



import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a__      :				int   	=			logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

a__      :				Optional[Any]   	=			{
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a__      :				int   	=			{
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a__      :				List[str]   	=			sorted(arg_to_scheduler.keys())
a__      :				Any   	=			"{" + ", ".join(arg_to_scheduler_choices) + "}"



class       UpperCAmelCase__(							pl.LightningModule	):

   '''simple docstring'''




   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			argparse.Namespace			,				lowerCAmelCase  :			List[str]=None			,				lowerCAmelCase  :			Tuple="base"			,				lowerCAmelCase  :			Any=None			,				lowerCAmelCase  :			Union[str, Any]=None			,				lowerCAmelCase  :			Any=None			,				**lowerCAmelCase  :			List[Any]			,				)	->				Optional[int]:




         """simple docstring"""

         super().__init__()
         # TODO: move to self.save_hyperparameters()
         # self.save_hyperparameters()
         # can also expand arguments into trainer signature for easier reading

         self.save_hyperparameters(lowerCAmelCase)
         lowercase__     =  0
         lowercase__     =  Path(self.hparams.output_dir)
         lowercase__     =  self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               lowercase__     =  AutoConfig.from_pretrained(
                   self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path			,				**({'num_labels': num_labels} if num_labels is not None else {})			,				cache_dir=lowerCAmelCase			,				**lowerCAmelCase			,				)
         else:
               lowercase__     =  config

         lowercase__     =  ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
         for p in extra_model_params:
               if getattr(self.hparams			,				lowerCAmelCase			,				lowerCAmelCase):
                     assert hasattr(self.config			,				lowerCAmelCase), f'''model config doesn\'t have a `{p}` attribute'''
                     setattr(self.config			,				lowerCAmelCase			,				getattr(self.hparams			,				lowerCAmelCase))

         if tokenizer is None:
               lowercase__     =  AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,				cache_dir=lowerCAmelCase			,				)
         else:
               lowercase__     =  tokenizer
         lowercase__     =  MODEL_MODES[mode]
         if model is None:
               lowercase__     =  self.model_type.from_pretrained(
                   self.hparams.model_name_or_path			,				from_tf=bool('.ckpt' in self.hparams.model_name_or_path)			,				config=self.config			,				cache_dir=lowerCAmelCase			,				)
         else:
               lowercase__     =  model


   def 			UpperCAmelCase		(						self  :			Dict			,				*lowerCAmelCase  :			Any			,				**lowerCAmelCase  :			Union[str, Any])	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  self.model_type.from_pretrained(*lowerCAmelCase			,				**lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Dict)	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  arg_to_scheduler[self.hparams.lr_scheduler]
         lowercase__     =  get_schedule_func(
             self.opt			,				num_warmup_steps=self.hparams.warmup_steps			,				num_training_steps=self.total_steps())
         lowercase__     =  {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
         return scheduler


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




         """simple docstring"""

         lowercase__     =  self.model
         lowercase__     =  ['bias', 'LayerNorm.weight']
         lowercase__     =  [
             {
                 'params': [
                     p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)
                 ],  # check this named paramters
                 'weight_decay': self.hparams.weight_decay,
             },
             {
                 'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
                 'weight_decay': 0.0,
             },
         ]
         if self.hparams.adafactor:
               lowercase__     =  Adafactor(
                   lowerCAmelCase			,				lr=self.hparams.learning_rate			,				scale_parameter=lowerCAmelCase			,				relative_step=lowerCAmelCase)

         else:
               lowercase__     =  AdamW(
                   lowerCAmelCase			,				lr=self.hparams.learning_rate			,				eps=self.hparams.adam_epsilon)
         lowercase__     =  optimizer

         lowercase__     =  self.get_lr_scheduler()

         return [optimizer], [scheduler]


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			Any)	->				int:




         """simple docstring"""

         return self.validation_step(lowerCAmelCase			,				lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			Optional[int])	->				List[Any]:




         """simple docstring"""

         return self.validation_end(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str)	->				int:




         """simple docstring"""

         lowercase__     =  max(1			,				self.hparams.gpus)  # TODO: consider num_tpu_cores
         lowercase__     =  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			Tuple)	->				str:




         """simple docstring"""

         if stage == "test":
               lowercase__     =  len(self.test_dataloader().dataset)
         else:
               lowercase__     =  self.get_dataloader('train'			,				self.hparams.train_batch_size			,				shuffle=lowerCAmelCase)
               lowercase__     =  len(self.train_dataloader().dataset)


