





import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


_snake_case  :    Dict						    =  {
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}

def   a_    ( lowerCAmelCase_      :			Optional[Any]	):
   assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
   assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
   if args.mlm:
      assert os.path.isfile(args.token_counts	)
      assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
   else:
      assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

   assert args.teacher_type == args.student_type or (
       args.student_type == "distilbert" and args.teacher_type == "bert"
   )
   assert os.path.isfile(args.student_config	)
   if args.student_pretrained_weights is not None:
      assert os.path.isfile(args.student_pretrained_weights	)

   if args.freeze_token_type_embds:
      assert args.student_type in ["roberta"]

   assert args.alpha_ce >= 0.0
   assert args.alpha_mlm >= 0.0
   assert args.alpha_clm >= 0.0
   assert args.alpha_mse >= 0.0
   assert args.alpha_cos >= 0.0
   assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0

def   a_    ( lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Union[str, Any]	):

   if args.student_type == "roberta":
      __lowerCAmelCase								=	False
   elif args.student_type == "gpt2":
      __lowerCAmelCase								=	False

def   a_    ( lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			Dict	):

   if args.student_type == "roberta":
      __lowerCAmelCase								=	False

def   a_    ( ):
   __lowerCAmelCase								=	argparse.ArgumentParser(description='Training'	)
   parser.add_argument('--force',	action='store_true',	help='Overwrite dump_path if it already exists.'	)

   parser.add_argument(
       '--dump_path',	type=__a,	required=__a,	help='The output directory (log, checkpoints, parameters, etc.)'	)
   parser.add_argument(
       '--data_file',	type=__a,	required=__a,	help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.',	)

   parser.add_argument(
       '--student_type',	type=__a,	choices=['distilbert', 'roberta', 'gpt2'],	required=__a,	help='The student type (DistilBERT, RoBERTa).',	)
   parser.add_argument('--student_config',	type=__a,	required=__a,	help='Path to the student configuration.'	)
   parser.add_argument(
       '--student_pretrained_weights',	default=__a,	type=__a,	help='Load student initialization checkpoint.'	)

   parser.add_argument(
       '--teacher_type',	choices=['bert', 'roberta', 'gpt2'],	required=__a,	help='Teacher type (BERT, RoBERTa).'	)
   parser.add_argument('--teacher_name',	type=__a,	required=__a,	help='The teacher model.'	)

   parser.add_argument('--temperature',	default=2.0,	type=__a,	help='Temperature for the softmax temperature.'	)
   parser.add_argument(
       '--alpha_ce',	default=0.5,	type=__a,	help='Linear weight for the distillation loss. Must be >=0.'	)
   parser.add_argument(
       '--alpha_mlm',	default=0.0,	type=__a,	help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.',	)
   parser.add_argument('--alpha_clm',	default=0.5,	type=__a,	help='Linear weight for the CLM loss. Must be >=0.'	)
   parser.add_argument('--alpha_mse',	default=0.0,	type=__a,	help='Linear weight of the MSE loss. Must be >=0.'	)
   parser.add_argument(
       '--alpha_cos',	default=0.0,	type=__a,	help='Linear weight of the cosine embedding loss. Must be >=0.'	)

   parser.add_argument(
       '--mlm',	action='store_true',	help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'	)
   parser.add_argument(
       '--mlm_mask_prop',	default=0.15,	type=__a,	help='Proportion of tokens for which we need to make a prediction.',	)
   parser.add_argument('--word_mask',	default=0.8,	type=__a,	help='Proportion of tokens to mask out.'	)
   parser.add_argument('--word_keep',	default=0.1,	type=__a,	help='Proportion of tokens to keep.'	)
   parser.add_argument('--word_rand',	default=0.1,	type=__a,	help='Proportion of tokens to randomly replace.'	)
   parser.add_argument(
       '--mlm_smoothing',	default=0.7,	type=__a,	help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).',	)
   parser.add_argument('--token_counts',	type=__a,	help='The token counts in the data_file for MLM.'	)

   parser.add_argument(
       '--restrict_ce_to_mask',	action='store_true',	help='If true, compute the distillation loss only the [MLM] prediction distribution.',	)
   parser.add_argument(
       '--freeze_pos_embs',	action='store_true',	help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.',	)
   parser.add_argument(
       '--freeze_token_type_embds',	action='store_true',	help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.',	)

