"""simple docstring"""



import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__magic_name__     					=       16
__magic_name__     					=       32





def 	_lowerCamelCase			(			UpperCAmelCase__,UpperCAmelCase__ = 16,UpperCAmelCase__ = "bert-base-cased"       )				->  Dict:


   '''simple docstring'''


   a__  					=		AutoTokenizer.from_pretrained(lowerCAmelCase__       )
   a__  					=		load_dataset('glue','mrpc'       )

   def tokenize_function(UpperCAmelCase__       ):
      # max_length=None => use the model max length (it's actually the default)
      a__  					=		tokenizer(examples['sentence1'],examples['sentence2'],truncation=lowerCAmelCase__,max_length=lowerCAmelCase__       )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   a__  					=		datasets.map(
       lowerCAmelCase__,batched=lowerCAmelCase__,remove_columns=['idx', 'sentence1', 'sentence2'],load_from_cache_file=lowerCAmelCase__       )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   a__  					=		tokenized_datasets.rename_column('label','labels'       )

   def collate_fn(UpperCAmelCase__       ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(lowerCAmelCase__,padding='max_length',max_length=1_28,return_tensors='pt'       )
      return tokenizer.pad(lowerCAmelCase__,padding='longest',return_tensors='pt'       )

   # Instantiate dataloaders.
   a__  					=		DataLoader(
       tokenized_datasets['train'],shuffle=lowerCAmelCase__,collate_fn=lowerCAmelCase__,batch_size=lowerCAmelCase__       )
   a__  					=		DataLoader(
       tokenized_datasets['validation'],shuffle=lowerCAmelCase__,collate_fn=lowerCAmelCase__,batch_size=lowerCAmelCase__       )

   return train_dataloader, eval_dataloader





def 	_lowerCamelCase			(			UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__       )				->  int:


   '''simple docstring'''


   model.eval()
   a__  					=		0
   for step, batch in enumerate(lowerCAmelCase__       ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device       )
      with torch.no_grad():
         a__  					=		model(**lowerCAmelCase__       )
      a__  					=		outputs.logits.argmax(dim=-1       )
      # It is slightly faster to call this once, than multiple times
      a__  					=		accelerator.gather(
          (predictions, batch['labels'])       )  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
         if step == len(lowerCAmelCase__       ) - 1:
            a__  					=		predictions[: len(eval_dataloader.dataset       ) - samples_seen]
            a__  					=		references[: len(eval_dataloader.dataset       ) - samples_seen]
         else:
            samples_seen += references.shape[0]
      metric.add_batch(
          predictions=lowerCAmelCase__,references=lowerCAmelCase__,)

   a__  					=		metric.compute()
   return eval_metric["accuracy"]





def 	_lowerCamelCase			(			UpperCAmelCase__,UpperCAmelCase__       )				->  Union[str, Any]:


   '''simple docstring'''


   a__  					=		Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   a__  					=		config['lr']
   a__  					=		int(config['num_epochs']       )
   a__  					=		int(config['seed']       )
   a__  					=		int(config['batch_size']       )
   a__  					=		args.model_name_or_path

   set_seed(lowerCAmelCase__       )
   a__  					=		get_dataloaders(lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__       )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   a__  					=		AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__,return_dict=lowerCAmelCase__       )

   # Instantiate optimizer
   a__  					=		(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   a__  					=		optimizer_cls(params=model.parameters(),lr=lowerCAmelCase__       )

   if accelerator.state.deepspeed_plugin is not None:
      a__  					=		accelerator.state.deepspeed_plugin.deepspeed_config[
          'gradient_accumulation_steps'
      ]
   else:
      a__  					=		1
   a__  					=		(len(lowerCAmelCase__       ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      a__  					=		get_linear_schedule_with_warmup(
          optimizer=lowerCAmelCase__,num_warmup_steps=0,num_training_steps=lowerCAmelCase__,)
   else:
      a__  					=		DummyScheduler(lowerCAmelCase__,total_num_steps=lowerCAmelCase__,warmup_num_steps=0       )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   a__  					=		accelerator.prepare(
       lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__       )

