






import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor

__lowerCamelCase	:		int										=   random.Random()







def 			A__							(      _a  :					Tuple		,  _a  :					Optional[Any]=1.0		,  _a  :					List[str]=None		,  _a  :					Optional[Any]=None	):





 '''simple docstring'''







 if rng is None:
  snake_case__  :    Any							       =global_rng

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

 return values







@require_torch
@require_torchaudio
class  _lowercase   (   unittest.TestCase						):
      def __init__(     self				,			a				,			a=7				,			a=4_0_0				,			a=2_0_0_0				,			a=2_4				,			a=2_4				,			a=0.0				,			a=1_6_0_0_0				,			a=True				,			a=True				,			):
       snake_case__  :    Union[str, Any]							       =parent
       snake_case__  :    Tuple							       =batch_size
       snake_case__  :    Dict							       =min_seq_length
       snake_case__  :    str							       =max_seq_length
       snake_case__  :    Optional[int]							       =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       snake_case__  :    Dict							       =feature_size
       snake_case__  :    Union[str, Any]							       =num_mel_bins
       snake_case__  :    List[str]							       =padding_value
       snake_case__  :    Tuple							       =sampling_rate
       snake_case__  :    List[Any]							       =return_attention_mask
       snake_case__  :    int							       =do_normalize
      def 						lowercase__					(     self   ):
       return {
           "feature_size": self.feature_size,
           "num_mel_bins": self.num_mel_bins,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
           "do_normalize": self.do_normalize,
       }



      def 						lowercase__					(     self				,			a=False				,			a=False   ):
       def _flatten(a   ):
        return list(itertools.chain(*A_   )   )

       if equal_length:
        snake_case__  :    Optional[Any]							       =[floats_list((self.max_seq_length, self.feature_size)   ) for _ in range(self.batch_size   )]
       else:
        # make sure that inputs increase in size
        snake_case__  :    int							       =[
            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__  :    str							       =[np.asarray(A_   ) for x in speech_inputs]
       return speech_inputs







@require_torch
@require_torchaudio
class  _lowercase   (   _SCREAMING_SNAKE_CASE						,				unittest.TestCase						):
      _a							:						Optional[int]										=							SpeechaTextFeatureExtractor if is_speech_available() else None
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =SpeechaTextFeatureExtractionTester(self   )
      def 						lowercase__					(     self				,			a   ):
       self.assertTrue(np.all(np.mean(A_				,			axis=0   ) < 1e-3   )   )
       self.assertTrue(np.all(np.abs(np.var(A_				,			axis=0   ) - 1   ) < 1e-3   )   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       # create three inputs of length 800, 1000, and 1200
       snake_case__  :    Optional[int]							       =[floats_list((1, x)   )[0] for x in range(8_0_0				,			1_4_0_0				,			2_0_0   )]
       snake_case__  :    Dict							       =[np.asarray(A_   ) for speech_input in speech_inputs]

       # Test feature size
       snake_case__  :    str							       =feature_extractor(A_				,			padding=A_				,			return_tensors="""np"""   ).input_features
       self.assertTrue(input_features.ndim == 3   )
       self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size   )

       # Test not batched input
       snake_case__  :    Any							       =feature_extractor(speech_inputs[0]				,			return_tensors="""np"""   ).input_features
       snake_case__  :    int							       =feature_extractor(np_speech_inputs[0]				,			return_tensors="""np"""   ).input_features
       self.assertTrue(np.allclose(A_				,			A_				,			atol=1e-3   )   )

       # Test batched
       snake_case__  :    int							       =feature_extractor(A_				,			return_tensors="""np"""   ).input_features
       snake_case__  :    Optional[int]							       =feature_extractor(A_				,			return_tensors="""np"""   ).input_features
       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__  :    List[str]							       =[floats_list((1, x)   )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
       snake_case__  :    List[Any]							       =np.asarray(A_   )
       snake_case__  :    List[Any]							       =feature_extractor(A_				,			return_tensors="""np"""   ).input_features
       snake_case__  :    List[str]							       =feature_extractor(A_				,			return_tensors="""np"""   ).input_features
       for enc_seq_a, enc_seq_a in zip(A_				,			A_   ):
        self.assertTrue(np.allclose(A_				,			A_				,			atol=1e-3   )   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       snake_case__  :    int							       =[floats_list((1, x)   )[0] for x in range(8_0_0				,			1_4_0_0				,			2_0_0   )]

       snake_case__  :    Optional[int]							       =["""longest""", """max_length""", """do_not_pad"""]
       snake_case__  :    List[Any]							       =[None, 1_6, None]
       for max_length, padding in zip(A_				,			A_   ):
        snake_case__  :    Dict							       =feature_extractor(
            A_				,			padding=A_				,			max_length=A_				,			return_attention_mask=A_   )
        snake_case__  :    List[str]							       =inputs.input_features
        snake_case__  :    Optional[Any]							       =inputs.attention_mask
        snake_case__  :    int							       =[np.sum(A_   ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       snake_case__  :    int							       =[floats_list((1, x)   )[0] for x in range(8_0_0				,			1_4_0_0				,			2_0_0   )]