   parser.add_argument('--n_epoch',	type=__a,	default=3,	help='Number of pass on the whole dataset.'	)
   parser.add_argument('--batch_size',	type=__a,	default=5,	help='Batch size (for each process).'	)
   parser.add_argument(
       '--group_by_size',	action='store_false',	help='If true, group sequences that have similar length into the same batch. Default is true.',	)

   parser.add_argument(
       '--gradient_accumulation_steps',	type=__a,	default=50,	help='Gradient accumulation for larger training batches.',	)
   parser.add_argument('--warmup_prop',	default=0.05,	type=__a,	help='Linear warmup proportion.'	)
   parser.add_argument('--weight_decay',	default=0.0,	type=__a,	help='Weight decay if we apply some.'	)
   parser.add_argument('--learning_rate',	default=5E-4,	type=__a,	help='The initial learning rate for Adam.'	)
   parser.add_argument('--adam_epsilon',	default=1E-6,	type=__a,	help='Epsilon for Adam optimizer.'	)
   parser.add_argument('--max_grad_norm',	default=5.0,	type=__a,	help='Max gradient norm.'	)
   parser.add_argument('--initializer_range',	default=0.02,	type=__a,	help='Random initialization range.'	)

   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='O1',	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_gpu',	type=__a,	default=1,	help='Number of GPUs in the node.'	)
   parser.add_argument('--local_rank',	type=__a,	default=-1,	help='Distributed training - Local rank'	)
   parser.add_argument('--seed',	type=__a,	default=56,	help='Random seed'	)

   parser.add_argument('--log_interval',	type=__a,	default=500,	help='Tensorboard logging interval.'	)
   parser.add_argument('--checkpoint_interval',	type=__a,	default=4000,	help='Checkpoint interval.'	)
   __lowerCAmelCase								=	parser.parse_args()
   sanity_checks(__a	)

   # ARGS #
   init_gpu_params(__a	)
   set_seed(__a	)
   if args.is_master:
      if os.path.exists(args.dump_path	):
         if not args.force:
            raise ValueError(
                F"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
                ' itUse `--force` if you want to overwrite it'	)
         else:
            shutil.rmtree(args.dump_path	)

      if not os.path.exists(args.dump_path	):
         os.makedirs(args.dump_path	)
      logger.info(F"""Experiment will be dumped and logged in {args.dump_path}"""	)

      # SAVE PARAMS #
      logger.info(F"""Param: {args}"""	)
      with open(os.path.join(args.dump_path,	'parameters.json'	),	'w'	) as f:
         json.dump(vars(__a	),	__a,	indent=4	)
      git_log(args.dump_path	)

   __lowerCAmelCase								=	MODEL_CLASSES[args.student_type]
   __lowerCAmelCase								=	MODEL_CLASSES[args.teacher_type]

   # TOKENIZER #
   __lowerCAmelCase								=	teacher_tokenizer_class.from_pretrained(args.teacher_name	)
   __lowerCAmelCase								=	{}
   for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
      __lowerCAmelCase								=	tokenizer.all_special_tokens.index(__a	)
      __lowerCAmelCase								=	tokenizer.all_special_ids[idx]
   logger.info(F"""Special tokens {special_tok_ids}"""	)
   __lowerCAmelCase								=	special_tok_ids
   __lowerCAmelCase								=	tokenizer.max_model_input_sizes[args.teacher_name]

   # DATA LOADER #
   logger.info(F"""Loading data from {args.data_file}"""	)
   with open(args.data_file,	'rb'	) as fp:
      __lowerCAmelCase								=	pickle.load(__a	)

   if args.mlm:
      logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)"""	)
      with open(args.token_counts,	'rb'	) as fp:
         __lowerCAmelCase								=	pickle.load(__a	)

      __lowerCAmelCase								=	np.maximum(__a,	1	) ** -args.mlm_smoothing
      for idx in special_tok_ids.values():
         __lowerCAmelCase								=	0.0  # do not predict special tokens
      __lowerCAmelCase								=	torch.from_numpy(__a	)
   else:
      __lowerCAmelCase								=	None

