




import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

a_   		=     random.Random()




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=1.0					,     lowercase_=None					,     lowercase_=None      )       ->	Dict:

    """simple docstring"""


    if rng is None:
        __UpperCamelCase  		=						global_rng

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

    return values

class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      def __init__(     self					: int ,       snake_case					: int ,       snake_case					: Union[str, Any]=7 ,       snake_case					: List[Any]=400 ,       snake_case					: int=2000 ,       snake_case					: List[Any]=2048 ,       snake_case					: int=128 ,       snake_case					: int=1 ,       snake_case					: Optional[Any]=512 ,       snake_case					: str=30 ,       snake_case					: str=44100 ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						min_seq_length
          __UpperCamelCase  		=						max_seq_length
          __UpperCamelCase  		=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          __UpperCamelCase  		=						spectrogram_length
          __UpperCamelCase  		=						feature_size
          __UpperCamelCase  		=						num_audio_channels
          __UpperCamelCase  		=						hop_length
          __UpperCamelCase  		=						chunk_length
          __UpperCamelCase  		=						sampling_rate







      def       snake_case	(     self					: Tuple				):
          return {
              "spectrogram_length": self.spectrogram_length,
              "feature_size": self.feature_size,
              "num_audio_channels": self.num_audio_channels,
              "hop_length": self.hop_length,
              "chunk_length": self.chunk_length,
              "sampling_rate": self.sampling_rate,
          }







      def       snake_case	(     self					: Dict ,       snake_case					: Union[str, Any]=False ,       snake_case					: str=False				):
          def _flatten(snake_case					: int				):
              return list(itertools.chain(*snake_case				)				)

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







@require_torch
@require_torchaudio
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					TvltFeatureExtractor







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						TvltFeatureExtractionTester(self				)







      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						self.feature_extraction_class(**self.feat_extract_dict				)
          self.assertTrue(hasattr(snake_case ,       '''spectrogram_length'''				)				)
          self.assertTrue(hasattr(snake_case ,       '''feature_size'''				)				)
          self.assertTrue(hasattr(snake_case ,       '''num_audio_channels'''				)				)
          self.assertTrue(hasattr(snake_case ,       '''hop_length'''				)				)
          self.assertTrue(hasattr(snake_case ,       '''chunk_length'''				)				)
          self.assertTrue(hasattr(snake_case ,       '''sampling_rate'''				)				)







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						self.feature_extraction_class(**self.feat_extract_dict				)

          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						feat_extract_first.save_pretrained(snake_case				)[0]
              check_json_file_has_correct_format(snake_case				)
              __UpperCamelCase  		=						self.feature_extraction_class.from_pretrained(snake_case				)

          __UpperCamelCase  		=						feat_extract_first.to_dict()
          __UpperCamelCase  		=						feat_extract_second.to_dict()
          __UpperCamelCase  		=						dict_first.pop('''mel_filters'''				)
          __UpperCamelCase  		=						dict_second.pop('''mel_filters'''				)
          self.assertTrue(np.allclose(snake_case ,       snake_case				)				)
          self.assertEqual(snake_case ,       snake_case				)







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						self.feature_extraction_class(**self.feat_extract_dict				)

          with tempfile.TemporaryDirectory() as tmpdirname:
              __UpperCamelCase  		=						os.path.join(snake_case ,       '''feat_extract.json'''				)
              feat_extract_first.to_json_file(snake_case				)
              __UpperCamelCase  		=						self.feature_extraction_class.from_json_file(snake_case				)

          __UpperCamelCase  		=						feat_extract_first.to_dict()
          __UpperCamelCase  		=						feat_extract_second.to_dict()
          __UpperCamelCase  		=						dict_first.pop('''mel_filters'''				)
          __UpperCamelCase  		=						dict_second.pop('''mel_filters'''				)
          self.assertTrue(np.allclose(snake_case ,       snake_case				)				)
          self.assertEqual(snake_case ,       snake_case				)







      def       snake_case	(     self					: Optional[Any]				):
          # Initialize feature_extractor
          __UpperCamelCase  		=						self.feature_extraction_class(**self.feat_extract_dict				)

          # create three inputs of length 800, 1000, and 1200
          __UpperCamelCase  		=						[floats_list((1, x)				)[0] for x in range(800 ,       1400 ,       200				)]
          __UpperCamelCase  		=						[np.asarray(snake_case				) for speech_input in speech_inputs]

          # Test not batched input
          __UpperCamelCase  		=						feature_extractor(np_speech_inputs[0] ,       return_tensors='''np''' ,       sampling_rate=44100				).audio_values

          self.assertTrue(encoded_audios.ndim == 4				)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

          # Test batched
          __UpperCamelCase  		=						feature_extractor(snake_case ,       return_tensors='''np''' ,       sampling_rate=44100				).audio_values

          self.assertTrue(encoded_audios.ndim == 4				)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

          # Test audio masking
          __UpperCamelCase  		=						feature_extractor(
              snake_case ,       return_tensors='''np''' ,       sampling_rate=44100 ,       mask_audio=snake_case				).audio_values

          self.assertTrue(encoded_audios.ndim == 4				)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

          # Test 2-D numpy arrays are batched.
          __UpperCamelCase  		=						[floats_list((1, x)				)[0] for x in (800, 800, 800)]
          __UpperCamelCase  		=						np.asarray(snake_case				)
          __UpperCamelCase  		=						feature_extractor(snake_case ,       return_tensors='''np''' ,       sampling_rate=44100				).audio_values
          self.assertTrue(encoded_audios.ndim == 4				)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)







      def       snake_case	(     self					: Dict ,       snake_case					: List[Any]				):
          __UpperCamelCase  		=						load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,       '''clean''' ,       split='''validation'''				)
          # automatic decoding with librispeech
          __UpperCamelCase  		=						ds.sort('''id'''				).select(range(snake_case				)				)[:num_samples]['''audio''']

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







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						self._load_datasamples(1				)
          __UpperCamelCase  		=						TvltFeatureExtractor()
          __UpperCamelCase  		=						feature_extractor(snake_case ,       return_tensors='''pt'''				).audio_values

          self.assertEquals(audio_values.shape ,       (1, 1, 192, 128)				)

          __UpperCamelCase  		=						torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]				)
          self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,       snake_case ,       atol=1E-4				)				)












import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
 print("Googling.....")
 a_   		=     "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
 a_   		=     requests.get(url, headers={"UserAgent": UserAgent().random})
 # res.raise_for_status()
 with open("project1a.html", "wb") as out_file:  # only for knowing the class
  for data in res.iter_content(1_0000):
   out_file.write(data)
 a_   		=     BeautifulSoup(res.text, "html.parser")
 a_   		=     list(soup.select(".eZt8xd"))[:5]

 print(len(links))
 for link in links:
  if link.text == "Maps":
   webbrowser.open(link.get("href"))
  else:
   webbrowser.open(f"""https://google.com{link.get('href')}""")