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			bool = False)	->				List[str]:




         """simple docstring"""

         raise NotImplementedError('You must implement this for your task')


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




         """simple docstring"""

         return self.train_loader


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Dict:




         """simple docstring"""

         return self.get_dataloader('dev'			,				self.hparams.eval_batch_size			,				shuffle=lowerCAmelCase)


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




         """simple docstring"""

         return self.get_dataloader('test'			,				self.hparams.eval_batch_size			,				shuffle=lowerCAmelCase)


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




         """simple docstring"""

         return os.path.join(
             self.hparams.data_dir			,				'cached_{}_{}_{}'.format(
                 lowerCAmelCase			,				list(filter(lowerCAmelCase			,				self.hparams.model_name_or_path.split('/'))).pop()			,				str(self.hparams.max_seq_length)			,				)			,				)


   @pl.utilities.rank_zero_only
   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			Dict[str, Any])	->				None:




         """simple docstring"""

         lowercase__     =  self.output_dir.joinpath('best_tfmr')
         lowercase__     =  self.step_count
         self.model.save_pretrained(lowerCAmelCase)
         self.tokenizer.save_pretrained(lowerCAmelCase)



   @staticmethod
   def 			UpperCAmelCase		(						lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			Any)	->				int:




         """simple docstring"""

         parser.add_argument(
             '--model_name_or_path'			,				default=lowerCAmelCase			,				type=lowerCAmelCase			,				required=lowerCAmelCase			,				help='Path to pretrained model or model identifier from huggingface.co/models'			,				)
         parser.add_argument(
             '--config_name'			,				default=''			,				type=lowerCAmelCase			,				help='Pretrained config name or path if not the same as model_name')
         parser.add_argument(
             '--tokenizer_name'			,				default=lowerCAmelCase			,				type=lowerCAmelCase			,				help='Pretrained tokenizer name or path if not the same as model_name'			,				)
         parser.add_argument(
             '--cache_dir'			,				default=str(Path(lowerCAmelCase).parent / 'test_run' / 'cache')			,				type=lowerCAmelCase			,				help='Where do you want to store the pre-trained models downloaded from huggingface.co'			,				)
         parser.add_argument(
             '--encoder_layerdrop'			,				type=lowerCAmelCase			,				help='Encoder layer dropout probability (Optional). Goes into model.config'			,				)
         parser.add_argument(
             '--decoder_layerdrop'			,				type=lowerCAmelCase			,				help='Decoder layer dropout probability (Optional). Goes into model.config'			,				)
         parser.add_argument(
             '--dropout'			,				type=lowerCAmelCase			,				help='Dropout probability (Optional). Goes into model.config'			,				)
         parser.add_argument(
             '--attention_dropout'			,				type=lowerCAmelCase			,				help='Attention dropout probability (Optional). Goes into model.config'			,				)
         parser.add_argument('--learning_rate'			,				default=5E-5			,				type=lowerCAmelCase			,				help='The initial learning rate for Adam.')
         parser.add_argument(
             '--lr_scheduler'			,				default='linear'			,				choices=lowerCAmelCase			,				metavar=lowerCAmelCase			,				type=lowerCAmelCase			,				help='Learning rate scheduler'			,				)
         parser.add_argument('--weight_decay'			,				default=0.0			,				type=lowerCAmelCase			,				help='Weight decay if we apply some.')
         parser.add_argument('--adam_epsilon'			,				default=1E-8			,				type=lowerCAmelCase			,				help='Epsilon for Adam optimizer.')
         parser.add_argument('--warmup_steps'			,				default=0			,				type=lowerCAmelCase			,				help='Linear warmup over warmup_steps.')
         parser.add_argument('--num_workers'			,				default=4			,				type=lowerCAmelCase			,				help='kwarg passed to DataLoader')
         parser.add_argument('--num_train_epochs'			,				dest='max_epochs'			,				default=3			,				type=lowerCAmelCase)
         parser.add_argument('--train_batch_size'			,				default=32			,				type=lowerCAmelCase)
         parser.add_argument('--eval_batch_size'			,				default=32			,				type=lowerCAmelCase)
         parser.add_argument('--adafactor'			,				action='store_true')



class       UpperCAmelCase__(							pl.Callback	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			List[Any])	->				Any:




         """simple docstring"""

         if (
             trainer.is_global_zero and trainer.global_rank == 0
         ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
               pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.



class       UpperCAmelCase__(							pl.Callback	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			int)	->				Union[str, Any]:




         """simple docstring"""

         for name, param in pl_module.model.rag.named_parameters():
               if param.grad is None:
                     print(lowerCAmelCase)



class       UpperCAmelCase__(							pl.Callback	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			List[Any]			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			List[Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  trainer.lr_schedulers[0]['scheduler']
         lowercase__     =  {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr())}
         pl_module.logger.log_metrics(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			pl.Trainer			,				lowerCAmelCase  :			pl.LightningModule)	->				List[str]:




         """simple docstring"""

         rank_zero_info('***** Validation results *****')
         lowercase__     =  trainer.callback_metrics
         # Log results
         for key in sorted(lowerCAmelCase):
               if key not in ["log", "progress_bar"]:
                     rank_zero_info('{} = {}\n'.format(lowerCAmelCase			,				str(metrics[key])))



   def 			UpperCAmelCase		(						self  :			Optional[Any]			,				lowerCAmelCase  :			pl.Trainer			,				lowerCAmelCase  :			pl.LightningModule)	->				str:




         """simple docstring"""

         rank_zero_info('***** Test results *****')
         lowercase__     =  trainer.callback_metrics
         # Log and save results to file
         lowercase__     =  os.path.join(pl_module.hparams.output_dir			,				'test_results.txt')
         with open(lowerCAmelCase			,				'w') as writer:
               for key in sorted(lowerCAmelCase):
                     if key not in ["log", "progress_bar"]:
                           rank_zero_info('{} = {}\n'.format(lowerCAmelCase			,				str(metrics[key])))
                           writer.write('{} = {}\n'.format(lowerCAmelCase			,				str(metrics[key])))

def   _lowerCAmelCase   (						A__						,  A__	):
      #  To allow all pl args uncomment the following line
      #  parser = pl.Trainer.add_argparse_args(parser)
      parser.add_argument(
          '--output_dir'						,  default=str(Path(A__	).parent / 'test_run' / 'model_checkpoints'	)						,  type=A__						,  help='The output directory where the model predictions and checkpoints will be written.'						,  )
      parser.add_argument(
          '--fp16'						,  action='store_true'						,  help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'						,  )

      parser.add_argument(
          '--fp16_opt_level'						,  type=A__						,  default='O2'						,  help=(
              'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
              'See details at https://nvidia.github.io/apex/amp.html'
          )						,  )
      parser.add_argument('--n_tpu_cores'						,  dest='tpu_cores'						,  type=A__	)
      parser.add_argument('--max_grad_norm'						,  dest='gradient_clip_val'						,  default=1.0						,  type=A__						,  help='Max gradient norm'	)
      parser.add_argument('--do_train'						,  action='store_true'						,  help='Whether to run training.'	)
      parser.add_argument('--do_predict'						,  action='store_true'						,  help='Whether to run predictions on the test set.'	)
      parser.add_argument(
          '--gradient_accumulation_steps'						,  dest='accumulate_grad_batches'						,  type=A__						,  default=1						,  help='Number of updates steps to accumulate before performing a backward/update pass.'						,  )
      parser.add_argument('--seed'						,  type=A__						,  default=42						,  help='random seed for initialization'	)
      parser.add_argument(
          '--data_dir'						,  default=str(Path(A__	).parent / 'test_run' / 'dummy-train-data'	)						,  type=A__						,  help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'						,  )

def   _lowerCAmelCase   (						A__						,  A__						,  A__=None						,  A__=True						,  A__=[]						,  A__=None						,  A__=None						,  **A__						,  ):
      pl.seed_everything(args.seed	)

