'''simple docstring'''

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

a        =						random.Random()

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=1.0							,	__UpperCAmelCase=None							,	__UpperCAmelCase=None      )						->					Union[str, Any]:

   '''simple docstring'''







   if rng is None:
      __SCREAMING_SNAKE_CASE      							=       global_rng

   __SCREAMING_SNAKE_CASE      							=       []
   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 __a (   unittest.TestCase			):





   def __init__(					self				:			Dict				,lowerCamelCase				:			str				,lowerCamelCase				:			Optional[int]=7				,lowerCamelCase				:			Dict=400				,lowerCamelCase				:			Dict=2000				,lowerCamelCase				:			str=24				,lowerCamelCase				:			Any=24				,lowerCamelCase				:			Optional[Any]=0.0				,lowerCamelCase				:			Optional[int]=1_6000				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Tuple=True				,):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      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      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			Optional[Any]=False				,lowerCamelCase				:			Optional[Any]=False    ):

      '''simple docstring'''

      def _flatten(lowerCamelCase				:			Any    ):
         return list(itertools.chain(*UpperCamelCase_    )    )

      if equal_length:
         __SCREAMING_SNAKE_CASE      							=       [floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
      else:
         # make sure that inputs increase in size
         __SCREAMING_SNAKE_CASE      							=       [
             floats_list((x, self.feature_size)    )
             for x in range(self.min_seq_length				,self.max_seq_length				,self.seq_length_diff    )
         ]
      if numpify:
         __SCREAMING_SNAKE_CASE      							=       [np.asarray(UpperCamelCase_    ) for x in speech_inputs]
      return speech_inputs







@require_torch
@require_torchaudio
class __a (   __lowerCamelCase,   unittest.TestCase			):
   __UpperCamelCase   : Any	      =	SpeechaTextFeatureExtractor if is_speech_available() else None





   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       SpeechaTextFeatureExtractionTester(self    )





   def      UpperCAmelCase__     (					self				:			Tuple				,lowerCamelCase				:			Dict    ):

      '''simple docstring'''

      self.assertTrue(np.all(np.mean(UpperCamelCase_				,axis=0    ) < 1E-3    )    )
      self.assertTrue(np.all(np.abs(np.var(UpperCamelCase_				,axis=0    ) - 1    ) < 1E-3    )    )





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
      # create three inputs of length 800, 1000, and 1200
      __SCREAMING_SNAKE_CASE      							=       [floats_list((1, x)    )[0] for x in range(800				,1400				,200    )]
      __SCREAMING_SNAKE_CASE      							=       [np.asarray(UpperCamelCase_    ) for speech_input in speech_inputs]

      # Test feature size
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(UpperCamelCase_				,padding=UpperCamelCase_				,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
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(speech_inputs[0]				,return_tensors="""np"""    ).input_features
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(np_speech_inputs[0]				,return_tensors="""np"""    ).input_features
      self.assertTrue(np.allclose(UpperCamelCase_				,UpperCamelCase_				,atol=1E-3    )    )

      # Test batched
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(UpperCamelCase_				,return_tensors="""np"""    ).input_features
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(UpperCamelCase_				,return_tensors="""np"""    ).input_features
      for enc_seq_a, enc_seq_a in zip(UpperCamelCase_				,UpperCamelCase_    ):
         self.assertTrue(np.allclose(UpperCamelCase_				,UpperCamelCase_				,atol=1E-3    )    )

      # Test 2-D numpy arrays are batched.
      __SCREAMING_SNAKE_CASE      							=       [floats_list((1, x)    )[0] for x in (800, 800, 800)]
      __SCREAMING_SNAKE_CASE      							=       np.asarray(UpperCamelCase_    )
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(UpperCamelCase_				,return_tensors="""np"""    ).input_features
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(UpperCamelCase_				,return_tensors="""np"""    ).input_features
      for enc_seq_a, enc_seq_a in zip(UpperCamelCase_				,UpperCamelCase_    ):
         self.assertTrue(np.allclose(UpperCamelCase_				,UpperCamelCase_				,atol=1E-3    )    )





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
      __SCREAMING_SNAKE_CASE      							=       [floats_list((1, x)    )[0] for x in range(800				,1400				,200    )]

      __SCREAMING_SNAKE_CASE      							=       ["""longest""", """max_length""", """do_not_pad"""]
      __SCREAMING_SNAKE_CASE      							=       [None, 16, None]
      for max_length, padding in zip(UpperCamelCase_				,UpperCamelCase_    ):
         __SCREAMING_SNAKE_CASE      							=       feature_extractor(
             UpperCamelCase_				,padding=UpperCamelCase_				,max_length=UpperCamelCase_				,return_attention_mask=UpperCamelCase_    )
         __SCREAMING_SNAKE_CASE      							=       inputs.input_features
         __SCREAMING_SNAKE_CASE      							=       inputs.attention_mask
         __SCREAMING_SNAKE_CASE      							=       [np.sum(UpperCamelCase_    ) 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      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
      __SCREAMING_SNAKE_CASE      							=       [floats_list((1, x)    )[0] for x in range(800				,1400				,200    )]

