



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Optional[Any]								=     (DEISMultistepScheduler,)
				A_   :     int								=     (('num_inference_steps', 2_5),)


				def __UpperCamelCase						(							self      :  Union[str, Any]						, **__UpperCamelCase      :  List[str]       )		->     Dict:
											A      						=    {
											    'num_train_timesteps': 1_000,
											    'beta_start': 0.0_0_0_1,
											    'beta_end': 0.0_2,
											    'beta_schedule': 'linear',
											    'solver_order': 2,
											}

											config.update(**__UpperCamelCase       )
											return config


				def __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  str=0						, **__UpperCamelCase      :  Optional[int]       )		->     List[Any]:
											A      						=    dict(self.forward_default_kwargs       )
											A      						=    kwargs.pop('num_inference_steps'						, __UpperCamelCase       )
											A      						=    self.dummy_sample
											A      						=    0.1 * sample
											A      						=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

											for scheduler_class in self.scheduler_classes:
																		A      						=    self.get_scheduler_config(**__UpperCamelCase       )
																		A      						=    scheduler_class(**__UpperCamelCase       )
																		scheduler.set_timesteps(__UpperCamelCase       )
																		# copy over dummy past residuals
																		A      						=    dummy_past_residuals[: scheduler.config.solver_order]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(__UpperCamelCase       )
																									A      						=    scheduler_class.from_pretrained(__UpperCamelCase       )
																									new_scheduler.set_timesteps(__UpperCamelCase       )
																									# copy over dummy past residuals
																									A      						=    dummy_past_residuals[: new_scheduler.config.solver_order]

																		A						,   A      						=    sample, sample
																		for t in range(__UpperCamelCase						, time_step + scheduler.config.solver_order + 1       ):
																									A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample
																									A      						=    new_scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample

																									assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"


				def __UpperCamelCase						(							self      :  str       )		->     Dict:
											pass


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  int=0						, **__UpperCamelCase      :  Dict       )		->     List[str]:
											A      						=    dict(self.forward_default_kwargs       )
											A      						=    kwargs.pop('num_inference_steps'						, __UpperCamelCase       )
											A      						=    self.dummy_sample
											A      						=    0.1 * sample
											A      						=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

											for scheduler_class in self.scheduler_classes:
																		A      						=    self.get_scheduler_config()
																		A      						=    scheduler_class(**__UpperCamelCase       )
																		scheduler.set_timesteps(__UpperCamelCase       )

																		# copy over dummy past residuals (must be after setting timesteps)
																		A      						=    dummy_past_residuals[: scheduler.config.solver_order]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																									scheduler.save_config(__UpperCamelCase       )
																									A      						=    scheduler_class.from_pretrained(__UpperCamelCase       )
																									# copy over dummy past residuals
																									new_scheduler.set_timesteps(__UpperCamelCase       )

																									# copy over dummy past residual (must be after setting timesteps)
																									A      						=    dummy_past_residuals[: new_scheduler.config.solver_order]

																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample
																		A      						=    new_scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample

																		assert torch.sum(torch.abs(output - new_output       )       ) < 1e-5, "Scheduler outputs are not identical"


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  Tuple=None						, **__UpperCamelCase      :  Dict       )		->     Tuple:
											if scheduler is None:
																		A      						=    self.scheduler_classes[0]
																		A      						=    self.get_scheduler_config(**__UpperCamelCase       )
																		A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config(**__UpperCamelCase       )
											A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    10
											A      						=    self.dummy_model()
											A      						=    self.dummy_sample_deter
											scheduler.set_timesteps(__UpperCamelCase       )

											for i, t in enumerate(scheduler.timesteps       ):
																		A      						=    model(__UpperCamelCase						, __UpperCamelCase       )
																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       ).prev_sample

											return sample


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    dict(self.forward_default_kwargs       )

											A      						=    kwargs.pop('num_inference_steps'						, __UpperCamelCase       )

											for scheduler_class in self.scheduler_classes:
																		A      						=    self.get_scheduler_config()
																		A      						=    scheduler_class(**__UpperCamelCase       )

																		A      						=    self.dummy_sample
																		A      						=    0.1 * sample

																		if num_inference_steps is not None and hasattr(__UpperCamelCase						, 'set_timesteps'       ):
																									scheduler.set_timesteps(__UpperCamelCase       )
																		elif num_inference_steps is not None and not hasattr(__UpperCamelCase						, 'set_timesteps'       ):
																									A      						=    num_inference_steps

																		# copy over dummy past residuals (must be done after set_timesteps)
																		A      						=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																		A      						=    dummy_past_residuals[: scheduler.config.solver_order]

																		A      						=    scheduler.timesteps[5]
																		A      						=    scheduler.timesteps[6]

																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample
																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, **__UpperCamelCase       ).prev_sample

																		self.assertEqual(output_a.shape						, sample.shape       )
																		self.assertEqual(output_a.shape						, output_a.shape       )


				def __UpperCamelCase						(							self      :  Any       )		->     int:
											# make sure that iterating over schedulers with same config names gives same results
											# for defaults
											A      						=    DEISMultistepScheduler(**self.get_scheduler_config()       )
											A      						=    self.full_loop(scheduler=__UpperCamelCase       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

											assert abs(result_mean.item() - 0.2_3_9_1_6       ) < 1e-3

											A      						=    DPMSolverSinglestepScheduler.from_config(scheduler.config       )
											A      						=    DPMSolverMultistepScheduler.from_config(scheduler.config       )
											A      						=    UniPCMultistepScheduler.from_config(scheduler.config       )
											A      						=    DEISMultistepScheduler.from_config(scheduler.config       )

											A      						=    self.full_loop(scheduler=__UpperCamelCase       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

											assert abs(result_mean.item() - 0.2_3_9_1_6       ) < 1e-3


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Dict:
											for timesteps in [25, 50, 100, 999, 1_000]:
																		self.check_over_configs(num_train_timesteps=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     List[Any]:
											self.check_over_configs(thresholding=__UpperCamelCase       )
											for order in [1, 2, 3]:
																		for solver_type in ["logrho"]:
																									for threshold in [0.5, 1.0, 2.0]:
																																for prediction_type in ["epsilon", "sample"]:
																																							self.check_over_configs(
																																							    thresholding=__UpperCamelCase						, prediction_type=__UpperCamelCase						, sample_max_value=__UpperCamelCase						, algorithm_type='deis'						, solver_order=__UpperCamelCase						, solver_type=__UpperCamelCase						, )


				def __UpperCamelCase						(							self      :  Dict       )		->     int:
											for prediction_type in ["epsilon", "v_prediction"]:
																		self.check_over_configs(prediction_type=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Any:
											for algorithm_type in ["deis"]:
																		for solver_type in ["logrho"]:
																									for order in [1, 2, 3]:
																																for prediction_type in ["epsilon", "sample"]:
																																							self.check_over_configs(
																																							    solver_order=__UpperCamelCase						, solver_type=__UpperCamelCase						, prediction_type=__UpperCamelCase						, algorithm_type=__UpperCamelCase						, )
																																							A      						=    self.full_loop(
																																							    solver_order=__UpperCamelCase						, solver_type=__UpperCamelCase						, prediction_type=__UpperCamelCase						, algorithm_type=__UpperCamelCase						, )
																																							assert not torch.isnan(__UpperCamelCase       ).any(), "Samples have nan numbers"


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Optional[Any]:
											self.check_over_configs(lower_order_final=__UpperCamelCase       )
											self.check_over_configs(lower_order_final=__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																		self.check_over_forward(num_inference_steps=__UpperCamelCase						, time_step=0       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     str:
											A      						=    self.full_loop()
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

											assert abs(result_mean.item() - 0.2_3_9_1_6       ) < 1e-3


				def __UpperCamelCase						(							self      :  int       )		->     Optional[Any]:
											A      						=    self.full_loop(prediction_type='v_prediction'       )
											A      						=    torch.mean(torch.abs(__UpperCamelCase       )       )

											assert abs(result_mean.item() - 0.0_9_1       ) < 1e-3


				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[str]:
											A      						=    self.scheduler_classes[0]
											A      						=    self.get_scheduler_config(thresholding=__UpperCamelCase						, dynamic_thresholding_ratio=0       )
											A      						=    scheduler_class(**__UpperCamelCase       )

											A      						=    10
											A      						=    self.dummy_model()
											A      						=    self.dummy_sample_deter.half()
											scheduler.set_timesteps(__UpperCamelCase       )

											for i, t in enumerate(scheduler.timesteps       ):
																		A      						=    model(__UpperCamelCase						, __UpperCamelCase       )
																		A      						=    scheduler.step(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       ).prev_sample

											assert sample.dtype == torch.floataa




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


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

__snake_case     :int			       =list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
__snake_case     :Optional[int]			       =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class        lowerCAmelCase__					:
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Model type selected in the list: ' + ', '.join(_lowerCamelCase       )}       )
				A_   :     str								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}       )
				A_   :     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.'
				        )
				    } ,				)
				A_   :     int								=     field(
				    default=1_2_8 ,				metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} ,				)
				A_   :     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.'
				        )
				    } ,				)
				A_   :     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.'
				        )
				    } ,				)
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'Overwrite the cached training and evaluation sets'}       )
				A_   :     bool								=     field(
				    default=_lowerCamelCase ,				metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}       )
				A_   :     float								=     field(
				    default=0.0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=2_0 ,				metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}       )
				A_   :     int								=     field(
				    default=0 ,				metadata={
				        'help': (
				            'language id of input for language-specific xlm models (see'
				            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
				        )
				    } ,				)
				A_   :     int								=     field(default=1 ,				metadata={'help': 'multiple threads for converting example to features'}       )





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     str								=     'train'
				A_   :     str								=     'dev'





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     SquadDataTrainingArguments
				A_   :     List[SquadFeatures]
				A_   :     Split
				A_   :     bool


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  SquadDataTrainingArguments						, __UpperCamelCase      :  PreTrainedTokenizer						, __UpperCamelCase      :  Optional[int] = None						, __UpperCamelCase      :  Union[str, Split] = Split.train						, __UpperCamelCase      :  Optional[bool] = False						, __UpperCamelCase      :  Optional[str] = None						, __UpperCamelCase      :  Optional[str] = "pt"						, )		->     Any:
											A      						=    args
											A      						=    is_language_sensitive
											A      						=    SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
											if isinstance(__UpperCamelCase						, __UpperCamelCase       ):
																		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(__UpperCamelCase       ):
																		if os.path.exists(__UpperCamelCase       ) and not args.overwrite_cache:
																									A      						=    time.time()
																									A      						=    torch.load(__UpperCamelCase       )

																									# 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'						, __UpperCamelCase       )
																									A      						=    self.old_features.get('examples'						, __UpperCamelCase       )
																									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=__UpperCamelCase						, 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=__UpperCamelCase						, )

																									A      						=    time.time()
																									torch.save(
																									    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}						, __UpperCamelCase						, )
																									# ^ 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      :  Optional[Any]       )		->     Tuple:
											return len(self.features       )


				def __getitem__(							self      :  Tuple						, __UpperCamelCase      :  List[Any]       )		->     Dict[str, torch.Tensor]:
											# Convert to Tensors and build dataset
											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