





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 					__A   (  a			):
				__A            = 42
				__A            = None





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase=0.9_9_9	,					_UpperCAmelCase="cosine"	,					)  ->     int:
				if alpha_transform_type == "cosine":

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

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

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




class 					__A   (  a			,    a			):
				__A            = [e.name for e in KarrasDiffusionSchedulers]
				__A            = 1


				@register_to_config
				def __init__(						self      ,       UpperCAmelCase_ = 1000      ,       UpperCAmelCase_ = 0.0_0_0_1      ,       UpperCAmelCase_ = 0.0_2      ,       UpperCAmelCase_ = "linear"      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = False      ,       UpperCAmelCase_ = "epsilon"      ,       UpperCAmelCase_ = "linspace"      ,       UpperCAmelCase_ = 0      ,       ):
								if trained_betas is not None:
												lowerCamelCase		       =torch.tensor(UpperCAmelCase_      ,       dtype=torch.floataa							)
								elif beta_schedule == "linear":
												lowerCamelCase		       =torch.linspace(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       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      ,       UpperCAmelCase_      ,       dtype=torch.floataa							) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												lowerCamelCase		       =betas_for_alpha_bar(UpperCAmelCase_							)
								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							)

								lowerCamelCase		       =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
								lowerCamelCase		       =np.concatenate([sigmas[::-1], [0.0]]							).astype(np.floataa							)
								lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							)

								# setable values
								lowerCamelCase		       =None
								lowerCamelCase		       =use_karras_sigmas
								self.set_timesteps(UpperCAmelCase_      ,       UpperCAmelCase_							)
								lowerCamelCase		       =[]
								lowerCamelCase		       =False


				@property
				def _snake_case				(						self							):
								# 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 _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								if isinstance(UpperCAmelCase_      ,       torch.Tensor							):
												lowerCamelCase		       =timestep.to(self.timesteps.device							)
								lowerCamelCase		       =(self.timesteps == timestep).nonzero().item()
								lowerCamelCase		       =self.sigmas[step_index]
								lowerCamelCase		       =sample / ((sigma**2 + 1) ** 0.5)
								lowerCamelCase		       =True
								return sample


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):

								def lms_derivative(UpperCAmelCase_							):
												lowerCamelCase		       =1.0
												for k in range(UpperCAmelCase_							):
																if current_order == k:
																				continue
																prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
												return prod

								lowerCamelCase		       =integrate.quad(UpperCAmelCase_      ,       self.sigmas[t]      ,       self.sigmas[t + 1]      ,       epsrel=1E-4							)[0]

								return integrated_coeff


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None							):
								lowerCamelCase		       =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		       =np.linspace(0      ,       self.config.num_train_timesteps - 1      ,       UpperCAmelCase_      ,       dtype=UpperCAmelCase_							)[
												    ::-1
												].copy()
								elif self.config.timestep_spacing == "leading":
												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      ,       UpperCAmelCase_							) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_							)
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												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(self.config.num_train_timesteps      ,       0      ,       -step_ratio							)).round().copy().astype(UpperCAmelCase_							)
												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		       =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
								lowerCamelCase		       =np.log(UpperCAmelCase_							)
								lowerCamelCase		       =np.interp(UpperCAmelCase_      ,       np.arange(0      ,       len(UpperCAmelCase_							)							)      ,       UpperCAmelCase_							)

								if self.use_karras_sigmas:
												lowerCamelCase		       =self._convert_to_karras(in_sigmas=UpperCAmelCase_							)
												lowerCamelCase		       =np.array([self._sigma_to_t(UpperCAmelCase_      ,       UpperCAmelCase_							) for sigma in sigmas]							)

								lowerCamelCase		       =np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)

								lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							).to(device=UpperCAmelCase_							)
								if str(UpperCAmelCase_							).startswith("""mps"""							):
												# mps does not support float64
												lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							).to(UpperCAmelCase_      ,       dtype=torch.floataa							)
								else:
												lowerCamelCase		       =torch.from_numpy(UpperCAmelCase_							).to(device=UpperCAmelCase_							)

								lowerCamelCase		       =[]


