





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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class    snake_case_				(		__lowercase						):
  A_										=    42
  A_										=    None






def        _SCREAMING_SNAKE_CASE		(			SCREAMING_SNAKE_CASE		:Tuple    ,    SCREAMING_SNAKE_CASE		:Optional[Any]=0.9_99    ,    SCREAMING_SNAKE_CASE		:Any="cosine"    ,    )			->    int:



 if alpha_transform_type == "cosine":

  def alpha_bar_fn(SCREAMING_SNAKE_CASE		:Union[str, Any]    ):
   return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(SCREAMING_SNAKE_CASE		:Union[str, Any]    ):
   return math.exp(t * -12.0    )

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

 __lowerCAmelCase     :       List[str]						=   []
 for i in range(UpperCamelCase_    ):
  __lowerCAmelCase     :       int						=   i / num_diffusion_timesteps
  __lowerCAmelCase     :       Optional[Any]						=   (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    snake_case_				(		__lowercase     ,__lowercase						):
  A_										=    [e.name for e in KarrasDiffusionSchedulers]
  A_										=    1






  @register_to_config
  def __init__(							self :     str   ,  _snake_case :     Tuple = 1000   ,  _snake_case :     List[Any] = 0.0_001   ,  _snake_case :     List[Any] = 0.02   ,  _snake_case :     Optional[Any] = "linear"   ,  _snake_case :     Tuple = None   ,  _snake_case :     Optional[int] = "epsilon"   ,  _snake_case :     str = "linspace"   ,  _snake_case :     str = 0   ,  )->Dict:






   '''simple docstring'''

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

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

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

   # setable values
   __lowerCAmelCase     :       Union[str, Any]						=   None
   __lowerCAmelCase     :       List[Any]						=   np.linspace(0   ,  num_train_timesteps - 1   ,  _UpperCamelCase   ,  dtype=_UpperCamelCase			)[::-1].copy()
   __lowerCAmelCase     :       int						=   torch.from_numpy(_UpperCamelCase			)
   __lowerCAmelCase     :       Dict						=   False






  @property
  def       UpperCAmelCase__						(							self :     Any			)->str:






   '''simple docstring'''

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

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






  def       UpperCAmelCase__						(							self :     Union[str, Any]   ,  _snake_case :     List[str]   ,  _snake_case :     Any			)->Tuple:






   '''simple docstring'''

   if isinstance(_UpperCamelCase   ,  torch.Tensor			):
    __lowerCAmelCase     :       List[str]						=   timestep.to(self.timesteps.device			)
   __lowerCAmelCase     :       List[str]						=   (self.timesteps == timestep).nonzero().item()
   __lowerCAmelCase     :       Dict						=   self.sigmas[step_index]
   __lowerCAmelCase     :       str						=   sample / ((sigma**2 + 1) ** 0.5)
   __lowerCAmelCase     :       Dict						=   True
   return sample






  def       UpperCAmelCase__						(							self :     List[Any]   ,  _snake_case :     Optional[int]   ,  _snake_case :     List[Any] = None			)->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase     :       List[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":
    __lowerCAmelCase     :       List[str]						=   np.linspace(0   ,  self.config.num_train_timesteps - 1   ,  _UpperCamelCase   ,  dtype=_UpperCamelCase			)[
        ::-1
    ].copy()
   elif self.config.timestep_spacing == "leading":
    __lowerCAmelCase     :       Optional[int]						=   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
    __lowerCAmelCase     :       List[str]						=   (np.arange(0   ,  _UpperCamelCase			) * step_ratio).round()[::-1].copy().astype(_UpperCamelCase			)
    timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
    __lowerCAmelCase     :       Dict						=   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
    __lowerCAmelCase     :       int						=   (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\'.'''			)

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






  def       UpperCAmelCase__						(							self :     List[str]   ,  _snake_case :     Any   ,  _snake_case :     Any   ,  _snake_case :     int   ,  _snake_case :     int = None   ,  _snake_case :     List[str] = True   ,  )->Any:






   '''simple docstring'''

   if (
       isinstance(_UpperCamelCase   ,  _UpperCamelCase			)
       or isinstance(_UpperCamelCase   ,  torch.IntTensor			)
       or isinstance(_UpperCamelCase   ,  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(_UpperCamelCase   ,  torch.Tensor			):
    __lowerCAmelCase     :       int						=   timestep.to(self.timesteps.device			)