   __lowerCAmelCase								=	LmSeqsDataset(params=__a,	data=__a	)
   logger.info('Data loader created.'	)

   # STUDENT #
   logger.info(F"""Loading student config from {args.student_config}"""	)
   __lowerCAmelCase								=	student_config_class.from_pretrained(args.student_config	)
   __lowerCAmelCase								=	True

   if args.student_pretrained_weights is not None:
      logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""	)
      __lowerCAmelCase								=	student_model_class.from_pretrained(args.student_pretrained_weights,	config=__a	)
   else:
      __lowerCAmelCase								=	student_model_class(__a	)

   if args.n_gpu > 0:
      student.to(F"""cuda:{args.local_rank}"""	)
   logger.info('Student loaded.'	)

   # TEACHER #
   __lowerCAmelCase								=	teacher_model_class.from_pretrained(args.teacher_name,	output_hidden_states=__a	)
   if args.n_gpu > 0:
      teacher.to(F"""cuda:{args.local_rank}"""	)
   logger.info(F"""Teacher loaded from {args.teacher_name}."""	)

   # FREEZING #
   if args.freeze_pos_embs:
      freeze_pos_embeddings(__a,	__a	)
   if args.freeze_token_type_embds:
      freeze_token_type_embeddings(__a,	__a	)

   # SANITY CHECKS #
   assert student.config.vocab_size == teacher.config.vocab_size
   assert student.config.hidden_size == teacher.config.hidden_size
   assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
   if args.mlm:
      assert token_probs.size(0	) == stu_architecture_config.vocab_size

   # DISTILLER #
   torch.cuda.empty_cache()
   __lowerCAmelCase								=	Distiller(
       params=__a,	dataset=__a,	token_probs=__a,	student=__a,	teacher=__a	)
   distiller.train()
   logger.info('Let\'s go get some drinks.'	)


if __name__ == "__main__":
     main()



'''simple docstring'''







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__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Union[str, Any]   =					None
  __UpperCAmelCase				:				List[Any]   =					None


  @property
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     return self.feat_extract_tester.prepare_feat_extract_dict()


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	Dict			=   self.feature_extraction_class(**self.feat_extract_dict       )
     self.assertTrue(hasattr(_a							,'feature_size'       )       )
     self.assertTrue(hasattr(_a							,'sampling_rate'       )       )
     self.assertTrue(hasattr(_a							,'padding_value'       )       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Any			=   self.feat_extract_tester.prepare_inputs_for_common()
     _a				:	str			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	int			=   feat_extract.model_input_names[0]

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

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

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

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

     if len(batch_features_input.shape       ) < 3:
        _a				:	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_torch
  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	List[Any]			=   self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a       )
     _a				:	Dict			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	int			=   feat_extract.model_input_names[0]

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

     _a				:	str			=   processed_features[input_name]

     if len(batch_features_input.shape       ) < 3:
        _a				:	str			=   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   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	int			=   self.feat_extract_tester.prepare_inputs_for_common(equal_length=_a       )
     _a				:	Dict			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	Tuple			=   feat_extract.model_input_names[0]

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

     _a				:	Optional[int]			=   processed_features[input_name]

     if len(batch_features_input.shape       ) < 3:
        _a				:	Optional[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   __lowercase		(		self      :     Dict							,_a      :     Any=False       ):


     '''simple docstring'''






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

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

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

     _a				:	int			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	Tuple			=   self.feat_extract_tester.prepare_inputs_for_common(numpify=_a       )
     _a				:	Union[str, Any]			=   feat_extract.model_input_names[0]

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

     _a				:	str			=   self.feat_extract_tester.seq_length_diff
     _a				:	Dict			=   self.feat_extract_tester.max_seq_length + pad_diff
     _a				:	Dict			=   self.feat_extract_tester.min_seq_length
     _a				:	Optional[Any]			=   self.feat_extract_tester.batch_size
     _a				:	Tuple			=   self.feat_extract_tester.feature_size

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

     _a				:	Tuple			=   feat_extract.pad(_a							,padding='longest'       )
     _a				:	Any			=   input_a[input_name]

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

     _a				:	List[str]			=   feat_extract.pad(_a							,padding='longest'							,return_tensors='np'       )
     _a				:	str			=   input_a[input_name]

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

     _a				:	int			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=_a							,return_tensors='np'       )
     _a				:	Optional[int]			=   input_a[input_name]

     self.assertFalse(_inputs_have_equal_length(_a       )       )
     self.assertTrue(_inputs_have_equal_length(_a       )       )
     self.assertTrue(_inputs_have_equal_length(_a       )       )
     self.assertTrue(_inputs_are_equal(_a							,_a       )       )
     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
     _a				:	Tuple			=   feat_extract.pad(_a							,pad_to_multiple_of=10       )
     _a				:	List[str]			=   input_a[input_name]

