"""simple docstring"""


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__  		=						random.Random()






def     __UpperCAmelCase					(			lowercase   ,lowercase=1.0   ,lowercase=None   ,lowercase=None		):







		"""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        a				(		unittest.TestCase   ):


				def __init__(					self	:  int   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  int=7   ,				__lowerCAmelCase	:  List[Any]=400   ,				__lowerCAmelCase	:  List[Any]=2000   ,				__lowerCAmelCase	:  Union[str, Any]=1   ,				__lowerCAmelCase	:  Any=0.0   ,				__lowerCAmelCase	:  Any=1_6000   ,				__lowerCAmelCase	:  Optional[Any]=True   ,				__lowerCAmelCase	:  Tuple=True   ,				):
						_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						 =      feature_size
						_UpperCAmelCase						 =      padding_value
						_UpperCAmelCase						 =      sampling_rate
						_UpperCAmelCase						 =      return_attention_mask
						_UpperCAmelCase						 =      do_normalize


				def      lowerCAmelCase_  (					self	:  str					):
						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      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  List[Any]=False   ,				__lowerCAmelCase	:  str=False					):
						def _flatten(__lowerCAmelCase	:  str					):
								return list(itertools.chain(*lowercase_					)					)

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

						return speech_inputs



class        a				(		_UpperCAmelCase	,							unittest.TestCase   ):
				_snake_case     :  int     							=							WavaVecaFeatureExtractor


				def      lowerCAmelCase_  (					self	:  Any					):
						_UpperCAmelCase						 =      WavaVecaFeatureExtractionTester(self					)


				def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[str]					):
						self.assertTrue(np.all(np.mean(lowercase_   ,				axis=0					) < 1e-3					)					)
						self.assertTrue(np.all(np.abs(np.var(lowercase_   ,				axis=0					) - 1					) < 1e-3					)					)


				def      lowerCAmelCase_  (					self	:  Any					):
						# Tests that all call wrap to encode_plus and batch_encode_plus
						_UpperCAmelCase						 =      self.feature_extraction_class(**self.feat_extract_tester.prepare_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(lowercase_					) for speech_input in speech_inputs]

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

						# Test batched
						_UpperCAmelCase						 =      feat_extract(lowercase_   ,				return_tensors="""np"""					).input_values
						_UpperCAmelCase						 =      feat_extract(lowercase_   ,				return_tensors="""np"""					).input_values
						for enc_seq_a, enc_seq_a in zip(lowercase_   ,				lowercase_					):
								self.assertTrue(np.allclose(lowercase_   ,				lowercase_   ,				atol=1e-3					)					)

						# Test 2-D numpy arrays are batched.
						_UpperCAmelCase						 =      [floats_list((1, x)					)[0] for x in (800, 800, 800)]
						_UpperCAmelCase						 =      np.asarray(lowercase_					)
						_UpperCAmelCase						 =      feat_extract(lowercase_   ,				return_tensors="""np"""					).input_values
						_UpperCAmelCase						 =      feat_extract(lowercase_   ,				return_tensors="""np"""					).input_values
						for enc_seq_a, enc_seq_a in zip(lowercase_   ,				lowercase_					):
								self.assertTrue(np.allclose(lowercase_   ,				lowercase_   ,				atol=1e-3					)					)


				def      lowerCAmelCase_  (					self	:  Tuple					):
						_UpperCAmelCase						 =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
						_UpperCAmelCase						 =      [floats_list((1, x)					)[0] for x in range(800   ,				1400   ,				200					)]

