


import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase   :    Any     =		random.Random()






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[Any]  ,							lowerCamelCase__		:						Optional[Any]=1.0  ,							lowerCamelCase__		:						str=None  ,							lowerCamelCase__		:						Dict=None      ):





			'''simple docstring'''




			if rng is None:
						lowerCamelCase    		=  global_rng

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

			return values







class        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A=7		,      A=4_00		,      A=20_00		,      A=1		,      A=0.0		,      A=1_60_00		,      A=True		,      A=True		,      )  -> Union[str, Any]:







					'''simple docstring'''







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







		def 					__A   ( self	)  -> List[Any]:







					'''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,
					    "do_normalize": self.do_normalize,
					}







		def 					__A   ( self		,      A=False		,      A=False	)  -> Any:







					'''simple docstring'''







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

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

					return speech_inputs







class        __lowercase       (							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					Any			       =		WavaVecaFeatureExtractor







		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  WavaVecaFeatureExtractionTester(self	)







		def 					__A   ( self		,      A	)  -> Optional[Any]:







					'''simple docstring'''







					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 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					# create three inputs of length 800, 1000, and 1200
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  [np.asarray(A	) for speech_input in speech_inputs]

					# Test not batched input
					lowerCamelCase    		=  feat_extract(speech_inputs[0]		,      return_tensors="""np"""	).input_values
					lowerCamelCase    		=  feat_extract(np_speech_inputs[0]		,      return_tensors="""np"""	).input_values
					self.assertTrue(np.allclose(A		,      A		,      atol=1e-3	)	)

					# Test batched
					lowerCamelCase    		=  feat_extract(A		,      return_tensors="""np"""	).input_values
					lowerCamelCase    		=  feat_extract(A		,      return_tensors="""np"""	).input_values
					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.
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in (8_00, 8_00, 8_00)]
					lowerCamelCase    		=  np.asarray(A	)
					lowerCamelCase    		=  feat_extract(A		,      return_tensors="""np"""	).input_values
					lowerCamelCase    		=  feat_extract(A		,      return_tensors="""np"""	).input_values
					for enc_seq_a, enc_seq_a in zip(A		,      A	):
								self.assertTrue(np.allclose(A		,      A		,      atol=1e-3	)	)







		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]

					lowerCamelCase    		=  ["""longest""", """max_length""", """do_not_pad"""]
					lowerCamelCase    		=  [None, 16_00, None]
					for max_length, padding in zip(A		,      A	):
								lowerCamelCase    		=  feat_extract(A		,      padding=A		,      max_length=A		,      return_tensors="""np"""	)
								lowerCamelCase    		=  processed.input_values

								self._check_zero_mean_unit_variance(input_values[0][:8_00]	)
								self.assertTrue(input_values[0][8_00:].sum() < 1e-6	)
								self._check_zero_mean_unit_variance(input_values[1][:10_00]	)
								self.assertTrue(input_values[0][10_00:].sum() < 1e-6	)
								self._check_zero_mean_unit_variance(input_values[2][:12_00]	)







		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  range(8_00		,      14_00		,      2_00	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in lengths]

					lowerCamelCase    		=  ["""longest""", """max_length""", """do_not_pad"""]
					lowerCamelCase    		=  [None, 16_00, None]

					for max_length, padding in zip(A		,      A	):
								lowerCamelCase    		=  feat_extract(A		,      max_length=A		,      padding=A	)
								lowerCamelCase    		=  processed.input_values

								self._check_zero_mean_unit_variance(input_values[0][:8_00]	)
								self._check_zero_mean_unit_variance(input_values[1][:10_00]	)
								self._check_zero_mean_unit_variance(input_values[2][:12_00]	)







		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  feat_extract(
					    A		,      truncation=A		,      max_length=10_00		,      padding="""max_length"""		,      return_tensors="""np"""	)
					lowerCamelCase    		=  processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :8_00]	)
					self._check_zero_mean_unit_variance(input_values[1]	)
					self._check_zero_mean_unit_variance(input_values[2]	)







		def 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  feat_extract(
					    A		,      truncation=A		,      max_length=10_00		,      padding="""longest"""		,      return_tensors="""np"""	)
					lowerCamelCase    		=  processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :8_00]	)
					self._check_zero_mean_unit_variance(input_values[1, :10_00]	)
					self._check_zero_mean_unit_variance(input_values[2]	)

					# make sure that if max_length < longest -> then pad to max_length
					self.assertTrue(input_values.shape == (3, 10_00)	)

					lowerCamelCase    		=  [floats_list((1, x)	)[0] for x in range(8_00		,      14_00		,      2_00	)]
					lowerCamelCase    		=  feat_extract(
					    A		,      truncation=A		,      max_length=20_00		,      padding="""longest"""		,      return_tensors="""np"""	)
					lowerCamelCase    		=  processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :8_00]	)
					self._check_zero_mean_unit_variance(input_values[1, :10_00]	)
					self._check_zero_mean_unit_variance(input_values[2]	)

					# make sure that if max_length > longest -> then pad to longest
					self.assertTrue(input_values.shape == (3, 12_00)	)







		@require_torch
		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					import torch

					lowerCamelCase    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
					lowerCamelCase    		=  np.random.rand(1_00	).astype(np.floataa	)
					lowerCamelCase    		=  np_speech_inputs.tolist()

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







		@slow
		@require_torch
		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
								lowerCamelCase    		=  WavaVecaConfig.from_pretrained(A	)
								lowerCamelCase    		=  WavaVecaFeatureExtractor.from_pretrained(A	)

								# only "layer" feature extraction norm should make use of
								# attention_mask
								self.assertEqual(feat_extract.return_attention_mask		,      config.feat_extract_norm == """layer"""	)





import os
from datetime import datetime as dt

from github import Github


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






def 	__lowerCamelCase						(							):





			'''simple docstring'''




			lowerCamelCase    		=  Github(os.environ["""GITHUB_TOKEN"""]      )
			lowerCamelCase    		=  g.get_repo("""huggingface/diffusers"""      )
			lowerCamelCase    		=  repo.get_issues(state="""open"""      )

			for issue in open_issues:
						lowerCamelCase    		=  sorted(issue.get_comments()  ,							key=lambda lowerCamelCase__      : i.created_at  ,							reverse=lowerCamelCase__      )
						lowerCamelCase    		=  comments[0] if len(lowerCamelCase__      ) > 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()