     _a				:	str			=   feat_extract.pad(_a							,padding='longest'							,pad_to_multiple_of=10       )
     _a				:	Tuple			=   input_a[input_name]

     _a				:	Optional[int]			=   feat_extract.pad(
         _a							,padding='max_length'							,pad_to_multiple_of=10							,max_length=_a       )
     _a				:	Any			=   input_a[input_name]

     _a				:	Optional[int]			=   feat_extract.pad(
         _a							,padding='max_length'							,pad_to_multiple_of=10							,max_length=_a							,return_tensors='np'							,)
     _a				:	Dict			=   input_a[input_name]

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

     _a				:	List[str]			=   pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
     self.assertTrue(all(len(_a       ) == 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
     _a				:	Any			=   (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   __lowercase		(		self      :     List[Any]							,_a      :     Optional[int]=False       ):


     '''simple docstring'''






     def _inputs_have_equal_length(_a      :     List[str]       ):
        _a				:	Union[str, Any]			=   len(input[0]       )
        for input_slice in input[1:]:
           if len(_a       ) != length:
              return False
        return True

     def _inputs_are_equal(_a      :     List[str]							,_a      :     List[str]       ):
        if len(_a       ) != len(_a       ):
           return False

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

     _a				:	Dict			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	str			=   self.feat_extract_tester.prepare_inputs_for_common(numpify=_a       )
     _a				:	Any			=   feat_extract.model_input_names[0]

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

     # truncate to smallest
     _a				:	Union[str, Any]			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[0]       )							,truncation=_a       )
     _a				:	str			=   input_a[input_name]

     _a				:	List[str]			=   feat_extract.pad(_a							,padding='max_length'							,max_length=len(speech_inputs[0]       )       )
     _a				:	Tuple			=   input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(_a       )       )
     self.assertFalse(_inputs_have_equal_length(_a       )       )

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

     _a				:	List[Any]			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[0]       )							,return_tensors='np'       )
     _a				:	int			=   input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(_a       )       )
     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(_a       )       )

     # truncate to middle
     _a				:	Dict			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[1]       )							,truncation=_a							,return_tensors='np'							,)
     _a				:	List[Any]			=   input_a[input_name]

     _a				:	Tuple			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[1]       )							,truncation=_a       )
     _a				:	Tuple			=   input_a[input_name]

     _a				:	Tuple			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=len(speech_inputs[1]       )							,return_tensors='np'       )
     _a				:	Dict			=   input_a[input_name]

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

     # 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(_a       )       )
     self.assertTrue(len(input_a[-1]       ) == len(speech_inputs[-1]       )       )

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

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

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

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

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

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

     # retrieve expected_length as multiple of pad_to_multiple_of
     _a				:	List[Any]			=   len(speech_inputs[0]       )
     if expected_length % pad_to_multiple_of != 0:
        _a				:	Union[str, Any]			=   ((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(_a       )       )
     self.assertFalse(_inputs_have_equal_length(_a       )       )


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     self._check_padding(numpify=_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     self._check_padding(numpify=_a       )


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     self._check_truncation(numpify=_a       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     self._check_truncation(numpify=_a       )


  @require_torch
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Any			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	List[Any]			=   self.feat_extract_tester.prepare_inputs_for_common()
     _a				:	Union[str, Any]			=   feat_extract.model_input_names[0]

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

     _a				:	List[Any]			=   feat_extract.pad(_a							,padding='longest'							,return_tensors='np'       )[input_name]
     _a				:	List[str]			=   feat_extract.pad(_a							,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   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	List[str]			=   self.feature_extraction_class(**self.feat_extract_dict       )
     _a				:	Optional[Any]			=   self.feat_extract_tester.prepare_inputs_for_common()
     _a				:	Dict			=   feat_extract.model_input_names[0]

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

     _a				:	Dict			=   feat_extract.pad(_a							,padding='longest'							,return_tensors='np'       )[input_name]
     _a				:	Any			=   feat_extract.pad(_a							,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   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






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

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

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


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






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

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

     _a				:	Dict			=   feat_extract.pad(
         _a							,padding='max_length'							,max_length=_a							,truncation=_a							,return_tensors='np'       )
     self.assertIn('attention_mask'							,_a       )
     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]       )



