



'''simple docstring'''





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


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

   from transformers.modeling_flax_pytorch_utils import (
       convert_pytorch_state_dict_to_flax,
       load_flax_weights_in_pytorch_model,
   )
   from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
   import torch






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Any      ,     _a				:     Union[str, Any]      ,     _a				:     List[Any]=14      ,     _a				:     Any=7      ,     _a				:     List[Any]=True      ,     _a				:     List[Any]=True      ,     _a				:     List[str]=False      ,     _a				:     str=True      ,     _a				:     Optional[int]=99      ,     _a				:     Optional[Any]=32      ,     _a				:     List[str]=4      ,     _a				:     List[str]=4      ,     _a				:     int=[[["global", "local"], 2]]      ,     _a				:     int=37      ,     _a				:     int="gelu"      ,     _a				:     Any=0.1      ,     _a				:     str=0.1      ,     _a				:     List[Any]=512      ,     _a				:     Dict=7      ,     _a				:     Any=0.02      ,     ) -> Dict:
    __lowerCamelCase	:	int   	=			parent
    __lowerCamelCase	:	Union[str, Any]   	=			batch_size
    __lowerCamelCase	:	List[Any]   	=			seq_length
    __lowerCamelCase	:	Any   	=			is_training
    __lowerCamelCase	:	Union[str, Any]   	=			use_input_mask
    __lowerCamelCase	:	Optional[Any]   	=			use_token_type_ids
    __lowerCamelCase	:	Tuple   	=			use_labels
    __lowerCamelCase	:	Optional[int]   	=			vocab_size
    __lowerCamelCase	:	Dict   	=			hidden_size
    __lowerCamelCase	:	Tuple   	=			num_hidden_layers
    __lowerCamelCase	:	List[str]   	=			num_attention_heads
    __lowerCamelCase	:	Optional[int]   	=			attention_types
    __lowerCamelCase	:	Dict   	=			intermediate_size
    __lowerCamelCase	:	Tuple   	=			hidden_act
    __lowerCamelCase	:	Tuple   	=			hidden_dropout_prob
    __lowerCamelCase	:	Dict   	=			attention_probs_dropout_prob
    __lowerCamelCase	:	List[Any]   	=			max_position_embeddings
    __lowerCamelCase	:	Dict   	=			window_size
    __lowerCamelCase	:	Dict   	=			initializer_range
    __lowerCamelCase	:	List[str]   	=			None
    __lowerCamelCase	:	Optional[int]   	=			vocab_size - 1
    __lowerCamelCase	:	Dict   	=			vocab_size - 1
    __lowerCamelCase	:	int   	=			vocab_size - 1




  def 						_lowercase					(			self				:     List[Any]       ) -> Union[str, Any]:
    __lowerCamelCase	:	str   	=			ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size       )

    __lowerCamelCase	:	Any   	=			None
    if self.use_input_mask:
      __lowerCamelCase	:	Optional[Any]   	=			random_attention_mask([self.batch_size, self.seq_length]       )

    __lowerCamelCase	:	Dict   	=			GPTNeoConfig(
        vocab_size=self.vocab_size      ,     hidden_size=self.hidden_size      ,     num_layers=self.num_hidden_layers      ,     num_heads=self.num_attention_heads      ,     max_position_embeddings=self.max_position_embeddings      ,     use_cache=_a      ,     bos_token_id=self.bos_token_id      ,     eos_token_id=self.eos_token_id      ,     pad_token_id=self.pad_token_id      ,     window_size=self.window_size      ,     attention_types=self.attention_types      ,     )

    return (config, input_ids, input_mask)




  def 						_lowercase					(			self				:     str       ) -> int:
    __lowerCamelCase	:	List[str]   	=			self.prepare_config_and_inputs()
    __lowerCamelCase	:	str   	=			config_and_inputs
    __lowerCamelCase	:	List[str]   	=			{'input_ids': input_ids, 'attention_mask': attention_mask}
    return config, inputs_dict




  def 						_lowercase					(			self				:     Tuple      ,     _a				:     int      ,     _a				:     Dict      ,     _a				:     str      ,     _a				:     List[Any]       ) -> Optional[Any]:
    __lowerCamelCase	:	Any   	=			20
    __lowerCamelCase	:	Optional[Any]   	=			model_class_name(_a       )

