



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def      lowerCAmelCase__		(   lowerCamelCase_		:		Dict	,lowerCamelCase_		:		Optional[int]=0.999	,lowerCamelCase_		:		str="cosine"	,):





				'''simple docstring'''

				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				lowerCAmelCase__					:    Dict												=					[]
				for i in range(lowerCamelCase_):
								lowerCAmelCase__					:    List[Any]												=					i / num_diffusion_timesteps
								lowerCAmelCase__					:    Dict												=					(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowerCamelCase_) / alpha_bar_fn(lowerCamelCase_)	,lowerCamelCase_))
				return torch.tensor(lowerCamelCase_	,dtype=torch.floataa)



class 	lowerCamelCase__      (							lowerCamelCase__				,    lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =[e.name for e in KarrasDiffusionSchedulers]
		snake_case_     =2



		@register_to_config
		def __init__(self		,__lowerCamelCase = 10_00		,__lowerCamelCase = 0.0_0085		,__lowerCamelCase = 0.012		,__lowerCamelCase = "linear"		,__lowerCamelCase = None		,__lowerCamelCase = "epsilon"		,__lowerCamelCase = "linspace"		,__lowerCamelCase = 0		,)					->      Optional[Any]:

						"""simple docstring"""






						if trained_betas is not None:
										lowerCAmelCase__					:    int												=					torch.tensor(__lowerCamelCase		,dtype=torch.floataa			)
						elif beta_schedule == "linear":
										lowerCAmelCase__					:    str												=					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__					:    int												=					1.0 - self.betas
						lowerCAmelCase__					:    int												=					torch.cumprod(self.alphas		,dim=0			)

						#  set all values
						self.set_timesteps(__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=None			)					->      str:

						"""simple docstring"""






						if schedule_timesteps is None:
										lowerCAmelCase__					:    Tuple												=					self.timesteps

						lowerCAmelCase__					:    Dict												=					(schedule_timesteps == timestep).nonzero()

						# The sigma index that is taken for the **very** first `step`
						# is always the second index (or the last index if there is only 1)
						# This way we can ensure we don't accidentally skip a sigma in
						# case we start in the middle of the denoising schedule (e.g. for image-to-image)
						if len(self._index_counter			) == 0:
										lowerCAmelCase__					:    Union[str, Any]												=					1 if len(__lowerCamelCase			) > 1 else 0
						else:
										lowerCAmelCase__					:    List[str]												=					timestep.cpu().item() if torch.is_tensor(__lowerCamelCase			) else timestep
										lowerCAmelCase__					:    Optional[int]												=					self._index_counter[timestep_int]

						return indices[pos].item()



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






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

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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,)					->      torch.FloatTensor:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.index_for_timestep(__lowerCamelCase			)

						if self.state_in_first_order:
										lowerCAmelCase__					:    Union[str, Any]												=					self.sigmas[step_index]
						else:
										lowerCAmelCase__					:    Tuple												=					self.sigmas_interpol[step_index]

						lowerCAmelCase__					:    Optional[Any]												=					sample / ((sigma**2 + 1) ** 0.5)
						return sample



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None		,__lowerCamelCase = None		,)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					num_inference_steps

						lowerCAmelCase__					:    Optional[Any]												=					num_train_timesteps or self.config.num_train_timesteps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
										lowerCAmelCase__					:    Dict												=					np.linspace(0		,num_train_timesteps - 1		,__lowerCamelCase		,dtype=__lowerCamelCase			)[::-1].copy()
						elif self.config.timestep_spacing == "leading":
										lowerCAmelCase__					:    Union[str, Any]												=					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__					:    Tuple												=					(np.arange(0		,__lowerCamelCase			) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase			)
										timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
										lowerCAmelCase__					:    Tuple												=					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__					:    Any												=					(np.arange(__lowerCamelCase		,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'."""			)

						lowerCAmelCase__					:    List[Any]												=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
						lowerCAmelCase__					:    str												=					torch.from_numpy(np.log(__lowerCamelCase			)			).to(__lowerCamelCase			)

						lowerCAmelCase__					:    Union[str, Any]												=					np.interp(__lowerCamelCase		,np.arange(0		,len(__lowerCamelCase			)			)		,__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
						lowerCAmelCase__					:    Tuple												=					torch.from_numpy(__lowerCamelCase			).to(device=__lowerCamelCase			)

						# interpolate sigmas
						lowerCAmelCase__					:    int												=					sigmas.log().lerp(sigmas.roll(1			).log()		,0.5			).exp()

						lowerCAmelCase__					:    List[str]												=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2			), sigmas[-1:]]			)
						lowerCAmelCase__					:    Dict												=					torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2			), sigmas_interpol[-1:]]			)

						if str(__lowerCamelCase			).startswith('''mps'''			):
										# mps does not support float64
										lowerCAmelCase__					:    List[Any]												=					torch.from_numpy(__lowerCamelCase			).to(__lowerCamelCase		,dtype=torch.floataa			)
						else:
										lowerCAmelCase__					:    Dict												=					torch.from_numpy(__lowerCamelCase			).to(__lowerCamelCase			)

						# interpolate timesteps
						lowerCAmelCase__					:    Dict												=					self.sigma_to_t(__lowerCamelCase			).to(__lowerCamelCase		,dtype=timesteps.dtype			)
						lowerCAmelCase__					:    Tuple												=					torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])		,dim=-1			).flatten()

						lowerCAmelCase__					:    str												=					torch.cat([timesteps[:1], interleaved_timesteps]			)

						lowerCAmelCase__					:    List[Any]												=					None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						lowerCAmelCase__					:    Dict												=					defaultdict(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					sigma.log()

