





import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
     import torch


_snake_case  :    Optional[int]						    =  random.Random()

def   a_    ( lowerCAmelCase_      :			List[Any],	lowerCAmelCase_      :			List[Any]=1.0,	lowerCAmelCase_      :			Dict=None,	lowerCAmelCase_      :			Tuple=None	):
   if rng is None:
      __lowerCAmelCase								=	global_rng

   __lowerCAmelCase								=	[]
   for batch_idx in range(shape[0]	):
      values.append([]	)
      for _ in range(shape[1]	):
         values[-1].append(rng.random() * scale	)

   return values



@require_torch
class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  def __init__(				self :		str					,  lowerCAmelCase_ :		Dict					,  lowerCAmelCase_ :		Tuple=7					,  lowerCAmelCase_ :		int=4_0_0					,  lowerCAmelCase_ :		Any=2_0_0_0					,  lowerCAmelCase_ :		Any=1					,  lowerCAmelCase_ :		List[Any]=0.0					,  lowerCAmelCase_ :		List[Any]=1_6_0_0_0					,  lowerCAmelCase_ :		List[str]=True					,  lowerCAmelCase_ :		List[Any]=8_0					,  lowerCAmelCase_ :		List[Any]=1_6					,  lowerCAmelCase_ :		Tuple=6_4					,  lowerCAmelCase_ :		Optional[Any]="hann_window"					,  lowerCAmelCase_ :		List[Any]=8_0					,  lowerCAmelCase_ :		str=7_6_0_0					,  lowerCAmelCase_ :		Optional[int]=1e-10					,  lowerCAmelCase_ :		Optional[int]=True					,  )							->						int:
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	min_seq_length
     __lowerCAmelCase								=	max_seq_length
     __lowerCAmelCase								=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     __lowerCAmelCase								=	feature_size
     __lowerCAmelCase								=	padding_value
     __lowerCAmelCase								=	sampling_rate
     __lowerCAmelCase								=	do_normalize
     __lowerCAmelCase								=	num_mel_bins
     __lowerCAmelCase								=	hop_length
     __lowerCAmelCase								=	win_length
     __lowerCAmelCase								=	win_function
     __lowerCAmelCase								=	fmin
     __lowerCAmelCase								=	fmax
     __lowerCAmelCase								=	mel_floor
     __lowerCAmelCase								=	return_attention_mask






  def 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     return {
         "feature_size": self.feature_size,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "do_normalize": self.do_normalize,
         "num_mel_bins": self.num_mel_bins,
         "hop_length": self.hop_length,
         "win_length": self.win_length,
         "win_function": self.win_function,
         "fmin": self.fmin,
         "fmax": self.fmax,
         "mel_floor": self.mel_floor,
         "return_attention_mask": self.return_attention_mask,
     }






  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		Tuple=False					,  lowerCAmelCase_ :		Union[str, Any]=False     )							->						Tuple:
     def _flatten(lowerCAmelCase_ :		Union[str, Any]     ):
        return list(itertools.chain(*lowerCAmelCase_     )     )

     if equal_length:
        __lowerCAmelCase								=	floats_list((self.batch_size, self.max_seq_length)     )
     else:
        # make sure that inputs increase in size
        __lowerCAmelCase								=	[
            _flatten(floats_list((x, self.feature_size)     )     )
            for x in range(self.min_seq_length					,  self.max_seq_length					,  self.seq_length_diff     )
        ]

     if numpify:
        __lowerCAmelCase								=	[np.asarray(lowerCAmelCase_     ) for x in speech_inputs]

     return speech_inputs






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		Union[str, Any]=False					,  lowerCAmelCase_ :		Optional[int]=False     )							->						Union[str, Any]:
     if equal_length:
        __lowerCAmelCase								=	[floats_list((self.max_seq_length, self.num_mel_bins)     ) for _ in range(self.batch_size     )]
     else:
        # make sure that inputs increase in size
        __lowerCAmelCase								=	[
            floats_list((x, self.num_mel_bins)     )
            for x in range(self.min_seq_length					,  self.max_seq_length					,  self.seq_length_diff     )
        ]

     if numpify:
        __lowerCAmelCase								=	[np.asarray(lowerCAmelCase_     ) for x in speech_inputs]

     return speech_inputs





@require_torch
class 				_UpperCAmelCase    (    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = SpeechTaFeatureExtractor






  def 				lowercase		(				self :		Any     )							->						Dict:
     __lowerCAmelCase								=	SpeechTaFeatureExtractionTester(self     )






  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		List[str]     )							->						Any:
     self.assertTrue(np.all(np.mean(lowerCAmelCase_					,  axis=0     ) < 1e-3     )     )
     self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase_					,  axis=0     ) - 1     ) < 1e-3     )     )






  def 				lowercase		(				self :		int     )							->						Dict:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     # create three inputs of length 800, 1000, and 1200
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]
     __lowerCAmelCase								=	[np.asarray(lowerCAmelCase_     ) for speech_input in speech_inputs]