    __lowerCamelCase	:	Tuple   	=			model.init_cache(input_ids.shape[0]      ,     _a       )
    __lowerCamelCase	:	Optional[Any]   	=			jnp.ones((input_ids.shape[0], max_decoder_length)      ,     dtype='i4'       )

    __lowerCamelCase	:	Dict   	=			jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1       )[None, :]      ,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )
    __lowerCamelCase	:	int   	=			model(
        input_ids[:, :-1]      ,     attention_mask=_a      ,     past_key_values=_a      ,     position_ids=_a      ,     )

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

    __lowerCamelCase	:	Any   	=			model(_a       )

    __lowerCamelCase	:	Dict   	=			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 						_lowercase					(			self				:     Tuple      ,     _a				:     Dict      ,     _a				:     List[Any]      ,     _a				:     Optional[int]      ,     _a				:     Union[str, Any]       ) -> List[Any]:
    __lowerCamelCase	:	Tuple   	=			20
    __lowerCamelCase	:	Union[str, Any]   	=			model_class_name(_a       )

    __lowerCamelCase	:	Optional[int]   	=			jnp.concatenate(
        [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )]      ,     axis=-1      ,     )

    __lowerCamelCase	:	List[str]   	=			model.init_cache(input_ids.shape[0]      ,     _a       )
    __lowerCamelCase	:	List[str]   	=			jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1       )[None, :]      ,     (input_ids.shape[0], input_ids.shape[-1] - 1)       )

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

    __lowerCamelCase	:	Union[str, Any]   	=			model(_a      ,     attention_mask=_a       )

    __lowerCamelCase	:	Tuple   	=			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_flax
class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       unittest.TestCase					):
  """simple docstring"""




  a_							 =(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
  a_							 =(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




  def 						_lowercase					(			self				:     List[str]       ) -> Tuple:
    __lowerCamelCase	:	Any   	=			FlaxGPTNeoModelTester(self       )




  def 						_lowercase					(			self				:     List[Any]       ) -> Dict:
    for model_class_name in self.all_model_classes:
      __lowerCamelCase	:	str   	=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward(_a      ,     _a      ,     _a      ,     _a       )




  def 						_lowercase					(			self				:     Dict       ) -> Optional[Any]:
    for model_class_name in self.all_model_classes:
      __lowerCamelCase	:	List[Any]   	=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward_with_attn_mask(
          _a      ,     _a      ,     _a      ,     _a       )




  @slow
  def 						_lowercase					(			self				:     List[str]       ) -> Optional[int]:
    __lowerCamelCase	:	Union[str, Any]   	=			GPTaTokenizer.from_pretrained('gpt2'      ,     pad_token='<|endoftext|>'      ,     padding_side='left'       )
    __lowerCamelCase	:	Optional[Any]   	=			tokenizer(['Hello this is a long string', 'Hey']      ,     return_tensors='np'      ,     padding=_a      ,     truncation=_a       )

    __lowerCamelCase	:	Optional[int]   	=			FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'       )
    __lowerCamelCase	:	str   	=			False
    __lowerCamelCase	:	str   	=			model.config.eos_token_id

    __lowerCamelCase	:	str   	=			jax.jit(model.generate       )

    __lowerCamelCase	:	str   	=			jit_generate(
        inputs['input_ids']      ,     attention_mask=inputs['attention_mask']      ,     pad_token_id=tokenizer.pad_token_id       ).sequences