   # We need to keep track of how many total steps we have iterated over
   a__  					=		0
   # We also need to keep track of the stating epoch so files are named properly
   a__  					=		0
   a__  					=		evaluate.load('glue','mrpc'       )
   a__  					=		num_epochs

   if args.partial_train_epoch is not None:
      a__  					=		args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint       )
      a__  					=		args.resume_from_checkpoint.split('epoch_'       )[1]
      a__  					=		''
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      a__  					=		int(lowerCAmelCase__       ) + 1
      a__  					=		evaluation_loop(lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__       )
      accelerator.print('resumed checkpoint performance:',lowerCAmelCase__       )
      accelerator.print('resumed checkpoint\'s scheduler\'s lr:',lr_scheduler.get_lr()[0]       )
      accelerator.print('resumed optimizers\'s lr:',optimizer.param_groups[0]['lr']       )
      with open(os.path.join(args.output_dir,f'''state_{starting_epoch-1}.json'''       ),'r'       ) as f:
         a__  					=		json.load(lowerCAmelCase__       )
         assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
         assert (
             resumed_state["lr"] == lr_scheduler.get_lr()[0]
         ), "Scheduler learning rate mismatch, loading from checkpoint failed"
         assert (
             resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
         ), "Optimizer learning rate mismatch, loading from checkpoint failed"
         assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
         return

    # Now we train the model
   a__  					=		{}
   for epoch in range(lowerCAmelCase__,lowerCAmelCase__       ):
      model.train()
      for step, batch in enumerate(lowerCAmelCase__       ):
         a__  					=		model(**lowerCAmelCase__       )
         a__  					=		outputs.loss
         a__  					=		loss / gradient_accumulation_steps
         accelerator.backward(lowerCAmelCase__       )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1
      a__  					=		f'''epoch_{epoch}'''
      a__  					=		os.path.join(args.output_dir,lowerCAmelCase__       )
      accelerator.save_state(lowerCAmelCase__       )
      a__  					=		evaluation_loop(lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__,lowerCAmelCase__       )
      a__  					=		accuracy
      a__  					=		lr_scheduler.get_lr()[0]
      a__  					=		optimizer.param_groups[0]['lr']
      a__  					=		epoch
      a__  					=		overall_step
      accelerator.print(f'''epoch {epoch}:''',lowerCAmelCase__       )

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         with open(os.path.join(args.output_dir,f'''state_{epoch}.json'''       ),'w'       ) as f:
            json.dump(lowerCAmelCase__,lowerCAmelCase__       )





def 	_lowerCamelCase			(			)				->  Optional[int]:


   '''simple docstring'''


   a__  					=		argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'       )
   parser.add_argument(
       '--model_name_or_path',type=lowerCAmelCase__,default='bert-base-cased',help='Path to pretrained model or model identifier from huggingface.co/models.',required=lowerCAmelCase__,)
   parser.add_argument(
       '--output_dir',type=lowerCAmelCase__,default='.',help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',)
   parser.add_argument(
       '--resume_from_checkpoint',type=lowerCAmelCase__,default=lowerCAmelCase__,help='If the training should continue from a checkpoint folder.',)
   parser.add_argument(
       '--partial_train_epoch',type=lowerCAmelCase__,default=lowerCAmelCase__,help='If passed, the training will stop after this number of epochs.',)
   parser.add_argument(
       '--num_epochs',type=lowerCAmelCase__,default=2,help='Number of train epochs.',)
   a__  					=		parser.parse_args()
   a__  					=		{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

   training_function(lowerCAmelCase__,lowerCAmelCase__       )


if __name__ == "__main__":
     main()









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_  (  snake_case__							):
					# to overwrite at feature extractactor specific tests
					_a      :						str						    =					None
					_a      :						Tuple						    =					None



					@property
					def   __a     (		self     :     str						):
							return self.feat_extract_tester.prepare_feat_extract_dict()



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     List[str] 						=      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   __a     (		self     :     List[Any]						):
							lowerCamelCase_	:     Dict 						=      self.feat_extract_tester.prepare_inputs_for_common()
							lowerCamelCase_	:     str 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     int 						=      feat_extract.model_input_names[0]

							lowerCamelCase_	:     Optional[int] 						=      BatchFeature({input_name: speech_inputs}						)

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

							lowerCamelCase_	:     Tuple 						=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase						)
							lowerCamelCase_	:     Union[str, Any] 						=      BatchFeature({input_name: speech_inputs}       ,      tensor_type='np'						)

							lowerCamelCase_	:     int 						=      processed_features[input_name]

							if len(batch_features_input.shape						) < 3:
									lowerCamelCase_	:     Union[str, Any] 						=      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   __a     (		self     :     Dict						):
							lowerCamelCase_	:     Any 						=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     Tuple 						=      feat_extract.model_input_names[0]

