



"""simple docstring"""




from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
     from .data.data_collator import DataCollator
     from .modeling_utils import PreTrainedModel
     from .tokenization_utils_base import PreTrainedTokenizerBase
     from .trainer_callback import TrainerCallback
     from .trainer_utils import EvalPrediction, PredictionOutput
     from .training_args import TrainingArguments


_UpperCamelCase:				Optional[Any]       							=  logging.get_logger(__name__)


class   a__		(						lowerCamelCase_     ):


   def __init__(							self      :							int,     lowerCAmelCase      :							int = None,     lowerCAmelCase      :							Union[str, Any] = None,     lowerCAmelCase      :							Union[str, Any] = None,     lowerCAmelCase      :							str = None,     lowerCAmelCase      :							Optional[int] = None,     lowerCAmelCase      :							Union[str, Any] = None,     lowerCAmelCase      :							Union[str, Any] = None,     lowerCAmelCase      :							Optional[int] = None,     lowerCAmelCase      :							int = None,     lowerCAmelCase      :							Optional[Any] = (None, None),     lowerCAmelCase      :							str = None,     )    ->       Any:
         super().__init__(
             model=lowerCAmelCase,     args=lowerCAmelCase,     data_collator=lowerCAmelCase,     train_dataset=lowerCAmelCase,     eval_dataset=lowerCAmelCase,     tokenizer=lowerCAmelCase,     model_init=lowerCAmelCase,     compute_metrics=lowerCAmelCase,     callbacks=lowerCAmelCase,     optimizers=lowerCAmelCase,     preprocess_logits_for_metrics=lowerCAmelCase,     )

         # Override self.model.generation_config if a GenerationConfig is specified in args.
         # Priority: args.generation_config > model.generation_config > default GenerationConfig.
         if self.args.generation_config is not None:
               lowercase  :      Any					=   self.load_generation_config(self.args.generation_config			)
               lowercase  :      List[Any]					=   gen_config


   @staticmethod
   def        lowercase				(							lowerCAmelCase      :							Union[str, Any]			)    ->       Optional[int]:







         if isinstance(lowerCAmelCase,     lowerCAmelCase			):
               return deepcopy(lowerCAmelCase			)

         # str or Path
         lowercase  :      Dict					=   Path(lowerCAmelCase			) if isinstance(lowerCAmelCase,     lowerCAmelCase			) else gen_config_arg
         lowercase  :      Tuple					=   None

         # Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
         # This step is required in order to determine config_file_name
         if pretrained_model_name.is_file():
               lowercase  :      List[Any]					=   pretrained_model_name.name
               lowercase  :      int					=   pretrained_model_name.parent
         # dir path
         elif pretrained_model_name.is_dir():
               pass
         # model id or URL
         else:
               lowercase  :      List[Any]					=   gen_config_arg

         lowercase  :      Tuple					=   GenerationConfig.from_pretrained(lowerCAmelCase,     lowerCAmelCase			)
         return gen_config


   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							Tuple = None,     lowerCAmelCase      :							Dict = None,     lowerCAmelCase      :							Dict = "eval",     **lowerCAmelCase      :							List[Any],     )    ->       Dict:
         lowercase  :      Tuple					=   gen_kwargs.copy()
         if gen_kwargs.get('max_length'			) is None and gen_kwargs.get('max_new_tokens'			) is None:
               lowercase  :      Optional[Any]					=   self.args.generation_max_length
         lowercase  :      str					=   (
             gen_kwargs["num_beams"] if gen_kwargs.get('num_beams'			) is not None else self.args.generation_num_beams
         )
         lowercase  :      Dict					=   gen_kwargs

         return super().evaluate(lowerCAmelCase,     ignore_keys=lowerCAmelCase,     metric_key_prefix=lowerCAmelCase			)


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							Any = None,     lowerCAmelCase      :							int = "test",     **lowerCAmelCase      :							Any,     )    ->       Tuple:
         lowercase  :      Dict					=   gen_kwargs.copy()
         if gen_kwargs.get('max_length'			) is None and gen_kwargs.get('max_new_tokens'			) is None:
               lowercase  :      Tuple					=   self.args.generation_max_length
         lowercase  :      str					=   (
             gen_kwargs["num_beams"] if gen_kwargs.get('num_beams'			) is not None else self.args.generation_num_beams
         )
         lowercase  :      Tuple					=   gen_kwargs

         return super().predict(lowerCAmelCase,     ignore_keys=lowerCAmelCase,     metric_key_prefix=lowerCAmelCase			)


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							str,     lowerCAmelCase      :							List[str] = None,     **lowerCAmelCase      :							Any,     )    ->       Optional[Any]:







         if not self.args.predict_with_generate or prediction_loss_only:
               return super().prediction_step(
                   lowerCAmelCase,     lowerCAmelCase,     prediction_loss_only=lowerCAmelCase,     ignore_keys=lowerCAmelCase			)

         lowercase  :      Any					=   "labels" in inputs
         lowercase  :      Dict					=   self._prepare_inputs(lowerCAmelCase			)