    __lowerCamelCase	:	Tuple   	=			tokenizer.batch_decode(_a      ,     skip_special_tokens=_a       )

    __lowerCamelCase	:	Union[str, Any]   	=			[
        'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
        'Hey, I\'m a little late to the party. I\'m going to',
    ]

    self.assertListEqual(_a      ,     _a       )




  @is_pt_flax_cross_test
  def 						_lowercase					(			self				:     Dict       ) -> str:
    __lowerCamelCase	:	Tuple   	=			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
        __lowerCamelCase	:	Optional[Any]   	=			self._prepare_for_class(_a      ,     _a       )
        __lowerCamelCase	:	Optional[Any]   	=			{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

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

        __lowerCamelCase	:	str   	=			pt_inputs['input_ids'].shape
        __lowerCamelCase	:	str   	=			np.random.randint(0      ,     seq_length - 1      ,     size=(batch_size,)       )
        for batch_idx, start_index in enumerate(_a       ):
          __lowerCamelCase	:	Any   	=			0
          __lowerCamelCase	:	int   	=			1
          __lowerCamelCase	:	Any   	=			0
          __lowerCamelCase	:	List[Any]   	=			1
        __lowerCamelCase	:	Tuple   	=			pt_model_class(_a       ).eval()
        __lowerCamelCase	:	Optional[int]   	=			model_class(_a      ,     dtype=jnp.floataa       )

        __lowerCamelCase	:	List[Any]   	=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,     _a       )
        __lowerCamelCase	:	List[str]   	=			fx_state

        with torch.no_grad():
          __lowerCamelCase	:	Union[str, Any]   	=			pt_model(**_a       ).to_tuple()

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

        with tempfile.TemporaryDirectory() as tmpdirname:
          pt_model.save_pretrained(_a       )
          __lowerCamelCase	:	Union[str, Any]   	=			model_class.from_pretrained(_a      ,     from_pt=_a       )

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




  @is_pt_flax_cross_test
  def 						_lowercase					(			self				:     Optional[Any]       ) -> List[Any]:
    __lowerCamelCase	:	List[str]   	=			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
        __lowerCamelCase	:	Union[str, Any]   	=			self._prepare_for_class(_a      ,     _a       )
        __lowerCamelCase	:	List[str]   	=			{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        __lowerCamelCase	:	Optional[int]   	=			model_class.__name__[4:]  # Skip the "Flax" at the beginning
        __lowerCamelCase	:	Tuple   	=			getattr(_a      ,     _a       )

        __lowerCamelCase	:	Optional[Any]   	=			pt_model_class(_a       ).eval()
        __lowerCamelCase	:	Union[str, Any]   	=			model_class(_a      ,     dtype=jnp.floataa       )

        __lowerCamelCase	:	str   	=			load_flax_weights_in_pytorch_model(_a      ,     fx_model.params       )
        __lowerCamelCase	:	List[Any]   	=			pt_inputs['input_ids'].shape
        __lowerCamelCase	:	Dict   	=			np.random.randint(0      ,     seq_length - 1      ,     size=(batch_size,)       )
        for batch_idx, start_index in enumerate(_a       ):
          __lowerCamelCase	:	Any   	=			0
          __lowerCamelCase	:	Union[str, Any]   	=			1
          __lowerCamelCase	:	str   	=			0
          __lowerCamelCase	:	Union[str, Any]   	=			1

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

        with torch.no_grad():
          __lowerCamelCase	:	Optional[Any]   	=			pt_model(**_a       ).to_tuple()

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

        with tempfile.TemporaryDirectory() as tmpdirname:
          fx_model.save_pretrained(_a       )
          __lowerCamelCase	:	Any   	=			pt_model_class.from_pretrained(_a      ,     from_flax=_a       )

        with torch.no_grad():
          __lowerCamelCase	:	List[str]   	=			pt_model_loaded(**_a       ).to_tuple()