       snake_case__  :    Dict							       =["""longest""", """max_length""", """do_not_pad"""]
       snake_case__  :    Optional[int]							       =[None, 1_6, None]
       for max_length, padding in zip(A_				,			A_   ):
        snake_case__  :    Optional[Any]							       =feature_extractor(
            A_				,			max_length=A_				,			padding=A_				,			return_tensors="""np"""				,			return_attention_mask=A_   )
        snake_case__  :    Dict							       =inputs.input_features
        snake_case__  :    List[Any]							       =inputs.attention_mask
        snake_case__  :    List[Any]							       =[np.sum(A_   ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
        self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6   )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
        self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6   )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       snake_case__  :    List[Any]							       =[floats_list((1, x)   )[0] for x in range(8_0_0				,			1_4_0_0				,			2_0_0   )]
       snake_case__  :    Any							       =feature_extractor(
           A_				,			padding="""max_length"""				,			max_length=4				,			truncation=A_				,			return_tensors="""np"""				,			return_attention_mask=A_				,			)
       snake_case__  :    Any							       =inputs.input_features
       snake_case__  :    List[Any]							       =inputs.attention_mask
       snake_case__  :    Dict							       =np.sum(attention_mask == 1				,			axis=1   )

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
       self._check_zero_mean_unit_variance(input_features[1]   )
       self._check_zero_mean_unit_variance(input_features[2]   )
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       snake_case__  :    List[Any]							       =[floats_list((1, x)   )[0] for x in range(8_0_0				,			1_4_0_0				,			2_0_0   )]
       snake_case__  :    Union[str, Any]							       =feature_extractor(
           A_				,			padding="""longest"""				,			max_length=4				,			truncation=A_				,			return_tensors="""np"""				,			return_attention_mask=A_				,			)
       snake_case__  :    List[Any]							       =inputs.input_features
       snake_case__  :    Any							       =inputs.attention_mask
       snake_case__  :    Dict							       =np.sum(attention_mask == 1				,			axis=1   )

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
       self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
       self._check_zero_mean_unit_variance(input_features[2]   )

       # make sure that if max_length < longest -> then pad to max_length
       self.assertEqual(input_features.shape				,			(3, 4, 2_4)   )

       snake_case__  :    Union[str, Any]							       =[floats_list((1, x)   )[0] for x in range(8_0_0				,			1_4_0_0				,			2_0_0   )]
       snake_case__  :    List[Any]							       =feature_extractor(
           A_				,			padding="""longest"""				,			max_length=1_6				,			truncation=A_				,			return_tensors="""np"""				,			return_attention_mask=A_				,			)
       snake_case__  :    List[str]							       =inputs.input_features
       snake_case__  :    Optional[Any]							       =inputs.attention_mask
       snake_case__  :    List[str]							       =np.sum(attention_mask == 1				,			axis=1   )

       self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
       self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
       self._check_zero_mean_unit_variance(input_features[2]   )

       # make sure that if max_length < longest -> then pad to max_length
       self.assertEqual(input_features.shape				,			(3, 6, 2_4)   )
      def 						lowercase__					(     self   ):
       import torch

       snake_case__  :    Optional[int]							       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       snake_case__  :    str							       =np.random.rand(1_0_0				,			3_2   ).astype(np.floataa   )
       snake_case__  :    List[Any]							       =np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
        snake_case__  :    str							       =feature_extractor.pad([{"""input_features""": inputs}]				,			return_tensors="""np"""   )
        self.assertTrue(np_processed.input_features.dtype == np.floataa   )
        snake_case__  :    List[str]							       =feature_extractor.pad([{"""input_features""": inputs}]				,			return_tensors="""pt"""   )
        self.assertTrue(pt_processed.input_features.dtype == torch.floataa   )
      def 						lowercase__					(     self				,			a   ):
       from datasets import load_dataset

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

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



      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =np.array([
           -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
           -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
           -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
       ]   )
       # fmt: on

       snake_case__  :    Union[str, Any]							       =self._load_datasamples(1   )
       snake_case__  :    Dict							       =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
       snake_case__  :    Optional[int]							       =feature_extractor(A_				,			return_tensors="""pt"""   ).input_features
       self.assertEquals(input_features.shape				,			(1, 5_8_4, 2_4)   )
       self.assertTrue(np.allclose(input_features[0, 0, :3_0]				,			A_				,			atol=1e-4   )   )













def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) <= 1:
  return [tuple(_a	)]

 snake_case__  :    Optional[int]							       =[]

 def generate(_a  :					int		,  _a  :					list	):
  if k == 1:
   res.append(tuple(arr[:]	)	)
   return

  generate(k - 1		,  _a	)

  for i in range(k - 1	):
   if k % 2 == 0:  # k is even
    snake_case__       ,   snake_case__  :    Dict							       =arr[k - 1], arr[i]
   else:  # k is odd
    snake_case__       ,   snake_case__  :    int							       =arr[k - 1], arr[0]
   generate(k - 1		,  _a	)

 generate(len(_a	)		,  _a	)
 return res


if __name__ == "__main__":
       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma:\n""").strip()
       __lowerCamelCase	:		Any										=   [int(item) for item in user_input.split(""",""")]
       print(heaps(arr))





