



"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class      UpperCAmelCase__		(      UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Optional[int] 							=     StableDiffusionInstructPixaPixPipeline
		lowerCAmelCase_       :	Union[str, Any] 							=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
		lowerCAmelCase_       :	Dict 							=     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowerCAmelCase_       :	Any 							=     IMAGE_TO_IMAGE_IMAGE_PARAMS
		lowerCAmelCase_       :	Optional[int] 							=     IMAGE_TO_IMAGE_IMAGE_PARAMS



		def 					A_		( self  :    str				)			->						List[Any]:






									'''simple docstring'''

									torch.manual_seed(0				)
									A         =					UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=8      ,							out_channels=4      ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,							cross_attention_dim=32      ,							)
									A         =					PNDMScheduler(skip_prk_steps=snake_case				)
									torch.manual_seed(0				)
									A         =					AutoencoderKL(
									    block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,							latent_channels=4      ,							)
									torch.manual_seed(0				)
									A         =					CLIPTextConfig(
									    bos_token_id=0      ,							eos_token_id=2      ,							hidden_size=32      ,							intermediate_size=37      ,							layer_norm_eps=1E-0_5      ,							num_attention_heads=4      ,							num_hidden_layers=5      ,							pad_token_id=1      ,							vocab_size=1_000      ,							)
									A         =					CLIPTextModel(snake_case				)
									A         =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

									A         =					{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components



		def 					A_		( self  :    str      ,							snake_case  :    Optional[int]      ,							snake_case  :    Optional[int]=0				)			->						Optional[int]:






									'''simple docstring'''

									A         =					floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(snake_case				)				).to(snake_case				)
									A         =					image.cpu().permute(0      ,							2      ,							3      ,							1				)[0]
									A         =					Image.fromarray(np.uinta(snake_case				)				).convert('RGB'				)
									if str(snake_case				).startswith('mps'				):
																A         =					torch.manual_seed(snake_case				)
									else:
																A         =					torch.Generator(device=snake_case				).manual_seed(snake_case				)
									A         =					{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'image_guidance_scale': 1,
									    'output_type': 'numpy',
									}
									return inputs



		def 					A_		( self  :    int				)			->						List[Any]:






									'''simple docstring'''

									A         =					'cpu'  # ensure determinism for the device-dependent torch.Generator
									A         =					self.get_dummy_components()
									A         =					StableDiffusionInstructPixaPixPipeline(**snake_case				)
									A         =					sd_pipe.to(snake_case				)
									sd_pipe.set_progress_bar_config(disable=snake_case				)

									A         =					self.get_dummy_inputs(snake_case				)
									A         =					sd_pipe(**snake_case				).images
									A         =					image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									A         =					np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3



		def 					A_		( self  :    Any				)			->						Any:






									'''simple docstring'''

									A         =					'cpu'  # ensure determinism for the device-dependent torch.Generator
									A         =					self.get_dummy_components()
									A         =					StableDiffusionInstructPixaPixPipeline(**snake_case				)
									A         =					sd_pipe.to(snake_case				)
									sd_pipe.set_progress_bar_config(disable=snake_case				)

									A         =					self.get_dummy_inputs(snake_case				)
									A         =					'french fries'
									A         =					sd_pipe(**snake_case      ,							negative_prompt=snake_case				)
									A         =					output.images
									A         =					image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									A         =					np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3



		def 					A_		( self  :    Union[str, Any]				)			->						Any:






									'''simple docstring'''

									A         =					'cpu'  # ensure determinism for the device-dependent torch.Generator
									A         =					self.get_dummy_components()
									A         =					StableDiffusionInstructPixaPixPipeline(**snake_case				)
									A         =					sd_pipe.to(snake_case				)
									sd_pipe.set_progress_bar_config(disable=snake_case				)

									A         =					self.get_dummy_inputs(snake_case				)
									A         =					[inputs['prompt']] * 2

									A         =					np.array(inputs['image']				).astype(np.floataa				) / 255.0
									A         =					torch.from_numpy(snake_case				).unsqueeze(0				).to(snake_case				)
									A         =					image / 2 + 0.5
									A         =					image.permute(0      ,							3      ,							1      ,							2				)
									A         =					image.repeat(2      ,							1      ,							1      ,							1				)

