






"""simple docstring"""
import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch


A_			: Tuple				=			random.Random()






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase=1.0			,   _lowerCamelCase=None			,   _lowerCamelCase=None				):
 if rng is None:
  lowerCamelCase__     :			int     				=						global_rng

 lowerCamelCase__     :			Union[str, 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
class 		a_      (	unittest.TestCase ):







      '''simple docstring'''

      def __init__(self,     lowerCamelCase_,     lowerCamelCase_=7,     lowerCamelCase_=4_0_0,     lowerCamelCase_=2_0_0_0,     lowerCamelCase_=1,     lowerCamelCase_=0.0,     lowerCamelCase_=2_4_0_0_0,     lowerCamelCase_=True,     ):

       '''simple docstring'''



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




      def 			a__							(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,
       }





      def 			a__							(self,     lowerCamelCase_=False,     lowerCamelCase_=False					):

       '''simple docstring'''



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

       if equal_length:
        lowerCamelCase__     :			str     				=						floats_list((self.batch_size, self.max_seq_length)					)
       else:
        # make sure that inputs increase in size
        lowerCamelCase__     :			Tuple     				=						[
            _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:
        lowerCamelCase__     :			int     				=						[np.asarray(lowerCamelCase_					) for x in audio_inputs]

       return audio_inputs



@require_torch
class 		a_      (	snake_case_  ,						unittest.TestCase ):







      '''simple docstring'''

      lowerCamelCase__       :				Dict =	EncodecFeatureExtractor




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[int]     				=						EnCodecFeatureExtractionTester(self					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			str     				=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       # create three inputs of length 800, 1000, and 1200
       lowerCamelCase__     :			List[str]     				=						[floats_list((1, x)					)[0] for x in range(8_0_0,     1_4_0_0,     2_0_0					)]
       lowerCamelCase__     :			Optional[int]     				=						[np.asarray(lowerCamelCase_					) for audio_input in audio_inputs]

       # Test not batched input
       lowerCamelCase__     :			int     				=						feat_extract(audio_inputs[0],     return_tensors='np'					).input_values
       lowerCamelCase__     :			Dict     				=						feat_extract(np_audio_inputs[0],     return_tensors='np'					).input_values
       self.assertTrue(np.allclose(lowerCamelCase_,     lowerCamelCase_,     atol=1e-3					)					)

       # Test batched
       lowerCamelCase__     :			Optional[int]     				=						feat_extract(lowerCamelCase_,     padding=lowerCamelCase_,     return_tensors='np'					).input_values
       lowerCamelCase__     :			Optional[int]     				=						feat_extract(lowerCamelCase_,     padding=lowerCamelCase_,     return_tensors='np'					).input_values
       for enc_seq_a, enc_seq_a in zip(lowerCamelCase_,     lowerCamelCase_					):
        self.assertTrue(np.allclose(lowerCamelCase_,     lowerCamelCase_,     atol=1e-3					)					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       lowerCamelCase__     :			List[str]     				=						np.random.rand(1_0_0					).astype(np.floataa					)
       lowerCamelCase__     :			Union[str, Any]     				=						np_audio_inputs.tolist()

       for inputs in [py_audio_inputs, np_audio_inputs]:
        lowerCamelCase__     :			Optional[Any]     				=						feature_extractor.pad([{'input_values': inputs}],     return_tensors='np'					)
        self.assertTrue(np_processed.input_values.dtype == np.floataa					)
        lowerCamelCase__     :			Tuple     				=						feature_extractor.pad([{'input_values': inputs}],     return_tensors='pt'					)
        self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)




      def 			a__							(self,     lowerCamelCase_					):

       '''simple docstring'''



       from datasets import load_dataset

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

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




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						torch.tensor(
           [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
            3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
            2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
            4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
            7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
            4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03]					)
       # fmt: on
       lowerCamelCase__     :			Dict     				=						self._load_datasamples(1					)
       lowerCamelCase__     :			Optional[Any]     				=						EncodecFeatureExtractor()
       lowerCamelCase__     :			List[Any]     				=						feature_extractor(lowerCamelCase_,     return_tensors='pt'					).input_values
       self.assertEquals(input_values.shape,     (1, 1, 9_3_6_8_0)					)
       self.assertTrue(torch.allclose(input_values[0, 0, :3_0],     lowerCamelCase_,     atol=1e-6					)					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Tuple     				=						torch.tensor(
           [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
            3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
            2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
            4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
            7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
            4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03]					)
       # fmt: on
       lowerCamelCase__     :			Union[str, Any]     				=						self._load_datasamples(1					)
       lowerCamelCase__     :			Optional[int]     				=						[np.tile(input_audio[0][None],     reps=(2, 1)					)]
       input_audio[0][1] *= 0.5
       lowerCamelCase__     :			Optional[int]     				=						EncodecFeatureExtractor(feature_size=2					)
       lowerCamelCase__     :			str     				=						feature_extractor(lowerCamelCase_,     return_tensors='pt'					).input_values
       self.assertEquals(input_values.shape,     (1, 2, 9_3_6_8_0)					)
       self.assertTrue(torch.allclose(input_values[0, 0, :3_0],     lowerCamelCase_,     atol=1e-6					)					)
       self.assertTrue(torch.allclose(input_values[0, 1, :3_0],     EXPECTED_INPUT_VALUES * 0.5,     atol=1e-6					)					)





