

"""simple docstring"""


import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__A						   =			"bert-base-cased"
__A						   =			"google/pegasus-xsum"
__A						   =			[" Sam ate lunch today.", "Sams lunch ingredients."]
__A						   =			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
__A						   =			"patrickvonplaten/t5-tiny-random"
__A						   =			"sshleifer/bart-tiny-random"
__A						   =			"sshleifer/tiny-mbart"
__A						   =			"sshleifer/tiny-marian-en-de"






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Union[str, Any]:
		__lowerCAmelCase:       Dict          =			"\n".join(__SCREAMING_SNAKE_CASE )
		Path(__SCREAMING_SNAKE_CASE ).open("w" ).writelines(__SCREAMING_SNAKE_CASE )






def     a__    (    __SCREAMING_SNAKE_CASE )	->	List[str]:
		for split in ["train", "val", "test"]:
				_dump_articles(os.path.join(__SCREAMING_SNAKE_CASE					, F"{split}.source" )					, __SCREAMING_SNAKE_CASE )
				_dump_articles(os.path.join(__SCREAMING_SNAKE_CASE					, F"{split}.target" )					, __SCREAMING_SNAKE_CASE )
		return tmp_dir





class     snake_case	(				__snake_case  ):

				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]     ,  )
				@slow
				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				List[Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AutoTokenizer.from_pretrained(UpperCamelCase__)
						__lowerCAmelCase:       Any          =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
						__lowerCAmelCase:       Dict          =			max(len(tokenizer.encode(UpperCamelCase__)) for a in ARTICLES)
						__lowerCAmelCase:       List[Any]          =			max(len(tokenizer.encode(UpperCamelCase__)) for a in SUMMARIES)
						__lowerCAmelCase:       Any          =			4
						__lowerCAmelCase:       str          =			8
						assert max_len_target > max_src_len  # Will be truncated
						assert max_len_source > max_src_len  # Will be truncated
						__lowerCAmelCase      , __lowerCAmelCase:       List[Any]          =			"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
						__lowerCAmelCase:       Any          =			SeqaSeqDataset(
						    UpperCamelCase__     ,  data_dir=UpperCamelCase__     ,  type_path="train"     ,  max_source_length=UpperCamelCase__     ,  max_target_length=UpperCamelCase__     ,  src_lang=UpperCamelCase__     ,  tgt_lang=UpperCamelCase__     ,  )
						__lowerCAmelCase:       List[str]          =			DataLoader(UpperCamelCase__     ,  batch_size=2     ,  collate_fn=train_dataset.collate_fn)
						for batch in dataloader:
								assert isinstance(UpperCamelCase__     ,  UpperCamelCase__)
								assert batch["attention_mask"].shape == batch["input_ids"].shape
								# show that articles were trimmed.
								assert batch["input_ids"].shape[1] == max_src_len
								# show that targets are the same len
								assert batch["labels"].shape[1] == max_tgt_len
								if tok_name != MBART_TINY:
										continue
								# check language codes in correct place
								__lowerCAmelCase:       Optional[int]          =			shift_tokens_right(batch["labels"]     ,  tokenizer.pad_token_id)
								assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
								assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
								assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
								assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

								break  # No need to test every batch

				@parameterized.expand([BART_TINY, BERT_BASE_CASED])
				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[Any])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			AutoTokenizer.from_pretrained(UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
						__lowerCAmelCase:       str          =			max(len(tokenizer.encode(UpperCamelCase__)) for a in ARTICLES)
						__lowerCAmelCase:       Optional[Any]          =			max(len(tokenizer.encode(UpperCamelCase__)) for a in SUMMARIES)
						__lowerCAmelCase:       Dict          =			4
						__lowerCAmelCase:       Optional[int]          =			LegacySeqaSeqDataset(
						    UpperCamelCase__     ,  data_dir=UpperCamelCase__     ,  type_path="train"     ,  max_source_length=2_0     ,  max_target_length=UpperCamelCase__     ,  )
						__lowerCAmelCase:       List[str]          =			DataLoader(UpperCamelCase__     ,  batch_size=2     ,  collate_fn=train_dataset.collate_fn)
						for batch in dataloader:
								assert batch["attention_mask"].shape == batch["input_ids"].shape
								# show that articles were trimmed.
								assert batch["input_ids"].shape[1] == max_len_source
								assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
								# show that targets were truncated
								assert batch["labels"].shape[1] == trunc_target  # Truncated
								assert max_len_target > trunc_target  # Truncated
								break  # No need to test every batch

				def 							lowercase_     (	self							:				str)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")

