



"""simple docstring"""
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 				lowerCamelCase  (			_SCREAMING_SNAKE_CASE				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     4_2
					_A		:			int						=     None

def    A_  (			_lowercase,					_lowercase=0.999,					_lowercase="cosine",					):







						'''simple docstring'''


						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						snake_case_ :List[str]						=			[]
						for i in range(A__	):
												snake_case_ :Optional[int]						=			i / num_diffusion_timesteps
												snake_case_ :List[str]						=			(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(A__	) / alpha_bar_fn(A__	),					A__	)	)
						return torch.tensor(A__,					dtype=torch.floataa	)

class 				lowerCamelCase  (			_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     1






					@register_to_config
					def __init__(       self:							str						,     snake_case:							int = 1_000						,     snake_case:							float = 0.0_0_0_1						,     snake_case:							float = 0.0_2						,     snake_case:							str = "linear"						,     snake_case:							Optional[Union[np.ndarray, List[float]]] = None						,     snake_case:							bool = True						,     snake_case:							bool = True						,     snake_case:							int = 0						,     snake_case:							str = "epsilon"						,     snake_case:							float = 1.0						,     **snake_case:							str						,     )   ->	Any:
											if kwargs.get("""set_alpha_to_one"""						,     snake_case						) is not None:
																	snake_case_ :Union[str, Any]						=			(
																	    """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
																	)
																	deprecate("""set_alpha_to_one"""						,     """1.0.0"""						,     snake_case						,     standard_warn=snake_case						)
																	snake_case_ :Tuple						=			kwargs["""set_alpha_to_one"""]
											if trained_betas is not None:
																	snake_case_ :int						=			torch.tensor(snake_case						,     dtype=torch.floataa						)
											elif beta_schedule == "linear":
																	snake_case_ :Optional[int]						=			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.
																	snake_case_ :Tuple						=			(
																	    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
																	snake_case_ :Any						=			betas_for_alpha_bar(snake_case						)
											else:
																	raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""						)

											snake_case_ :int						=			1.0 - self.betas
											snake_case_ :List[str]						=			torch.cumprod(self.alphas						,     dim=0						)

											# At every step in inverted ddim, we are looking into the next alphas_cumprod
											# For the final step, there is no next alphas_cumprod, and the index is out of bounds
											# `set_alpha_to_zero` decides whether we set this parameter simply to zero
											# in this case, self.step() just output the predicted noise
											# or whether we use the final alpha of the "non-previous" one.
											snake_case_ :int						=			torch.tensor(0.0						) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											snake_case_ :Optional[int]						=			1.0

											# setable values
											snake_case_ :Optional[Any]						=			None
											snake_case_ :List[Any]						=			torch.from_numpy(np.arange(0						,     snake_case						).copy().astype(np.intaa						)						)






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							torch.FloatTensor						,     snake_case:							Optional[int] = None						)   ->	torch.FloatTensor:
											return sample






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							int						,     snake_case:							Union[str, torch.device] = None						)   ->	int:

											if num_inference_steps > self.config.num_train_timesteps:
																	raise ValueError(
																	    f"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																	    f""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																	    f""" maximal {self.config.num_train_timesteps} timesteps."""						)

											snake_case_ :List[Any]						=			num_inference_steps
											snake_case_ :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
											snake_case_ :Optional[Any]						=			(np.arange(0						,     snake_case						) * step_ratio).round().copy().astype(np.intaa						)
											snake_case_ :Tuple						=			torch.from_numpy(snake_case						).to(snake_case						)
											self.timesteps += self.config.steps_offset






					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							torch.FloatTensor						,     snake_case:							int						,     snake_case:							torch.FloatTensor						,     snake_case:							float = 0.0						,     snake_case:							bool = False						,     snake_case:							Optional[torch.FloatTensor] = None						,     snake_case:							bool = True						,     )   ->	Union[DDIMSchedulerOutput, Tuple]:
											# 1. get previous step value (=t+1)
											snake_case_ :Optional[int]						=			timestep + self.config.num_train_timesteps // self.num_inference_steps

