

import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp
						import numpy as np

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
						import torch






@require_flax
class 			__magic_name__					:





			def __init__(	self      ,						__snake_case      ,						__snake_case=14      ,						__snake_case=7      ,						__snake_case=True      ,						__snake_case=True      ,						__snake_case=True      ,						__snake_case=99      ,						__snake_case=32      ,						__snake_case=5      ,						__snake_case=4      ,						__snake_case=37      ,						__snake_case="gelu"      ,						__snake_case=0.1      ,						__snake_case=0.1      ,						__snake_case=512      ,						__snake_case=0.02      ,						__snake_case=None      ,						)     ->				Optional[Any]:




						'''simple docstring'''
						__a						   =parent
						__a						   =batch_size
						__a						   =seq_length
						__a						   =is_training
						__a						   =use_input_mask
						__a						   =use_labels
						__a						   =vocab_size
						__a						   =d_model
						__a						   =num_hidden_layers
						__a						   =num_attention_heads
						__a						   =ffn_dim
						__a						   =activation_function
						__a						   =activation_dropout
						__a						   =attention_dropout
						__a						   =max_position_embeddings
						__a						   =initializer_range
						__a						   =None
						__a						   =0
						__a						   =2
						__a						   =1





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						__a						   =np.clip(ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size      )      ,						3      ,						self.vocab_size      )

						__a						   =None
						if self.use_input_mask:
									__a						   =random_attention_mask([self.batch_size, self.seq_length]      )

						__a						   =XGLMConfig(
						    vocab_size=self.vocab_size      ,						d_model=self.hidden_size      ,						num_layers=self.num_hidden_layers      ,						attention_heads=self.num_attention_heads      ,						ffn_dim=self.ffn_dim      ,						activation_function=self.activation_function      ,						activation_dropout=self.activation_dropout      ,						attention_dropout=self.attention_dropout      ,						max_position_embeddings=self.max_position_embeddings      ,						initializer_range=self.initializer_range      ,						use_cache=__snake_case      ,						bos_token_id=self.bos_token_id      ,						eos_token_id=self.eos_token_id      ,						pad_token_id=self.pad_token_id      ,						)

						return (config, input_ids, input_mask)





			def 	__magic_name__					(	self      )     ->				Any:




						'''simple docstring'''
						__a						   =self.prepare_config_and_inputs()
						__a   ,					__a   ,					__a						   =config_and_inputs
						__a						   ={'input_ids': input_ids, 'attention_mask': attention_mask}
						return config, inputs_dict





			def 	__magic_name__					(	self      )     ->				List[Any]:




						'''simple docstring'''
						__a   ,					__a   ,					__a						   =self.prepare_config_and_inputs()

						__a						   =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
						__a						   =ids_tensor([self.batch_size, self.seq_length]      ,						vocab_size=2      )

						return (
						    config,
						    input_ids,
						    attention_mask,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				List[Any]:




						'''simple docstring'''
						__a						   =20
						__a						   =model_class_name(__snake_case      )

						__a						   =model.init_cache(input_ids.shape[0]      ,						__snake_case      )
						__a						   =jnp.ones((input_ids.shape[0], max_decoder_length)      ,						dtype='i4'      )

						__a						   =jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1      )[None, :]      ,						(input_ids.shape[0], input_ids.shape[-1] - 1)      )
						__a						   =model(
						    input_ids[:, :-1]      ,						attention_mask=__snake_case      ,						past_key_values=__snake_case      ,						position_ids=__snake_case      ,						)

						__a						   =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,						dtype='i4'      )
						__a						   =model(
						    input_ids[:, -1:]      ,						attention_mask=__snake_case      ,						past_key_values=outputs_cache.past_key_values      ,						position_ids=__snake_case      ,						)

						__a						   =model(__snake_case      )

						__a						   =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
						self.parent.assertTrue(diff < 1e-3      ,						msg=f'Max diff is {diff}'      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				Tuple:




						'''simple docstring'''
						__a						   =20
						__a						   =model_class_name(__snake_case      )

						__a						   =jnp.concatenate(
						    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]      ,						axis=-1      ,						)

						__a						   =model.init_cache(input_ids.shape[0]      ,						__snake_case      )
						__a						   =jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1      )[None, :]      ,						(input_ids.shape[0], input_ids.shape[-1] - 1)      )

						__a						   =model(
						    input_ids[:, :-1]      ,						attention_mask=__snake_case      ,						past_key_values=__snake_case      ,						position_ids=__snake_case      ,						)
						__a						   =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,						dtype='i4'      )
						__a						   =model(
						    input_ids[:, -1:]      ,						past_key_values=outputs_cache.past_key_values      ,						attention_mask=__snake_case      ,						position_ids=__snake_case      ,						)