						__lowerCAmelCase:       Any          =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
						__lowerCAmelCase:       str          =			tmp_dir.joinpath("train.source").open().readlines()
						__lowerCAmelCase:       List[str]          =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
						pack_data_dir(UpperCamelCase__     ,  UpperCamelCase__     ,  1_2_8     ,  UpperCamelCase__)
						__lowerCAmelCase:       List[Any]          =			{x.name for x in tmp_dir.iterdir()}
						__lowerCAmelCase:       Optional[int]          =			{x.name for x in save_dir.iterdir()}
						__lowerCAmelCase:       Optional[Any]          =			save_dir.joinpath("train.source").open().readlines()
						# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
						# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
						assert len(UpperCamelCase__) < len(UpperCamelCase__)
						assert len(UpperCamelCase__) == 1
						assert len(packed_examples[0]) == sum(len(UpperCamelCase__) for x in orig_examples)
						assert orig_paths == new_paths

				@pytest.mark.skipif(not FAIRSEQ_AVAILABLE     ,  reason="This test requires fairseq")
				def 							lowercase_     (	self							:				Tuple)->   List[Any]:



						'''simple docstring'''


						if not FAIRSEQ_AVAILABLE:
								return
						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       Union[str, Any]          =			self._get_dataset(max_len=6_4)
						__lowerCAmelCase:       str          =			6_4
						__lowerCAmelCase:       Optional[Any]          =			ds.make_dynamic_sampler(UpperCamelCase__     ,  required_batch_size_multiple=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			[len(UpperCamelCase__) for x in batch_sampler]
						assert len(set(UpperCamelCase__)) > 1  # it's not dynamic batch size if every batch is the same length
						assert sum(UpperCamelCase__) == len(UpperCamelCase__)  # no dropped or added examples
						__lowerCAmelCase:       Union[str, Any]          =			DataLoader(UpperCamelCase__     ,  batch_sampler=UpperCamelCase__     ,  collate_fn=ds.collate_fn     ,  num_workers=2)
						__lowerCAmelCase:       Optional[Any]          =			[]
						__lowerCAmelCase:       List[str]          =			[]
						for batch in data_loader:
								__lowerCAmelCase:       int          =			batch["input_ids"].shape
								__lowerCAmelCase:       Dict          =			src_shape[0]
								assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
								__lowerCAmelCase:       Optional[int]          =			np.product(batch["input_ids"].shape)
								num_src_per_batch.append(UpperCamelCase__)
								if num_src_tokens > (max_tokens * 1.1):
										failures.append(UpperCamelCase__)
						assert num_src_per_batch[0] == max(UpperCamelCase__)
						if failures:
								raise AssertionError(f"too many tokens in {len(UpperCamelCase__)} batches")

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



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       List[Any]          =			self._get_dataset(max_len=5_1_2)
						__lowerCAmelCase:       Tuple          =			2
						__lowerCAmelCase:       List[str]          =			ds.make_sortish_sampler(UpperCamelCase__     ,  shuffle=UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			DataLoader(UpperCamelCase__     ,  batch_size=UpperCamelCase__     ,  collate_fn=ds.collate_fn     ,  num_workers=2)
						__lowerCAmelCase:       List[Any]          =			DataLoader(UpperCamelCase__     ,  batch_size=UpperCamelCase__     ,  collate_fn=ds.collate_fn     ,  num_workers=2     ,  sampler=UpperCamelCase__)

						__lowerCAmelCase:       str          =			tokenizer.pad_token_id

						def count_pad_tokens(UpperCamelCase__							:				Tuple     ,  UpperCamelCase__							:				Union[str, Any]="input_ids"):
								return [batch[k].eq(UpperCamelCase__).sum().item() for batch in data_loader]

						assert sum(count_pad_tokens(UpperCamelCase__     ,  k="labels")) < sum(count_pad_tokens(UpperCamelCase__     ,  k="labels"))
						assert sum(count_pad_tokens(UpperCamelCase__)) < sum(count_pad_tokens(UpperCamelCase__))
						assert len(UpperCamelCase__) == len(UpperCamelCase__)

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				List[str]=1_0_0_0     ,  UpperCamelCase__							:				List[Any]=1_2_8)->   Tuple:



						'''simple docstring'''


						if os.getenv("USE_REAL_DATA"     ,  UpperCamelCase__):
								__lowerCAmelCase:       Tuple          =			"examples/seq2seq/wmt_en_ro"
								__lowerCAmelCase:       str          =			max_len * 2 * 6_4
								if not Path(UpperCamelCase__).joinpath("train.len").exists():
										save_len_file(UpperCamelCase__     ,  UpperCamelCase__)
						else:
								__lowerCAmelCase:       Dict          =			"examples/seq2seq/test_data/wmt_en_ro"
								__lowerCAmelCase:       List[str]          =			max_len * 4
								save_len_file(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       int          =			AutoTokenizer.from_pretrained(UpperCamelCase__)
						__lowerCAmelCase:       Union[str, Any]          =			SeqaSeqDataset(
						    UpperCamelCase__     ,  data_dir=UpperCamelCase__     ,  type_path="train"     ,  max_source_length=UpperCamelCase__     ,  max_target_length=UpperCamelCase__     ,  n_obs=UpperCamelCase__     ,  )
						return ds, max_tokens, tokenizer

				def 							lowercase_     (	self							:				Optional[Any])->   int:



						'''simple docstring'''


						__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase:       Union[str, Any]          =			self._get_dataset()
						__lowerCAmelCase:       List[Any]          =			set(DistributedSortishSampler(UpperCamelCase__     ,  2_5_6     ,  num_replicas=2     ,  rank=0     ,  add_extra_examples=UpperCamelCase__))
						__lowerCAmelCase:       int          =			set(DistributedSortishSampler(UpperCamelCase__     ,  2_5_6     ,  num_replicas=2     ,  rank=1     ,  add_extra_examples=UpperCamelCase__))
						assert idsa.intersection(UpperCamelCase__) == set()




				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]     ,  )
				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				Any)->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			AutoTokenizer.from_pretrained(UpperCamelCase__     ,  use_fast=UpperCamelCase__)
						if tok_name == MBART_TINY:
								__lowerCAmelCase:       str          =			SeqaSeqDataset(
								    UpperCamelCase__     ,  data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())     ,  type_path="train"     ,  max_source_length=4     ,  max_target_length=8     ,  src_lang="EN"     ,  tgt_lang="FR"     ,  )
								__lowerCAmelCase:       int          =			train_dataset.dataset_kwargs
								assert "src_lang" in kwargs and "tgt_lang" in kwargs
						else:
								__lowerCAmelCase:       Optional[Any]          =			SeqaSeqDataset(
								    UpperCamelCase__     ,  data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())     ,  type_path="train"     ,  max_source_length=4     ,  max_target_length=8     ,  )
								__lowerCAmelCase:       List[Any]          =			train_dataset.dataset_kwargs
								assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
								assert len(UpperCamelCase__) == 1 if tok_name == BART_TINY else len(UpperCamelCase__) == 0





"""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     snake_case	(				__snake_case  ):
				# to overwrite at feature extractactor specific tests
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  None

				@property
				def 							lowercase_     (	self							:				str)->   Any:



						'''simple docstring'''


						return self.feat_extract_tester.prepare_feat_extract_dict()

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



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						self.assertTrue(hasattr(UpperCamelCase__     ,  "feature_size"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "sampling_rate"))
						self.assertTrue(hasattr(UpperCamelCase__     ,  "padding_value"))

				def 							lowercase_     (	self							:				Dict)->   Any:



						'''simple docstring'''


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

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

						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="np")

						__lowerCAmelCase:       Optional[int]          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			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							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       int          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

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

						__lowerCAmelCase:       str          =			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))

				@require_tf
				def 							lowercase_     (	self							:				str)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase__)
						__lowerCAmelCase:       Optional[int]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs}     ,  tensor_type="tf")

						__lowerCAmelCase:       int          =			processed_features[input_name]

						if len(batch_features_input.shape) < 3:
								__lowerCAmelCase:       Dict          =			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							:				Union[str, Any]     ,  UpperCamelCase__							:				List[str]=False)->   Optional[int]:



						'''simple docstring'''


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

						def _inputs_are_equal(UpperCamelCase__							:				str     ,  UpperCamelCase__							:				str):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

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

						__lowerCAmelCase:       List[str]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			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:       str          =			self.feat_extract_tester.max_seq_length + pad_diff
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.min_seq_length
						__lowerCAmelCase:       Union[str, Any]          =			self.feat_extract_tester.batch_size
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.feature_size

