





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  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=1.0		,						_SCREAMING_SNAKE_CASE=None		,						_SCREAMING_SNAKE_CASE=None		)    ->       Union[str, Any]:
  if rng is None:
    lowercase__           =  global_rng

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

  return values
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   def __init__(   self      :    Optional[int]      ,					a      :    Optional[Any]      ,					a      :    Optional[Any]=7      ,					a      :    Optional[int]=400      ,					a      :    Any=2_000      ,					a      :    Any=1      ,					a      :    Optional[int]=0.0      ,					a      :    Any=16_000      ,					a      :    Optional[Any]=True      ,					a      :    Optional[int]=True      ,					)->       List[str]:





     """simple docstring"""







     lowercase__           =  parent
     lowercase__           =  batch_size
     lowercase__           =  min_seq_length
     lowercase__           =  max_seq_length
     lowercase__           =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     lowercase__           =  feature_size
     lowercase__           =  padding_value
     lowercase__           =  sampling_rate
     lowercase__           =  return_attention_mask
     lowercase__           =  do_normalize


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





     """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 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    Dict=False      ,					a      :    int=False	)->       str:





     """simple docstring"""







     def _flatten(a      :    Dict	):
       return list(itertools.chain(*a	)	)

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

     return speech_inputs






@require_torch
@require_torchaudio
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		Optional[Any]			 =	ASTFeatureExtractor


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





     """simple docstring"""







     lowercase__           =  ASTFeatureExtractionTester(self	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     # create three inputs of length 800, 1000, and 1200
     lowercase__           =  [floats_list((1, x)	)[0] for x in range(800      ,					1_400      ,					200	)]
     lowercase__           =  [np.asarray(a	) for speech_input in speech_inputs]

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

     # Test batched
     lowercase__           =  feat_extract(a      ,					padding=a      ,					return_tensors='np'	).input_values
     lowercase__           =  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.
     lowercase__           =  [floats_list((1, x)	)[0] for x in (800, 800, 800)]
     lowercase__           =  np.asarray(a	)
     lowercase__           =  feat_extract(a      ,					return_tensors='np'	).input_values
     lowercase__           =  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 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Union[str, Any]:





     """simple docstring"""







     import torch

     lowercase__           =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     lowercase__           =  np.random.rand(100	).astype(np.floataa	)
     lowercase__           =  np_speech_inputs.tolist()

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					a      :    Any	)->       Optional[int]:





     """simple docstring"""







     from datasets import load_dataset

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

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


   @require_torch
   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Tuple:





     """simple docstring"""







     lowercase__           =  torch.tensor(
         [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
          -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
          -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
          -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]	)
     # fmt: on

     lowercase__           =  self._load_datasamples(1	)
     lowercase__           =  ASTFeatureExtractor()
     lowercase__           =  feature_extractor(a      ,					return_tensors='pt'	).input_values
     self.assertEquals(input_values.shape      ,					(1, 1_024, 128)	)
     self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,					a      ,					atol=1E-4	)	)









class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   pass






class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   pass






class     SCREAMING_SNAKE_CASE       :


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





     """simple docstring"""







     lowercase__           =  [
         [],
         [],
         [],
     ]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    int      ,					a      :    int	)->       None:





     """simple docstring"""







     try:
       if len(self.queues[priority]	) >= 100:
         raise OverflowError('Maximum queue size is 100'	)
       self.queues[priority].append(a	)
     except IndexError:
       raise ValueError('Valid priorities are 0, 1, and 2'	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       int:





     """simple docstring"""







     for queue in self.queues:
       if queue:
         return queue.pop(0	)
     raise UnderFlowError('All queues are empty'	)


   def __str__(   self      :    Dict	)->       str:





     """simple docstring"""







     return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues	)	)






class     SCREAMING_SNAKE_CASE       :


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





     """simple docstring"""







     lowercase__           =  []


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    int	)->       None:





     """simple docstring"""







     if len(self.queue	) == 100:
       raise OverFlowError('Maximum queue size is 100'	)
     self.queue.append(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       int:





     """simple docstring"""







     if not self.queue:
       raise UnderFlowError('The queue is empty'	)
     else:
       lowercase__           =  min(self.queue	)
       self.queue.remove(a	)
       return data


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





     """simple docstring"""







     return str(self.queue	)






def       __UpperCamelCase  ()    ->       str:
  lowercase__           =  FixedPriorityQueue()
  fpq.enqueue(0		,						10		)
  fpq.enqueue(1		,						70		)
  fpq.enqueue(0		,						100		)
  fpq.enqueue(2		,						1		)
  fpq.enqueue(2		,						5		)
  fpq.enqueue(1		,						7		)
  fpq.enqueue(2		,						4		)
  fpq.enqueue(1		,						64		)
  fpq.enqueue(0		,						128		)
  print(_SCREAMING_SNAKE_CASE		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(_SCREAMING_SNAKE_CASE		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)




def       __UpperCamelCase  ()    ->       List[str]:
  lowercase__           =  ElementPriorityQueue()
  epq.enqueue(10		)
  epq.enqueue(70		)
  epq.enqueue(100		)
  epq.enqueue(1		)
  epq.enqueue(5		)
  epq.enqueue(7		)
  epq.enqueue(4		)
  epq.enqueue(64		)
  epq.enqueue(128		)
  print(_SCREAMING_SNAKE_CASE		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(_SCREAMING_SNAKE_CASE		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)


if __name__ == "__main__":
      fixed_priority_queue()
      element_priority_queue()


