
'''simple docstring'''







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_lowercase					:    List[str]       				=			logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 					lowerCAmelCase__  (    lowerCamelCase_	):
					lowerCAmelCase_	  =	42
					lowerCAmelCase_	  =	None







def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					Tuple=0.999 , __SCREAMING_SNAKE_CASE   :					Optional[int]="cosine" , ):



						"""simple docstring"""





						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(__SCREAMING_SNAKE_CASE   :					Optional[int]				):
																		return math.exp(t * -12.0				)

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

						lowercase_      :	Any						=			[]
						for i in range(lowerCamelCase__				):
												lowercase_      :	Optional[Any]						=			i / num_diffusion_timesteps
												lowercase_      :	Optional[Any]						=			(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_	):
					lowerCAmelCase_	  =	[e.name for e in KarrasDiffusionSchedulers]
					lowerCAmelCase_	  =	1






					@register_to_config
					def __init__(  self							,				__SCREAMING_SNAKE_CASE = 10_00							,				__SCREAMING_SNAKE_CASE = 0.0_001							,				__SCREAMING_SNAKE_CASE = 0.02							,				__SCREAMING_SNAKE_CASE = "linear"							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = "epsilon"							,				__SCREAMING_SNAKE_CASE = "linear"							,				__SCREAMING_SNAKE_CASE = False							,				__SCREAMING_SNAKE_CASE = "linspace"							,				__SCREAMING_SNAKE_CASE = 0							,				):


											"""simple docstring"""





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

											lowercase_      :	List[Any]						=			1.0 - self.betas
											lowercase_      :	Optional[int]						=			torch.cumprod(self.alphas							,				dim=0						)

											lowercase_      :	Optional[int]						=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
											lowercase_      :	Tuple						=			np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
											lowercase_      :	Dict						=			torch.from_numpy(__SCREAMING_SNAKE_CASE						)

											# setable values
											lowercase_      :	int						=			None
											lowercase_      :	List[Any]						=			np.linspace(0							,				num_train_timesteps - 1							,				__SCREAMING_SNAKE_CASE							,				dtype=__SCREAMING_SNAKE_CASE						)[::-1].copy()
											lowercase_      :	Optional[int]						=			torch.from_numpy(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	List[str]						=			False
											lowercase_      :	Optional[Any]						=			use_karras_sigmas






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





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

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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											if isinstance(__SCREAMING_SNAKE_CASE							,				torch.Tensor						):
																	lowercase_      :	Dict						=			timestep.to(self.timesteps.device						)
											lowercase_      :	List[str]						=			(self.timesteps == timestep).nonzero().item()
											lowercase_      :	Dict						=			self.sigmas[step_index]

											lowercase_      :	List[str]						=			sample / ((sigma**2 + 1) ** 0.5)

											lowercase_      :	Union[str, Any]						=			True
											return sample






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = None						):


											"""simple docstring"""





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

											lowercase_      :	Any						=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
											lowercase_      :	List[Any]						=			np.log(__SCREAMING_SNAKE_CASE						)

											if self.config.interpolation_type == "linear":
																	lowercase_      :	str						=			np.interp(__SCREAMING_SNAKE_CASE							,				np.arange(0							,				len(__SCREAMING_SNAKE_CASE						)						)							,				__SCREAMING_SNAKE_CASE						)
											elif self.config.interpolation_type == "log_linear":
																	lowercase_      :	Optional[Any]						=			torch.linspace(np.log(sigmas[-1]						)							,				np.log(sigmas[0]						)							,				num_inference_steps + 1						).exp()
											else:
																	raise ValueError(
																	    F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
																	    ''' \'linear\' or \'log_linear\''''						)

											if self.use_karras_sigmas:
																	lowercase_      :	int						=			self._convert_to_karras(in_sigmas=__SCREAMING_SNAKE_CASE							,				num_inference_steps=self.num_inference_steps						)
																	lowercase_      :	List[Any]						=			np.array([self._sigma_to_t(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						) for sigma in sigmas]						)