						__a						   =model(__snake_case      ,						attention_mask=__snake_case      )
						__a						   =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
						self.parent.assertTrue(diff < 1e-3      ,						msg=f'Max diff is {diff}'      )






@require_sentencepiece
@require_flax
class 			__magic_name__					(    lowerCAmelCase_						,					lowerCAmelCase_						,					unittest.TestCase   ):
			SCREAMING_SNAKE_CASE							     =		(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			SCREAMING_SNAKE_CASE							     =		(FlaxXGLMForCausalLM,) if is_flax_available() else ()





			def 	__magic_name__					(	self      )     ->				List[str]:




						'''simple docstring'''
						__a						   =FlaxXGLMModelTester(self      )





			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						for model_class_name in self.all_model_classes:
									__a   ,					__a   ,					__a						   =self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )





			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						for model_class_name in self.all_model_classes:
									__a   ,					__a   ,					__a						   =self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    __snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )





			@slow
			def 	__magic_name__					(	self      )     ->				List[str]:




						'''simple docstring'''
						__a						   =XGLMTokenizer.from_pretrained('XGLM'      ,						padding_side='left'      )
						__a						   =tokenizer(['Hello this is a long string', 'Hey']      ,						return_tensors='np'      ,						padding=__snake_case      ,						truncation=__snake_case      )

						__a						   =FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )
						__a						   =1
						__a						   =False

						__a						   =jax.jit(model.generate      )

						__a						   =jit_generate(inputs['input_ids']      ,						attention_mask=inputs['attention_mask']      ).sequences

						__a						   =tokenizer.batch_decode(__snake_case      ,						skip_special_tokens=__snake_case      )

						__a						   =[
						    'Hello this is a long string of questions, but I\'m not sure if I\'m',
						    'Hey, I\'m a newbie to the forum and I\'',
						]

						self.assertListEqual(__snake_case      ,						__snake_case      )





			@is_pt_flax_cross_test
			def 	__magic_name__					(	self      )     ->				str:




						'''simple docstring'''
						__a   ,					__a						   =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__      ):
												# prepare inputs
												__a						   =self._prepare_for_class(__snake_case      ,						__snake_case      )
												__a						   ={k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

												# load corresponding PyTorch class
												__a						   =model_class.__name__[4:]  # Skip the "Flax" at the beginning
												__a						   =getattr(__snake_case      ,						__snake_case      )

												__a   ,					__a						   =pt_inputs['input_ids'].shape
												__a						   =np.random.randint(0      ,						seq_length - 1      ,						size=(batch_size,)      )
												for batch_idx, start_index in enumerate(__snake_case      ):
															__a						   =0
															__a						   =1
															__a						   =0
															__a						   =1
												__a						   =pt_model_class(__snake_case      ).eval()
												# Flax models don't use the `use_cache` option and cache is not returned as a default.
												# So we disable `use_cache` here for PyTorch model.
												__a						   =False
												__a						   =model_class(__snake_case      ,						dtype=jnp.floataa      )
												__a						   =convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,						__snake_case      )
												__a						   =fx_state

												with torch.no_grad():
															__a						   =pt_model(**__snake_case      ).to_tuple()

												__a						   =fx_model(**__snake_case      ).to_tuple()
												self.assertEqual(len(__snake_case      )      ,						len(__snake_case      )      ,						'Output lengths differ between Flax and PyTorch'      )
												for fx_output, pt_output in zip(__snake_case      ,						__snake_case      ):
															self.assert_almost_equals(fx_output[:, -1]      ,						pt_output[:, -1].numpy()      ,						4e-2      )

												with tempfile.TemporaryDirectory() as tmpdirname:
															pt_model.save_pretrained(__snake_case      )
															__a						   =model_class.from_pretrained(__snake_case      ,						from_pt=__snake_case      )

												__a						   =fx_model_loaded(**__snake_case      ).to_tuple()
												self.assertEqual(
												    len(__snake_case      )      ,						len(__snake_case      )      ,						'Output lengths differ between Flax and PyTorch'      )
												for fx_output_loaded, pt_output in zip(__snake_case      ,						__snake_case      ):
															self.assert_almost_equals(fx_output_loaded[:, -1]      ,						pt_output[:, -1].numpy()      ,						4e-2      )





			@is_pt_flax_cross_test
			def 	__magic_name__					(	self      )     ->				str:




						'''simple docstring'''
						__a   ,					__a						   =self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__      ):
												# prepare inputs
												__a						   =self._prepare_for_class(__snake_case      ,						__snake_case      )
												__a						   ={k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

												# load corresponding PyTorch class
												__a						   =model_class.__name__[4:]  # Skip the "Flax" at the beginning
												__a						   =getattr(__snake_case      ,						__snake_case      )

