
"""simple docstring"""





import os
import sys

_a		       =							os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_a		       =							[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]







@add_start_docstrings(AutoConfig.__doc__  )
def 	__a     (		*__lowerCamelCase,		**__lowerCamelCase  ):
   return AutoConfig.from_pretrained(*__lowerCamelCase,		**__lowerCamelCase  )







@add_start_docstrings(AutoTokenizer.__doc__  )
def 	__a     (		*__lowerCamelCase,		**__lowerCamelCase  ):
   return AutoTokenizer.from_pretrained(*__lowerCamelCase,		**__lowerCamelCase  )







@add_start_docstrings(AutoModel.__doc__  )
def 	__a     (		*__lowerCamelCase,		**__lowerCamelCase  ):
   return AutoModel.from_pretrained(*__lowerCamelCase,		**__lowerCamelCase  )







@add_start_docstrings(AutoModelForCausalLM.__doc__  )
def 	__a     (		*__lowerCamelCase,		**__lowerCamelCase  ):
   return AutoModelForCausalLM.from_pretrained(*__lowerCamelCase,		**__lowerCamelCase  )







@add_start_docstrings(AutoModelForMaskedLM.__doc__  )
def 	__a     (		*__lowerCamelCase,		**__lowerCamelCase  ):
   return AutoModelForMaskedLM.from_pretrained(*__lowerCamelCase,		**__lowerCamelCase  )







@add_start_docstrings(AutoModelForSequenceClassification.__doc__  )
def 	__a     (		*__lowerCamelCase,		**__lowerCamelCase  ):
   return AutoModelForSequenceClassification.from_pretrained(*__lowerCamelCase,		**__lowerCamelCase  )







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__  )
def 	__a     (		*__lowerCamelCase,		**__lowerCamelCase  ):
   return AutoModelForQuestionAnswering.from_pretrained(*__lowerCamelCase,		**__lowerCamelCase  )







# 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							(   lowerCAmelCase	):





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






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





					if alpha_transform_type == "cosine":

										def alpha_bar_fn(A       :    Optional[int]       ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(A       :    Any       ):
															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							=							1




	@register_to_config
	def __init__(self		,       lowerCamelCase_ = 1000		,       lowerCamelCase_ = 0.0001		,       lowerCamelCase_ = 0.02		,       lowerCamelCase_ = "linear"		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       lowerCamelCase_ = True		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = "epsilon"		,       lowerCamelCase_ = 1.0		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						if kwargs.get("set_alpha_to_one"		,       lowerCamelCase_		) is not None:
											a					     =   (
											    "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_		)
											a					     =   kwargs["set_alpha_to_one"]
						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		)

						# 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.
						a					     =   torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

						# standard deviation of the initial noise distribution
						a					     =   1.0

						# setable values
						a					     =   None
						a					     =   torch.from_numpy(np.arange(0		,       lowerCamelCase_		).copy().astype(np.intaa		)		)




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

						return sample




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

						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.'''		)

						a					     =   num_inference_steps
						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().copy().astype(np.intaa		)
						a					     =   torch.from_numpy(lowerCamelCase_		).to(lowerCamelCase_		)
						self.timesteps += self.config.steps_offset




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 0.0		,       lowerCamelCase_ = False		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       ):
						"""simple docstring"""

						a					     =   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
						a					     =   self.alphas_cumprod[timestep]
						a					     =   (
						    self.alphas_cumprod[prev_timestep]
						    if prev_timestep < self.config.num_train_timesteps
						    else self.final_alpha_cumprod
						)

						a					     =   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":
											a					     =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											a					     =   model_output
						elif self.config.prediction_type == "sample":
											a					     =   model_output
											a					     =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
						elif self.config.prediction_type == "v_prediction":
											a					     =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											a					     =   (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:
											a					     =   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
						a					     =   (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
						a					     =   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		):
						"""simple docstring"""

						return self.config.num_train_timesteps