									A         =					sd_pipe(**snake_case				).images
									A         =					image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									A         =					np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3



		def 					A_		( self  :    Optional[int]				)			->						str:






									'''simple docstring'''

									A         =					'cpu'  # ensure determinism for the device-dependent torch.Generator
									A         =					self.get_dummy_components()
									A         =					EulerAncestralDiscreteScheduler(
									    beta_start=0.00085      ,							beta_end=0.012      ,							beta_schedule='scaled_linear'				)
									A         =					StableDiffusionInstructPixaPixPipeline(**snake_case				)
									A         =					sd_pipe.to(snake_case				)
									sd_pipe.set_progress_bar_config(disable=snake_case				)

									A         =					self.get_dummy_inputs(snake_case				)
									A         =					sd_pipe(**snake_case				).images
									A         =					image[0, -3:, -3:, -1]

									A         =					[round(snake_case      ,							4				) for x in image_slice.flatten().tolist()]
									print(','.join([str(snake_case				) for x in slice]				)				)

									assert image.shape == (1, 32, 32, 3)
									A         =					np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3



		def 					A_		( self  :    int				)			->						List[Any]:






									'''simple docstring'''

									super().test_inference_batch_single_identical(expected_max_diff=3E-3				)



		def 					A_		( self  :    Any				)			->						Any:






									'''simple docstring'''

									A         =					self.get_dummy_components()
									A         =					StableDiffusionInstructPixaPixPipeline(**snake_case				)
									A         =					VaeImageProcessor(do_resize=snake_case      ,							do_normalize=snake_case				)
									A         =					pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)

									A         =					pipe(**self.get_dummy_inputs_by_type(snake_case      ,							input_image_type='pt'				)				)[0]

									A         =					components['vae']
									A         =					self.get_dummy_inputs_by_type(snake_case      ,							input_image_type='pt'				)

									for image_param in self.image_latents_params:
																if image_param in inputs.keys():
																							A         =					vae.encode(inputs[image_param]				).latent_dist.mode()

									A         =					pipe(**snake_case				)[0]

									A         =					np.abs(out - out_latents_inputs				).max()
									self.assertLess(snake_case      ,							1E-4      ,							'passing latents as image input generate different result from passing image'				)






@slow
@require_torch_gpu
class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    Optional[int]				)			->						str:






									'''simple docstring'''

									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



		def 					A_		( self  :    Any      ,							snake_case  :    List[Any]=0				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					torch.manual_seed(snake_case				)
									A         =					load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'				)
									A         =					{
									    'prompt': 'turn him into a cyborg',
									    'image': image,
									    'generator': generator,
									    'num_inference_steps': 3,
									    'guidance_scale': 7.5,
									    'image_guidance_scale': 1.0,
									    'output_type': 'numpy',
									}
									return inputs



		def 					A_		( self  :    Union[str, Any]				)			->						Tuple:






									'''simple docstring'''

									A         =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    'timbrooks/instruct-pix2pix'      ,							safety_checker=snake_case				)
									pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)
									pipe.enable_attention_slicing()

									A         =					self.get_inputs()
									A         =					pipe(**snake_case				).images
									A         =					image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									A         =					np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]				)

									assert np.abs(expected_slice - image_slice				).max() < 1E-3



		def 					A_		( self  :    List[str]				)			->						Optional[int]:






									'''simple docstring'''

									A         =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    'timbrooks/instruct-pix2pix'      ,							safety_checker=snake_case				)
									A         =					LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
									pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)
									pipe.enable_attention_slicing()

									A         =					self.get_inputs()
									A         =					pipe(**snake_case				).images
									A         =					image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									A         =					np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]				)

									assert np.abs(expected_slice - image_slice				).max() < 1E-3



		def 					A_		( self  :    List[Any]				)			->						List[str]:






									'''simple docstring'''

									A         =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    'timbrooks/instruct-pix2pix'      ,							safety_checker=snake_case				)
									A         =					DDIMScheduler.from_config(pipe.scheduler.config				)
									pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)
									pipe.enable_attention_slicing()

									A         =					self.get_inputs()
									A         =					pipe(**snake_case				).images
									A         =					image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									A         =					np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]				)

									assert np.abs(expected_slice - image_slice				).max() < 1E-3



		def 					A_		( self  :    List[Any]				)			->						Any:






									'''simple docstring'''

									A         =					0

									def callback_fn(snake_case  :    int      ,							snake_case  :    int      ,							snake_case  :    torch.FloatTensor				) -> None:
																A         =					True
																nonlocal number_of_steps
																number_of_steps += 1
																if step == 1:
																							A         =					latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 64, 64)
																							A         =					latents[0, -3:, -3:, -1]
																							A         =					np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]				)

																							assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5E-2
																elif step == 2:
																							A         =					latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 64, 64)
																							A         =					latents[0, -3:, -3:, -1]
																							A         =					np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]				)

																							assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5E-2

									A         =					False

									A         =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    'timbrooks/instruct-pix2pix'      ,							safety_checker=snake_case      ,							torch_dtype=torch.floataa				)
									A         =					pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)
									pipe.enable_attention_slicing()

									A         =					self.get_inputs()
									pipe(**snake_case      ,							callback=snake_case      ,							callback_steps=1				)
									assert callback_fn.has_been_called
									assert number_of_steps == 3



		def 					A_		( self  :    Union[str, Any]				)			->						int:






									'''simple docstring'''

									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									A         =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    'timbrooks/instruct-pix2pix'      ,							safety_checker=snake_case      ,							torch_dtype=torch.floataa				)
									A         =					pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)
									pipe.enable_attention_slicing(1				)
									pipe.enable_sequential_cpu_offload()

									A         =					self.get_inputs()
									A         =					pipe(**snake_case				)

									A         =					torch.cuda.max_memory_allocated()
									# make sure that less than 2.2 GB is allocated
									assert mem_bytes < 2.2 * 10**9



		def 					A_		( self  :    int				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									A         =					inputs['image'].resize((504, 504)				)

									A         =					'timbrooks/instruct-pix2pix'
									A         =					StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    snake_case      ,							safety_checker=snake_case      ,							)
									pipe.to(snake_case				)
									pipe.set_progress_bar_config(disable=snake_case				)
									pipe.enable_attention_slicing()

									A         =					pipe(**snake_case				)
									A         =					output.images[0]

									A         =					image[255:258, 383:386, -1]

									assert image.shape == (504, 504, 3)
									A         =					np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-3








"""simple docstring"""







import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


A    =			logging.get_logger(__name__)

A    =			list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A    =			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class      UpperCAmelCase__		:
		lowerCAmelCase_       :	str 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Model type selected in the list: """ + """, """.join(UpperCamelCase     )}     )
		lowerCAmelCase_       :	str 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""}     )
		lowerCAmelCase_       :	int 							=     field(
		    default=1_28   ,metadata={
		        """help""": (
		            """The maximum total input sequence length after tokenization. Sequences longer """
		            """than this will be truncated, sequences shorter will be padded."""
		        )
		    }   ,)
		lowerCAmelCase_       :	int 							=     field(
		    default=1_28   ,metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""}   ,)
		lowerCAmelCase_       :	int 							=     field(
		    default=64   ,metadata={
		        """help""": (
		            """The maximum number of tokens for the question. Questions longer than this will """
		            """be truncated to this length."""
		        )
		    }   ,)
		lowerCAmelCase_       :	int 							=     field(
		    default=30   ,metadata={
		        """help""": (
		            """The maximum length of an answer that can be generated. This is needed because the start """
		            """and end predictions are not conditioned on one another."""
		        )
		    }   ,)
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""}     )
		lowerCAmelCase_       :	float 							=     field(
		    default=0.0   ,metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}     )
		lowerCAmelCase_       :	int 							=     field(
		    default=20   ,metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}     )
		lowerCAmelCase_       :	int 							=     field(
		    default=0   ,metadata={
		        """help""": (
		            """language id of input for language-specific xlm models (see"""
		            """ tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
		        )
		    }   ,)
		lowerCAmelCase_       :	int 							=     field(default=1   ,metadata={"""help""": """multiple threads for converting example to features"""}     )






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	str 							=     """train"""
		lowerCAmelCase_       :	Union[str, Any] 							=     """dev"""