												__a						   =pt_model_class(__snake_case      ).eval()
												__a						   =False
												__a						   =model_class(__snake_case      ,						dtype=jnp.floataa      )

												__a						   =load_flax_weights_in_pytorch_model(__snake_case      ,						fx_model.params      )
												__a   ,					__a						   =pt_inputs['input_ids'].shape
												__a						   =np.random.randint(0      ,						seq_length - 1      ,						size=(batch_size,)      )
												for batch_idx, start_index in enumerate(__snake_case      ):
															__a						   =0
															__a						   =1
															__a						   =0
															__a						   =1

												# make sure weights are tied in PyTorch
												pt_model.tie_weights()

												with torch.no_grad():
															__a						   =pt_model(**__snake_case      ).to_tuple()

												__a						   =fx_model(**__snake_case      ).to_tuple()
												self.assertEqual(len(__snake_case      )      ,						len(__snake_case      )      ,						'Output lengths differ between Flax and PyTorch'      )
												for fx_output, pt_output in zip(__snake_case      ,						__snake_case      ):
															self.assert_almost_equals(fx_output[:, -1]      ,						pt_output[:, -1].numpy()      ,						4e-2      )

												with tempfile.TemporaryDirectory() as tmpdirname:
															fx_model.save_pretrained(__snake_case      )
															__a						   =pt_model_class.from_pretrained(__snake_case      ,						from_flax=__snake_case      )

												with torch.no_grad():
															__a						   =pt_model_loaded(**__snake_case      ).to_tuple()

												self.assertEqual(
												    len(__snake_case      )      ,						len(__snake_case      )      ,						'Output lengths differ between Flax and PyTorch'      )
												for fx_output, pt_output in zip(__snake_case      ,						__snake_case      ):
															self.assert_almost_equals(fx_output[:, -1]      ,						pt_output[:, -1].numpy()      ,						4e-2      )





			@slow
			def 	__magic_name__					(	self      )     ->				Any:




						'''simple docstring'''
						for model_class_name in self.all_model_classes:
									__a						   =model_class_name.from_pretrained('facebook/xglm-564M'      )
									__a						   =model(np.ones((1, 1)      )      )
									self.assertIsNotNone(__snake_case      )









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  UpperCamelCase_(							_snake_case     :						List[Any] ,  _snake_case     :						Optional[int]=0.999 ,  _snake_case     :						List[Any]="cosine" ,  ):




			"""simple docstring"""






			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_snake_case     :						Union[str, Any]			):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

			elif alpha_transform_type == "exp":

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

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

			__a						   =[]
			for i in range(_snake_case			):
						__a						   =i / num_diffusion_timesteps
						__a						   =(i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(_snake_case			) / alpha_bar_fn(_snake_case			) ,  _snake_case			)			)
			return torch.tensor(_snake_case ,  dtype=torch.floataa			)






class 			__magic_name__					(    lowerCAmelCase_						,					lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		[e.name for e in KarrasDiffusionSchedulers]
			SCREAMING_SNAKE_CASE							     =		2





			@register_to_config
			def __init__(	self      ,						__snake_case = 1000      ,						__snake_case = 0.0_0085      ,						__snake_case = 0.012      ,						__snake_case = "linear"      ,						__snake_case = None      ,						__snake_case = "epsilon"      ,						__snake_case = "linspace"      ,						__snake_case = 0      ,						)     ->				Union[str, Any]:




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

						__a						   =1.0 - self.betas
						__a						   =torch.cumprod(self.alphas      ,						dim=0      )

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





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case=None      )     ->				Any:




						'''simple docstring'''
						if schedule_timesteps is None:
									__a						   =self.timesteps

						__a						   =(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:
									__a						   =1 if len(__snake_case      ) > 1 else 0
						else:
									__a						   =timestep.cpu().item() if torch.is_tensor(__snake_case      ) else timestep
									__a						   =self._index_counter[timestep_int]

						return indices[pos].item()





			@property
			def 	__magic_name__					(	self      )     ->				Any:




						'''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 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						)     ->				torch.FloatTensor:




						'''simple docstring'''
						__a						   =self.index_for_timestep(__snake_case      )

						if self.state_in_first_order:
									__a						   =self.sigmas[step_index]
						else:
									__a						   =self.sigmas_interpol[step_index - 1]

						__a						   =sample / ((sigma**2 + 1) ** 0.5)
						return sample





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = None      ,						)     ->				List[str]:




						'''simple docstring'''
						__a						   =num_inference_steps

						__a						   =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":
									__a						   =np.linspace(0      ,						num_train_timesteps - 1      ,						__snake_case      ,						dtype=__snake_case      )[::-1].copy()
						elif self.config.timestep_spacing == "leading":
									__a						   =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
									__a						   =(np.arange(0      ,						__snake_case      ) * step_ratio).round()[::-1].copy().astype(__snake_case      )
									timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
									__a						   =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
									__a						   =(np.arange(__snake_case      ,						0      ,						-step_ratio      )).round().copy().astype(__snake_case      )
									timesteps -= 1
						else:
									raise ValueError(
									    f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'      )