   __lowerCAmelCase     :       Tuple						=   (self.timesteps == timestep).nonzero().item()
   __lowerCAmelCase     :       List[str]						=   self.sigmas[step_index]

   # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
   if self.config.prediction_type == "epsilon":
    __lowerCAmelCase     :       List[str]						=   sample - sigma * model_output
   elif self.config.prediction_type == "v_prediction":
    # * c_out + input * c_skip
    __lowerCAmelCase     :       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`'''			)

   __lowerCAmelCase     :       Union[str, Any]						=   self.sigmas[step_index]
   __lowerCAmelCase     :       str						=   self.sigmas[step_index + 1]
   __lowerCAmelCase     :       Union[str, Any]						=   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
   __lowerCAmelCase     :       int						=   (sigma_to**2 - sigma_up**2) ** 0.5

   # 2. Convert to an ODE derivative
   __lowerCAmelCase     :       List[Any]						=   (sample - pred_original_sample) / sigma

   __lowerCAmelCase     :       Any						=   sigma_down - sigma

   __lowerCAmelCase     :       Dict						=   sample + derivative * dt

   __lowerCAmelCase     :       Any						=   model_output.device
   __lowerCAmelCase     :       Optional[int]						=   randn_tensor(model_output.shape   ,  dtype=model_output.dtype   ,  device=_UpperCamelCase   ,  generator=_UpperCamelCase			)

   __lowerCAmelCase     :       str						=   prev_sample + noise * sigma_up

   if not return_dict:
    return (prev_sample,)

   return EulerAncestralDiscreteSchedulerOutput(
       prev_sample=_UpperCamelCase   ,  pred_original_sample=_UpperCamelCase			)






  def       UpperCAmelCase__						(							self :     Union[str, Any]   ,  _snake_case :     Any   ,  _snake_case :     List[Any]   ,  _snake_case :     Tuple   ,  )->List[Any]:






   '''simple docstring'''

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

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

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

   __lowerCAmelCase     :       Dict						=   original_samples + noise * sigma
   return noisy_samples






  def __len__(							self :     int			)->int:






   '''simple docstring'''

   return self.config.num_train_timesteps







import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


__snake_case			:			Union[str, Any]			   =   logging.get_logger(__name__)


@dataclass
class 			__SCREAMING_SNAKE_CASE    :
						_SCREAMING_SNAKE_CASE		:				str
						_SCREAMING_SNAKE_CASE		:				str
						_SCREAMING_SNAKE_CASE		:				Optional[str]            =    None
						_SCREAMING_SNAKE_CASE		:				Optional[str]            =    None






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return json.dumps(dataclasses.asdict(self      )   ,  indent=2      ) + "\n"




@dataclass(frozen=__lowercase)
class 			__SCREAMING_SNAKE_CASE    :
						_SCREAMING_SNAKE_CASE		:				List[int]
						_SCREAMING_SNAKE_CASE		:				Optional[List[int]]            =    None
						_SCREAMING_SNAKE_CASE		:				Optional[List[int]]            =    None
						_SCREAMING_SNAKE_CASE		:				Optional[Union[int, float]]            =    None






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return json.dumps(dataclasses.asdict(self      )      ) + "\n"




class 			__SCREAMING_SNAKE_CASE    :






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												raise NotImplementedError()






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												raise NotImplementedError()






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												raise NotImplementedError()






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												raise NotImplementedError()






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												raise NotImplementedError()






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												if len(self.get_labels()      ) > 1:
																		lowerCAmelCase__			  =  self.get_labels()[int(example.label      )]
												return example







						@classmethod
						def 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase=None      ):






												"""simple docstring"""
												with open(_UpperCamelCase   ,  'r'   ,  encoding='utf-8-sig'      ) as f:
																		return list(csv.reader(_UpperCamelCase   ,  delimiter='\t'   ,  quotechar=_UpperCamelCase      )      )




class 			__SCREAMING_SNAKE_CASE    (      __lowercase):






						def __init__(		self   ,  _UpperCamelCase=None   ,  _UpperCamelCase=None   ,  _UpperCamelCase="classification"   ,  _UpperCamelCase=False      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  [] if labels is None else labels
												lowerCAmelCase__			  =  [] if examples is None else examples
												lowerCAmelCase__			  =  mode
												lowerCAmelCase__			  =  verbose






						def __len__(		self      ):






