmelCase						,	commit_description=_lowerCAmelCase						,	repo_type="model"						,	create_pr=_lowerCAmelCase						,	).pr_url
															self._logger.warning(F"PR open in {hub_pr_url}"						)








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

import flax
import jax.numpy as jnp
from scipy import integrate

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

@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					A__								=     42

					# setable values
					A__								=     42
					A__								=     42
					A__								=     42
					A__								=     None

					# running values
					A__								=     None





					@classmethod
					def        __magic_name__			(			cls						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										return cls(common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						)

@dataclass
class SCREAMING_SNAKE_CASE_			(       lowercase_ ):





					"""simple docstring"""


					A__								=     42

class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_ ):





					"""simple docstring"""


					A__								=     [e.name for e in FlaxKarrasDiffusionSchedulers]

					A__								=     42





					@property
					def        __magic_name__			(			self						):
										return True





					@register_to_config
					def __init__(			self						,	_lowerCAmelCase = 1000						,	_lowerCAmelCase = 0.0_001						,	_lowerCAmelCase = 0.02						,	_lowerCAmelCase = "linear"						,	_lowerCAmelCase = None						,	_lowerCAmelCase = "epsilon"						,	_lowerCAmelCase = jnp.floataa						,	):
										lowerCamelCase__      =							dtype





					def        __magic_name__			(			self						,	_lowerCAmelCase = None						):
										if common is None:
															lowerCamelCase__      =							CommonSchedulerState.create(self						)

										lowerCamelCase__      =							jnp.arange(0						,	self.config.num_train_timesteps						).round()[::-1]
										lowerCamelCase__      =							((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

										# standard deviation of the initial noise distribution
										lowerCamelCase__      =							sigmas.max()

										return LMSDiscreteSchedulerState.create(
										    common=_lowerCAmelCase						,	init_noise_sigma=_lowerCAmelCase						,	timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										((lowerCamelCase__)     ,		)      =							jnp.where(state.timesteps == timestep						,	size=1						)
										lowerCamelCase__      =							step_index[0]

										lowerCamelCase__      =							state.sigmas[step_index]
										lowerCamelCase__      =							sample / ((sigma**2 + 1) ** 0.5)
										return sample





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						):

										def lms_derivative(_lowerCAmelCase						):
															lowerCamelCase__      =							1.0
															for k in range(_lowerCAmelCase						):
																				if current_order == k:
																									continue
																				prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
															return prod

										lowerCamelCase__      =							integrate.quad(_lowerCAmelCase						,	state.sigmas[t]						,	state.sigmas[t + 1]						,	epsrel=1E-4						)[0]

										return integrated_coeff





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = ()						):
										lowerCamelCase__      =							jnp.linspace(self.config.num_train_timesteps - 1						,	0						,	_lowerCAmelCase						,	dtype=self.dtype						)

										lowerCamelCase__      =							jnp.floor(_lowerCAmelCase						).astype(jnp.intaa						)
										lowerCamelCase__      =							jnp.ceil(_lowerCAmelCase						).astype(jnp.intaa						)

										lowerCamelCase__      =							jnp.mod(_lowerCAmelCase						,	1.0						)

										lowerCamelCase__      =							((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
										lowerCamelCase__      =							(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
										lowerCamelCase__      =							jnp.concatenate([sigmas, jnp.array([0.0]						,	dtype=self.dtype						)]						)

										lowerCamelCase__      =							timesteps.astype(jnp.intaa						)

										# initial running values
										lowerCamelCase__      =							jnp.zeros((0,) + shape						,	dtype=self.dtype						)

										return state.replace(
										    timesteps=_lowerCAmelCase						,	sigmas=_lowerCAmelCase						,	num_inference_steps=_lowerCAmelCase						,	derivatives=_lowerCAmelCase						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase = 4						,	_lowerCAmelCase = True						,	):
										if state.num_inference_steps is None:
															raise ValueError(
															    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"						)

										lowerCamelCase__      =							state.sigmas[timestep]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
															lowerCamelCase__      =							sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
															# * c_out + input * c_skip
															lowerCamelCase__      =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										else:
															raise ValueError(
															    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

										# 2. Convert to an ODE derivative
										lowerCamelCase__      =							(sample - pred_original_sample) / sigma
										lowerCamelCase__      =							state.replace(derivatives=jnp.append(state.derivatives						,	_lowerCAmelCase						)						)
										if len(state.derivatives						) > order:
															lowerCamelCase__      =							state.replace(derivatives=jnp.delete(state.derivatives						,	0						)						)

										# 3. Compute linear multistep coefficients
										lowerCamelCase__      =							min(timestep + 1						,	_lowerCAmelCase						)
										lowerCamelCase__      =							[self.get_lms_coefficient(_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						) for curr_order in range(_lowerCAmelCase						)]

										# 4. Compute previous sample based on the derivatives path
										lowerCamelCase__      =							sample + sum(
										    coeff * derivative for coeff, derivative in zip(_lowerCAmelCase						,	reversed(state.derivatives						)						)						)

										if not return_dict:
															return (prev_sample, state)

										return FlaxLMSSchedulerOutput(prev_sample=_lowerCAmelCase						,	state=_lowerCAmelCase						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							state.sigmas[timesteps].flatten()
										lowerCamelCase__      =							broadcast_to_shape_from_left(_lowerCAmelCase						,	noise.shape						)

										lowerCamelCase__      =							original_samples + noise * sigma

										return noisy_samples






					def __len__(			self						):
										return self.config.num_train_timesteps