											lowercase_      :	Optional[int]						=			np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
											lowercase_      :	int						=			torch.from_numpy(__SCREAMING_SNAKE_CASE						).to(device=__SCREAMING_SNAKE_CASE						)
											if str(__SCREAMING_SNAKE_CASE						).startswith('''mps'''						):
																	# mps does not support float64
																	lowercase_      :	int						=			torch.from_numpy(__SCREAMING_SNAKE_CASE						).to(__SCREAMING_SNAKE_CASE							,				dtype=torch.floataa						)
											else:
																	lowercase_      :	int						=			torch.from_numpy(__SCREAMING_SNAKE_CASE						).to(device=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			np.log(__SCREAMING_SNAKE_CASE						)

											# get distribution
											lowercase_      :	Optional[int]						=			log_sigma - log_sigmas[:, np.newaxis]

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

											lowercase_      :	Dict						=			log_sigmas[low_idx]
											lowercase_      :	Dict						=			log_sigmas[high_idx]

											# interpolate sigmas
											lowercase_      :	List[str]						=			(low - log_sigma) / (low - high)
											lowercase_      :	Union[str, Any]						=			np.clip(__SCREAMING_SNAKE_CASE							,				0							,				1						)

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






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			in_sigmas[-1].item()
											lowercase_      :	str						=			in_sigmas[0].item()

											lowercase_      :	List[str]						=			7.0  # 7.0 is the value used in the paper
											lowercase_      :	Optional[Any]						=			np.linspace(0							,				1							,				__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Optional[Any]						=			sigma_min ** (1 / rho)
											lowercase_      :	str						=			sigma_max ** (1 / rho)
											lowercase_      :	Any						=			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE = 0.0							,				__SCREAMING_SNAKE_CASE = 0.0							,				__SCREAMING_SNAKE_CASE = float('''inf'''						)							,				__SCREAMING_SNAKE_CASE = 1.0							,				__SCREAMING_SNAKE_CASE = None							,				__SCREAMING_SNAKE_CASE = True							,				):


											"""simple docstring"""





											if (
											    isinstance(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)
											    or isinstance(__SCREAMING_SNAKE_CASE							,				torch.IntTensor						)
											    or isinstance(__SCREAMING_SNAKE_CASE							,				torch.LongTensor						)
											):
																	raise ValueError(
																	    (
																	        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																	        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
																	        ''' one of the `scheduler.timesteps` as a timestep.'''
																	    )							,				)

											if not self.is_scale_input_called:
																	logger.warning(
																	    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
																	    '''See `StableDiffusionPipeline` for a usage example.'''						)

											if isinstance(__SCREAMING_SNAKE_CASE							,				torch.Tensor						):
																	lowercase_      :	Any						=			timestep.to(self.timesteps.device						)

											lowercase_      :	str						=			(self.timesteps == timestep).nonzero().item()
											lowercase_      :	Optional[int]						=			self.sigmas[step_index]

											lowercase_      :	Dict						=			min(s_churn / (len(self.sigmas						) - 1)							,				2**0.5 - 1						) if s_tmin <= sigma <= s_tmax else 0.0

											lowercase_      :	Optional[int]						=			randn_tensor(
											    model_output.shape							,				dtype=model_output.dtype							,				device=model_output.device							,				generator=__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Dict						=			noise * s_noise
											lowercase_      :	Dict						=			sigma * (gamma + 1)

											if gamma > 0:
																	lowercase_      :	Dict						=			sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											# NOTE: "original_sample" should not be an expected prediction_type but is left in for
											# backwards compatibility
											if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																	lowercase_      :	Any						=			model_output
											elif self.config.prediction_type == "epsilon":
																	lowercase_      :	Optional[int]						=			sample - sigma_hat * model_output
											elif self.config.prediction_type == "v_prediction":
																	# * c_out + input * c_skip
																	lowercase_      :	List[str]						=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											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
											lowercase_      :	Optional[Any]						=			(sample - pred_original_sample) / sigma_hat