												"""simple docstring"""
												return len(self.examples      )






						def __getitem__(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												if isinstance(_UpperCamelCase   ,  _UpperCamelCase      ):
																		return SingleSentenceClassificationProcessor(labels=self.labels   ,  examples=self.examples[idx]      )
												return self.examples[idx]






						@classmethod
						def 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase=""   ,  _UpperCamelCase=0   ,  _UpperCamelCase=1   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  cls(**_UpperCamelCase      )
												processor.add_examples_from_csv(
												    _UpperCamelCase   ,  split_name=_UpperCamelCase   ,  column_label=_UpperCamelCase   ,  column_text=_UpperCamelCase   ,  column_id=_UpperCamelCase   ,  skip_first_row=_UpperCamelCase   ,  overwrite_labels=_UpperCamelCase   ,  overwrite_examples=_UpperCamelCase   ,  )
												return processor






						@classmethod
						def 					UpperCamelCase__					(		cls   ,  _UpperCamelCase   ,  _UpperCamelCase=None   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  cls(**_UpperCamelCase      )
												processor.add_examples(_UpperCamelCase   ,  labels=_UpperCamelCase      )
												return processor






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase=""   ,  _UpperCamelCase=0   ,  _UpperCamelCase=1   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  _UpperCamelCase=False   ,  _UpperCamelCase=False   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self._read_tsv(_UpperCamelCase      )
												if skip_first_row:
																		lowerCAmelCase__			  =  lines[1:]
												lowerCAmelCase__			  =  []
												lowerCAmelCase__			  =  []
												lowerCAmelCase__			  =  []
												for i, line in enumerate(_UpperCamelCase      ):
																		texts.append(line[column_text]      )
																		labels.append(line[column_label]      )
																		if column_id is not None:
																								ids.append(line[column_id]      )
																		else:
																								lowerCAmelCase__			  =  F"{split_name}-{i}" if split_name else str(_UpperCamelCase      )
																								ids.append(_UpperCamelCase      )

												return self.add_examples(
												    _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  overwrite_labels=_UpperCamelCase   ,  overwrite_examples=_UpperCamelCase      )






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase=None   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  _UpperCamelCase=False      ):






												"""simple docstring"""
												if labels is not None and len(_UpperCamelCase      ) != len(_UpperCamelCase      ):
																		raise ValueError(
																		    F"Text and labels have mismatched lengths {len(_UpperCamelCase      )} and {len(_UpperCamelCase      )}"      )
												if ids is not None and len(_UpperCamelCase      ) != len(_UpperCamelCase      ):
																		raise ValueError(F"Text and ids have mismatched lengths {len(_UpperCamelCase      )} and {len(_UpperCamelCase      )}"      )
												if ids is None:
																		lowerCAmelCase__			  =  [None] * len(_UpperCamelCase      )
												if labels is None:
																		lowerCAmelCase__			  =  [None] * len(_UpperCamelCase      )
												lowerCAmelCase__			  =  []
												lowerCAmelCase__			  =  set()
												for text_or_text_and_label, label, guid in zip(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      ):
																		if isinstance(_UpperCamelCase   ,  (tuple, list)      ) and label is None:
																								lowerCAmelCase__  ,				lowerCAmelCase__			  =  text_or_text_and_label
																		else:
																								lowerCAmelCase__			  =  text_or_text_and_label
																		added_labels.add(_UpperCamelCase      )
																		examples.append(InputExample(guid=_UpperCamelCase   ,  text_a=_UpperCamelCase   ,  text_b=_UpperCamelCase   ,  label=_UpperCamelCase      )      )

												# Update examples
												if overwrite_examples:
																		lowerCAmelCase__			  =  examples
												else:
																		self.examples.extend(_UpperCamelCase      )

												# Update labels
												if overwrite_labels:
																		lowerCAmelCase__			  =  list(_UpperCamelCase      )
												else:
																		lowerCAmelCase__			  =  list(set(self.labels      ).union(_UpperCamelCase      )      )

												return self.examples







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase=None   ,  _UpperCamelCase=False   ,  _UpperCamelCase=0   ,  _UpperCamelCase=True   ,  _UpperCamelCase=None   ,  ):






