



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


_lowercase:				Any       =	logging.get_logger(__name__)

_lowercase:				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 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__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	, lowerCamelCase_=50257	, lowerCamelCase_=1024	, lowerCamelCase_=768	, lowerCamelCase_=12	, lowerCamelCase_=12	, lowerCamelCase_=None	, lowerCamelCase_="gelu_new"	, lowerCamelCase_=0.1	, lowerCamelCase_=0.1	, lowerCamelCase_=0.1	, lowerCamelCase_=1E-5	, lowerCamelCase_=0.02	, lowerCamelCase_="cls_index"	, lowerCamelCase_=True	, lowerCamelCase_=None	, lowerCamelCase_=True	, lowerCamelCase_=0.1	, lowerCamelCase_=True	, lowerCamelCase_=True	, lowerCamelCase_=50256	, lowerCamelCase_=50256	, lowerCamelCase_=False	, lowerCamelCase_=False	, **lowerCamelCase_	, ):
						"""simple docstring"""

						a        =     vocab_size
						a        =     n_positions
						a        =     n_embd
						a        =     n_layer
						a        =     n_head
						a        =     n_inner
						a        =     activation_function
						a        =     resid_pdrop
						a        =     embd_pdrop
						a        =     attn_pdrop
						a        =     layer_norm_epsilon
						a        =     initializer_range
						a        =     summary_type
						a        =     summary_use_proj
						a        =     summary_activation
						a        =     summary_first_dropout
						a        =     summary_proj_to_labels
						a        =     scale_attn_weights
						a        =     use_cache
						a        =     scale_attn_by_inverse_layer_idx
						a        =     reorder_and_upcast_attn

						a        =     bos_token_id
						a        =     eos_token_id

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




class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_ = "default"	, lowerCamelCase_ = None	, lowerCamelCase_ = False	, ):
						"""simple docstring"""

						super().__init__(lowerCamelCase_	, task=lowerCamelCase_	, patching_specs=lowerCamelCase_	, use_past=lowerCamelCase_	)
						if not getattr(self._config	, "pad_token_id"	, lowerCamelCase_	):
											# TODO: how to do that better?
											a        =     0




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
						if self.use_past:
											self.fill_with_past_key_values_(lowerCamelCase_	, direction="inputs"	)
											a        =     {0: "batch", 1: "past_sequence + sequence"}
						else:
											a        =     {0: "batch", 1: "sequence"}

						return common_inputs




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return self._config.n_layer




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return self._config.n_head




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = -1	, lowerCamelCase_ = -1	, lowerCamelCase_ = False	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						a        =     super(lowerCamelCase_	, self	).generate_dummy_inputs(
						    lowerCamelCase_	, batch_size=lowerCamelCase_	, seq_length=lowerCamelCase_	, is_pair=lowerCamelCase_	, framework=lowerCamelCase_	)

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

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

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

						return ordered_inputs




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return 13





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 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							None






def 					a(   A :					Dict				,  A :					Optional[int]=0.999				,  A :					Optional[int]="cosine"				,  )	-> List[str]:
					"""simple docstring"""





					if alpha_transform_type == "cosine":

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

					elif alpha_transform_type == "exp":

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

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

					a        =     []
					for i in range(A ):
										a        =     i / num_diffusion_timesteps
										a        =     (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 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""
	__A					=							[e.name for e in KarrasDiffusionSchedulers]
	__A					=							1




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 1000	, lowerCamelCase_ = 0.0001	, lowerCamelCase_ = 0.02	, lowerCamelCase_ = "linear"	, lowerCamelCase_ = None	, lowerCamelCase_ = False	, lowerCamelCase_ = "epsilon"	, lowerCamelCase_ = "linspace"	, lowerCamelCase_ = 0	, ):
						"""simple docstring"""

						if trained_betas is not None:
											a        =     torch.tensor(lowerCamelCase_	, dtype=torch.floataa	)
						elif beta_schedule == "linear":
											a        =     torch.linspace(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, dtype=torch.floataa	)
						elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											a        =     (
											    torch.linspace(beta_start**0.5	, beta_end**0.5	, lowerCamelCase_	, dtype=torch.floataa	) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											a        =     betas_for_alpha_bar(lowerCamelCase_	)
						else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

						a        =     1.0 - self.betas
						a        =     torch.cumprod(self.alphas	, dim=0	)

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

						# setable values
						a        =     None
						a        =     use_karras_sigmas
						self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	)
						a        =     []
						a        =     False




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						if self.config.timestep_spacing in ["linspace", "trailing"]:
											return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						if isinstance(lowerCamelCase_	, torch.Tensor	):
											a        =     timestep.to(self.timesteps.device	)
						a        =     (self.timesteps == timestep).nonzero().item()
						a        =     self.sigmas[step_index]
						a        =     sample / ((sigma**2 + 1) ** 0.5)
						a        =     True
						return sample




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

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

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

						return integrated_coeff




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     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":
											a        =     np.linspace(0	, self.config.num_train_timesteps - 1	, lowerCamelCase_	, dtype=lowerCamelCase_	)[
											    ::-1
											].copy()
						elif self.config.timestep_spacing == "leading":
											a        =     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
											a        =     (np.arange(0	, lowerCamelCase_	) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_	)
											timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
											a        =     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
											a        =     (np.arange(self.config.num_train_timesteps	, 0	, -step_ratio	)).round().copy().astype(lowerCamelCase_	)
											timesteps -= 1
						else:
											raise ValueError(
											    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

						a        =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
						a        =     np.log(lowerCamelCase_	)
						a        =     np.interp(lowerCamelCase_	, np.arange(0	, len(lowerCamelCase_	)	)	, lowerCamelCase_	)

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

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

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

						a        =     []




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     np.log(lowerCamelCase_	)

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

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

						a        =     log_sigmas[low_idx]
						a        =     log_sigmas[high_idx]

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

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     in_sigmas[-1].item()
						a        =     in_sigmas[0].item()

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = 4	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						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(lowerCamelCase_	, torch.Tensor	):
											a        =     timestep.to(self.timesteps.device	)
						a        =     (self.timesteps == timestep).nonzero().item()
						a        =     self.sigmas[step_index]

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

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

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

						if not return_dict:
											return (prev_sample,)

						return LMSDiscreteSchedulerOutput(prev_sample=lowerCamelCase_	, pred_original_sample=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, ):
						"""simple docstring"""

						a        =     self.sigmas.to(device=original_samples.device	, dtype=original_samples.dtype	)
						if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_	):
											# mps does not support float64
											a        =     self.timesteps.to(original_samples.device	, dtype=torch.floataa	)
											a        =     timesteps.to(original_samples.device	, dtype=torch.floataa	)
						else:
											a        =     self.timesteps.to(original_samples.device	)
											a        =     timesteps.to(original_samples.device	)

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

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

						a        =     original_samples + noise * sigma
						return noisy_samples




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