						# get distribution
						lowerCAmelCase__					:    Optional[int]												=					log_sigma - self.log_sigmas[:, None]

						# get sigmas range
						lowerCAmelCase__					:    List[str]												=					dists.ge(0			).cumsum(dim=0			).argmax(dim=0			).clamp(max=self.log_sigmas.shape[0] - 2			)
						lowerCAmelCase__					:    Tuple												=					low_idx + 1

						lowerCAmelCase__					:    Dict												=					self.log_sigmas[low_idx]
						lowerCAmelCase__					:    Dict												=					self.log_sigmas[high_idx]

						# interpolate sigmas
						lowerCAmelCase__					:    List[str]												=					(low - log_sigma) / (low - high)
						lowerCAmelCase__					:    List[str]												=					w.clamp(0		,1			)

						# transform interpolation to time range
						lowerCAmelCase__					:    List[str]												=					(1 - w) * low_idx + w * high_idx
						lowerCAmelCase__					:    Tuple												=					t.view(sigma.shape			)
						return t



		@property
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						return self.sample is None



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase = True		,)					->      Union[SchedulerOutput, Tuple]:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					self.index_for_timestep(__lowerCamelCase			)

						# advance index counter by 1
						lowerCAmelCase__					:    Tuple												=					timestep.cpu().item() if torch.is_tensor(__lowerCamelCase			) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
										lowerCAmelCase__					:    List[Any]												=					self.sigmas[step_index]
										lowerCAmelCase__					:    str												=					self.sigmas_interpol[step_index + 1]
										lowerCAmelCase__					:    Tuple												=					self.sigmas[step_index + 1]
						else:
										# 2nd order / KDPM2's method
										lowerCAmelCase__					:    List[Any]												=					self.sigmas[step_index - 1]
										lowerCAmelCase__					:    Union[str, Any]												=					self.sigmas_interpol[step_index]
										lowerCAmelCase__					:    Tuple												=					self.sigmas[step_index]

						# currently only gamma=0 is supported. This usually works best anyways.
						# We can support gamma in the future but then need to scale the timestep before
						# passing it to the model which requires a change in API
						lowerCAmelCase__					:    List[Any]												=					0
						lowerCAmelCase__					:    Union[str, Any]												=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										lowerCAmelCase__					:    Dict												=					sigma_hat if self.state_in_first_order else sigma_interpol
										lowerCAmelCase__					:    str												=					sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
										lowerCAmelCase__					:    Optional[Any]												=					sigma_hat if self.state_in_first_order else sigma_interpol
										lowerCAmelCase__					:    Tuple												=					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
										    sample / (sigma_input**2 + 1)
										)
						elif self.config.prediction_type == "sample":
										raise NotImplementedError('''prediction_type not implemented yet: sample'''			)
						else:
										raise ValueError(
										    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""			)

						if self.state_in_first_order:
										# 2. Convert to an ODE derivative for 1st order
										lowerCAmelCase__					:    Any												=					(sample - pred_original_sample) / sigma_hat
										# 3. delta timestep
										lowerCAmelCase__					:    Any												=					sigma_interpol - sigma_hat

										# store for 2nd order step
										lowerCAmelCase__					:    Any												=					sample
						else:
										# DPM-Solver-2
										# 2. Convert to an ODE derivative for 2nd order
										lowerCAmelCase__					:    Optional[Any]												=					(sample - pred_original_sample) / sigma_interpol

										# 3. delta timestep
										lowerCAmelCase__					:    Any												=					sigma_next - sigma_hat

										lowerCAmelCase__					:    Dict												=					self.sample
										lowerCAmelCase__					:    Optional[Any]												=					None

						lowerCAmelCase__					:    Union[str, Any]												=					sample + derivative * dt

						if not return_dict:
										return (prev_sample,)

						return SchedulerOutput(prev_sample=__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,)					->      torch.FloatTensor:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					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
										lowerCAmelCase__					:    Any												=					self.timesteps.to(original_samples.device		,dtype=torch.floataa			)
										lowerCAmelCase__					:    Union[str, Any]												=					timesteps.to(original_samples.device		,dtype=torch.floataa			)
						else:
										lowerCAmelCase__					:    str												=					self.timesteps.to(original_samples.device			)
										lowerCAmelCase__					:    Optional[Any]												=					timesteps.to(original_samples.device			)

						lowerCAmelCase__					:    Tuple												=					[self.index_for_timestep(__lowerCamelCase		,__lowerCamelCase			) for t in timesteps]

						lowerCAmelCase__					:    Optional[Any]												=					sigmas[step_indices].flatten()
						while len(sigma.shape			) < len(original_samples.shape			):
										lowerCAmelCase__					:    int												=					sigma.unsqueeze(-1			)

						lowerCAmelCase__					:    Union[str, Any]												=					original_samples + noise * sigma
						return noisy_samples



		def __len__(self			)					->      Optional[Any]:

						"""simple docstring"""






						return self.config.num_train_timesteps






from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case    :		Dict  =HfArgumentParser(InitializationArguments)
__snake_case    :		Tuple  =parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case    :		Optional[int]  =AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case    :		List[str]  ={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
__snake_case    :		List[Any]  =AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__snake_case    :		int  =AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