     # Test not batched input
     __lowerCAmelCase								=	feat_extract(speech_inputs[0]					,  return_tensors='np'     ).input_values
     __lowerCAmelCase								=	feat_extract(np_speech_inputs[0]					,  return_tensors='np'     ).input_values
     self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-3     )     )

     # Test batched
     __lowerCAmelCase								=	feat_extract(lowerCAmelCase_					,  return_tensors='np'     ).input_values
     __lowerCAmelCase								=	feat_extract(lowerCAmelCase_					,  return_tensors='np'     ).input_values
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-3     )     )






  def 				lowercase		(				self :		Optional[int]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]

     __lowerCAmelCase								=	['longest', 'max_length', 'do_not_pad']
     __lowerCAmelCase								=	[None, 1_6_0_0, None]
     for max_length, padding in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        __lowerCAmelCase								=	feat_extract(lowerCAmelCase_					,  padding=lowerCAmelCase_					,  max_length=lowerCAmelCase_					,  return_tensors='np'     )
        __lowerCAmelCase								=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0][:8_0_0]     )
        self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6     )
        self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]     )
        self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6     )
        self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]     )






  def 				lowercase		(				self :		List[Any]     )							->						List[str]:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	range(8_0_0					,  1_4_0_0					,  2_0_0     )
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in lengths]

     __lowerCAmelCase								=	['longest', 'max_length', 'do_not_pad']
     __lowerCAmelCase								=	[None, 1_6_0_0, None]

     for max_length, padding in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        __lowerCAmelCase								=	feat_extract(lowerCAmelCase_					,  max_length=lowerCAmelCase_					,  padding=lowerCAmelCase_     )
        __lowerCAmelCase								=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0][:8_0_0]     )
        self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]     )
        self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]     )






  def 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]
     __lowerCAmelCase								=	feat_extract(
         lowerCAmelCase_					,  truncation=lowerCAmelCase_					,  max_length=1_0_0_0					,  padding='max_length'					,  return_tensors='np'     )
     __lowerCAmelCase								=	processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
     self._check_zero_mean_unit_variance(input_values[1]     )
     self._check_zero_mean_unit_variance(input_values[2]     )






  def 				lowercase		(				self :		int     )							->						Optional[int]:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]
     __lowerCAmelCase								=	feat_extract(
         lowerCAmelCase_					,  truncation=lowerCAmelCase_					,  max_length=1_0_0_0					,  padding='longest'					,  return_tensors='np'     )
     __lowerCAmelCase								=	processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
     self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]     )
     self._check_zero_mean_unit_variance(input_values[2]     )

     # make sure that if max_length < longest -> then pad to max_length
     self.assertTrue(input_values.shape == (3, 1_0_0_0)     )

     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]
     __lowerCAmelCase								=	feat_extract(
         lowerCAmelCase_					,  truncation=lowerCAmelCase_					,  max_length=2_0_0_0					,  padding='longest'					,  return_tensors='np'     )
     __lowerCAmelCase								=	processed.input_values

     self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
     self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]     )
     self._check_zero_mean_unit_variance(input_values[2]     )

     # make sure that if max_length > longest -> then pad to longest
     self.assertTrue(input_values.shape == (3, 1_2_0_0)     )






  def 				lowercase		(				self :		Any     )							->						Tuple:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	np.random.rand(1_0_0     ).astype(np.floataa     )
     __lowerCAmelCase								=	np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
        __lowerCAmelCase								=	feature_extractor.pad([{'input_values': inputs}]					,  return_tensors='np'     )
        self.assertTrue(np_processed.input_values.dtype == np.floataa     )
        __lowerCAmelCase								=	feature_extractor.pad([{'input_values': inputs}]					,  return_tensors='pt'     )
        self.assertTrue(pt_processed.input_values.dtype == torch.floataa     )






  def 				lowercase		(				self :		List[Any]     )							->						Union[str, Any]:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     # create three inputs of length 800, 1000, and 1200
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]
     __lowerCAmelCase								=	[np.asarray(lowerCAmelCase_     ) for speech_input in speech_inputs]

     # Test feature size
     __lowerCAmelCase								=	feature_extractor(audio_target=lowerCAmelCase_					,  padding=lowerCAmelCase_					,  return_tensors='np'     ).input_values
     self.assertTrue(input_values.ndim == 3     )
     self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins     )

     # Test not batched input
     __lowerCAmelCase								=	feature_extractor(speech_inputs[0]					,  return_tensors='np'     ).input_values
     __lowerCAmelCase								=	feature_extractor(np_speech_inputs[0]					,  return_tensors='np'     ).input_values
     self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-3     )     )

     # Test batched
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  return_tensors='np'     ).input_values
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  return_tensors='np'     ).input_values
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-3     )     )

     # Test 2-D numpy arrays are batched.
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     __lowerCAmelCase								=	np.asarray(lowerCAmelCase_     )
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  return_tensors='np'     ).input_values
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  return_tensors='np'     ).input_values
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-3     )     )






  def 				lowercase		(				self :		Any     )							->						int:
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_target()
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )

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

     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase_     )
     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}					,  tensor_type='np'     )

     __lowerCAmelCase								=	processed_features[input_name]

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

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






  @require_torch
  def 				lowercase		(				self :		Dict     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase_     )
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

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

     __lowerCAmelCase								=	processed_features[input_name]

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

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






  @require_torch
  def 				lowercase		(				self :		Optional[Any]     )							->						Any:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_dict     )
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_target()
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )

     __lowerCAmelCase								=	feat_extract.num_mel_bins  # hack!

     __lowerCAmelCase								=	feat_extract.pad(lowerCAmelCase_					,  padding='longest'					,  return_tensors='np'     )[input_name]
     __lowerCAmelCase								=	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     )






  def 				lowercase		(				self :		List[str]     )							->						Dict:
     __lowerCAmelCase								=	self.feat_extract_dict
     __lowerCAmelCase								=	True
     __lowerCAmelCase								=	self.feature_extraction_class(**lowerCAmelCase_     )
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_target()
     __lowerCAmelCase								=	[len(lowerCAmelCase_     ) for x in speech_inputs]
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )

     __lowerCAmelCase								=	feat_extract.num_mel_bins  # hack!

     __lowerCAmelCase								=	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 				lowercase		(				self :		List[Any]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	self.feat_extract_dict
     __lowerCAmelCase								=	True
     __lowerCAmelCase								=	self.feature_extraction_class(**lowerCAmelCase_     )
     __lowerCAmelCase								=	self.feat_extract_tester.prepare_inputs_for_target()
     __lowerCAmelCase								=	[len(lowerCAmelCase_     ) for x in speech_inputs]
     __lowerCAmelCase								=	feat_extract.model_input_names[0]

     __lowerCAmelCase								=	BatchFeature({input_name: speech_inputs}     )
     __lowerCAmelCase								=	min(lowerCAmelCase_     )

     __lowerCAmelCase								=	feat_extract.num_mel_bins  # hack!

     __lowerCAmelCase								=	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]     )






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		List[str]     )							->						Tuple:
     from datasets import load_dataset

     __lowerCAmelCase								=	load_dataset('hf-internal-testing/librispeech_asr_dummy'					,  'clean'					,  split='validation'     )
     # automatic decoding with librispeech
     __lowerCAmelCase								=	ds.sort('id'     ).select(range(lowerCAmelCase_     )     )[:num_samples]['audio']

     return [x["array"] for x in speech_samples]






  def 				lowercase		(				self :		Union[str, Any]     )							->						Optional[Any]:
     # fmt: off
     __lowerCAmelCase								=	torch.tensor(
         [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
          3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
          2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
          4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
          7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
          4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]     )
     # fmt: on

     __lowerCAmelCase								=	self._load_datasamples(1     )
     __lowerCAmelCase								=	SpeechTaFeatureExtractor()
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  return_tensors='pt'     ).input_values
     self.assertEquals(input_values.shape					,  (1, 9_3_6_8_0)     )
     self.assertTrue(torch.allclose(input_values[0, :3_0]					,  lowerCAmelCase_					,  atol=1e-6     )     )






  def 				lowercase		(				self :		str     )							->						List[str]:
     # fmt: off
     __lowerCAmelCase								=	torch.tensor(
         [-2.68_70, -3.01_04, -3.13_56, -3.53_52, -3.00_44, -3.03_53, -3.47_19, -3.67_77,
          -3.15_20, -2.94_35, -2.65_53, -2.87_95, -2.99_44, -2.59_21, -3.02_79, -3.03_86,
          -3.08_64, -3.12_91, -3.23_53, -2.74_44, -2.68_31, -2.72_87, -3.17_61, -3.15_71,
          -3.27_26, -3.05_82, -3.10_07, -3.45_33, -3.46_95, -3.09_98]     )
     # fmt: on

     __lowerCAmelCase								=	self._load_datasamples(1     )
     __lowerCAmelCase								=	SpeechTaFeatureExtractor()
     __lowerCAmelCase								=	feature_extractor(audio_target=lowerCAmelCase_					,  return_tensors='pt'     ).input_values
     self.assertEquals(input_values.shape					,  (1, 3_6_6, 8_0)     )
     self.assertTrue(torch.allclose(input_values[0, 0, :3_0]					,  lowerCAmelCase_					,  atol=1e-4     )     )








import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	batch['labels'].ne(lowerCAmelCase_	).sum(1	).tolist()
         if consider_target:
            for src, tgt in zip(lowerCAmelCase_,	lowerCAmelCase_	):
               max_lens.append(max(lowerCAmelCase_,	lowerCAmelCase_	)	)
         else:
            max_lens.extend(lowerCAmelCase_	)
      return max_lens

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   pickle_save(lowerCAmelCase_,	train_ds.len_file	)
   pickle_save(lowerCAmelCase_,	val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)