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

							lowerCamelCase_	:     Optional[int] 						=      processed_features[input_name]

							if len(batch_features_input.shape						) < 3:
									lowerCamelCase_	:     Optional[int] 						=      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   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     int 						=      self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     int 						=      feat_extract.model_input_names[0]

							lowerCamelCase_	:     List[str] 						=      BatchFeature({input_name: speech_inputs}       ,      tensor_type='tf'						)

							lowerCamelCase_	:     Union[str, Any] 						=      processed_features[input_name]

							if len(batch_features_input.shape						) < 3:
									lowerCamelCase_	:     Any 						=      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   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     int=False						):
							def _inputs_have_equal_length(lowerCamelCase     :     Union[str, Any]						):
									lowerCamelCase_	:     Any 						=      len(input[0]						)
									for input_slice in input[1:]:
											if len(lowerCamelCase						) != length:
													return False
									return True

							def _inputs_are_equal(lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     Dict						):
									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

							lowerCamelCase_	:     List[str] 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     str 						=      self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      feat_extract.model_input_names[0]

							lowerCamelCase_	:     List[Any] 						=      BatchFeature({input_name: speech_inputs}						)

							lowerCamelCase_	:     Optional[int] 						=      self.feat_extract_tester.seq_length_diff
							lowerCamelCase_	:     int 						=      self.feat_extract_tester.max_seq_length + pad_diff
							lowerCamelCase_	:     Optional[int] 						=      self.feat_extract_tester.min_seq_length
							lowerCamelCase_	:     List[Any] 						=      self.feat_extract_tester.batch_size
							lowerCamelCase_	:     int 						=      self.feat_extract_tester.feature_size

							# test padding for List[int] + numpy
							lowerCamelCase_	:     int 						=      feat_extract.pad(lowerCamelCase       ,      padding=lowerCamelCase						)
							lowerCamelCase_	:     Tuple 						=      input_a[input_name]

							lowerCamelCase_	:     List[str] 						=      feat_extract.pad(lowerCamelCase       ,      padding='longest'						)
							lowerCamelCase_	:     Optional[Any] 						=      input_a[input_name]

							lowerCamelCase_	:     List[Any] 						=      feat_extract.pad(lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[-1]						)						)
							lowerCamelCase_	:     List[str] 						=      input_a[input_name]

							lowerCamelCase_	:     Union[str, Any] 						=      feat_extract.pad(lowerCamelCase       ,      padding='longest'       ,      return_tensors='np'						)
							lowerCamelCase_	:     int 						=      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]

							lowerCamelCase_	:     Union[str, Any] 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=lowerCamelCase       ,      return_tensors='np'						)
							lowerCamelCase_	:     Dict 						=      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
							lowerCamelCase_	:     Optional[Any] 						=      feat_extract.pad(lowerCamelCase       ,      pad_to_multiple_of=10						)
							lowerCamelCase_	:     Dict 						=      input_a[input_name]

							lowerCamelCase_	:     List[Any] 						=      feat_extract.pad(lowerCamelCase       ,      padding='longest'       ,      pad_to_multiple_of=10						)
							lowerCamelCase_	:     Union[str, Any] 						=      input_a[input_name]

							lowerCamelCase_	:     str 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      pad_to_multiple_of=10       ,      max_length=lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      input_a[input_name]

							lowerCamelCase_	:     Any 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      pad_to_multiple_of=10       ,      max_length=lowerCamelCase       ,      return_tensors='np'       ,      )
							lowerCamelCase_	:     str 						=      input_a[input_name]

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

							lowerCamelCase_	:     Dict 						=      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
							lowerCamelCase_	:     Dict 						=      (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   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     Any=False						):
							def _inputs_have_equal_length(lowerCamelCase     :     Dict						):
									lowerCamelCase_	:     Dict 						=      len(input[0]						)
									for input_slice in input[1:]:
											if len(lowerCamelCase						) != length:
													return False
									return True

							def _inputs_are_equal(lowerCamelCase     :     int       ,      lowerCamelCase     :     List[str]						):
									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

							lowerCamelCase_	:     List[str] 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     str 						=      self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase						)
							lowerCamelCase_	:     Any 						=      feat_extract.model_input_names[0]

							lowerCamelCase_	:     str 						=      BatchFeature({input_name: speech_inputs}						)

							# truncate to smallest
							lowerCamelCase_	:     Dict 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[0]						)       ,      truncation=lowerCamelCase						)
							lowerCamelCase_	:     int 						=      input_a[input_name]

							lowerCamelCase_	:     str 						=      feat_extract.pad(lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[0]						)						)
							lowerCamelCase_	:     Union[str, Any] 						=      input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(lowerCamelCase						)						)
							self.assertFalse(_inputs_have_equal_length(lowerCamelCase						)						)

