




"""simple docstring"""


import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


_A													=      os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
  from run_translation import main  # noqa


set_seed(4_2)
_A													=      """sshleifer/student_marian_en_ro_6_1"""
_A													=      """sshleifer/tiny-mbart"""


@require_torch
class _lowerCamelCase    (					a_						):






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		Optional[Any]=False   ,     UpperCamelCase       :		Dict=None   ,     UpperCamelCase       :		int=True   ,     UpperCamelCase       :		Union[str, Any]=True   ,     UpperCamelCase       :		Union[str, Any]=True   ,     UpperCamelCase       :		str=True   ,     )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.run_trainer(
             eval_steps=1   ,     max_len=12   ,     model_name=UpperCamelCase   ,     num_train_epochs=1   ,     distributed=UpperCamelCase   ,     extra_args_str=UpperCamelCase   ,     predict_with_generate=UpperCamelCase   ,     do_train=UpperCamelCase   ,     do_eval=UpperCamelCase   ,     do_predict=UpperCamelCase   ,     )
         lowerCAmelCase__      :			Union[str, Any]   =	TrainerState.load_from_json(os.path.join(UpperCamelCase   ,     """trainer_state.json"""   )   ).log_history

         if not do_eval:
               return

         lowerCAmelCase__      :			Optional[Any]   =	[log for log in logs if """eval_loss""" in log.keys()]

         lowerCAmelCase__      :			List[str]   =	eval_metrics[0]
         if predict_with_generate:
               assert "eval_bleu" in first_step_stats

               lowerCAmelCase__      :			Any   =	eval_metrics[-1]
               assert isinstance(last_step_stats["""eval_bleu"""]   ,     UpperCamelCase   )
               assert not math.isnan(float(last_step_stats["""eval_loss"""]   )   ), "eval_loss must not be `nan`"






   @require_torch_non_multi_gpu
   def 	_lowerCAmelCase   (		self       :		str   )			->						List[str]:

         """simple docstring"""
         self.run_seqaseq_quick()






   @require_torch_multi_gpu
   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						List[str]:

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   )






   @require_torch_multi_gpu
   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						str:

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   )






   @unittest.skip("""Requires an update of the env running those tests"""   )
   @require_torch_multi_gpu
   @require_fairscale
   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Union[str, Any]:

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   ,     extra_args_str="""--sharded_ddp simple"""   )






   @unittest.skip("""Requires an update of the env running those tests"""   )
   @require_torch_multi_gpu
   @require_fairscale
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						str:

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   ,     extra_args_str="""--sharded_ddp simple --fp16"""   )






   @unittest.skip("""Requires an update of the env running those tests"""   )
   @require_torch_multi_gpu
   @require_fairscale
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Union[str, Any]:

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   ,     extra_args_str="""--sharded_ddp zero_dp_2"""   ,     predict_with_generate=UpperCamelCase   )






   @unittest.skip("""Requires an update of the env running those tests"""   )
   @require_torch_multi_gpu
   @require_fairscale
   def 	_lowerCAmelCase   (		self       :		Any   )			->						List[str]:

         """simple docstring"""
         self.run_seqaseq_quick(
             distributed=UpperCamelCase   ,     extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""   ,     predict_with_generate=UpperCamelCase   )






   @require_apex
   @require_torch_gpu
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						List[str]:

         """simple docstring"""
         # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
         # program and it breaks other tests that run from the same pytest worker, therefore until this is
         # sorted out it must be run only in an external program, that is distributed=True in this
         # test and only under one or more gpus - if we want cpu will need to make a special test
         #
         # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
         # 2nd main() call it botches the future eval.
         #
         self.run_seqaseq_quick(distributed=UpperCamelCase   ,     extra_args_str="""--fp16 --fp16_backend=apex"""   )
         # test 2nd time - was getting eval_loss': nan'
         # to reproduce the problem set distributed=False
         self.run_seqaseq_quick(distributed=UpperCamelCase   ,     extra_args_str="""--fp16 --fp16_backend=apex"""   )






   @parameterized.expand(["""base""", """low""", """high""", """mixed"""]   )
   @require_torch_multi_gpu
   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		Any   )			->						int:

         """simple docstring"""
         # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
         lowerCAmelCase__      :			Tuple   =	{
             # test with the default log_level - should be info and thus log info once
             """base""": {"""extra_args_str""": """""", """n_matches""": 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
             """low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
             """high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
             """mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
         }

         lowerCAmelCase__      :			Union[str, Any]   =	experiments[experiment_id]
         lowerCAmelCase__      :			Dict   =	{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
         lowerCAmelCase__      :			Union[str, Any]   =	"""Running training"""
         with CaptureStderr() as cl:
               self.run_seqaseq_quick(**UpperCamelCase   ,     extra_args_str=data["""extra_args_str"""]   )
         lowerCAmelCase__      :			Union[str, Any]   =	len(re.findall(UpperCamelCase   ,     cl.err   )   )
         self.assertEqual(UpperCamelCase   ,     data["""n_matches"""]   )