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

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest")
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[-1]))
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[int]          =			input_a[input_name]

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

						__lowerCAmelCase:       Any          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  return_tensors="np")
						__lowerCAmelCase:       Optional[Any]          =			input_a[input_name]

						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))
						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:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						__lowerCAmelCase:       Tuple          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  pad_to_multiple_of=1_0)
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__)
						__lowerCAmelCase:       str          =			input_a[input_name]

						__lowerCAmelCase:       Dict          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  pad_to_multiple_of=1_0     ,  max_length=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       List[str]          =			input_a[input_name]

						self.assertTrue(all(len(UpperCamelCase__) % 1_0 == 0 for x in input_a))
						self.assertTrue(_inputs_are_equal(UpperCamelCase__     ,  UpperCamelCase__))

						__lowerCAmelCase:       Optional[Any]          =			pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
						self.assertTrue(all(len(UpperCamelCase__) == 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:       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							:				Optional[int]     ,  UpperCamelCase__							:				Optional[int]=False)->   str:



						'''simple docstring'''


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

						def _inputs_are_equal(UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				List[Any]):
								if len(UpperCamelCase__) != len(UpperCamelCase__):
										return False

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

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

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

						# truncate to smallest
						__lowerCAmelCase:       int          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			input_a[input_name]

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0]))
						__lowerCAmelCase:       Tuple          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

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

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  return_tensors="np")
						__lowerCAmelCase:       Any          =			input_a[input_name]

						self.assertTrue(_inputs_have_equal_length(UpperCamelCase__))
						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(UpperCamelCase__))

						# truncate to middle
						__lowerCAmelCase:       Union[str, Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__     ,  return_tensors="np"     ,  )
						__lowerCAmelCase:       int          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  truncation=UpperCamelCase__)
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[Any]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[1])     ,  return_tensors="np")
						__lowerCAmelCase:       str          =			input_a[input_name]

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

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

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

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

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

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

						# test truncation for `pad_to_multiple_of` for List[int] + numpy
						__lowerCAmelCase:       Tuple          =			1_2
						__lowerCAmelCase:       Optional[int]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  )
						__lowerCAmelCase:       Dict          =			input_a[input_name]

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=len(speech_inputs[0])     ,  pad_to_multiple_of=UpperCamelCase__     ,  )
						__lowerCAmelCase:       str          =			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:       Optional[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(UpperCamelCase__))
						self.assertFalse(_inputs_have_equal_length(UpperCamelCase__))

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



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

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



						'''simple docstring'''


						self._check_padding(numpify=UpperCamelCase__)

				def 							lowercase_     (	self							:				List[Any])->   Tuple:



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

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



						'''simple docstring'''


						self._check_truncation(numpify=UpperCamelCase__)

				@require_torch
				def 							lowercase_     (	self							:				Dict)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Optional[Any]          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       Any          =			feat_extract.model_input_names[0]

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

						__lowerCAmelCase:       str          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  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							:				Tuple)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.feature_extraction_class(**self.feat_extract_dict)
						__lowerCAmelCase:       Tuple          =			self.feat_extract_tester.prepare_inputs_for_common()
						__lowerCAmelCase:       List[Any]          =			feat_extract.model_input_names[0]

						__lowerCAmelCase:       Any          =			BatchFeature({input_name: speech_inputs})

						__lowerCAmelCase:       Dict          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")[input_name]
						__lowerCAmelCase:       Any          =			feat_extract.pad(UpperCamelCase__     ,  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							:				Optional[int])->   List[Any]:



						'''simple docstring'''


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

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

						__lowerCAmelCase:       Optional[Any]          =			feat_extract.pad(UpperCamelCase__     ,  padding="longest"     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						self.assertListEqual(list(processed.attention_mask.shape)     ,  list(processed[input_name].shape[:2]))
						self.assertListEqual(processed.attention_mask.sum(-1).tolist()     ,  UpperCamelCase__)




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



						'''simple docstring'''


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

						__lowerCAmelCase:       Optional[int]          =			BatchFeature({input_name: speech_inputs})
						__lowerCAmelCase:       List[str]          =			min(UpperCamelCase__)

						__lowerCAmelCase:       List[str]          =			feat_extract.pad(
						    UpperCamelCase__     ,  padding="max_length"     ,  max_length=UpperCamelCase__     ,  truncation=UpperCamelCase__     ,  return_tensors="np")
						self.assertIn("attention_mask"     ,  UpperCamelCase__)
						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])