												"""simple docstring"""
												if max_length is None:
																		lowerCAmelCase__			  =  tokenizer.max_len

												lowerCAmelCase__			  =  {label: i for i, label in enumerate(self.labels      )}

												lowerCAmelCase__			  =  []
												for ex_index, example in enumerate(self.examples      ):
																		if ex_index % 1_00_00 == 0:
																								logger.info(F"Tokenizing example {ex_index}"      )

																		lowerCAmelCase__			  =  tokenizer.encode(
																		    example.text_a   ,  add_special_tokens=_UpperCamelCase   ,  max_length=min(_UpperCamelCase   ,  tokenizer.max_len      )   ,  )
																		all_input_ids.append(_UpperCamelCase      )

												lowerCAmelCase__			  =  max(len(_UpperCamelCase      ) for input_ids in all_input_ids      )

												lowerCAmelCase__			  =  []
												for ex_index, (input_ids, example) in enumerate(zip(_UpperCamelCase   ,  self.examples      )      ):
																		if ex_index % 1_00_00 == 0:
																								logger.info(F"Writing example {ex_index}/{len(self.examples      )}"      )
																		# The mask has 1 for real tokens and 0 for padding tokens. Only real
																		# tokens are attended to.
																		lowerCAmelCase__			  =  [1 if mask_padding_with_zero else 0] * len(_UpperCamelCase      )

																		# Zero-pad up to the sequence length.
																		lowerCAmelCase__			  =  batch_length - len(_UpperCamelCase      )
																		if pad_on_left:
																								lowerCAmelCase__			  =  ([pad_token] * padding_length) + input_ids
																								lowerCAmelCase__			  =  ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
																		else:
																								lowerCAmelCase__			  =  input_ids + ([pad_token] * padding_length)
																								lowerCAmelCase__			  =  attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

																		if len(_UpperCamelCase      ) != batch_length:
																								raise ValueError(F"Error with input length {len(_UpperCamelCase      )} vs {batch_length}"      )
																		if len(_UpperCamelCase      ) != batch_length:
																								raise ValueError(F"Error with input length {len(_UpperCamelCase      )} vs {batch_length}"      )

																		if self.mode == "classification":
																								lowerCAmelCase__			  =  label_map[example.label]
																		elif self.mode == "regression":
																								lowerCAmelCase__			  =  float(example.label      )
																		else:
																								raise ValueError(self.mode      )

																		if ex_index < 5 and self.verbose:
																								logger.info('*** Example ***'      )
																								logger.info(F"guid: {example.guid}"      )
																								logger.info(F"input_ids: {' '.join([str(_UpperCamelCase      ) for x in input_ids]      )}"      )
																								logger.info(F"attention_mask: {' '.join([str(_UpperCamelCase      ) for x in attention_mask]      )}"      )
																								logger.info(F"label: {example.label} (id = {label})"      )

																		features.append(InputFeatures(input_ids=_UpperCamelCase   ,  attention_mask=_UpperCamelCase   ,  label=_UpperCamelCase      )      )

												if return_tensors is None:
																		return features
												elif return_tensors == "tf":
																		if not is_tf_available():
																								raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'      )
																		import tensorflow as tf

																		def gen():
																								for ex in features:
																														yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

																		lowerCAmelCase__			  =  tf.data.Dataset.from_generator(
																		    _UpperCamelCase   ,  ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)   ,  ({'input_ids': tf.TensorShape([None]      ), 'attention_mask': tf.TensorShape([None]      )}, tf.TensorShape([]      ))   ,  )
																		return dataset
												elif return_tensors == "pt":
																		if not is_torch_available():
																								raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'      )
																		import torch
																		from torch.utils.data import TensorDataset

																		lowerCAmelCase__			  =  torch.tensor([f.input_ids for f in features]   ,  dtype=torch.long      )
																		lowerCAmelCase__			  =  torch.tensor([f.attention_mask for f in features]   ,  dtype=torch.long      )
																		if self.mode == "classification":
																								lowerCAmelCase__			  =  torch.tensor([f.label for f in features]   ,  dtype=torch.long      )
																		elif self.mode == "regression":
																								lowerCAmelCase__			  =  torch.tensor([f.label for f in features]   ,  dtype=torch.float      )

																		lowerCAmelCase__			  =  TensorDataset(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      )
																		return dataset
												else:
																		raise ValueError('return_tensors should be one of \'tf\' or \'pt\''      )