   @slow
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self.run_trainer(
             eval_steps=2   ,     max_len=1_28   ,     model_name=UpperCamelCase   ,     learning_rate=3E-4   ,     num_train_epochs=10   ,     distributed=UpperCamelCase   ,     )

         # Check metrics
         lowerCAmelCase__      :			int   =	TrainerState.load_from_json(os.path.join(UpperCamelCase   ,     """trainer_state.json"""   )   ).log_history
         lowerCAmelCase__      :			Optional[int]   =	[log for log in logs if """eval_loss""" in log.keys()]
         lowerCAmelCase__      :			int   =	eval_metrics[0]
         lowerCAmelCase__      :			Any   =	eval_metrics[-1]

         assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
         assert isinstance(last_step_stats["""eval_bleu"""]   ,     UpperCamelCase   )

         # test if do_predict saves generations and metrics
         lowerCAmelCase__      :			Tuple   =	os.listdir(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	{os.path.basename(UpperCamelCase   ) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents






   @slow
   @require_bitsandbytes
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Tuple:

         """simple docstring"""
         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(UpperCamelCase       :		str   ) -> Tuple[int, float]:
               lowerCAmelCase__      :			Tuple   =	"""--skip_memory_metrics 0"""

               lowerCAmelCase__      :			List[Any]   =	self.run_trainer(
                   max_len=1_28   ,     model_name=UpperCamelCase   ,     learning_rate=3E-4   ,     num_train_epochs=1   ,     optim=UpperCamelCase   ,     distributed=UpperCamelCase   ,     extra_args_str=UpperCamelCase   ,     do_eval=UpperCamelCase   ,     do_predict=UpperCamelCase   ,     n_gpus_to_use=1   ,     )

               # Check metrics
               lowerCAmelCase__      :			List[Any]   =	TrainerState.load_from_json(Path(UpperCamelCase   ,     """trainer_state.json"""   )   ).log_history
               lowerCAmelCase__      :			Dict   =	int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20   )
               lowerCAmelCase__      :			Dict   =	int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20   )

               lowerCAmelCase__      :			List[Any]   =	logs[0]["""train_loss"""]
               return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			List[Any]   =	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value   )
         lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Tuple   =	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value   )

         lowerCAmelCase__      :			str   =	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         lowerCAmelCase__      :			Optional[Any]   =	gpu_peak_mem_orig + gpu_alloc_mem_orig
         lowerCAmelCase__      :			Any   =	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         lowerCAmelCase__      :			str   =	gpu_total_mem_orig - gpu_total_mem_bnb

         # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
         # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
         # in 2 bytes and the diff in optim memory usage is derived as so:
         #
         # - normal 25*8=~200MB (8 bytes per param)
         # - bnb    25*2= ~50MB (2 bytes per param)
         #
         # Thus we should expect ~150MB total memory saved.
         #
         # Peak memory should be the same - the total should be different by about that same margin
         #
         # After leaving a small margin to accommodate for differences between gpus let's check
         # that we have at least 120MB in savings
         lowerCAmelCase__      :			Union[str, Any]   =	1_20

         # uncomment the following if this test starts failing - requires py38 for a new print feature
         # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
         # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
         # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
         # print(f"{gpu_alloc_mem_diff=}MB")
         # print(f"{gpu_peak_mem_diff=}MB")
         # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
         # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

         self.assertGreater(
             UpperCamelCase   ,     UpperCamelCase   ,     """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
             f""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
             f""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"""   ,     )

         self.assertGreater(
             UpperCamelCase   ,     UpperCamelCase   ,     """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
             f""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
             f""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"""   ,     )

         self.assertEqual(
             UpperCamelCase   ,     UpperCamelCase   ,     f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		float = 3E-3   ,     UpperCamelCase       :		str = "adafactor"   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		str = None   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		int = None   ,     )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
         lowerCAmelCase__      :			Any   =	self.get_auto_remove_tmp_dir()
         lowerCAmelCase__      :			List[str]   =	f"""
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(UpperCamelCase   )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase   )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

         lowerCAmelCase__      :			Union[str, Any]   =	f"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(UpperCamelCase   )}
        """.split()

         lowerCAmelCase__      :			Optional[int]   =	"""
            --do_predict
        """.split()

         lowerCAmelCase__      :			Tuple   =	[]
         if do_train:
               args += args_train