						_UpperCAmelCase						 =      ["""longest""", """max_length""", """do_not_pad"""]
						_UpperCAmelCase						 =      [None, 1600, None]
						for max_length, padding in zip(lowercase_   ,				lowercase_					):
								_UpperCAmelCase						 =      feat_extract(lowercase_   ,				padding=lowercase_   ,				max_length=lowercase_   ,				return_tensors="""np"""					)
								_UpperCAmelCase						 =      processed.input_values

								self._check_zero_mean_unit_variance(input_values[0][:800]					)
								self.assertTrue(input_values[0][800:].sum() < 1e-6					)
								self._check_zero_mean_unit_variance(input_values[1][:1000]					)
								self.assertTrue(input_values[0][1000:].sum() < 1e-6					)
								self._check_zero_mean_unit_variance(input_values[2][:1200]					)


				def      lowerCAmelCase_  (					self	:  int					):
						_UpperCAmelCase						 =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
						_UpperCAmelCase						 =      range(800   ,				1400   ,				200					)
						_UpperCAmelCase						 =      [floats_list((1, x)					)[0] for x in lengths]

						_UpperCAmelCase						 =      ["""longest""", """max_length""", """do_not_pad"""]
						_UpperCAmelCase						 =      [None, 1600, None]

						for max_length, padding in zip(lowercase_   ,				lowercase_					):
								_UpperCAmelCase						 =      feat_extract(lowercase_   ,				max_length=lowercase_   ,				padding=lowercase_					)
								_UpperCAmelCase						 =      processed.input_values

								self._check_zero_mean_unit_variance(input_values[0][:800]					)
								self._check_zero_mean_unit_variance(input_values[1][:1000]					)
								self._check_zero_mean_unit_variance(input_values[2][:1200]					)


				def      lowerCAmelCase_  (					self	:  int					):
						_UpperCAmelCase						 =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
						_UpperCAmelCase						 =      [floats_list((1, x)					)[0] for x in range(800   ,				1400   ,				200					)]
						_UpperCAmelCase						 =      feat_extract(
						    lowercase_   ,				truncation=lowercase_   ,				max_length=1000   ,				padding="""max_length"""   ,				return_tensors="""np"""					)
						_UpperCAmelCase						 =      processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]					)
						self._check_zero_mean_unit_variance(input_values[1]					)
						self._check_zero_mean_unit_variance(input_values[2]					)


				def      lowerCAmelCase_  (					self	:  List[Any]					):
						_UpperCAmelCase						 =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
						_UpperCAmelCase						 =      [floats_list((1, x)					)[0] for x in range(800   ,				1400   ,				200					)]
						_UpperCAmelCase						 =      feat_extract(
						    lowercase_   ,				truncation=lowercase_   ,				max_length=1000   ,				padding="""longest"""   ,				return_tensors="""np"""					)
						_UpperCAmelCase						 =      processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]					)
						self._check_zero_mean_unit_variance(input_values[1, :1000]					)
						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, 1000)					)

						_UpperCAmelCase						 =      [floats_list((1, x)					)[0] for x in range(800   ,				1400   ,				200					)]
						_UpperCAmelCase						 =      feat_extract(
						    lowercase_   ,				truncation=lowercase_   ,				max_length=2000   ,				padding="""longest"""   ,				return_tensors="""np"""					)
						_UpperCAmelCase						 =      processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]					)
						self._check_zero_mean_unit_variance(input_values[1, :1000]					)
						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, 1200)					)


				@require_torch
				def      lowerCAmelCase_  (					self	:  List[Any]					):
						import torch

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

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


				@slow
				@require_torch
				def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
						# this test makes sure that models that are using
						# group norm don't have their feature extractor return the
						# attention_mask
						for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
								_UpperCAmelCase						 =      WavaVecaConfig.from_pretrained(lowercase_					)
								_UpperCAmelCase						 =      WavaVecaFeatureExtractor.from_pretrained(lowercase_					)

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

"""simple docstring"""


import random






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase = False		):







  """simple docstring"""
  _UpperCAmelCase						 =      {i: [] for i in range(lowercase		)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowercase		)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowercase		):
    for j in range(i + 1   ,lowercase		):
      if random.random() < probability:
        graph[i].append(lowercase		)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowercase		)
  return graph






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return {
      i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
  }


if __name__ == "__main__":
    import doctest

    doctest.testmod()