         # XXX: adapt synced_gpus for fairscale as well
         # Priority (handled in generate):
         # gen_kwargs > model.generation_config > default GenerationConfig()

         if len(lowerCAmelCase			) == 0 and hasattr(self,     '_gen_kwargs'			):
               lowercase  :      Optional[Any]					=   self._gen_kwargs.copy()

         if gen_kwargs.get('max_length'			) is None and gen_kwargs.get('max_new_tokens'			) is None:
               lowercase  :      str					=   self.model.config.max_length
         lowercase  :      Optional[int]					=   (
             gen_kwargs["num_beams"] if gen_kwargs.get('num_beams'			) is not None else self.model.config.num_beams
         )
         lowercase  :      Union[str, Any]					=   True if is_deepspeed_zeroa_enabled() else False
         lowercase  :      int					=   (
             gen_kwargs["synced_gpus"] if gen_kwargs.get('synced_gpus'			) is not None else default_synced_gpus
         )

         # If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
         # (otherwise, it would continue generating from the padded `decoder_input_ids`)
         if (
             "labels" in inputs
             and "decoder_input_ids" in inputs
             and inputs["labels"].shape == inputs["decoder_input_ids"].shape
         ):
               lowercase  :      Optional[int]					=   {k: v for k, v in inputs.items() if k != "decoder_input_ids"}
         lowercase  :      List[Any]					=   self.model.generate(**lowerCAmelCase,     **lowerCAmelCase			)

         # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
         # TODO: remove this hack when the legacy code that initializes generation_config from a model config is
         # removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
         if self.model.generation_config._from_model_config:
               lowercase  :      str					=   False

         # Retrieves GenerationConfig from model.generation_config
         lowercase  :      Optional[Any]					=   self.model.generation_config
         # in case the batch is shorter than max length, the output should be padded
         if generated_tokens.shape[-1] < gen_config.max_length:
               lowercase  :      str					=   self._pad_tensors_to_max_len(lowerCAmelCase,     gen_config.max_length			)
         elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
               lowercase  :      str					=   self._pad_tensors_to_max_len(lowerCAmelCase,     gen_config.max_new_tokens + 1			)

         with torch.no_grad():
               if has_labels:
                     with self.compute_loss_context_manager():
                           lowercase  :      Optional[Any]					=   model(**lowerCAmelCase			)
                     if self.label_smoother is not None:
                           lowercase  :      Union[str, Any]					=   self.label_smoother(lowerCAmelCase,     inputs['labels']			).mean().detach()
                     else:
                           lowercase  :      Tuple					=   (outputs["loss"] if isinstance(lowerCAmelCase,     lowerCAmelCase			) else outputs[0]).mean().detach()
               else:
                     lowercase  :      Dict					=   None

         if self.args.prediction_loss_only:
               return loss, None, None

         if has_labels:
               lowercase  :      List[str]					=   inputs["labels"]
               if labels.shape[-1] < gen_config.max_length:
                     lowercase  :      int					=   self._pad_tensors_to_max_len(lowerCAmelCase,     gen_config.max_length			)
               elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
                     lowercase  :      Optional[Any]					=   self._pad_tensors_to_max_len(lowerCAmelCase,     gen_config.max_new_tokens + 1			)
         else:
               lowercase  :      Union[str, Any]					=   None

         return loss, generated_tokens, labels






   def        lowercase				(							self      :							Union[str, Any],     lowerCAmelCase      :							Union[str, Any],     lowerCAmelCase      :							List[Any]			)    ->       List[Any]:







         if self.tokenizer is not None and hasattr(self.tokenizer,     'pad_token_id'			):
               # If PAD token is not defined at least EOS token has to be defined
               lowercase  :      List[str]					=   (
                   self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
               )
         else:
               if self.model.config.pad_token_id is not None:
                     lowercase  :      Any					=   self.model.config.pad_token_id
               else:
                     raise ValueError('Pad_token_id must be set in the configuration of the model, in order to pad tensors'			)

         lowercase  :      List[str]					=   pad_token_id * torch.ones(
             (tensor.shape[0], max_length),     dtype=tensor.dtype,     device=tensor.device			)
         lowercase  :      Union[str, Any]					=   tensor
         return padded_tensor





"""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


_UpperCamelCase:				str    							=  logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				None




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase=0.9_9_9	,      _UpperCAmelCase="cosine"	,      )     ->			Optional[Any]:






      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.exp(t * -1_2.0				)

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

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





class   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				[e.name for e in KarrasDiffusionSchedulers]
   _lowerCamelCase												=				1