						__a						   =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
						__a						   =torch.from_numpy(np.log(__snake_case      )      ).to(__snake_case      )

						__a						   =np.interp(__snake_case      ,						np.arange(0      ,						len(__snake_case      )      )      ,						__snake_case      )
						__a						   =np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
						__a						   =torch.from_numpy(__snake_case      ).to(device=__snake_case      )

						# compute up and down sigmas
						__a						   =sigmas.roll(-1      )
						__a						   =0.0
						__a						   =(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
						__a						   =(sigmas_next**2 - sigmas_up**2) ** 0.5
						__a						   =0.0

						# compute interpolated sigmas
						__a						   =sigmas.log().lerp(sigmas_down.log()      ,						0.5      ).exp()
						__a						   =0.0

						# set sigmas
						__a						   =torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
						__a						   =torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
						__a						   =torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
						__a						   =torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

						if str(__snake_case      ).startswith('mps'      ):
									# mps does not support float64
									__a						   =torch.from_numpy(__snake_case      ).to(__snake_case      ,						dtype=torch.floataa      )
						else:
									__a						   =torch.from_numpy(__snake_case      ).to(__snake_case      )

						__a						   =self.sigma_to_t(__snake_case      ).to(__snake_case      ,						dtype=timesteps.dtype      )
						__a						   =torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])      ,						dim=-1      ).flatten()

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

						__a						   =None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						__a						   =defaultdict(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				int:




						'''simple docstring'''
						# get log sigma
						__a						   =sigma.log()

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

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

						__a						   =self.log_sigmas[low_idx]
						__a						   =self.log_sigmas[high_idx]

						# interpolate sigmas
						__a						   =(low - log_sigma) / (low - high)
						__a						   =w.clamp(0      ,						1      )

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





			@property
			def 	__magic_name__					(	self      )     ->				Optional[int]:




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





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case = None      ,						__snake_case = True      ,						)     ->				Union[SchedulerOutput, Tuple]:




						'''simple docstring'''
						__a						   =self.index_for_timestep(__snake_case      )

						# advance index counter by 1
						__a						   =timestep.cpu().item() if torch.is_tensor(__snake_case      ) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
									__a						   =self.sigmas[step_index]
									__a						   =self.sigmas_interpol[step_index]
									__a						   =self.sigmas_up[step_index]
									__a						   =self.sigmas_down[step_index - 1]
						else:
									# 2nd order / KPDM2's method
									__a						   =self.sigmas[step_index - 1]
									__a						   =self.sigmas_interpol[step_index - 1]
									__a						   =self.sigmas_up[step_index - 1]
									__a						   =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
						__a						   =0
						__a						   =sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

						__a						   =model_output.device
						__a						   =randn_tensor(model_output.shape      ,						dtype=model_output.dtype      ,						device=__snake_case      ,						generator=__snake_case      )

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
									__a						   =sigma_hat if self.state_in_first_order else sigma_interpol
									__a						   =sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
									__a						   =sigma_hat if self.state_in_first_order else sigma_interpol
									__a						   =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
									__a						   =(sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									__a						   =sigma_interpol - sigma_hat

									# store for 2nd order step
									__a						   =sample
									__a						   =dt
									__a						   =sample + derivative * dt
						else:
									# DPM-Solver-2
									# 2. Convert to an ODE derivative for 2nd order
									__a						   =(sample - pred_original_sample) / sigma_interpol
									# 3. delta timestep
									__a						   =sigma_down - sigma_hat

									__a						   =self.sample
									__a						   =None

									__a						   =sample + derivative * dt
									__a						   =prev_sample + noise * sigma_up

						if not return_dict:
									return (prev_sample,)

						return SchedulerOutput(prev_sample=__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						)     ->				torch.FloatTensor:




						'''simple docstring'''
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						__a						   =self.sigmas.to(device=original_samples.device      ,						dtype=original_samples.dtype      )
						if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case      ):
									# mps does not support float64
									__a						   =self.timesteps.to(original_samples.device      ,						dtype=torch.floataa      )
									__a						   =timesteps.to(original_samples.device      ,						dtype=torch.floataa      )
						else:
									__a						   =self.timesteps.to(original_samples.device      )
									__a						   =timesteps.to(original_samples.device      )

						__a						   =[self.index_for_timestep(__snake_case      ,						__snake_case      ) for t in timesteps]

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

						__a						   =original_samples + noise * sigma
						return noisy_samples





			def __len__(	self      )     ->				Optional[int]:




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