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




  @slow
  def 						_lowercase					(			self				:     Tuple       ) -> str:
    for model_class_name in self.all_model_classes:
      __lowerCamelCase	:	Optional[Any]   	=			model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'       )
      __lowerCamelCase	:	List[str]   	=			model(np.ones((1, 1)       )       )
      self.assertIsNotNone(_a       )











'''simple docstring'''





import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =42
  a_							 =None




def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase=0.999	,_lowerCAmelCase="cosine"	,)				->				Union[str, Any]:
  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

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






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =[e.name for e in KarrasDiffusionSchedulers]
  a_							 =1




  @register_to_config
  def __init__(			self				:     List[Any]      ,     _a				:     int = 1000      ,     _a				:     float = 0.0001      ,     _a				:     float = 0.02      ,     _a				:     str = "linear"      ,     _a				:     Optional[Union[np.ndarray, List[float]]] = None      ,     _a				:     Optional[bool] = False      ,     _a				:     str = "epsilon"      ,     _a				:     str = "linspace"      ,     _a				:     int = 0      ,     ) -> Tuple:
    if trained_betas is not None:
      __lowerCamelCase	:	Optional[int]   	=			torch.tensor(_a      ,     dtype=torch.floataa       )
    elif beta_schedule == "linear":
      __lowerCamelCase	:	List[str]   	=			torch.linspace(_a      ,     _a      ,     _a      ,     dtype=torch.floataa       )
    elif beta_schedule == "scaled_linear":
      # this schedule is very specific to the latent diffusion model.
      __lowerCamelCase	:	Optional[int]   	=			(
          torch.linspace(beta_start**0.5      ,     beta_end**0.5      ,     _a      ,     dtype=torch.floataa       ) ** 2
      )
    elif beta_schedule == "squaredcos_cap_v2":
      # Glide cosine schedule
      __lowerCamelCase	:	Any   	=			betas_for_alpha_bar(_a       )
    else:
      raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'       )

    __lowerCamelCase	:	Optional[Any]   	=			1.0 - self.betas
    __lowerCamelCase	:	Optional[int]   	=			torch.cumprod(self.alphas      ,     dim=0       )

    __lowerCamelCase	:	str   	=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    __lowerCamelCase	:	Optional[Any]   	=			np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
    __lowerCamelCase	:	Dict   	=			torch.from_numpy(_a       )

    # setable values
    __lowerCamelCase	:	List[str]   	=			None
    __lowerCamelCase	:	int   	=			use_karras_sigmas
    self.set_timesteps(_a      ,     _a       )
    __lowerCamelCase	:	int   	=			[]
    __lowerCamelCase	:	Dict   	=			False




  @property
  def 						_lowercase					(			self				:     Optional[Any]       ) -> Any:
    # 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 						_lowercase					(			self				:     List[Any]      ,     _a				:     torch.FloatTensor      ,     _a				:     Union[float, torch.FloatTensor]       ) -> torch.FloatTensor:
    if isinstance(_a      ,     torch.Tensor       ):
      __lowerCamelCase	:	List[Any]   	=			timestep.to(self.timesteps.device       )
    __lowerCamelCase	:	int   	=			(self.timesteps == timestep).nonzero().item()
    __lowerCamelCase	:	Any   	=			self.sigmas[step_index]
    __lowerCamelCase	:	List[str]   	=			sample / ((sigma**2 + 1) ** 0.5)
    __lowerCamelCase	:	Any   	=			True
    return sample




  def 						_lowercase					(			self				:     Optional[int]      ,     _a				:     str      ,     _a				:     List[str]      ,     _a				:     Union[str, Any]       ) -> Optional[Any]:

    def lms_derivative(_a				:     List[str]       ):
      __lowerCamelCase	:	Any   	=			1.0
      for k in range(_a       ):
        if current_order == k:
          continue
        prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
      return prod