   @register_to_config
   def __init__(							self      :							str,     lowerCAmelCase      :							int = 1000,     lowerCAmelCase      :							float = 0.0001,     lowerCAmelCase      :							float = 0.02,     lowerCAmelCase      :							str = "linear",     lowerCAmelCase      :							Optional[Union[np.ndarray, List[float]]] = None,     lowerCAmelCase      :							str = "epsilon",     lowerCAmelCase      :							str = "linspace",     lowerCAmelCase      :							int = 0,     )    ->       List[Any]:
         if trained_betas is not None:
               lowercase  :      Union[str, Any]					=   torch.tensor(lowerCAmelCase,     dtype=torch.floataa			)
         elif beta_schedule == "linear":
               lowercase  :      Union[str, Any]					=   torch.linspace(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     dtype=torch.floataa			)
         elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               lowercase  :      str					=   (
                   torch.linspace(beta_start**0.5,     beta_end**0.5,     lowerCAmelCase,     dtype=torch.floataa			) ** 2
               )
         elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               lowercase  :      List[Any]					=   betas_for_alpha_bar(lowerCAmelCase			)
         else:
               raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

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

         lowercase  :      Optional[Any]					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      Optional[int]					=   np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
         lowercase  :      int					=   torch.from_numpy(lowerCAmelCase			)

         # setable values
         lowercase  :      List[str]					=   None
         lowercase  :      Tuple					=   np.linspace(0,     num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[::-1].copy()
         lowercase  :      Dict					=   torch.from_numpy(lowerCAmelCase			)
         lowercase  :      str					=   False


   @property
   def        lowercase				(							self      :							Tuple			)    ->       Tuple:
         # 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        lowercase				(							self      :							Any,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor]			)    ->       torch.FloatTensor:
         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      Any					=   timestep.to(self.timesteps.device			)
         lowercase  :      List[Any]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Union[str, Any]					=   self.sigmas[step_index]
         lowercase  :      Dict					=   sample / ((sigma**2 + 1) ** 0.5)
         lowercase  :      Union[str, Any]					=   True
         return sample


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							int,     lowerCAmelCase      :							Union[str, torch.device] = None			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   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,     lowerCAmelCase,     dtype=lowerCAmelCase			)[
                   ::-1
               ].copy()
         elif self.config.timestep_spacing == "leading":
               lowercase  :      List[str]					=   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(0,     lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase			)
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               lowercase  :      Optional[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  :      Optional[int]					=   (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\'.'''			)

         lowercase  :      int					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      List[str]					=   np.interp(lowerCAmelCase,     np.arange(0,     len(lowerCAmelCase			)			),     lowerCAmelCase			)
         lowercase  :      Any					=   np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
         lowercase  :      Optional[Any]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)
         if str(lowerCAmelCase			).startswith('mps'			):
               # mps does not support float64
               lowercase  :      List[Any]					=   torch.from_numpy(lowerCAmelCase			).to(lowerCAmelCase,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Optional[torch.Generator] = None,     lowerCAmelCase      :							bool = True,     )    ->       Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

         if (
             isinstance(lowerCAmelCase,     lowerCAmelCase			)
             or isinstance(lowerCAmelCase,     torch.IntTensor			)
             or isinstance(lowerCAmelCase,     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(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      List[str]					=   timestep.to(self.timesteps.device			)

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

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
               lowercase  :      Optional[Any]					=   sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               lowercase  :      int					=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'''			)

         lowercase  :      List[Any]					=   self.sigmas[step_index]
         lowercase  :      str					=   self.sigmas[step_index + 1]
         lowercase  :      Optional[int]					=   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
         lowercase  :      Optional[int]					=   (sigma_to**2 - sigma_up**2) ** 0.5

         # 2. Convert to an ODE derivative
         lowercase  :      Tuple					=   (sample - pred_original_sample) / sigma

         lowercase  :      str					=   sigma_down - sigma

         lowercase  :      int					=   sample + derivative * dt

         lowercase  :      Dict					=   model_output.device
         lowercase  :      List[Any]					=   randn_tensor(model_output.shape,     dtype=model_output.dtype,     device=lowerCAmelCase,     generator=lowerCAmelCase			)

         lowercase  :      List[str]					=   prev_sample + noise * sigma_up

         if not return_dict:
               return (prev_sample,)

         return EulerAncestralDiscreteSchedulerOutput(
             prev_sample=lowerCAmelCase,     pred_original_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							int,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							torch.FloatTensor,     )    ->       torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         lowercase  :      Tuple					=   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
               lowercase  :      Dict					=   self.timesteps.to(original_samples.device,     dtype=torch.floataa			)
               lowercase  :      Union[str, Any]					=   timesteps.to(original_samples.device,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   self.timesteps.to(original_samples.device			)
               lowercase  :      Tuple					=   timesteps.to(original_samples.device			)

         lowercase  :      Union[str, Any]					=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         lowercase  :      Union[str, Any]					=   sigmas[step_indices].flatten()
         while len(sigma.shape			) < len(original_samples.shape			):
               lowercase  :      Dict					=   sigma.unsqueeze(-1			)

         lowercase  :      Optional[Any]					=   original_samples + noise * sigma
         return noisy_samples






   def __len__(							self      :							int			)    ->       List[str]:
         return self.config.num_train_timesteps