      # init model
      lowercase__     =  Path(model.hparams.output_dir	)
      odir.mkdir(exist_ok=A__	)

      # add custom checkpoints
      if checkpoint_callback is None:
            lowercase__     =  pl.callbacks.ModelCheckpoint(
                filepath=args.output_dir						,  prefix='checkpoint'						,  monitor='val_loss'						,  mode='min'						,  save_top_k=1	)
      if early_stopping_callback:
            extra_callbacks.append(A__	)
      if logging_callback is None:
            lowercase__     =  LoggingCallback()

      lowercase__     =  {}

      if args.fpaa:
            lowercase__     =  16

      if args.gpus > 1:
            lowercase__     =  'auto'
            lowercase__     =  'ddp'

      lowercase__     =  args.accumulate_grad_batches
      lowercase__     =  None
      lowercase__     =  'auto'

      lowercase__     =  pl.Trainer.from_argparse_args(
          A__						,  weights_summary=A__						,  callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]						,  logger=A__						,  val_check_interval=1						,  num_sanity_val_steps=2						,  **A__						,  )

      if args.do_train:
            trainer.fit(A__	)

      else:
            print('RAG modeling tests with new set functions successfuly executed!'	)
      return trainer





import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[Any]       				=    None
   A    :  Optional[int]       				=    None


   @property
   def 			UpperCAmelCase		(						self  :			str)	->				Union[str, Any]:




         """simple docstring"""

         return self.feat_extract_tester.prepare_feat_extract_dict()


   def 			UpperCAmelCase		(						self  :			int)	->				Any:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         self.assertTrue(hasattr(lowerCAmelCase			,				'feature_size'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'sampling_rate'))
         self.assertTrue(hasattr(lowerCAmelCase			,				'padding_value'))


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




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         self.assertTrue(all(len(lowerCAmelCase) == len(lowerCAmelCase) for x, y in zip(lowerCAmelCase			,				processed_features[input_name])))

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='np')

         lowercase__     =  processed_features[input_name]

         if len(batch_features_input.shape) < 3:
               lowercase__     =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))


   @require_torch
   def 			UpperCAmelCase		(						self  :			Dict)	->				int:




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='pt')

         lowercase__     =  processed_features[input_name]

         if len(batch_features_input.shape) < 3:
               lowercase__     =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))


   @require_tf
   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='tf')

         lowercase__     =  processed_features[input_name]

         if len(batch_features_input.shape) < 3:
               lowercase__     =  batch_features_input[:, :, None]

         self.assertTrue(
             batch_features_input.shape
             == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))


   def 			UpperCAmelCase		(						self  :			str			,				lowerCAmelCase  :			str=False)	->				Union[str, Any]:




         """simple docstring"""

         def _inputs_have_equal_length(lowerCAmelCase  :			int):
               lowercase__     =  len(input[0])
               for input_slice in input[1:]:
                     if len(lowerCAmelCase) != length:
                           return False
               return True

         def _inputs_are_equal(lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			Tuple):
               if len(lowerCAmelCase) != len(lowerCAmelCase):
                     return False

               for input_slice_a, input_slice_a in zip(lowerCAmelCase			,				lowerCAmelCase):
                     if not np.allclose(np.asarray(lowerCAmelCase)			,				np.asarray(lowerCAmelCase)			,				atol=1E-3):
                           return False
               return True

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  self.feat_extract_tester.seq_length_diff
         lowercase__     =  self.feat_extract_tester.max_seq_length + pad_diff
         lowercase__     =  self.feat_extract_tester.min_seq_length
         lowercase__     =  self.feat_extract_tester.batch_size
         lowercase__     =  self.feat_extract_tester.feature_size

         # test padding for List[int] + numpy
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest')
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[-1]))
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         # max_length parameter has to be provided when setting `padding="max_length"`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='max_length')[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=lowerCAmelCase			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_are_equal(lowerCAmelCase			,				lowerCAmelCase))
         self.assertTrue(len(input_a[0]) == pad_min_length)
         self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
         self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
         self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

         if feature_size > 1:
               self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

         # test padding for `pad_to_multiple_of` for List[int] + numpy
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				pad_to_multiple_of=10)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				pad_to_multiple_of=10)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				pad_to_multiple_of=10			,				max_length=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				pad_to_multiple_of=10			,				max_length=lowerCAmelCase			,				return_tensors='np'			,				)
         lowercase__     =  input_a[input_name]

         self.assertTrue(all(len(lowerCAmelCase) % 10 == 0 for x in input_a))
         self.assertTrue(_inputs_are_equal(lowerCAmelCase			,				lowerCAmelCase))

         lowercase__     =  pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         self.assertTrue(all(len(lowerCAmelCase) == expected_mult_pad_length for x in input_a))
         self.assertEqual(input_a.shape[:2]			,				(batch_size, expected_mult_pad_length))

         if feature_size > 1:
               self.assertTrue(input_a.shape[2] == feature_size)