    __lowerCamelCase	:	Any   	=			integrate.quad(_a      ,     self.sigmas[t]      ,     self.sigmas[t + 1]      ,     epsrel=1e-4       )[0]

    return integrated_coeff




  def 						_lowercase					(			self				:     int      ,     _a				:     int      ,     _a				:     Union[str, torch.device] = None       ) -> str:
    __lowerCamelCase	:	Optional[Any]   	=			num_inference_steps

    # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
    if self.config.timestep_spacing == "linspace":
      __lowerCamelCase	:	Any   	=			np.linspace(0      ,     self.config.num_train_timesteps - 1      ,     _a      ,     dtype=_a       )[
          ::-1
      ].copy()
    elif self.config.timestep_spacing == "leading":
      __lowerCamelCase	:	Union[str, Any]   	=			self.config.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
      __lowerCamelCase	:	Union[str, Any]   	=			(np.arange(0      ,     _a       ) * step_ratio).round()[::-1].copy().astype(_a       )
      timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
      __lowerCamelCase	:	int   	=			self.config.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
      __lowerCamelCase	:	str   	=			(np.arange(self.config.num_train_timesteps      ,     0      ,     -step_ratio       )).round().copy().astype(_a       )
      timesteps -= 1
    else:
      raise ValueError(
          f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'       )

    __lowerCamelCase	:	Optional[Any]   	=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
    __lowerCamelCase	:	List[Any]   	=			np.log(_a       )
    __lowerCamelCase	:	Optional[int]   	=			np.interp(_a      ,     np.arange(0      ,     len(_a       )       )      ,     _a       )

    if self.use_karras_sigmas:
      __lowerCamelCase	:	int   	=			self._convert_to_karras(in_sigmas=_a       )
      __lowerCamelCase	:	str   	=			np.array([self._sigma_to_t(_a      ,     _a       ) for sigma in sigmas]       )

    __lowerCamelCase	:	Optional[Any]   	=			np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )

    __lowerCamelCase	:	Dict   	=			torch.from_numpy(_a       ).to(device=_a       )
    if str(_a       ).startswith('mps'       ):
      # mps does not support float64
      __lowerCamelCase	:	List[str]   	=			torch.from_numpy(_a       ).to(_a      ,     dtype=torch.floataa       )
    else:
      __lowerCamelCase	:	str   	=			torch.from_numpy(_a       ).to(device=_a       )

    __lowerCamelCase	:	List[Any]   	=			[]




  def 						_lowercase					(			self				:     List[Any]      ,     _a				:     List[Any]      ,     _a				:     int       ) -> Tuple:
    # get log sigma
    __lowerCamelCase	:	Optional[int]   	=			np.log(_a       )

    # get distribution
    __lowerCamelCase	:	int   	=			log_sigma - log_sigmas[:, np.newaxis]

    # get sigmas range
    __lowerCamelCase	:	Dict   	=			np.cumsum((dists >= 0)      ,     axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
    __lowerCamelCase	:	List[Any]   	=			low_idx + 1

    __lowerCamelCase	:	Optional[Any]   	=			log_sigmas[low_idx]
    __lowerCamelCase	:	str   	=			log_sigmas[high_idx]

    # interpolate sigmas
    __lowerCamelCase	:	List[str]   	=			(low - log_sigma) / (low - high)
    __lowerCamelCase	:	List[str]   	=			np.clip(_a      ,     0      ,     1       )

    # transform interpolation to time range
    __lowerCamelCase	:	Dict   	=			(1 - w) * low_idx + w * high_idx
    __lowerCamelCase	:	List[str]   	=			t.reshape(sigma.shape       )
    return t




  def 						_lowercase					(			self				:     int      ,     _a				:     torch.FloatTensor       ) -> torch.FloatTensor:
    __lowerCamelCase	:	float   	=			in_sigmas[-1].item()
    __lowerCamelCase	:	float   	=			in_sigmas[0].item()