      __SCREAMING_SNAKE_CASE      							=       ["""longest""", """max_length""", """do_not_pad"""]
      __SCREAMING_SNAKE_CASE      							=       [None, 16, None]
      for max_length, padding in zip(UpperCamelCase_				,UpperCamelCase_    ):
         __SCREAMING_SNAKE_CASE      							=       feature_extractor(
             UpperCamelCase_				,max_length=UpperCamelCase_				,padding=UpperCamelCase_				,return_tensors="""np"""				,return_attention_mask=UpperCamelCase_    )
         __SCREAMING_SNAKE_CASE      							=       inputs.input_features
         __SCREAMING_SNAKE_CASE      							=       inputs.attention_mask
         __SCREAMING_SNAKE_CASE      							=       [np.sum(UpperCamelCase_    ) 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      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
      __SCREAMING_SNAKE_CASE      							=       [floats_list((1, x)    )[0] for x in range(800				,1400				,200    )]
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(
          UpperCamelCase_				,padding="""max_length"""				,max_length=4				,truncation=UpperCamelCase_				,return_tensors="""np"""				,return_attention_mask=UpperCamelCase_				,)
      __SCREAMING_SNAKE_CASE      							=       inputs.input_features
      __SCREAMING_SNAKE_CASE      							=       inputs.attention_mask
      __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
      __SCREAMING_SNAKE_CASE      							=       [floats_list((1, x)    )[0] for x in range(800				,1400				,200    )]
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(
          UpperCamelCase_				,padding="""longest"""				,max_length=4				,truncation=UpperCamelCase_				,return_tensors="""np"""				,return_attention_mask=UpperCamelCase_				,)
      __SCREAMING_SNAKE_CASE      							=       inputs.input_features
      __SCREAMING_SNAKE_CASE      							=       inputs.attention_mask
      __SCREAMING_SNAKE_CASE      							=       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, 24)    )

      __SCREAMING_SNAKE_CASE      							=       [floats_list((1, x)    )[0] for x in range(800				,1400				,200    )]
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(
          UpperCamelCase_				,padding="""longest"""				,max_length=16				,truncation=UpperCamelCase_				,return_tensors="""np"""				,return_attention_mask=UpperCamelCase_				,)
      __SCREAMING_SNAKE_CASE      							=       inputs.input_features
      __SCREAMING_SNAKE_CASE      							=       inputs.attention_mask
      __SCREAMING_SNAKE_CASE      							=       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, 24)    )





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      import torch

      __SCREAMING_SNAKE_CASE      							=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
      __SCREAMING_SNAKE_CASE      							=       np.random.rand(100				,32    ).astype(np.floataa    )
      __SCREAMING_SNAKE_CASE      							=       np_speech_inputs.tolist()

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





   def      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			Optional[Any]    ):

      '''simple docstring'''

      from datasets import load_dataset

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

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





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       np.array([
          -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
          -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
          -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
      ]    )
      # fmt: on

      __SCREAMING_SNAKE_CASE      							=       self._load_datasamples(1    )
      __SCREAMING_SNAKE_CASE      							=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
      __SCREAMING_SNAKE_CASE      							=       feature_extractor(UpperCamelCase_				,return_tensors="""pt"""    ).input_features
      self.assertEquals(input_features.shape				,(1, 584, 24)    )
      self.assertTrue(np.allclose(input_features[0, 0, :30]				,UpperCamelCase_				,atol=1E-4    )    )












'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       BeautifulSoup(requests.get(__UpperCAmelCase							,	params=__UpperCAmelCase      ).content							,	"""html.parser"""      )
   __SCREAMING_SNAKE_CASE      							=       soup.find("""div"""							,	attrs={"""class""": """gs_ri"""}      )
   __SCREAMING_SNAKE_CASE      							=       div.find("""div"""							,	attrs={"""class""": """gs_fl"""}      ).find_all("""a"""      )
   return anchors[2].get_text()


if __name__ == "__main__":
    a        =						{
        "title": (
            "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
            "capacitance, volumetric capacitance, and energy density"
        ),
        "journal": "Chem. Mater.",
        "volume": 30,
        "pages": "3979-3990",
        "year": 2018,
        "hl": "en",
    }
    print(get_citation("https://scholar.google.com/scholar_lookup", params=params))