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_							):
								# get log sigma
								lowerCamelCase		       =np.log(UpperCAmelCase_							)

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

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

								lowerCamelCase		       =log_sigmas[low_idx]
								lowerCamelCase		       =log_sigmas[high_idx]

								# interpolate sigmas
								lowerCamelCase		       =(low - log_sigma) / (low - high)
								lowerCamelCase		       =np.clip(UpperCAmelCase_      ,       0      ,       1							)

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


				def _snake_case				(						self      ,       UpperCAmelCase_							):
								lowerCamelCase		       =in_sigmas[-1].item()
								lowerCamelCase		       =in_sigmas[0].item()

								lowerCamelCase		       =7.0  # 7.0 is the value used in the paper
								lowerCamelCase		       =np.linspace(0      ,       1      ,       self.num_inference_steps							)
								lowerCamelCase		       =sigma_min ** (1 / rho)
								lowerCamelCase		       =sigma_max ** (1 / rho)
								lowerCamelCase		       =(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = 4      ,       UpperCAmelCase_ = True      ,       ):
								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(UpperCAmelCase_      ,       torch.Tensor							):
												lowerCamelCase		       =timestep.to(self.timesteps.device							)
								lowerCamelCase		       =(self.timesteps == timestep).nonzero().item()
								lowerCamelCase		       =self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
												lowerCamelCase		       =sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												lowerCamelCase		       =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
												lowerCamelCase		       =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		       =(sample - pred_original_sample) / sigma
								self.derivatives.append(UpperCAmelCase_							)
								if len(self.derivatives							) > order:
												self.derivatives.pop(0							)

								# 3. Compute linear multistep coefficients
								lowerCamelCase		       =min(step_index + 1      ,       UpperCAmelCase_							)
								lowerCamelCase		       =[self.get_lms_coefficient(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_							) for curr_order in range(UpperCAmelCase_							)]

								# 4. Compute previous sample based on the derivatives path
								lowerCamelCase		       =sample + sum(
								    coeff * derivative for coeff, derivative in zip(UpperCAmelCase_      ,       reversed(self.derivatives							)							)							)

								if not return_dict:
												return (prev_sample,)

								return LMSDiscreteSchedulerOutput(prev_sample=UpperCAmelCase_      ,       pred_original_sample=UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       ):
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowerCamelCase		       =self.sigmas.to(device=original_samples.device      ,       dtype=original_samples.dtype							)
								if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_							):
												# mps does not support float64
												lowerCamelCase		       =self.timesteps.to(original_samples.device      ,       dtype=torch.floataa							)
												lowerCamelCase		       =timesteps.to(original_samples.device      ,       dtype=torch.floataa							)
								else:
												lowerCamelCase		       =self.timesteps.to(original_samples.device							)
												lowerCamelCase		       =timesteps.to(original_samples.device							)

								lowerCamelCase		       =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

								lowerCamelCase		       =original_samples + noise * sigma
								return noisy_samples


				def __len__(						self							):
								return self.config.num_train_timesteps












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

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


UpperCAmelCase__  :	str     =logging.get_logger(__name__)

