






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 					__SCREAMING_SNAKE_CASE(		a_		):
							_UpperCAmelCase	      =    42
							_UpperCAmelCase	      =    None


def        a_	( _A ,				_A=0.999 ,				_A="cosine" ,				)     ->     Optional[int]:







				"""simple docstring"""
				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

				snake_case__													=       []
				for i in range(_A					):
								snake_case__													=       i / num_diffusion_timesteps
								snake_case__													=       (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 					__SCREAMING_SNAKE_CASE(		a_      ,					a_		):
							_UpperCAmelCase	      =    [e.name for e in KarrasDiffusionSchedulers]
							_UpperCAmelCase	      =    1
							@register_to_config
							def __init__(       self: int    ,		UpperCamelCase: int = 10_00    ,		UpperCamelCase: float = 0.0_001    ,		UpperCamelCase: float = 0.02    ,		UpperCamelCase: str = "linear"    ,		UpperCamelCase: Optional[Union[np.ndarray, List[float]]] = None    ,		UpperCamelCase: Optional[bool] = False    ,		UpperCamelCase: str = "epsilon"    ,		UpperCamelCase: str = "linspace"    ,		UpperCamelCase: int = 0    ,		)						->						Union[str, Any]:
											if trained_betas is not None:
															snake_case__													=       torch.tensor(UpperCamelCase    ,		dtype=torch.floataa  )
											elif beta_schedule == "linear":
															snake_case__													=       torch.linspace(UpperCamelCase    ,		UpperCamelCase    ,		UpperCamelCase    ,		dtype=torch.floataa  )
											elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															snake_case__													=       (
															    torch.linspace(beta_start**0.5    ,		beta_end**0.5    ,		UpperCamelCase    ,		dtype=torch.floataa  ) ** 2
															)
											elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															snake_case__													=       betas_for_alpha_bar(UpperCamelCase  )
											else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''  )

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

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

											# setable values
											snake_case__													=       None
											snake_case__													=       use_karras_sigmas
											self.set_timesteps(UpperCamelCase    ,		UpperCamelCase  )
											snake_case__													=       []
											snake_case__													=       False
							@property
							def lowerCAmelCase_	(       self: List[str]  )						->						List[Any]:
											# 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 lowerCAmelCase_	(       self: Optional[Any]    ,		UpperCamelCase: torch.FloatTensor    ,		UpperCamelCase: Union[float, torch.FloatTensor]  )						->						torch.FloatTensor:
											if isinstance(UpperCamelCase    ,		torch.Tensor  ):
															snake_case__													=       timestep.to(self.timesteps.device  )
											snake_case__													=       (self.timesteps == timestep).nonzero().item()
											snake_case__													=       self.sigmas[step_index]
											snake_case__													=       sample / ((sigma**2 + 1) ** 0.5)
											snake_case__													=       True
											return sample
							def lowerCAmelCase_	(       self: Union[str, Any]    ,		UpperCamelCase: Optional[Any]    ,		UpperCamelCase: str    ,		UpperCamelCase: Any  )						->						Union[str, Any]:

											def lms_derivative(UpperCamelCase: Dict  ):
															snake_case__													=       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

											snake_case__													=       integrate.quad(UpperCamelCase    ,		self.sigmas[t]    ,		self.sigmas[t + 1]    ,		epsrel=1e-4  )[0]

											return integrated_coeff
							def lowerCAmelCase_	(       self: int    ,		UpperCamelCase: int    ,		UpperCamelCase: Union[str, torch.device] = None  )						->						int:
											snake_case__													=       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":
															snake_case__													=       np.linspace(0    ,		self.config.num_train_timesteps - 1    ,		UpperCamelCase    ,		dtype=UpperCamelCase  )[
															    ::-1
															].copy()
											elif self.config.timestep_spacing == "leading":
															snake_case__													=       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
															snake_case__													=       (np.arange(0    ,		UpperCamelCase  ) * step_ratio).round()[::-1].copy().astype(UpperCamelCase  )
															timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
															snake_case__													=       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
															snake_case__													=       (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\'.'''  )

