





'''simple docstring'''

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler
@dataclass
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	int									=       0
       _snake_case      :	bool									=       False
       _snake_case      :	float									=       3.0

class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





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

            '''simple docstring'''




            self.assertDictEqual(MockClass().to_kwargs()			,					{}	)
            self.assertDictEqual(MockClass(a=2	).to_kwargs()			,					{'''a''': 2}	)
            self.assertDictEqual(MockClass(a=2			,					b=lowerCAmelCase__	).to_kwargs()			,					{'''a''': 2, '''b''': True}	)
            self.assertDictEqual(MockClass(a=2			,					c=2.25	).to_kwargs()			,					{'''a''': 2, '''c''': 2.25}	)





       @require_cuda
       def snake_case__		(				self  :						int	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			GradScalerKwargs(init_scale=1024			,					growth_factor=2	)
            AcceleratorState._reset_state()
            _UpperCamelCase							       =			Accelerator(mixed_precision='''fp16'''			,					kwargs_handlers=[scaler_handler]	)
            print(accelerator.use_fpaa	)
            _UpperCamelCase							       =			accelerator.scaler

            # Check the kwargs have been applied
            self.assertEqual(scaler._init_scale			,					1024.0	)
            self.assertEqual(scaler._growth_factor			,					2.0	)

            # Check the other values are at the default
            self.assertEqual(scaler._backoff_factor			,					0.5	)
            self.assertEqual(scaler._growth_interval			,					2000	)
            self.assertEqual(scaler._enabled			,					lowerCAmelCase__	)







       @require_multi_gpu
       def snake_case__		(				self  :						List[Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			['''torchrun''', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__	)]
            execute_subprocess_async(lowerCAmelCase__			,					env=os.environ.copy()	)


if __name__ == "__main__":
   lowercase__		:     Dict			=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   lowercase__		:     List[str]			=							Accelerator(kwargs_handlers=[ddp_scaler])
   lowercase__		:     List[Any]			=							torch.nn.Linear(1_00, 2_00)
   lowercase__		:     Dict			=							accelerator.prepare(model)

   # Check the values changed in kwargs
   lowercase__		:     Any			=							''
   lowercase__		:     Union[str, Any]			=							model.bucket_bytes_cap // (10_24 * 10_24)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)











import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase    				=   random.Random()

if is_torch_available():
	import torch





def 		__UpperCAmelCase     (	a_				,	a_=1.0				,	a_=None				,	a_=None):
							if rng is None:
														snake_case_           =				global_rng

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

							return values




class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a=7			,     a=4_00			,     a=20_00			,     a=1			,     a=0.0			,     a=1_60_00			,     a=True			,     a=True			,     )			->       Tuple:
								snake_case_           =				parent
								snake_case_           =				batch_size
								snake_case_           =				min_seq_length
								snake_case_           =				max_seq_length
								snake_case_           =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								snake_case_           =				feature_size
								snake_case_           =				padding_value
								snake_case_           =				sampling_rate
								snake_case_           =				return_attention_mask
								snake_case_           =				do_normalize




	def 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "return_attention_mask": self.return_attention_mask,
								    "do_normalize": self.do_normalize,
								}






	def 				_UpperCamelCase     (			self			,     a=False			,     a=False   )			->       Optional[int]:
								def _flatten(a   ):
															return list(itertools.chain(*a   )   )

								if equal_length:
															snake_case_           =				floats_list((self.batch_size, self.max_seq_length)   )
								else:
															# make sure that inputs increase in size
															snake_case_           =				[
															    _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:
															snake_case_           =				[np.asarray(a   ) for x in speech_inputs]

								return speech_inputs






@require_torch
@require_torchaudio
class 					UpperCamelCase_ (     snake_case_			,   unittest.TestCase							):



	'''simple docstring'''




	lowerCAmelCase						=					ASTFeatureExtractor




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				ASTFeatureExtractionTester(self   )




	def 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								snake_case_           =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								snake_case_           =				[floats_list((1, x)   )[0] for x in range(8_00			,     14_00			,     2_00   )]
								snake_case_           =				[np.asarray(a   ) for speech_input in speech_inputs]

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

								# Test batched
								snake_case_           =				feat_extract(a			,     padding=a			,     return_tensors='np'   ).input_values
								snake_case_           =				feat_extract(a			,     padding=a			,     return_tensors='np'   ).input_values
								for enc_seq_a, enc_seq_a in zip(a			,     a   ):
															self.assertTrue(np.allclose(a			,     a			,     atol=1E-3   )   )

								# Test 2-D numpy arrays are batched.
								snake_case_           =				[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
								snake_case_           =				np.asarray(a   )
								snake_case_           =				feat_extract(a			,     return_tensors='np'   ).input_values
								snake_case_           =				feat_extract(a			,     return_tensors='np'   ).input_values
								for enc_seq_a, enc_seq_a in zip(a			,     a   ):
															self.assertTrue(np.allclose(a			,     a			,     atol=1E-3   )   )




	@require_torch
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								import torch

								snake_case_           =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								snake_case_           =				np.random.rand(1_00   ).astype(np.floataa   )
								snake_case_           =				np_speech_inputs.tolist()

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




	def 				_UpperCamelCase     (			self			,     a   )			->       Tuple:
								from datasets import load_dataset

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

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






	@require_torch
	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								# fmt: off
								snake_case_           =				torch.tensor(
								    [-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776,
								     -1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133,
								     -1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936,
								     -0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869]   )
								# fmt: on

								snake_case_           =				self._load_datasamples(1   )
								snake_case_           =				ASTFeatureExtractor()
								snake_case_           =				feature_extractor(a			,     return_tensors='pt'   ).input_values
								self.assertEquals(input_values.shape			,     (1, 10_24, 1_28)   )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]			,     a			,     atol=1E-4   )   )