      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Dict     				=						self._load_datasamples(2					)
       # would be easier if the stride was like
       lowerCamelCase__     :			Union[str, Any]     				=						EncodecFeatureExtractor(feature_size=1,     chunk_length_s=1,     overlap=0.01					)

       # pad and trunc raise an error ?
       with self.assertRaisesRegex(
           lowerCamelCase_,     '^Both padding and truncation were set. Make sure you only set one.$',     ):
        lowerCamelCase__     :			Dict     				=						feature_extractor(
            lowerCamelCase_,     padding='max_length',     truncation=lowerCamelCase_,     return_tensors='pt'					).input_values

       # truncate to chunk
       lowerCamelCase__     :			str     				=						feature_extractor(lowerCamelCase_,     truncation=lowerCamelCase_,     return_tensors='pt'					).input_values
       self.assertEquals(truncated_outputs.shape,     (2, 1, 7_1_5_2_0)					)  # 2 chunks

       # force truncate to max_length
       lowerCamelCase__     :			Any     				=						feature_extractor(
           lowerCamelCase_,     truncation=lowerCamelCase_,     max_length=4_8_0_0_0,     return_tensors='pt'					).input_values
       self.assertEquals(truncated_outputs.shape,     (2, 1, 4_8_0_0_0)					)

       # pad to chunk
       lowerCamelCase__     :			Optional[Any]     				=						feature_extractor(lowerCamelCase_,     padding=lowerCamelCase_,     return_tensors='pt'					).input_values
       self.assertEquals(padded_outputs.shape,     (2, 1, 9_5_2_8_0)					)

       # pad to chunk
       lowerCamelCase__     :			List[str]     				=						feature_extractor(lowerCamelCase_,     return_tensors='pt'					).input_values
       self.assertEquals(truncated_outputs.shape,     (2, 1, 9_5_2_8_0)					)

       # force pad to max length
       lowerCamelCase__     :			str     				=						feature_extractor(
           lowerCamelCase_,     padding='max_length',     max_length=1_0_0_0_0_0,     return_tensors='pt'					).input_values
       self.assertEquals(truncated_outputs.shape,     (2, 1, 1_0_0_0_0_0)					)

       # force no pad
       with self.assertRaisesRegex(
           lowerCamelCase_,     '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',     ):
        lowerCamelCase__     :			Any     				=						feature_extractor(lowerCamelCase_,     padding=lowerCamelCase_,     return_tensors='pt'					).input_values

       lowerCamelCase__     :			List[str]     				=						feature_extractor(input_audio[0],     padding=lowerCamelCase_,     return_tensors='pt'					).input_values
       self.assertEquals(truncated_outputs.shape,     (1, 1, 9_3_6_8_0)					)

       # no pad if no chunk_length_s
       lowerCamelCase__     :			Optional[int]     				=						None
       with self.assertRaisesRegex(
           lowerCamelCase_,     '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',     ):
        lowerCamelCase__     :			List[Any]     				=						feature_extractor(lowerCamelCase_,     padding=lowerCamelCase_,     return_tensors='pt'					).input_values

       lowerCamelCase__     :			Any     				=						feature_extractor(input_audio[0],     padding=lowerCamelCase_,     return_tensors='pt'					).input_values
       self.assertEquals(truncated_outputs.shape,     (1, 1, 9_3_6_8_0)					)

       # no pad if no overlap
       lowerCamelCase__     :			Any     				=						2
       lowerCamelCase__     :			Optional[int]     				=						None
       with self.assertRaisesRegex(
           lowerCamelCase_,     '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',     ):
        lowerCamelCase__     :			Optional[int]     				=						feature_extractor(lowerCamelCase_,     padding=lowerCamelCase_,     return_tensors='pt'					).input_values

       lowerCamelCase__     :			List[str]     				=						feature_extractor(input_audio[0],     padding=lowerCamelCase_,     return_tensors='pt'					).input_values
       self.assertEquals(truncated_outputs.shape,     (1, 1, 9_3_6_8_0)					)









"""simple docstring"""
import bsa
import requests






def        lowerCamelCase_  (						_lowerCamelCase				):
 return {
     "name": soup.ha.a.text,
     "genre": soup.find('span'			,   class_='genre'				).text.strip(),
     "rating": soup.strong.text,
     "page_link": f'''https://www.imdb.com{soup.a.get('href'				)}''',
 }






def        lowerCamelCase_  (						_lowerCamelCase = 5				):
 lowerCamelCase__     :			Optional[int]     				=						int(float(_lowerCamelCase				)				)
 if num_movies < 1:
  return ()
 lowerCamelCase__     :			Any     				=						(
     'https://www.imdb.com/search/title?title_type='
     f'''feature&sort=num_votes,desc&count={num_movies}'''
 )
 lowerCamelCase__     :			Union[str, Any]     				=						bsa.BeautifulSoup(requests.get(_lowerCamelCase				).content			,   'html.parser'				)
 return tuple(
     get_movie_data_from_soup(_lowerCamelCase				)
     for movie in source.find_all('div'			,   class_='lister-item mode-advanced'				)				)


if __name__ == "__main__":
      import json

      A_			: str				=			int(input("How many movies would you like to see? "))
      print(
          ", ".join(
              json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
          )
      )

