'''simple docstring'''

# 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      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Dict=0.9_9_9						,		snake_case_       :       List[Any]="cosine"						,		)					->     Optional[Any]:






 '''simple docstring'''

 if alpha_transform_type == "cosine":

  def alpha_bar_fn(snake_case_       :       Optional[Any]      ):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2      ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(snake_case_       :       Any      ):
   return math.exp(t * -1_2.0      )

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

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







class 				_lowercase     (				UpperCAmelCase__  ,							UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					1
 @register_to_config
 def __init__(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[str] = 10_00      ,       SCREAMING_SNAKE_CASE__   :	Optional[int] = 0.0_0_0_1      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any] = 0.0_2      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any] = "linear"      ,       SCREAMING_SNAKE_CASE__   :	Optional[int] = None      ,       SCREAMING_SNAKE_CASE__   :	str = True      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any] = True      ,       SCREAMING_SNAKE_CASE__   :	Tuple = 0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any] = "epsilon"      ,       SCREAMING_SNAKE_CASE__   :	int = 1.0      ,       **SCREAMING_SNAKE_CASE__   :	int      ,       )  ->    int:







  if kwargs.get("""set_alpha_to_one"""      ,       SCREAMING_SNAKE_CASE__							) is not None:
   __lowerCAmelCase											=						(
       """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
   )
   deprecate("""set_alpha_to_one"""      ,       """1.0.0"""      ,       SCREAMING_SNAKE_CASE__      ,       standard_warn=SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase											=						kwargs["""set_alpha_to_one"""]
  if trained_betas is not None:
   __lowerCAmelCase											=						torch.tensor(SCREAMING_SNAKE_CASE__      ,       dtype=torch.floataa							)
  elif beta_schedule == "linear":
   __lowerCAmelCase											=						torch.linspace(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       dtype=torch.floataa							)
  elif beta_schedule == "scaled_linear":
   # this schedule is very specific to the latent diffusion model.
   __lowerCAmelCase											=						(
       torch.linspace(beta_start**0.5      ,       beta_end**0.5      ,       SCREAMING_SNAKE_CASE__      ,       dtype=torch.floataa							) ** 2
   )
  elif beta_schedule == "squaredcos_cap_v2":
   # Glide cosine schedule
   __lowerCAmelCase											=						betas_for_alpha_bar(SCREAMING_SNAKE_CASE__							)
  else:
   raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

  __lowerCAmelCase											=						1.0 - self.betas
  __lowerCAmelCase											=						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											=						torch.tensor(0.0							) if set_alpha_to_zero else self.alphas_cumprod[-1]

  # standard deviation of the initial noise distribution
  __lowerCAmelCase											=						1.0

  # setable values
  __lowerCAmelCase											=						None
  __lowerCAmelCase											=						torch.from_numpy(np.arange(0      ,       SCREAMING_SNAKE_CASE__							).copy().astype(np.intaa							)							)
 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Any = None							)  ->    Any:
  return sample
 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tuple = None							)  ->    str:







  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											=						num_inference_steps
  __lowerCAmelCase											=						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											=						(np.arange(0      ,       SCREAMING_SNAKE_CASE__							) * step_ratio).round().copy().astype(np.intaa							)
  __lowerCAmelCase											=						torch.from_numpy(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
  self.timesteps += self.config.steps_offset
 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Dict = 0.0      ,       SCREAMING_SNAKE_CASE__   :	str = False      ,       SCREAMING_SNAKE_CASE__   :	str = None      ,       SCREAMING_SNAKE_CASE__   :	Dict = True      ,       )  ->    Any:
  __lowerCAmelCase											=						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											=						self.alphas_cumprod[timestep]
  __lowerCAmelCase											=						(
      self.alphas_cumprod[prev_timestep]
      if prev_timestep < self.config.num_train_timesteps
      else self.final_alpha_cumprod
  )

  __lowerCAmelCase											=						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											=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   __lowerCAmelCase											=						model_output
  elif self.config.prediction_type == "sample":
   __lowerCAmelCase											=						model_output
   __lowerCAmelCase											=						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  elif self.config.prediction_type == "v_prediction":
   __lowerCAmelCase											=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
   __lowerCAmelCase											=						(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											=						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											=						(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											=						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=SCREAMING_SNAKE_CASE__      ,       pred_original_sample=SCREAMING_SNAKE_CASE__							)





 def __len__(      self   :	Tuple							)  ->    List[Any]:
  return self.config.num_train_timesteps








