
import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




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


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   1
   lowercase__	: Optional[int]     		=   3
   lowercase__	: Optional[int]     		=   (32, 32)

   lowercase__	: int     		=   floats_tensor((batch_size, num_channels) + sizes       ,	rng=random.Random(0)).to(SCREAMING_SNAKE_CASE_)
   return image


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   torch.manual_seed(0)
   lowercase__	: str     		=   UNetaDConditionModel(
       block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,	cross_attention_dim=32       ,	)
   return model


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   torch.manual_seed(0)
   lowercase__	: Optional[Any]     		=   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       ,	)
   return model


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   torch.manual_seed(0)
   lowercase__	: Dict     		=   CLIPTextConfig(
       bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=32       ,	intermediate_size=37       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=10_00       ,	)
   return CLIPTextModel(SCREAMING_SNAKE_CASE_)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   def extract(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):



     class    _snake_case :


      def __init__(       self):


        '''simple docstring'''




        lowercase__	: List[Any]     		=   torch.ones([0])



      def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


        '''simple docstring'''




        self.pixel_values.to(SCREAMING_SNAKE_CASE_)
        return self

     return Out()

   return extract


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
   lowercase__	: str     		=   self.dummy_cond_unet
   lowercase__	: str     		=   DDIMScheduler(
       beta_start=0.0_0_0_8_5       ,	beta_end=0.0_1_2       ,	beta_schedule="""scaled_linear"""       ,	clip_sample=SCREAMING_SNAKE_CASE_       ,	set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: Union[str, Any]     		=   self.dummy_vae
   lowercase__	: Tuple     		=   self.dummy_text_encoder
   lowercase__	: Tuple     		=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

   # make sure here that pndm scheduler skips prk
   lowercase__	: Any     		=   StableDiffusionPipeline(
       unet=SCREAMING_SNAKE_CASE_       ,	scheduler=SCREAMING_SNAKE_CASE_       ,	vae=SCREAMING_SNAKE_CASE_       ,	text_encoder=SCREAMING_SNAKE_CASE_       ,	tokenizer=SCREAMING_SNAKE_CASE_       ,	safety_checker=SCREAMING_SNAKE_CASE_       ,	feature_extractor=self.dummy_extractor       ,	)
   lowercase__	: Optional[Any]     		=   sd_pipe.to(SCREAMING_SNAKE_CASE_)
   sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   """A painting of a squirrel eating a burger"""

   lowercase__	: int     		=   torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(0)
   lowercase__	: Any     		=   sd_pipe([prompt]       ,	generator=SCREAMING_SNAKE_CASE_       ,	guidance_scale=6.0       ,	num_inference_steps=2       ,	output_type="""np""")
   lowercase__	: Dict     		=   output.images

   lowercase__	: List[str]     		=   torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(0)
   lowercase__	: int     		=   sd_pipe(
       [prompt]       ,	generator=SCREAMING_SNAKE_CASE_       ,	guidance_scale=6.0       ,	num_inference_steps=2       ,	output_type="""np"""       ,	return_dict=SCREAMING_SNAKE_CASE_       ,	)[0]

   lowercase__	: List[str]     		=   image[0, -3:, -3:, -1]
   lowercase__	: List[Any]     		=   image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   lowercase__	: Tuple     		=   np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
   lowercase__	: Tuple     		=   self.dummy_cond_unet
   lowercase__	: Dict     		=   PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   self.dummy_vae
   lowercase__	: Optional[int]     		=   self.dummy_text_encoder
   lowercase__	: List[Any]     		=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

   # make sure here that pndm scheduler skips prk
   lowercase__	: Tuple     		=   StableDiffusionPipeline(
       unet=SCREAMING_SNAKE_CASE_       ,	scheduler=SCREAMING_SNAKE_CASE_       ,	vae=SCREAMING_SNAKE_CASE_       ,	text_encoder=SCREAMING_SNAKE_CASE_       ,	tokenizer=SCREAMING_SNAKE_CASE_       ,	safety_checker=SCREAMING_SNAKE_CASE_       ,	feature_extractor=self.dummy_extractor       ,	)
   lowercase__	: List[Any]     		=   sd_pipe.to(SCREAMING_SNAKE_CASE_)
   sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   """A painting of a squirrel eating a burger"""
   lowercase__	: str     		=   torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(0)
   lowercase__	: Dict     		=   sd_pipe([prompt]       ,	generator=SCREAMING_SNAKE_CASE_       ,	guidance_scale=6.0       ,	num_inference_steps=2       ,	output_type="""np""")

   lowercase__	: Any     		=   output.images

   lowercase__	: Union[str, Any]     		=   torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(0)
   lowercase__	: Any     		=   sd_pipe(
       [prompt]       ,	generator=SCREAMING_SNAKE_CASE_       ,	guidance_scale=6.0       ,	num_inference_steps=2       ,	output_type="""np"""       ,	return_dict=SCREAMING_SNAKE_CASE_       ,	)[0]

   lowercase__	: Union[str, Any]     		=   image[0, -3:, -3:, -1]
   lowercase__	: Optional[Any]     		=   image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 64, 64, 3)
   lowercase__	: int     		=   np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   StableDiffusionPipeline.from_pretrained(
       """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""       ,	safety_checker=SCREAMING_SNAKE_CASE_)
   assert isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   assert isinstance(pipe.scheduler       ,	SCREAMING_SNAKE_CASE_)
   assert pipe.safety_checker is None

   lowercase__	: Dict     		=   pipe("""example prompt"""       ,	num_inference_steps=2).images[0]
   assert image is not None

   # check that there's no error when saving a pipeline with one of the models being None
   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(SCREAMING_SNAKE_CASE_)
     lowercase__	: List[Any]     		=   StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_)

   # sanity check that the pipeline still works
   assert pipe.safety_checker is None
   lowercase__	: int     		=   pipe("""example prompt"""       ,	num_inference_steps=2).images[0]
   assert image is not None



 @unittest.skipIf(torch_device != """cuda"""       ,	"""This test requires a GPU""")
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.dummy_cond_unet
   lowercase__	: List[str]     		=   PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_)
   lowercase__	: int     		=   self.dummy_vae
   lowercase__	: int     		=   self.dummy_text_encoder
   lowercase__	: Union[str, Any]     		=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

   # put models in fp16
   lowercase__	: Optional[Any]     		=   unet.half()
   lowercase__	: List[Any]     		=   vae.half()
   lowercase__	: Any     		=   bert.half()

   # make sure here that pndm scheduler skips prk
   lowercase__	: Dict     		=   StableDiffusionPipeline(
       unet=SCREAMING_SNAKE_CASE_       ,	scheduler=SCREAMING_SNAKE_CASE_       ,	vae=SCREAMING_SNAKE_CASE_       ,	text_encoder=SCREAMING_SNAKE_CASE_       ,	tokenizer=SCREAMING_SNAKE_CASE_       ,	safety_checker=SCREAMING_SNAKE_CASE_       ,	feature_extractor=self.dummy_extractor       ,	)
   lowercase__	: Optional[Any]     		=   sd_pipe.to(SCREAMING_SNAKE_CASE_)
   sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   """A painting of a squirrel eating a burger"""
   lowercase__	: List[Any]     		=   sd_pipe([prompt]       ,	num_inference_steps=2       ,	output_type="""np""").images

   assert image.shape == (1, 64, 64, 3)



@nightly
@require_torch_gpu
class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       ,	safety_checker=SCREAMING_SNAKE_CASE_)
   lowercase__	: str     		=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
   lowercase__	: List[Any]     		=   sd_pipe.to(SCREAMING_SNAKE_CASE_)
   sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   (
       """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
       """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
       """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
       """ children from bahnhof zoo, detailed """
   )
   lowercase__	: Any     		=   40_03_66_03_46
   lowercase__	: Dict     		=   7

   # without safety guidance (sld_guidance_scale = 0)
   lowercase__	: Optional[Any]     		=   torch.manual_seed(SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   sd_pipe(
       [prompt]       ,	generator=SCREAMING_SNAKE_CASE_       ,	guidance_scale=SCREAMING_SNAKE_CASE_       ,	num_inference_steps=50       ,	output_type="""np"""       ,	width=5_12       ,	height=5_12       ,	sld_guidance_scale=0       ,	)

   lowercase__	: str     		=   output.images
   lowercase__	: List[str]     		=   image[0, -3:, -3:, -1]
   lowercase__	: List[str]     		=   [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

   assert image.shape == (1, 5_12, 5_12, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

   # without safety guidance (strong configuration)
   lowercase__	: List[str]     		=   torch.manual_seed(SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   sd_pipe(
       [prompt]       ,	generator=SCREAMING_SNAKE_CASE_       ,	guidance_scale=SCREAMING_SNAKE_CASE_       ,	num_inference_steps=50       ,	output_type="""np"""       ,	width=5_12       ,	height=5_12       ,	sld_guidance_scale=20_00       ,	sld_warmup_steps=7       ,	sld_threshold=0.0_2_5       ,	sld_momentum_scale=0.5       ,	sld_mom_beta=0.7       ,	)

   lowercase__	: Dict     		=   output.images
   lowercase__	: List[str]     		=   image[0, -3:, -3:, -1]
   lowercase__	: Union[str, Any]     		=   [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

   assert image.shape == (1, 5_12, 5_12, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       ,	safety_checker=SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
   lowercase__	: Union[str, Any]     		=   sd_pipe.to(SCREAMING_SNAKE_CASE_)
   sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   """padme amidala taking a bath artwork, safe for work, no nudity"""
   lowercase__	: Tuple     		=   27_34_97_17_55
   lowercase__	: List[str]     		=   7

   lowercase__	: Dict     		=   torch.manual_seed(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   sd_pipe(
       [prompt]       ,	generator=SCREAMING_SNAKE_CASE_       ,	guidance_scale=SCREAMING_SNAKE_CASE_       ,	num_inference_steps=50       ,	output_type="""np"""       ,	width=5_12       ,	height=5_12       ,	sld_guidance_scale=0       ,	)

   lowercase__	: Tuple     		=   output.images
   lowercase__	: List[str]     		=   image[0, -3:, -3:, -1]
   lowercase__	: int     		=   [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

   assert image.shape == (1, 5_12, 5_12, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

   lowercase__	: Optional[int]     		=   torch.manual_seed(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   sd_pipe(
       [prompt]       ,	generator=SCREAMING_SNAKE_CASE_       ,	guidance_scale=SCREAMING_SNAKE_CASE_       ,	num_inference_steps=50       ,	output_type="""np"""       ,	width=5_12       ,	height=5_12       ,	sld_guidance_scale=20_00       ,	sld_warmup_steps=7       ,	sld_threshold=0.0_2_5       ,	sld_momentum_scale=0.5       ,	sld_mom_beta=0.7       ,	)

   lowercase__	: List[str]     		=   output.images
   lowercase__	: int     		=   image[0, -3:, -3:, -1]
   lowercase__	: List[Any]     		=   [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

   assert image.shape == (1, 5_12, 5_12, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""")
   lowercase__	: Optional[int]     		=   sd_pipe.to(SCREAMING_SNAKE_CASE_)
   sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   (
       """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
       """ leyendecker"""
   )
   lowercase__	: Any     		=   10_44_35_52_34
   lowercase__	: List[str]     		=   12

   lowercase__	: List[str]     		=   torch.manual_seed(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   sd_pipe(
       [prompt]       ,	generator=SCREAMING_SNAKE_CASE_       ,	guidance_scale=SCREAMING_SNAKE_CASE_       ,	num_inference_steps=50       ,	output_type="""np"""       ,	width=5_12       ,	height=5_12       ,	sld_guidance_scale=0       ,	)

   lowercase__	: Tuple     		=   output.images
   lowercase__	: str     		=   image[0, -3:, -3:, -1]
   lowercase__	: Any     		=   np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

   assert image.shape == (1, 5_12, 5_12, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-7

   lowercase__	: Optional[int]     		=   torch.manual_seed(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   sd_pipe(
       [prompt]       ,	generator=SCREAMING_SNAKE_CASE_       ,	guidance_scale=SCREAMING_SNAKE_CASE_       ,	num_inference_steps=50       ,	output_type="""np"""       ,	width=5_12       ,	height=5_12       ,	sld_guidance_scale=20_00       ,	sld_warmup_steps=7       ,	sld_threshold=0.0_2_5       ,	sld_momentum_scale=0.5       ,	sld_mom_beta=0.7       ,	)

   lowercase__	: Optional[int]     		=   output.images
   lowercase__	: Any     		=   image[0, -3:, -3:, -1]
   lowercase__	: Optional[int]     		=   np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1])
   assert image.shape == (1, 5_12, 5_12, 3)

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2








'''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


_lowercase          =						logging.get_logger(__name__)

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




@dataclass
class     UpperCAmelCase_	:






						'''simple docstring'''






						_lowercase						: str										=  field(
						    default=_SCREAMING_SNAKE_CASE			,				metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(_SCREAMING_SNAKE_CASE					)}					)
						_lowercase						: str										=  field(
						    default=_SCREAMING_SNAKE_CASE			,				metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''}					)
						_lowercase						: int										=  field(
						    default=1_2_8			,				metadata={
						        '''help''': (
						            '''The maximum total input sequence length after tokenization. Sequences longer '''
						            '''than this will be truncated, sequences shorter will be padded.'''
						        )
						    }			,				)
						_lowercase						: int										=  field(
						    default=1_2_8			,				metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''}			,				)
						_lowercase						: int										=  field(
						    default=6_4			,				metadata={
						        '''help''': (
						            '''The maximum number of tokens for the question. Questions longer than this will '''
						            '''be truncated to this length.'''
						        )
						    }			,				)
						_lowercase						: int										=  field(
						    default=3_0			,				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.'''
						        )
						    }			,				)
						_lowercase						: bool										=  field(
						    default=_SCREAMING_SNAKE_CASE			,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)
						_lowercase						: bool										=  field(
						    default=_SCREAMING_SNAKE_CASE			,				metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''}					)
						_lowercase						: float										=  field(
						    default=0.0			,				metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}					)
						_lowercase						: int										=  field(
						    default=2_0			,				metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}					)
						_lowercase						: int										=  field(
						    default=0			,				metadata={
						        '''help''': (
						            '''language id of input for language-specific xlm models (see'''
						            ''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
						        )
						    }			,				)
						_lowercase						: int										=  field(default=1			,				metadata={'''help''': '''multiple threads for converting example to features'''}					)






class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: str										=  '''train'''
						_lowercase						: Union[str, Any]										=  '''dev'''






class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: SquadDataTrainingArguments
						_lowercase						: List[SquadFeatures]
						_lowercase						: Split
						_lowercase						: bool




						def __init__(   self       ,  _lowercase       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = Split.train       ,  _lowercase = False       ,  _lowercase = None       ,  _lowercase = "pt"       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					args
												_lowerCAmelCase    		=					is_language_sensitive
												_lowerCAmelCase    		=					SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
												if isinstance(_lowercase       ,  _lowercase ):
																		try:
																								_lowerCAmelCase    		=					Split[mode]
																		except KeyError:
																								raise KeyError("""mode is not a valid split name""" )
												_lowerCAmelCase    		=					mode
												# Load data features from cache or dataset file
												_lowerCAmelCase    		=					"""v2""" if args.version_2_with_negative else """v1"""
												_lowerCAmelCase    		=					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.
												_lowerCAmelCase    		=					cached_features_file + """.lock"""
												with FileLock(_lowercase ):
																		if os.path.exists(_lowercase ) and not args.overwrite_cache:
																								_lowerCAmelCase    		=					time.time()
																								_lowerCAmelCase    		=					torch.load(_lowercase )

																								# Legacy cache files have only features, while new cache files
																								# will have dataset and examples also.
																								_lowerCAmelCase    		=					self.old_features["""features"""]
																								_lowerCAmelCase    		=					self.old_features.get("""dataset"""       ,  _lowercase )
																								_lowerCAmelCase    		=					self.old_features.get("""examples"""       ,  _lowercase )
																								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:
																														_lowerCAmelCase    		=					self.processor.get_dev_examples(args.data_dir )
																								else:
																														_lowerCAmelCase    		=					self.processor.get_train_examples(args.data_dir )

																								_lowerCAmelCase		,       _lowerCAmelCase    		=					squad_convert_examples_to_features(
																								    examples=self.examples       ,  tokenizer=_lowercase       ,  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=_lowercase       ,  )

																								_lowerCAmelCase    		=					time.time()
																								torch.save(
																								    {"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples}       ,  _lowercase       ,  )
																								# ^ 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 ):

												"""simple docstring"""



												return len(self.features )




						def __getitem__(   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.features[i]

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

												_lowerCAmelCase    		=					{
												    """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:
																		_lowerCAmelCase    		=					torch.tensor(feature.start_position       ,  dtype=torch.long )
																		_lowerCAmelCase    		=					torch.tensor(feature.end_position       ,  dtype=torch.long )
																		inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )

												return inputs