							# truncate to smallest with np
							lowerCamelCase_	:     Optional[int] 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[0]						)       ,      return_tensors='np'       ,      truncation=lowerCamelCase       ,      )
							lowerCamelCase_	:     List[str] 						=      input_a[input_name]

							lowerCamelCase_	:     int 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[0]						)       ,      return_tensors='np'						)
							lowerCamelCase_	:     Tuple 						=      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
							lowerCamelCase_	:     Tuple 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[1]						)       ,      truncation=lowerCamelCase       ,      return_tensors='np'       ,      )
							lowerCamelCase_	:     Union[str, Any] 						=      input_a[input_name]

							lowerCamelCase_	:     str 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[1]						)       ,      truncation=lowerCamelCase						)
							lowerCamelCase_	:     Any 						=      input_a[input_name]

							lowerCamelCase_	:     int 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[1]						)       ,      return_tensors='np'						)
							lowerCamelCase_	:     Optional[int] 						=      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
							lowerCamelCase_	:     str 						=      12
							lowerCamelCase_	:     List[Any] 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[0]						)       ,      pad_to_multiple_of=lowerCamelCase       ,      truncation=lowerCamelCase       ,      )
							lowerCamelCase_	:     List[str] 						=      input_a[input_name]

							lowerCamelCase_	:     List[Any] 						=      feat_extract.pad(
							    lowerCamelCase       ,      padding='max_length'       ,      max_length=len(speech_inputs[0]						)       ,      pad_to_multiple_of=lowerCamelCase       ,      )
							lowerCamelCase_	:     Dict 						=      input_a[input_name]

							# retrieve expected_length as multiple of pad_to_multiple_of
							lowerCamelCase_	:     Optional[Any] 						=      len(speech_inputs[0]						)
							if expected_length % pad_to_multiple_of != 0:
									lowerCamelCase_	:     str 						=      ((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   __a     (		self     :     Any						):
							self._check_padding(numpify=lowerCamelCase						)



					def   __a     (		self     :     List[Any]						):
							self._check_padding(numpify=lowerCamelCase						)



					def   __a     (		self     :     int						):
							self._check_truncation(numpify=lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							self._check_truncation(numpify=lowerCamelCase						)



					@require_torch
					def   __a     (		self     :     str						):
							lowerCamelCase_	:     List[Any] 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     str 						=      self.feat_extract_tester.prepare_inputs_for_common()
							lowerCamelCase_	:     Optional[Any] 						=      feat_extract.model_input_names[0]

							lowerCamelCase_	:     Optional[Any] 						=      BatchFeature({input_name: speech_inputs}						)

							lowerCamelCase_	:     int 						=      feat_extract.pad(lowerCamelCase       ,      padding='longest'       ,      return_tensors='np'						)[input_name]
							lowerCamelCase_	:     Dict 						=      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   __a     (		self     :     Any						):
							lowerCamelCase_	:     Union[str, Any] 						=      self.feature_extraction_class(**self.feat_extract_dict						)
							lowerCamelCase_	:     List[str] 						=      self.feat_extract_tester.prepare_inputs_for_common()
							lowerCamelCase_	:     Tuple 						=      feat_extract.model_input_names[0]

							lowerCamelCase_	:     Tuple 						=      BatchFeature({input_name: speech_inputs}						)

							lowerCamelCase_	:     Dict 						=      feat_extract.pad(lowerCamelCase       ,      padding='longest'       ,      return_tensors='np'						)[input_name]
							lowerCamelCase_	:     List[str] 						=      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   __a     (		self     :     str						):
							lowerCamelCase_	:     Any 						=      self.feat_extract_dict
							lowerCamelCase_	:     Optional[Any] 						=      True
							lowerCamelCase_	:     Dict 						=      self.feature_extraction_class(**lowerCamelCase						)
							lowerCamelCase_	:     Optional[int] 						=      self.feat_extract_tester.prepare_inputs_for_common()
							lowerCamelCase_	:     List[str] 						=      [len(lowerCamelCase						) for x in speech_inputs]
							lowerCamelCase_	:     List[Any] 						=      feat_extract.model_input_names[0]

							lowerCamelCase_	:     List[str] 						=      BatchFeature({input_name: speech_inputs}						)

							lowerCamelCase_	:     Tuple 						=      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   __a     (		self     :     Dict						):
							lowerCamelCase_	:     int 						=      self.feat_extract_dict
							lowerCamelCase_	:     Any 						=      True
							lowerCamelCase_	:     int 						=      self.feature_extraction_class(**lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      self.feat_extract_tester.prepare_inputs_for_common()
							lowerCamelCase_	:     str 						=      [len(lowerCamelCase						) for x in speech_inputs]
							lowerCamelCase_	:     Optional[Any] 						=      feat_extract.model_input_names[0]

							lowerCamelCase_	:     Any 						=      BatchFeature({input_name: speech_inputs}						)
							lowerCamelCase_	:     Optional[Any] 						=      min(lowerCamelCase						)

							lowerCamelCase_	:     List[Any] 						=      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]						)