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

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

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

											snake_case__													=       torch.from_numpy(UpperCamelCase  ).to(device=UpperCamelCase  )
											if str(UpperCamelCase  ).startswith('mps'  ):
															# mps does not support float64
															snake_case__													=       torch.from_numpy(UpperCamelCase  ).to(UpperCamelCase    ,		dtype=torch.floataa  )
											else:
															snake_case__													=       torch.from_numpy(UpperCamelCase  ).to(device=UpperCamelCase  )

											snake_case__													=       []
							def lowerCAmelCase_	(       self: Optional[int]    ,		UpperCamelCase: Any    ,		UpperCamelCase: Union[str, Any]  )						->						int:
											# get log sigma
											snake_case__													=       np.log(UpperCamelCase  )

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

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

											snake_case__													=       log_sigmas[low_idx]
											snake_case__													=       log_sigmas[high_idx]

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

											# transform interpolation to time range
											snake_case__													=       (1 - w) * low_idx + w * high_idx
											snake_case__													=       t.reshape(sigma.shape  )
											return t
							def lowerCAmelCase_	(       self: Optional[int]    ,		UpperCamelCase: torch.FloatTensor  )						->						torch.FloatTensor:
											snake_case__													=       in_sigmas[-1].item()
											snake_case__													=       in_sigmas[0].item()

											snake_case__													=       7.0  # 7.0 is the value used in the paper
											snake_case__													=       np.linspace(0    ,		1    ,		self.num_inference_steps  )
											snake_case__													=       sigma_min ** (1 / rho)
											snake_case__													=       sigma_max ** (1 / rho)
											snake_case__													=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas
							def lowerCAmelCase_	(       self: Union[str, Any]    ,		UpperCamelCase: torch.FloatTensor    ,		UpperCamelCase: Union[float, torch.FloatTensor]    ,		UpperCamelCase: torch.FloatTensor    ,		UpperCamelCase: int = 4    ,		UpperCamelCase: bool = True    ,		)						->						Union[LMSDiscreteSchedulerOutput, Tuple]:
											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  ):
															snake_case__													=       timestep.to(self.timesteps.device  )
											snake_case__													=       (self.timesteps == timestep).nonzero().item()
											snake_case__													=       self.sigmas[step_index]

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

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

											# 4. Compute previous sample based on the derivatives path
											snake_case__													=       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 lowerCAmelCase_	(       self: str    ,		UpperCamelCase: torch.FloatTensor    ,		UpperCamelCase: torch.FloatTensor    ,		UpperCamelCase: torch.FloatTensor    ,		)						->						torch.FloatTensor:
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											snake_case__													=       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
															snake_case__													=       self.timesteps.to(original_samples.device    ,		dtype=torch.floataa  )
															snake_case__													=       timesteps.to(original_samples.device    ,		dtype=torch.floataa  )
											else:
															snake_case__													=       self.timesteps.to(original_samples.device  )
															snake_case__													=       timesteps.to(original_samples.device  )

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

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

											snake_case__													=       original_samples + noise * sigma
											return noisy_samples


							def __len__(       self: Tuple  )						->						Optional[int]:
											return self.config.num_train_timesteps












import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__UpperCamelCase   :						List[Any]							=  logging.getLogger(__name__)
def        a_	( )     ->     int:







				"""simple docstring"""
				snake_case__													=       argparse.ArgumentParser(description='PyTorch Transformer Language Model'					)
				parser.add_argument('--model_name' ,				type=_A ,				default='transfo-xl-wt103' ,				help='pretrained model name'					)
				parser.add_argument(
				    '--split' ,				type=_A ,				default='test' ,				choices=['all', 'valid', 'test'] ,				help='which split to evaluate'					)
				parser.add_argument('--batch_size' ,				type=_A ,				default=10 ,				help='batch size'					)
				parser.add_argument('--tgt_len' ,				type=_A ,				default=128 ,				help='number of tokens to predict'					)
				parser.add_argument('--ext_len' ,				type=_A ,				default=0 ,				help='length of the extended context'					)
				parser.add_argument('--mem_len' ,				type=_A ,				default=1600 ,				help='length of the retained previous heads'					)
				parser.add_argument('--clamp_len' ,				type=_A ,				default=1000 ,				help='max positional embedding index'					)
				parser.add_argument('--no_cuda' ,				action='store_true' ,				help='Do not use CUDA even though CUA is available'					)
				parser.add_argument('--work_dir' ,				type=_A ,				required=_A ,				help='path to the work_dir'					)
				parser.add_argument('--no_log' ,				action='store_true' ,				help='do not log the eval result'					)
				parser.add_argument('--same_length' ,				action='store_true' ,				help='set same length attention with masking'					)
				parser.add_argument('--server_ip' ,				type=_A ,				default='' ,				help='Can be used for distant debugging.'					)
				parser.add_argument('--server_port' ,				type=_A ,				default='' ,				help='Can be used for distant debugging.'					)
				snake_case__													=       parser.parse_args()
				assert args.ext_len >= 0, "extended context length must be non-negative"

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print('Waiting for debugger attach'					)
								ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,				redirect_output=_A					)
								ptvsd.wait_for_attach()

				snake_case__													=       torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'					)
				logger.info('device: {}'.format(_A					)					)

				# Load a pre-processed dataset
				# You can also build the corpus yourself using TransfoXLCorpus methods
				# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
				# and tokenizing the dataset
				# The pre-processed corpus is a convertion (using the conversion script )
				snake_case__													=       TransfoXLCorpus.from_pretrained(args.model_name					)

				snake_case__													=       corpus.get_iterator('valid' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)
				snake_case__													=       corpus.get_iterator('test' ,				args.batch_size ,				args.tgt_len ,				device=_A ,				ext_len=args.ext_len					)

				# Load a pre-trained model
				snake_case__													=       TransfoXLLMHeadModel.from_pretrained(args.model_name					)
				model.to(_A					)

				logger.info(
				    'Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
				        args.batch_size ,				args.tgt_len ,				args.ext_len ,				args.mem_len ,				args.clamp_len					)					)

				model.reset_memory_length(args.mem_len					)
				if args.clamp_len > 0:
								snake_case__													=       args.clamp_len
				if args.same_length:
								snake_case__													=       True

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(_A					):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								snake_case__							,  snake_case__													=       0, 0.0
								snake_case__													=       time.time()
								with torch.no_grad():
												snake_case__													=       None
												for idx, (data, target, seq_len) in enumerate(_A					):
																snake_case__													=       model(_A ,				lm_labels=_A ,				mems=_A					)
																snake_case__							,  snake_case__							,  snake_case__													=       ret
																snake_case__													=       loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												snake_case__													=       time.time() - start_time
								logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(_A ,				1000 * total_time / (idx + 1)					)					)
								return total_loss / total_len

				# Run on test data.
				if args.split == "all":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       evaluate(_A					)
				elif args.split == "valid":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       None
				elif args.split == "test":
								snake_case__													=       evaluate(_A					)
								snake_case__													=       None

				def format_log(_A ,				_A					):
								snake_case__													=       '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(_A ,				_A ,				math.exp(_A					)					)
								return log_str

				snake_case__													=       ''
				if valid_loss is not None:
								log_str += format_log(_A ,				'valid'					)
				if test_loss is not None:
								log_str += format_log(_A ,				'test'					)

				logger.info('=' * 100					)
				logger.info(_A					)
				logger.info('=' * 100					)


if __name__ == "__main__":
							main()