											lowercase_      :	List[str]						=			self.sigmas[step_index + 1] - sigma_hat

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

											if not return_dict:
																	return (prev_sample,)

											return EulerDiscreteSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE							,				pred_original_sample=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			self.sigmas.to(device=original_samples.device							,				dtype=original_samples.dtype						)
											if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE						):
																	# mps does not support float64
																	lowercase_      :	Optional[int]						=			self.timesteps.to(original_samples.device							,				dtype=torch.floataa						)
																	lowercase_      :	Any						=			timesteps.to(original_samples.device							,				dtype=torch.floataa						)
											else:
																	lowercase_      :	Tuple						=			self.timesteps.to(original_samples.device						)
																	lowercase_      :	Dict						=			timesteps.to(original_samples.device						)

											lowercase_      :	Optional[int]						=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

											lowercase_      :	List[str]						=			sigmas[step_indices].flatten()
											while len(sigma.shape						) < len(original_samples.shape						):
																	lowercase_      :	Any						=			sigma.unsqueeze(-1						)

											lowercase_      :	Tuple						=			original_samples + noise * sigma
											return noisy_samples






					def __len__(  self						):


											"""simple docstring"""





											return self.config.num_train_timesteps








"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


A     =      logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase__		:
		lowerCAmelCase_							:						str							=  field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
		lowerCAmelCase_							:						bool							=  field(default=UpperCamelCase ,metadata={"""help""": """Set this flag to use fast tokenization."""}  )
		# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
		# or just modify its tokenizer_config.json.
		lowerCAmelCase_							:						Optional[str]							=  field(
		    default=UpperCamelCase ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,)



def 	lowerCAmelCase__      (  )			->				Optional[Any]:
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							A    =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

							if len(sys.argv      ) == 2 and sys.argv[1].endswith('.json'      ):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														A   ,	A   ,	A    =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
							else:
														A   ,	A   ,	A    =       parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir      )
							    and os.listdir(training_args.output_dir      )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
														    ' --overwrite_output_dir to overcome.'      )

							# Setup logging
							logging.basicConfig(
							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,   datefmt='%m/%d/%Y %H:%M:%S'					,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,   )
							logger.warning(
							    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'					,   training_args.local_rank					,   training_args.device					,   training_args.n_gpu					,   bool(training_args.local_rank != -1      )					,   training_args.fpaa					,   )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank      ):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info('Training/evaluation parameters %s'					,   lowerCamelCase__      )

							# Prepare Question-Answering task
							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							A    =       AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,   cache_dir=model_args.cache_dir					,   )
							A    =       AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,   cache_dir=model_args.cache_dir					,   use_fast=lowerCamelCase__					,   )
							A    =       AutoModelForQuestionAnswering.from_pretrained(
							    model_args.model_name_or_path					,   from_tf=bool('.ckpt' in model_args.model_name_or_path      )					,   config=lowerCamelCase__					,   cache_dir=model_args.cache_dir					,   )

							# Get datasets
							A    =       hasattr(model.config					,   'lang2id'      )
							A    =       (
							    SquadDataset(
							        lowerCamelCase__					,   tokenizer=lowerCamelCase__					,   is_language_sensitive=lowerCamelCase__					,   cache_dir=model_args.cache_dir      )
							    if training_args.do_train
							    else None
							)
							A    =       (
							    SquadDataset(
							        lowerCamelCase__					,   tokenizer=lowerCamelCase__					,   mode='dev'					,   is_language_sensitive=lowerCamelCase__					,   cache_dir=model_args.cache_dir					,   )
							    if training_args.do_eval
							    else None
							)

							# Data collator
							A    =       DataCollatorWithPadding(lowerCamelCase__					,   pad_to_multiple_of=8      ) if training_args.fpaa else None

							# Initialize our Trainer
							A    =       Trainer(
							    model=lowerCamelCase__					,   args=lowerCamelCase__					,   train_dataset=lowerCamelCase__					,   eval_dataset=lowerCamelCase__					,   data_collator=lowerCamelCase__					,   )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir      )

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
							main()