         if do_eval:
               args += args_eval

         if do_predict:
               args += args_predict

         if predict_with_generate:
               args += "--predict_with_generate".split()

         if do_train:
               if optim == "adafactor":
                     args += "--adafactor".split()
               else:
                     args += f"""--optim {optim}""".split()

         if extra_args_str is not None:
               args += extra_args_str.split()

         if distributed:
               if n_gpus_to_use is None:
                     lowerCAmelCase__      :			List[str]   =	get_gpu_count()
               lowerCAmelCase__      :			List[Any]   =	get_torch_dist_unique_port()
               lowerCAmelCase__      :			Optional[int]   =	f"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
               lowerCAmelCase__      :			List[Any]   =	[sys.executable] + distributed_args + args
               # keep for quick debug
               # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
               execute_subprocess_async(UpperCamelCase   ,     env=self.get_env()   )
         else:
               lowerCAmelCase__      :			str   =	["""run_translation.py"""] + args
               with patch.object(UpperCamelCase   ,     """argv"""   ,     UpperCamelCase   ):
                     main()

         return output_dir











"""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 _lowerCamelCase    (					a_						):
   # to overwrite at feature extractactor specific tests
   _lowerCamelCase    :Optional[int]     				=     None
   _lowerCamelCase    :List[Any]     				=     None






   @property
   def 	_lowerCAmelCase   (		self       :		int   )			->						Tuple:

         """simple docstring"""
         return self.feat_extract_tester.prepare_feat_extract_dict()






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

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	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 	_lowerCAmelCase   (		self       :		Any   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__      :			List[Any]   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Union[str, Any]   =	feat_extract.model_input_names[0]

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

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

         lowerCAmelCase__      :			List[str]   =	self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	BatchFeature({input_name: speech_inputs}   ,     tensor_type="""np"""   )

         lowerCAmelCase__      :			Dict   =	processed_features[input_name]

         if len(batch_features_input.shape   ) < 3:
               lowerCAmelCase__      :			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 	_lowerCAmelCase   (		self       :		Tuple   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase   )
         lowerCAmelCase__      :			str   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Optional[int]   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			Union[str, Any]   =	BatchFeature({input_name: speech_inputs}   ,     tensor_type="""pt"""   )

         lowerCAmelCase__      :			str   =	processed_features[input_name]

         if len(batch_features_input.shape   ) < 3:
               lowerCAmelCase__      :			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 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase   )
         lowerCAmelCase__      :			str   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Dict   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			Tuple   =	BatchFeature({input_name: speech_inputs}   ,     tensor_type="""tf"""   )

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

         if len(batch_features_input.shape   ) < 3:
               lowerCAmelCase__      :			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 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		Union[str, Any]=False   )			->						int:

         """simple docstring"""
         def _inputs_have_equal_length(UpperCamelCase       :		int   ):
               lowerCAmelCase__      :			Optional[Any]   =	len(input[0]   )
               for input_slice in input[1:]:
                     if len(UpperCamelCase   ) != length:
                           return False
               return True

         def _inputs_are_equal(UpperCamelCase       :		int   ,     UpperCamelCase       :		Dict   ):
               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__      :			str   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Dict   =	self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase   )
         lowerCAmelCase__      :			str   =	feat_extract.model_input_names[0]

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

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

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

         lowerCAmelCase__      :			Tuple   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   )
         lowerCAmelCase__      :			Any   =	input_a[input_name]

         lowerCAmelCase__      :			List[str]   =	feat_extract.pad(UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[-1]   )   )
         lowerCAmelCase__      :			Dict   =	input_a[input_name]

         lowerCAmelCase__      :			str   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     return_tensors="""np"""   )
         lowerCAmelCase__      :			Optional[Any]   =	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__      :			Optional[int]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=UpperCamelCase   ,     return_tensors="""np"""   )
         lowerCAmelCase__      :			int   =	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__      :			Any   =	feat_extract.pad(UpperCamelCase   ,     pad_to_multiple_of=10   )
         lowerCAmelCase__      :			int   =	input_a[input_name]

         lowerCAmelCase__      :			Dict   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     pad_to_multiple_of=10   )
         lowerCAmelCase__      :			Union[str, Any]   =	input_a[input_name]

         lowerCAmelCase__      :			Optional[int]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     pad_to_multiple_of=10   ,     max_length=UpperCamelCase   )
         lowerCAmelCase__      :			str   =	input_a[input_name]

         lowerCAmelCase__      :			Union[str, Any]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     pad_to_multiple_of=10   ,     max_length=UpperCamelCase   ,     return_tensors="""np"""   ,     )
         lowerCAmelCase__      :			List[Any]   =	input_a[input_name]