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase  :					List[str]						    =							logging.get_logger(__name__)

lowercase  :					Optional[Any]						    =							{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				"gpt_neo"
    _a		:						Optional[int]=				["past_key_values"]
    _a		:						Dict=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
    def __init__(  self		,snake_case=50257		,snake_case=2048		,snake_case=2048		,snake_case=24		,snake_case=[[["global", "local"], 12]]		,snake_case=16		,snake_case=None		,snake_case=256		,snake_case="gelu_new"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.0		,snake_case=0.1		,snake_case=1e-5		,snake_case=0.02		,snake_case=True		,snake_case=50256		,snake_case=50256		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     int       	=      vocab_size
           lowercase							:     Union[str, Any]       	=      max_position_embeddings
           lowercase							:     Dict       	=      hidden_size
           lowercase							:     Union[str, Any]       	=      num_layers
           lowercase							:     Union[str, Any]       	=      num_heads
           lowercase							:     Optional[int]       	=      intermediate_size
           lowercase							:     List[str]       	=      window_size
           lowercase							:     Optional[int]       	=      activation_function
           lowercase							:     List[str]       	=      resid_dropout
           lowercase							:     int       	=      embed_dropout
           lowercase							:     Optional[int]       	=      attention_dropout
           lowercase							:     Tuple       	=      classifier_dropout
           lowercase							:     Optional[int]       	=      layer_norm_epsilon
           lowercase							:     Dict       	=      initializer_range
           lowercase							:     List[str]       	=      use_cache

           lowercase							:     Optional[int]       	=      bos_token_id
           lowercase							:     int       	=      eos_token_id

           lowercase							:     Union[str, Any]       	=      attention_types
           lowercase							:     Dict       	=      self.expand_attention_types_params(snake_case      )

           if len(self.attention_layers      ) != self.num_layers:
                  raise ValueError(
                      """Configuration for convolutional module is incorrect. """
                      """It is required that `len(config.attention_layers)` == `config.num_layers` """
                      f"but is `len(config.attention_layers) = {len(self.attention_layers      )}`, "
                      f"`config.num_layers = {self.num_layers}`. "
                      """`config.attention_layers` is prepared using `config.attention_types`. """
                      """Please verify the value of `config.attention_types` argument."""      )

           super().__init__(bos_token_id=snake_case		,eos_token_id=snake_case		,**snake_case      )
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      []
           for item in attention_types:
                  for _ in range(item[1]      ):
                         attentions.extend(item[0]      )
           return attentions







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       import torch

       lowercase							:     Tuple       	=      input.size()
       lowercase							:     int       	=      len(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      shape[dimension]

       lowercase							:     int       	=      torch.arange(0					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.div(sizedim - size					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				) + 1
       lowercase							:     Dict       	=      torch.arange(SCREAMING_SNAKE_CASE__				) + low_indices[:min_length][:, None]

       lowercase							:     Union[str, Any]       	=      [slice(SCREAMING_SNAKE_CASE__				)] * rank
       lowercase							:     Optional[Any]       	=      indices
       lowercase							:     List[str]       	=      input[s]

       lowercase							:     Optional[int]       	=      list(range(0					,	rank + 1				)				)
       perm.append(perm.pop(dimension + 1				)				)

       return sliced.permute(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       import torch

       lowercase							:     Union[str, Any]       	=      torch.arange(1					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.remainder(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      remainders == 0
       lowercase							:     Optional[int]       	=      candidates[divisor_indices]
       lowercase							:     List[Any]       	=      torch.max(SCREAMING_SNAKE_CASE__				)
       return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				)




class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""      )
                  lowercase							:     Dict       	=      {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                  lowercase							:     List[str]       	=      {0: """batch""", 1: """sequence"""}

           return common_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._config.num_heads
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      super(snake_case		,self      ).generate_dummy_inputs(
               snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           # We need to order the input in the way they appears in the forward()
           lowercase							:     List[str]       	=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
                  else:
                         import torch

                         lowercase	,			lowercase							:     List[Any]       	=      common_inputs["""input_ids"""].shape
                         # Not using the same length for past_key_values
                         lowercase							:     Optional[int]       	=      seqlen + 2
                         lowercase							:     int       	=      (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowercase							:     Optional[Any]       	=      [
                             (torch.zeros(snake_case      ), torch.zeros(snake_case      )) for _ in range(self.num_layers      )
                         ]

           lowercase							:     Optional[Any]       	=      common_inputs["""attention_mask"""]
           if self.use_past:
                  lowercase							:     Any       	=      ordered_inputs["""attention_mask"""].dtype
                  lowercase							:     Union[str, Any]       	=      torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(snake_case		,snake_case		,dtype=snake_case      )]		,dim=1      )

           return ordered_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 13