											# 2. compute alphas, betas
											# change original implementation to exactly match noise levels for analogous forward process
											snake_case_ :Union[str, Any]						=			self.alphas_cumprod[timestep]
											snake_case_ :Any						=			(
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											snake_case_ :Union[str, Any]						=			1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																	snake_case_ :Tuple						=			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																	snake_case_ :str						=			model_output
											elif self.config.prediction_type == "sample":
																	snake_case_ :List[str]						=			model_output
																	snake_case_ :Optional[int]						=			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																	snake_case_ :Optional[Any]						=			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																	snake_case_ :Tuple						=			(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																	raise ValueError(
																	    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																	    """ `v_prediction`"""						)

											# 4. Clip or threshold "predicted x_0"
											if self.config.clip_sample:
																	snake_case_ :Tuple						=			pred_original_sample.clamp(
																	    -self.config.clip_sample_range						,     self.config.clip_sample_range						)

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											snake_case_ :List[str]						=			(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											snake_case_ :Dict						=			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																	return (prev_sample, pred_original_sample)
											return DDIMSchedulerOutput(prev_sample=snake_case						,     pred_original_sample=snake_case						)






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




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A					:  List[Any]											=      logging.get_logger(__name__)

__A					:  Optional[Any]											=      {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'gpt_neox'

    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Tuple=5_04_32    ,						lowerCamelCase    :  Optional[int]=61_44    ,						lowerCamelCase    :  Tuple=44    ,						lowerCamelCase    :  Any=64    ,						lowerCamelCase    :  List[Any]=2_45_76    ,						lowerCamelCase    :  List[Any]="gelu"    ,						lowerCamelCase    :  Optional[Any]=0.25    ,						lowerCamelCase    :  Any=1_00_00    ,						lowerCamelCase    :  Any=0.0    ,						lowerCamelCase    :  str=0.0    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[Any]=20_48    ,						lowerCamelCase    :  List[Any]=0.02    ,						lowerCamelCase    :  Any=1E-5    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=0    ,						lowerCamelCase    :  List[str]=2    ,						lowerCamelCase    :  Dict=False    ,						lowerCamelCase    :  Tuple=True    ,						lowerCamelCase    :  Optional[int]=None    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[Any]:
      super().__init__(bos_token_id=lowerCamelCase    ,						eos_token_id=lowerCamelCase    ,						**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   vocab_size
      lowerCAmelCase_			:      Union[str, Any]			=   max_position_embeddings
      lowerCAmelCase_			:      Any			=   hidden_size
      lowerCAmelCase_			:      List[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[int]			=   num_attention_heads
      lowerCAmelCase_			:      str			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      List[Any]			=   rotary_pct
      lowerCAmelCase_			:      Any			=   rotary_emb_base
      lowerCAmelCase_			:      List[str]			=   attention_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_dropout
      lowerCAmelCase_			:      Tuple			=   classifier_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_range
      lowerCAmelCase_			:      Any			=   layer_norm_eps
      lowerCAmelCase_			:      str			=   use_cache
      lowerCAmelCase_			:      str			=   tie_word_embeddings
      lowerCAmelCase_			:      str			=   use_parallel_residual
      lowerCAmelCase_			:      Any			=   rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""		)





    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,						lowerCamelCase		) or len(self.rope_scaling		) != 2:
        raise ValueError(
            """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
            F'got {self.rope_scaling}'		)
      lowerCAmelCase_			:      Optional[Any]			=   self.rope_scaling.get("""type"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      int			=   self.rope_scaling.get("""factor"""    ,						lowerCamelCase		)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
      if rope_scaling_factor is None or not isinstance(lowerCamelCase    ,						lowerCamelCase		) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)