UpperCAmelCase__  :	Optional[int]     ={
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}

class 					__A   (  a			):
				__A            = """gpt2"""
				__A            = ["""past_key_values"""]
				__A            = {
				    """hidden_size""": """n_embd""",
				    """max_position_embeddings""": """n_positions""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}


				def __init__(						self      ,       UpperCAmelCase_=50257      ,       UpperCAmelCase_=1024      ,       UpperCAmelCase_=768      ,       UpperCAmelCase_=12      ,       UpperCAmelCase_=12      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_="gelu_new"      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=1E-5      ,       UpperCAmelCase_=0.0_2      ,       UpperCAmelCase_="cls_index"      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=None      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=0.1      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=True      ,       UpperCAmelCase_=50256      ,       UpperCAmelCase_=50256      ,       UpperCAmelCase_=False      ,       UpperCAmelCase_=False      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =vocab_size
								lowerCamelCase		       =n_positions
								lowerCamelCase		       =n_embd
								lowerCamelCase		       =n_layer
								lowerCamelCase		       =n_head
								lowerCamelCase		       =n_inner
								lowerCamelCase		       =activation_function
								lowerCamelCase		       =resid_pdrop
								lowerCamelCase		       =embd_pdrop
								lowerCamelCase		       =attn_pdrop
								lowerCamelCase		       =layer_norm_epsilon
								lowerCamelCase		       =initializer_range
								lowerCamelCase		       =summary_type
								lowerCamelCase		       =summary_use_proj
								lowerCamelCase		       =summary_activation
								lowerCamelCase		       =summary_first_dropout
								lowerCamelCase		       =summary_proj_to_labels
								lowerCamelCase		       =scale_attn_weights
								lowerCamelCase		       =use_cache
								lowerCamelCase		       =scale_attn_by_inverse_layer_idx
								lowerCamelCase		       =reorder_and_upcast_attn

								lowerCamelCase		       =bos_token_id
								lowerCamelCase		       =eos_token_id

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




class 					__A   (  a			):


				def __init__(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = "default"      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = False      ,       ):
								super().__init__(UpperCAmelCase_      ,       task=UpperCAmelCase_      ,       patching_specs=UpperCAmelCase_      ,       use_past=UpperCAmelCase_							)
								if not getattr(self._config      ,       """pad_token_id"""      ,       UpperCAmelCase_							):
												# TODO: how to do that better?
												lowerCamelCase		       =0


				@property
				def _snake_case				(						self							):
								lowerCamelCase		       =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
								if self.use_past:
												self.fill_with_past_key_values_(UpperCAmelCase_      ,       direction="""inputs"""							)
												lowerCamelCase		       ={0: """batch""", 1: """past_sequence + sequence"""}
								else:
												lowerCamelCase		       ={0: """batch""", 1: """sequence"""}

								return common_inputs


				@property
				def _snake_case				(						self							):
								return self._config.n_layer


				@property
				def _snake_case				(						self							):
								return self._config.n_head


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = -1      ,       UpperCAmelCase_ = -1      ,       UpperCAmelCase_ = False      ,       UpperCAmelCase_ = None      ,       ):
								lowerCamelCase		       =super(UpperCAmelCase_      ,       self							).generate_dummy_inputs(
								    UpperCAmelCase_      ,       batch_size=UpperCAmelCase_      ,       seq_length=UpperCAmelCase_      ,       is_pair=UpperCAmelCase_      ,       framework=UpperCAmelCase_							)

								# We need to order the input in the way they appears in the forward()
								lowerCamelCase		       =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

																lowerCamelCase       ,     lowerCamelCase		       =common_inputs["""input_ids"""].shape
																# Not using the same length for past_key_values
																lowerCamelCase		       =seqlen + 2
																lowerCamelCase		       =(
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																lowerCamelCase		       =[
																    (torch.zeros(UpperCAmelCase_							), torch.zeros(UpperCAmelCase_							)) for _ in range(self.num_layers							)
																]

								lowerCamelCase		       =common_inputs["""attention_mask"""]
								if self.use_past:
												lowerCamelCase		       =ordered_inputs["""attention_mask"""].dtype
												lowerCamelCase		       =torch.cat(
												    [ordered_inputs["""attention_mask"""], torch.ones(UpperCAmelCase_      ,       UpperCAmelCase_      ,       dtype=UpperCAmelCase_							)]      ,       dim=1							)

								return ordered_inputs


				@property
				def _snake_case				(						self							):
								return 13