         # Check padding value is correct
         lowercase__     =  (np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
         self.assertTrue(
             abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
             < 1E-3)
         self.assertTrue(
             abs(
                 np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
             < 1E-3)
         self.assertTrue(
             abs(
                 np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
                 - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
             < 1E-3)
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1E-3)
         self.assertTrue(
             abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
             < 1E-3)


   def 			UpperCAmelCase		(						self  :			Tuple			,				lowerCAmelCase  :			Dict=False)	->				str:




         """simple docstring"""

         def _inputs_have_equal_length(lowerCAmelCase  :			int):
               lowercase__     =  len(input[0])
               for input_slice in input[1:]:
                     if len(lowerCAmelCase) != length:
                           return False
               return True

         def _inputs_are_equal(lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[Any]):
               if len(lowerCAmelCase) != len(lowerCAmelCase):
                     return False

               for input_slice_a, input_slice_a in zip(lowerCAmelCase			,				lowerCAmelCase):
                     if not np.allclose(np.asarray(lowerCAmelCase)			,				np.asarray(lowerCAmelCase)			,				atol=1E-3):
                           return False
               return True

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCAmelCase)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         # truncate to smallest
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				truncation=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0]))
         lowercase__     =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))

         # truncate to smallest with np
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				return_tensors='np'			,				truncation=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))

         # truncate to middle
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				truncation=lowerCAmelCase			,				return_tensors='np'			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				truncation=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				return_tensors='np')
         lowercase__     =  input_a[input_name]

         self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(_inputs_are_equal(lowerCAmelCase			,				lowerCAmelCase))

         # since truncation forces padding to be smaller than longest input
         # function can't return `np.ndarray`, but has to return list
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))
         self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				truncation=lowerCAmelCase)[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				truncation=lowerCAmelCase)[input_name]

         # padding has to be max_length when setting `truncation=True`
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				truncation=lowerCAmelCase)[input_name]

         # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
         with self.assertRaises(lowerCAmelCase):
               feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				truncation=lowerCAmelCase)[input_name]

         # test truncation for `pad_to_multiple_of` for List[int] + numpy
         lowercase__     =  12
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				pad_to_multiple_of=lowerCAmelCase			,				truncation=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				pad_to_multiple_of=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         lowercase__     =  len(speech_inputs[0])
         if expected_length % pad_to_multiple_of != 0:
               lowercase__     =  ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

         self.assertTrue(len(input_a[0]) == expected_length)
         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))


   def 			UpperCAmelCase		(						self  :			List[str])	->				List[str]:




         """simple docstring"""

         self._check_padding(numpify=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				Optional[Any]:




         """simple docstring"""

         self._check_padding(numpify=lowerCAmelCase)


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




         """simple docstring"""

         self._check_truncation(numpify=lowerCAmelCase)


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




         """simple docstring"""

         self._check_truncation(numpify=lowerCAmelCase)


   @require_torch
   def 			UpperCAmelCase		(						self  :			Dict)	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')[input_name]
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='pt')[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1E-2)


   @require_tf
   def 			UpperCAmelCase		(						self  :			str)	->				str:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')[input_name]
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='tf')[input_name]

         self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1E-2)


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.feat_extract_dict
         lowercase__     =  True
         lowercase__     =  self.feature_extraction_class(**lowerCAmelCase)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  [len(lowerCAmelCase) for x in speech_inputs]
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')
         self.assertIn('attention_mask'			,				lowerCAmelCase)
         self.assertListEqual(list(processed.attention_mask.shape)			,				list(processed[input_name].shape[:2]))
         self.assertListEqual(processed.attention_mask.sum(-1).tolist()			,				lowerCAmelCase)



   def 			UpperCAmelCase		(						self  :			str)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.feat_extract_dict
         lowercase__     =  True
         lowercase__     =  self.feature_extraction_class(**lowerCAmelCase)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  [len(lowerCAmelCase) for x in speech_inputs]
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})
         lowercase__     =  min(lowerCAmelCase)

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=lowerCAmelCase			,				truncation=lowerCAmelCase			,				return_tensors='np')
         self.assertIn('attention_mask'			,				lowerCAmelCase)
         self.assertListEqual(
             list(processed_pad.attention_mask.shape)			,				[processed_pad[input_name].shape[0], max_length])
         self.assertListEqual(
             processed_pad.attention_mask[:, :max_length].sum(-1).tolist()			,				[max_length for x in speech_inputs])