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	SquadDataTrainingArguments
		lowerCAmelCase_       :	List[SquadFeatures]
		lowerCAmelCase_       :	Split
		lowerCAmelCase_       :	bool



		def __init__( self  :    List[Any]      ,							snake_case  :    SquadDataTrainingArguments      ,							snake_case  :    PreTrainedTokenizer      ,							snake_case  :    Optional[int] = None      ,							snake_case  :    Union[str, Split] = Split.train      ,							snake_case  :    Optional[bool] = False      ,							snake_case  :    Optional[str] = None      ,							snake_case  :    Optional[str] = "pt"      ,							)			->						Optional[Any]:






									'''simple docstring'''

									A         =					args
									A         =					is_language_sensitive
									A         =					SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
									if isinstance(snake_case      ,							snake_case				):
																try:
																							A         =					Split[mode]
																except KeyError:
																							raise KeyError('mode is not a valid split name'				)
									A         =					mode
									# Load data features from cache or dataset file
									A         =					'v2' if args.version_2_with_negative else 'v1'
									A         =					os.path.join(
									    cache_dir if cache_dir is not None else args.data_dir      ,							f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"""      ,							)

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									A         =					cached_features_file + '.lock'
									with FileLock(snake_case				):
																if os.path.exists(snake_case				) and not args.overwrite_cache:
																							A         =					time.time()
																							A         =					torch.load(snake_case				)

																							# Legacy cache files have only features, while new cache files
																							# will have dataset and examples also.
																							A         =					self.old_features['features']
																							A         =					self.old_features.get('dataset'      ,							snake_case				)
																							A         =					self.old_features.get('examples'      ,							snake_case				)
																							logger.info(
																							    f"""Loading features from cached file {cached_features_file} [took %.3f s]"""      ,							time.time() - start				)

																							if self.dataset is None or self.examples is None:
																														logger.warning(
																														    f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
																														    ' future run'				)
																else:
																							if mode == Split.dev:
																														A         =					self.processor.get_dev_examples(args.data_dir				)
																							else:
																														A         =					self.processor.get_train_examples(args.data_dir				)

																							A      ,  A         =					squad_convert_examples_to_features(
																							    examples=self.examples      ,							tokenizer=snake_case      ,							max_seq_length=args.max_seq_length      ,							doc_stride=args.doc_stride      ,							max_query_length=args.max_query_length      ,							is_training=mode == Split.train      ,							threads=args.threads      ,							return_dataset=snake_case      ,							)

																							A         =					time.time()
																							torch.save(
																							    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}      ,							snake_case      ,							)
																							# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																							logger.info(
																							    f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""				)



		def __len__( self  :    Any				)			->						Dict:






									'''simple docstring'''

									return len(self.features				)



		def __getitem__( self  :    Union[str, Any]      ,							snake_case  :    List[str]				)			->						Dict[str, torch.Tensor]:






									'''simple docstring'''

									A         =					self.features[i]

									A         =					torch.tensor(feature.input_ids      ,							dtype=torch.long				)
									A         =					torch.tensor(feature.attention_mask      ,							dtype=torch.long				)
									A         =					torch.tensor(feature.token_type_ids      ,							dtype=torch.long				)
									A         =					torch.tensor(feature.cls_index      ,							dtype=torch.long				)
									A         =					torch.tensor(feature.p_mask      ,							dtype=torch.float				)
									A         =					torch.tensor(feature.is_impossible      ,							dtype=torch.float				)

									A         =					{
									    'input_ids': input_ids,
									    'attention_mask': attention_mask,
									    'token_type_ids': token_type_ids,
									}

									if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
																del inputs["token_type_ids"]

									if self.args.model_type in ["xlnet", "xlm"]:
																inputs.update({'cls_index': cls_index, 'p_mask': p_mask}				)
																if self.args.version_2_with_negative:
																							inputs.update({'is_impossible': is_impossible}				)
																if self.is_language_sensitive:
																							inputs.update({'langs': (torch.ones(input_ids.shape      ,							dtype=torch.intaa				) * self.args.lang_id)}				)

									if self.mode == Split.train:
																A         =					torch.tensor(feature.start_position      ,							dtype=torch.long				)
																A         =					torch.tensor(feature.end_position      ,							dtype=torch.long				)
																inputs.update({'start_positions': start_positions, 'end_positions': end_positions}				)

									return inputs