    __lowerCamelCase	:	Optional[Any]   	=			7.0  # 7.0 is the value used in the paper
    __lowerCamelCase	:	Any   	=			np.linspace(0      ,     1      ,     self.num_inference_steps       )
    __lowerCamelCase	:	Optional[Any]   	=			sigma_min ** (1 / rho)
    __lowerCamelCase	:	Tuple   	=			sigma_max ** (1 / rho)
    __lowerCamelCase	:	str   	=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas




  def 						_lowercase					(			self				:     Dict      ,     _a				:     torch.FloatTensor      ,     _a				:     Union[float, torch.FloatTensor]      ,     _a				:     torch.FloatTensor      ,     _a				:     int = 4      ,     _a				:     bool = True      ,     ) -> Union[LMSDiscreteSchedulerOutput, Tuple]:
    if not self.is_scale_input_called:
      warnings.warn(
          'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
          'See `StableDiffusionPipeline` for a usage example.'       )

    if isinstance(_a      ,     torch.Tensor       ):
      __lowerCamelCase	:	Optional[int]   	=			timestep.to(self.timesteps.device       )
    __lowerCamelCase	:	List[str]   	=			(self.timesteps == timestep).nonzero().item()
    __lowerCamelCase	:	Optional[int]   	=			self.sigmas[step_index]

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
      __lowerCamelCase	:	Optional[Any]   	=			sample - sigma * model_output
    elif self.config.prediction_type == "v_prediction":
      # * c_out + input * c_skip
      __lowerCamelCase	:	List[str]   	=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
    elif self.config.prediction_type == "sample":
      __lowerCamelCase	:	Union[str, Any]   	=			model_output
    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	:	str   	=			(sample - pred_original_sample) / sigma
    self.derivatives.append(_a       )
    if len(self.derivatives       ) > order:
      self.derivatives.pop(0       )

    # 3. Compute linear multistep coefficients
    __lowerCamelCase	:	List[str]   	=			min(step_index + 1      ,     _a       )
    __lowerCamelCase	:	int   	=			[self.get_lms_coefficient(_a      ,     _a      ,     _a       ) for curr_order in range(_a       )]

    # 4. Compute previous sample based on the derivatives path
    __lowerCamelCase	:	Optional[Any]   	=			sample + sum(
        coeff * derivative for coeff, derivative in zip(_a      ,     reversed(self.derivatives       )       )       )

    if not return_dict:
      return (prev_sample,)

    return LMSDiscreteSchedulerOutput(prev_sample=_a      ,     pred_original_sample=_a       )




  def 						_lowercase					(			self				:     List[str]      ,     _a				:     torch.FloatTensor      ,     _a				:     torch.FloatTensor      ,     _a				:     torch.FloatTensor      ,     ) -> torch.FloatTensor:
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    __lowerCamelCase	:	Any   	=			self.sigmas.to(device=original_samples.device      ,     dtype=original_samples.dtype       )
    if original_samples.device.type == "mps" and torch.is_floating_point(_a       ):
      # mps does not support float64
      __lowerCamelCase	:	Optional[int]   	=			self.timesteps.to(original_samples.device      ,     dtype=torch.floataa       )
      __lowerCamelCase	:	Optional[int]   	=			timesteps.to(original_samples.device      ,     dtype=torch.floataa       )
    else:
      __lowerCamelCase	:	int   	=			self.timesteps.to(original_samples.device       )
      __lowerCamelCase	:	Optional[Any]   	=			timesteps.to(original_samples.device       )

    __lowerCamelCase	:	List[str]   	=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

    __lowerCamelCase	:	Union[str, Any]   	=			sigmas[step_indices].flatten()
    while len(sigma.shape       ) < len(original_samples.shape       ):
      __lowerCamelCase	:	List[Any]   	=			sigma.unsqueeze(-1       )

    __lowerCamelCase	:	Optional[Any]   	=			original_samples + noise * sigma
    return noisy_samples




  def __len__(			self				:     Union[str, Any]       ) -> int:
    return self.config.num_train_timesteps






