# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
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 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				torch.FloatTensor
    SCREAMING_SNAKE_CASE:				Optional[torch.FloatTensor]          =    None





def 		snake_case__	(		lowercase							,				lowercase=0.999							,				lowercase="cosine"							,				):
   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}'''					)

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







class 					_lowercase    (     UpperCAmelCase__ ,  UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				str          =    1




    @register_to_config
    def __init__(    self       ,						lowerCamelCase__ = 1_000       ,						lowerCamelCase__ = 0.0_0_0_1       ,						lowerCamelCase__ = 0.0_2       ,						lowerCamelCase__ = "linear"       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = True       ,						lowerCamelCase__ = True       ,						lowerCamelCase__ = 0       ,						lowerCamelCase__ = "epsilon"       ,						lowerCamelCase__ = 1.0       ,						**lowerCamelCase__       ,						):
       if kwargs.get("set_alpha_to_one"       ,						lowerCamelCase__ ) is not None:
          lowerCAmelCase_:							Optional[int]       =	(
              "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
          )
          deprecate("set_alpha_to_one"       ,						"1.0.0"       ,						lowerCamelCase__       ,						standard_warn=lowerCamelCase__ )
          lowerCAmelCase_:							List[Any]       =	kwargs["set_alpha_to_one"]
       if trained_betas is not None:
          lowerCAmelCase_:							Any       =	torch.tensor(lowerCamelCase__       ,						dtype=torch.floataa )
       elif beta_schedule == "linear":
          lowerCAmelCase_:							List[Any]       =	torch.linspace(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						dtype=torch.floataa )
       elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowerCAmelCase_:							List[Any]       =	(
              torch.linspace(beta_start**0.5       ,						beta_end**0.5       ,						lowerCamelCase__       ,						dtype=torch.floataa ) ** 2
          )
       elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowerCAmelCase_:							Tuple       =	betas_for_alpha_bar(lowerCamelCase__ )
       else:
          raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}''' )

       lowerCAmelCase_:							Any       =	1.0 - self.betas
       lowerCAmelCase_:							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.
       lowerCAmelCase_:							str       =	torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       lowerCAmelCase_:							Tuple       =	1.0

       # setable values
       lowerCAmelCase_:							Optional[int]       =	None
       lowerCAmelCase_:							int       =	torch.from_numpy(np.arange(0       ,						lowerCamelCase__ ).copy().astype(np.intaa ) )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       return sample




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):

       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.''' )

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




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = 0.0       ,						lowerCamelCase__ = False       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = True       ,						):
       # 1. get previous step value (=t+1)
       lowerCAmelCase_:							Union[str, Any]       =	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
       lowerCAmelCase_:							Union[str, Any]       =	self.alphas_cumprod[timestep]
       lowerCAmelCase_:							List[Any]       =	(
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       lowerCAmelCase_:							Optional[int]       =	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":
          lowerCAmelCase_:							Optional[int]       =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          lowerCAmelCase_:							Union[str, Any]       =	model_output
       elif self.config.prediction_type == "sample":
          lowerCAmelCase_:							Any       =	model_output
          lowerCAmelCase_:							Optional[int]       =	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
          lowerCAmelCase_:							int       =	(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          lowerCAmelCase_:							Dict       =	(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:
          lowerCAmelCase_:							str       =	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
       lowerCAmelCase_:							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
       lowerCAmelCase_:							Optional[int]       =	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=lowerCamelCase__       ,						pred_original_sample=lowerCamelCase__ )




    def __len__(    self ):
       return self.config.num_train_timesteps
from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

a  :      Union[str, Any]            =    {
    """bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[str]          =    'gpt_bigcode'
    SCREAMING_SNAKE_CASE:				Union[str, Any]          =    ['past_key_values']
    SCREAMING_SNAKE_CASE:				Tuple          =    {
        'hidden_size': 'n_embd',
        'max_position_embeddings': 'n_positions',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }




    def __init__(    self       ,						lowerCamelCase__=50_257       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=768       ,						lowerCamelCase__=12       ,						lowerCamelCase__=12       ,						lowerCamelCase__=None       ,						lowerCamelCase__="gelu_pytorch_tanh"       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=1E-5       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=50_256       ,						lowerCamelCase__=50_256       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							List[Any]       =	vocab_size
       lowerCAmelCase_:							List[str]       =	n_positions
       lowerCAmelCase_:							Union[str, Any]       =	n_embd
       lowerCAmelCase_:							Union[str, Any]       =	n_layer
       lowerCAmelCase_:							Union[str, Any]       =	n_head
       lowerCAmelCase_:							List[Any]       =	n_inner
       lowerCAmelCase_:							List[Any]       =	activation_function
       lowerCAmelCase_:							Optional[int]       =	resid_pdrop
       lowerCAmelCase_:							Union[str, Any]       =	embd_pdrop
       lowerCAmelCase_:							Any       =	attn_pdrop
       lowerCAmelCase_:							Union[str, Any]       =	layer_norm_epsilon
       lowerCAmelCase_:							Optional[Any]       =	initializer_range
       lowerCAmelCase_:							int       =	scale_attn_weights
       lowerCAmelCase_:							int       =	use_cache
       lowerCAmelCase_:							int       =	attention_softmax_in_fpaa
       lowerCAmelCase_:							int       =	scale_attention_softmax_in_fpaa
       lowerCAmelCase_:							Union[str, Any]       =	multi_query

       lowerCAmelCase_:							Union[str, Any]       =	bos_token_id
       lowerCAmelCase_:							List[str]       =	eos_token_id

       super().__init__(bos_token_id=lowerCamelCase__       ,						eos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )