'''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


_lowerCAmelCase  :Tuple  				=    logging.get_logger(__name__)

_lowerCAmelCase  :List[str]  				=    list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_lowerCAmelCase  :int  				=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 				UpperCAmelCase :




    '''simple docstring'''







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






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[Any]							=						"train"
    snake_case__							:   Dict							=						"dev"






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







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






    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = Split.train    ,							lowercase__ = False    ,							lowercase__ = None    ,							lowercase__ = "pt"    ,							)							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       args
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       is_language_sensitive
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
         if isinstance(lowercase__    ,							lowercase__ ):
              try:
                   SCREAMING_SNAKE_CASE					:			int    	=       Split[mode]
              except KeyError:
                   raise KeyError('mode is not a valid split name' )
         SCREAMING_SNAKE_CASE					:			int    	=       mode
         # Load data features from cache or dataset file
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       'v2' if args.version_2_with_negative else 'v1'
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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.
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       cached_features_file + '.lock'
         with FileLock(lowercase__ ):
              if os.path.exists(lowercase__ ) and not args.overwrite_cache:
                   SCREAMING_SNAKE_CASE					:			Any    	=       time.time()
                   SCREAMING_SNAKE_CASE					:			Dict    	=       torch.load(lowercase__ )

                   # Legacy cache files have only features, while new cache files
                   # will have dataset and examples also.
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.old_features['features']
                   SCREAMING_SNAKE_CASE					:			str    	=       self.old_features.get('dataset'    ,							lowercase__ )
                   SCREAMING_SNAKE_CASE					:			str    	=       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:
                        SCREAMING_SNAKE_CASE					:			Any    	=       self.processor.get_dev_examples(args.data_dir )
                   else:
                        SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.processor.get_train_examples(args.data_dir )

                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       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__    ,							)

                   SCREAMING_SNAKE_CASE					:			Tuple    	=       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 )							-> int:
         return len(self.features )






    def __getitem__(       self    ,							lowercase__ )							-> Dict[str, torch.Tensor]:
         # Convert to Tensors and build dataset
         SCREAMING_SNAKE_CASE					:			str    	=       self.features[i]

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor(feature.input_ids    ,							dtype=torch.long )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor(feature.attention_mask    ,							dtype=torch.long )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor(feature.token_type_ids    ,							dtype=torch.long )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.tensor(feature.cls_index    ,							dtype=torch.long )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor(feature.p_mask    ,							dtype=torch.float )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor(feature.is_impossible    ,							dtype=torch.float )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {
             '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:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor(feature.start_position    ,							dtype=torch.long )
              SCREAMING_SNAKE_CASE					:			int    	=       torch.tensor(feature.end_position    ,							dtype=torch.long )
              inputs.update({'start_positions': start_positions, 'end_positions': end_positions} )

         return inputs




'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						(DDPMParallelScheduler,)






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {
             'num_train_timesteps': 1_000,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**lowercase__ )
         return config






    def       _UpperCamelCase    (       self )							-> int:
         for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,							[0.0_0_2, 0.0_2, 0.2, 2] ):
              self.check_over_configs(beta_start=lowercase__    ,							beta_end=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         self.check_over_configs(thresholding=lowercase__ )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=lowercase__    ,							prediction_type=lowercase__    ,							sample_max_value=lowercase__    ,							)






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






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for t in [0, 500, 999]:
              self.check_over_forward(time_step=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			str    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.dummy_sample_deter + 0.1
         SCREAMING_SNAKE_CASE					:			int    	=       self.dummy_sample_deter - 0.1

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       samplea.shape[0]
         SCREAMING_SNAKE_CASE					:			str    	=       torch.stack([samplea, samplea, samplea]    ,							dim=0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.arange(lowercase__ )[0:3, None].repeat(1    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(samples.flatten(0    ,							1 )    ,							timesteps.flatten(0    ,							1 ) )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler.batch_step_no_noise(lowercase__    ,							timesteps.flatten(0    ,							1 )    ,							samples.flatten(0    ,							1 ) )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1E-2
         assert abs(result_mean.item() - 0.5_0_0_5 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Dict    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[str]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
         assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_scheduler_config(prediction_type='v_prediction' )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			int    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
         assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       scheduler.timesteps

         for i, timestep in enumerate(lowercase__ ):
              if i == len(lowercase__ ) - 1:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       -1
              else:
                   SCREAMING_SNAKE_CASE					:			Any    	=       timesteps[i + 1]

              SCREAMING_SNAKE_CASE					:			str    	=       scheduler.previous_timestep(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       prev_t.item()

              self.assertEqual(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       [100, 87, 50, 51, 0]

         with self.assertRaises(lowercase__    ,							msg='`custom_timesteps` must be in descending order.' ):
              scheduler.set_timesteps(timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       len(lowercase__ )

         with self.assertRaises(lowercase__    ,							msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
              scheduler.set_timesteps(num_inference_steps=lowercase__    ,							timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             lowercase__    ,							msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    ,							):
              scheduler.set_timesteps(timesteps=lowercase__ )



