




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

lowerCAmelCase_					    = random.Random()



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase=1.0	,      UpperCamelCase=None	,      UpperCamelCase=None      )     ->	Dict:
  if rng is None:
    lowerCAmelCase__				:  Dict		       =     global_rng

  lowerCAmelCase__				:  Optional[Any]		       =     []
  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	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=7      ,				__UpperCAmelCase=400      ,				__UpperCAmelCase=2000      ,				__UpperCAmelCase=2048      ,				__UpperCAmelCase=128      ,				__UpperCAmelCase=1      ,				__UpperCAmelCase=512      ,				__UpperCAmelCase=30      ,				__UpperCAmelCase=4_4100      ,				):
        lowerCAmelCase__				:  Optional[int]		       =     parent
        lowerCAmelCase__				:  Dict		       =     batch_size
        lowerCAmelCase__				:  Dict		       =     min_seq_length
        lowerCAmelCase__				:  Optional[int]		       =     max_seq_length
        lowerCAmelCase__				:  List[str]		       =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowerCAmelCase__				:  Dict		       =     spectrogram_length
        lowerCAmelCase__				:  Dict		       =     feature_size
        lowerCAmelCase__				:  str		       =     num_audio_channels
        lowerCAmelCase__				:  Optional[int]		       =     hop_length
        lowerCAmelCase__				:  Optional[int]		       =     chunk_length
        lowerCAmelCase__				:  Optional[Any]		       =     sampling_rate

      def 	__magic_name__(   self		):
        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 	__magic_name__(   self      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=False		):
        def _flatten(__UpperCAmelCase		):
          return list(itertools.chain(*__UpperCAmelCase		)		)

        if equal_length:
          lowerCAmelCase__				:  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
          lowerCAmelCase__				:  Optional[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:
          lowerCAmelCase__				:  Optional[Any]		       =     [np.asarray(__UpperCAmelCase		) for x in speech_inputs]
        return speech_inputs






@require_torch
@require_torchaudio
class  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      A__		     =		TvltFeatureExtractor

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     TvltFeatureExtractionTester(self		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     self.feature_extraction_class(**self.feat_extract_dict		)
        self.assertTrue(hasattr(__UpperCAmelCase      ,				'''spectrogram_length'''		)		)
        self.assertTrue(hasattr(__UpperCAmelCase      ,				'''feature_size'''		)		)
        self.assertTrue(hasattr(__UpperCAmelCase      ,				'''num_audio_channels'''		)		)
        self.assertTrue(hasattr(__UpperCAmelCase      ,				'''hop_length'''		)		)
        self.assertTrue(hasattr(__UpperCAmelCase      ,				'''chunk_length'''		)		)
        self.assertTrue(hasattr(__UpperCAmelCase      ,				'''sampling_rate'''		)		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     self.feature_extraction_class(**self.feat_extract_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
          lowerCAmelCase__				:  int		       =     feat_extract_first.save_pretrained(__UpperCAmelCase		)[0]
          check_json_file_has_correct_format(__UpperCAmelCase		)
          lowerCAmelCase__				:  Tuple		       =     self.feature_extraction_class.from_pretrained(__UpperCAmelCase		)

        lowerCAmelCase__				:  Optional[int]		       =     feat_extract_first.to_dict()
        lowerCAmelCase__				:  int		       =     feat_extract_second.to_dict()
        lowerCAmelCase__				:  int		       =     dict_first.pop('''mel_filters'''		)
        lowerCAmelCase__				:  List[str]		       =     dict_second.pop('''mel_filters'''		)
        self.assertTrue(np.allclose(__UpperCAmelCase      ,				__UpperCAmelCase		)		)
        self.assertEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.feature_extraction_class(**self.feat_extract_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
          lowerCAmelCase__				:  Tuple		       =     os.path.join(__UpperCAmelCase      ,				'''feat_extract.json'''		)
          feat_extract_first.to_json_file(__UpperCAmelCase		)
          lowerCAmelCase__				:  List[str]		       =     self.feature_extraction_class.from_json_file(__UpperCAmelCase		)

        lowerCAmelCase__				:  int		       =     feat_extract_first.to_dict()
        lowerCAmelCase__				:  Tuple		       =     feat_extract_second.to_dict()
        lowerCAmelCase__				:  Optional[Any]		       =     dict_first.pop('''mel_filters'''		)
        lowerCAmelCase__				:  Optional[int]		       =     dict_second.pop('''mel_filters'''		)
        self.assertTrue(np.allclose(__UpperCAmelCase      ,				__UpperCAmelCase		)		)
        self.assertEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        # Initialize feature_extractor
        lowerCAmelCase__				:  List[str]		       =     self.feature_extraction_class(**self.feat_extract_dict		)

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

        # Test not batched input
        lowerCAmelCase__				:  int		       =     feature_extractor(np_speech_inputs[0]      ,				return_tensors='''np'''      ,				sampling_rate=4_4100		).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
        lowerCAmelCase__				:  Any		       =     feature_extractor(__UpperCAmelCase      ,				return_tensors='''np'''      ,				sampling_rate=4_4100		).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
        lowerCAmelCase__				:  int		       =     feature_extractor(
            __UpperCAmelCase      ,				return_tensors='''np'''      ,				sampling_rate=4_4100      ,				mask_audio=__UpperCAmelCase		).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.
        lowerCAmelCase__				:  Union[str, Any]		       =     [floats_list((1, x)		)[0] for x in (800, 800, 800)]
        lowerCAmelCase__				:  List[Any]		       =     np.asarray(__UpperCAmelCase		)
        lowerCAmelCase__				:  Union[str, Any]		       =     feature_extractor(__UpperCAmelCase      ,				return_tensors='''np'''      ,				sampling_rate=4_4100		).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 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      ,				'''clean'''      ,				split='''validation'''		)
        # automatic decoding with librispeech
        lowerCAmelCase__				:  str		       =     ds.sort('''id'''		).select(range(__UpperCAmelCase		)		)[:num_samples]['''audio''']

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







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self._load_datasamples(1		)
        lowerCAmelCase__				:  Optional[int]		       =     TvltFeatureExtractor()
        lowerCAmelCase__				:  Tuple		       =     feature_extractor(__UpperCAmelCase      ,				return_tensors='''pt'''		).audio_values

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

        lowerCAmelCase__				:  List[Any]		       =     torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,				__UpperCAmelCase      ,				atol=1e-4		)		)







import os
from datetime import datetime as dt

from github import Github


lowerCAmelCase_					    = [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]



def      __lowerCAmelCase			(					)     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     Github(os.environ['''GITHUB_TOKEN''']      )
  lowerCAmelCase__				:  str		       =     g.get_repo('''huggingface/diffusers'''      )
  lowerCAmelCase__				:  List[str]		       =     repo.get_issues(state='''open'''      )

  for issue in open_issues:
    lowerCAmelCase__				:  Optional[Any]		       =     sorted(issue.get_comments()	,      key=lambda UpperCamelCase      : i.created_at	,      reverse=UpperCamelCase      )
    lowerCAmelCase__				:  int		       =     comments[0] if len(UpperCamelCase      ) > 0 else None
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and (dt.utcnow() - issue.updated_at).days > 7
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''      )
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''      )
      issue.remove_from_labels('''stale'''      )
    elif (
        (dt.utcnow() - issue.updated_at).days > 23
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      issue.create_comment(
          '''This issue has been automatically marked as stale because it has not had '''
          '''recent activity. If you think this still needs to be addressed '''
          '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
          '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''      )
      issue.add_to_labels('''stale'''      )


if __name__ == "__main__":
     main()

