





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


__lowerCamelCase       :     str   =     random.Random()

if is_torch_available():
    import torch



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase=1.0							,   _lowerCAmelCase=None							,   _lowerCAmelCase=None     )						-> Optional[Any]:
 if rng is None:
  UpperCamelCase	:					Optional[int]       	=   global_rng

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

 return values



class 					A__   (       unittest.TestCase			):





      def __init__( self       ,							A_       ,							A_=7       ,							A_=400       ,							A_=2000       ,							A_=1       ,							A_=0.0       ,							A_=1_6000       ,							A_=True       ,							A_=True       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   parent
       UpperCamelCase	:					List[Any]       	=   batch_size
       UpperCamelCase	:					List[Any]       	=   min_seq_length
       UpperCamelCase	:					List[str]       	=   max_seq_length
       UpperCamelCase	:					int       	=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       UpperCamelCase	:					Union[str, Any]       	=   feature_size
       UpperCamelCase	:					List[str]       	=   padding_value
       UpperCamelCase	:					Optional[Any]       	=   sampling_rate
       UpperCamelCase	:					List[str]       	=   return_attention_mask
       UpperCamelCase	:					List[Any]       	=   do_normalize





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       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   ):
       '''simple docstring'''




       def _flatten(A_   ):
        return list(itertools.chain(*A_   )   )

       if equal_length:
        UpperCamelCase	:					List[str]       	=   floats_list((self.batch_size, self.max_seq_length)   )
       else:
        # make sure that inputs increase in size
        UpperCamelCase	:					Dict       	=   [
            _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:
        UpperCamelCase	:					Union[str, Any]       	=   [np.asarray(A_   ) for x in speech_inputs]

       return speech_inputs



@require_torch
@require_torchaudio
class 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Optional[Any]						    =   ASTFeatureExtractor





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   ASTFeatureExtractionTester(self   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       # create three inputs of length 800, 1000, and 1200
       UpperCamelCase	:					Tuple       	=   [floats_list((1, x)   )[0] for x in range(800       ,							1400       ,							200   )]
       UpperCamelCase	:					Dict       	=   [np.asarray(A_   ) for speech_input in speech_inputs]

       # Test not batched input
       UpperCamelCase	:					Dict       	=   feat_extract(speech_inputs[0]       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					Union[str, Any]       	=   feat_extract(np_speech_inputs[0]       ,							return_tensors="np"   ).input_values
       self.assertTrue(np.allclose(A_       ,							A_       ,							atol=1e-3   )   )

       # Test batched
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							padding=A_       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					Any       	=   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.
       UpperCamelCase	:					Dict       	=   [floats_list((1, x)   )[0] for x in (800, 800, 800)]
       UpperCamelCase	:					int       	=   np.asarray(A_   )
       UpperCamelCase	:					Any       	=   feat_extract(A_       ,							return_tensors="np"   ).input_values
       UpperCamelCase	:					List[str]       	=   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   ):
       '''simple docstring'''




       import torch

       UpperCamelCase	:					List[Any]       	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       UpperCamelCase	:					int       	=   np.random.rand(100   ).astype(np.floataa   )
       UpperCamelCase	:					str       	=   np_speech_inputs.tolist()

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





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       from datasets import load_dataset

       UpperCamelCase	:					Dict       	=   load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,							"clean"       ,							split="validation"   )
       # automatic decoding with librispeech
       UpperCamelCase	:					Any       	=   ds.sort("id"   ).select(range(A_   )   )[:num_samples]["audio"]

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





      @require_torch
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   torch.tensor(
           [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
            -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
            -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
            -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]   )
       # fmt: on

       UpperCamelCase	:					List[Any]       	=   self._load_datasamples(1   )
       UpperCamelCase	:					Tuple       	=   ASTFeatureExtractor()
       UpperCamelCase	:					str       	=   feature_extractor(A_       ,							return_tensors="pt"   ).input_values
       self.assertEquals(input_values.shape       ,							(1, 1024, 128)   )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,							A_       ,							atol=1e-4   )   )


"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.nn.Linear(10   ,       10    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   torch.optim.SGD(model.parameters()   ,       0.1    )
											SCREAMING_SNAKE_CASE__						:       int           =   Accelerator()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   accelerator.prepare(SCREAMING_SNAKE_CASE__    )
											try:
																		pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE__    )    )
											except Exception as e:
																		self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
											AcceleratorState._reset_state()