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

         lowerCAmelCase__      :			Optional[int]   =	pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
         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__      :			str   =	(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 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		int=False   )			->						List[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       :		Union[str, Any]   ,     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__      :			Dict   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			Any   =	self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase   )
         lowerCAmelCase__      :			str   =	feat_extract.model_input_names[0]

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

         # truncate to smallest
         lowerCAmelCase__      :			str   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   ,     truncation=UpperCamelCase   )
         lowerCAmelCase__      :			Dict   =	input_a[input_name]

         lowerCAmelCase__      :			str   =	feat_extract.pad(UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   )
         lowerCAmelCase__      :			Optional[int]   =	input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(UpperCamelCase   )   )
         self.assertFalse(_inputs_have_equal_length(UpperCamelCase   )   )

         # truncate to smallest with np
         lowerCAmelCase__      :			Tuple   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   ,     return_tensors="""np"""   ,     truncation=UpperCamelCase   ,     )
         lowerCAmelCase__      :			List[Any]   =	input_a[input_name]

         lowerCAmelCase__      :			List[Any]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   ,     return_tensors="""np"""   )
         lowerCAmelCase__      :			Union[str, 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__      :			List[Any]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[1]   )   ,     truncation=UpperCamelCase   ,     return_tensors="""np"""   ,     )
         lowerCAmelCase__      :			List[Any]   =	input_a[input_name]

         lowerCAmelCase__      :			List[Any]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[1]   )   ,     truncation=UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	input_a[input_name]

         lowerCAmelCase__      :			Any   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[1]   )   ,     return_tensors="""np"""   )
         lowerCAmelCase__      :			Dict   =	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__      :			Any   =	12
         lowerCAmelCase__      :			Optional[int]   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   ,     pad_to_multiple_of=UpperCamelCase   ,     truncation=UpperCamelCase   ,     )
         lowerCAmelCase__      :			List[Any]   =	input_a[input_name]

         lowerCAmelCase__      :			Any   =	feat_extract.pad(
             UpperCamelCase   ,     padding="""max_length"""   ,     max_length=len(speech_inputs[0]   )   ,     pad_to_multiple_of=UpperCamelCase   ,     )
         lowerCAmelCase__      :			Optional[Any]   =	input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         lowerCAmelCase__      :			Union[str, Any]   =	len(speech_inputs[0]   )
         if expected_length % pad_to_multiple_of != 0:
               lowerCAmelCase__      :			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 	_lowerCAmelCase   (		self       :		List[str]   )			->						Optional[int]:

         """simple docstring"""
         self._check_padding(numpify=UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						List[Any]:

         """simple docstring"""
         self._check_padding(numpify=UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Dict   )			->						Optional[int]:

         """simple docstring"""
         self._check_truncation(numpify=UpperCamelCase   )






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

         """simple docstring"""
         self._check_truncation(numpify=UpperCamelCase   )






   @require_torch
   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.feature_extraction_class(**self.feat_extract_dict   )
         lowerCAmelCase__      :			str   =	self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__      :			str   =	feat_extract.model_input_names[0]

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

         lowerCAmelCase__      :			Dict   =	feat_extract.pad(UpperCamelCase   ,     padding="""longest"""   ,     return_tensors="""np"""   )[input_name]
         lowerCAmelCase__      :			str   =	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 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						str:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	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__      :			int   =	BatchFeature({input_name: speech_inputs}   )

         lowerCAmelCase__      :			int   =	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 	_lowerCAmelCase   (		self       :		List[Any]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.feat_extract_dict
         lowerCAmelCase__      :			int   =	True
         lowerCAmelCase__      :			str   =	self.feature_extraction_class(**UpperCamelCase   )
         lowerCAmelCase__      :			int   =	self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__      :			List[Any]   =	[len(UpperCamelCase   ) for x in speech_inputs]
         lowerCAmelCase__      :			Optional[Any]   =	feat_extract.model_input_names[0]

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

         lowerCAmelCase__      :			Tuple   =	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 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.feat_extract_dict
         lowerCAmelCase__      :			List[Any]   =	True
         lowerCAmelCase__      :			str   =	self.feature_extraction_class(**UpperCamelCase   )
         lowerCAmelCase__      :			int   =	self.feat_extract_tester.prepare_inputs_for_common()
         lowerCAmelCase__      :			Any   =	[len(UpperCamelCase   ) for x in speech_inputs]
         lowerCAmelCase__      :			int   =	feat_extract.model_input_names[0]

         lowerCAmelCase__      :			Optional[Any]   =	BatchFeature({input_name: speech_inputs}   )
         lowerCAmelCase__      :			Any   =	min(UpperCamelCase   )

         lowerCAmelCase__      :			Tuple   =	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]   )





