




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


_snake_case										=       random.Random()







def 				lowerCAmelCase_	(     snake_case_,snake_case_=1.0,snake_case_=None,snake_case_=None     ):

      if rng is None:
            _A							:     Tuple			=						global_rng

      _A							:     str			=						[]
      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		):

     def __init__(  self		,     _a		,     _a=7		,     _a=400		,     _a=2000		,     _a=1		,     _a=0.0		,     _a=1_6000		,     _a=True		,     _a=True		,     )						->							Any:
           _A							:     Union[str, Any]			=						parent
           _A							:     Dict			=						batch_size
           _A							:     int			=						min_seq_length
           _A							:     int			=						max_seq_length
           _A							:     Optional[Any]			=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _A							:     Any			=						feature_size
           _A							:     List[str]			=						padding_value
           _A							:     int			=						sampling_rate
           _A							:     Dict			=						return_attention_mask
           _A							:     Any			=						do_normalize

     def 					a__	(  self				)						->							List[Any]:
           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				)						->							str:
           def _flatten(_a				):
                 return list(itertools.chain(*lowercase_				)				)

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

           return speech_inputs







class 						lowercase							(   _lowerCamelCase,unittest.TestCase		):
     _a							 =						WavaVecaFeatureExtractor

     def 					a__	(  self				)						->							Tuple:
           _A							:     Dict			=						WavaVecaFeatureExtractionTester(self				)

     def 					a__	(  self		,     _a				)						->							Optional[Any]:
           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 					a__	(  self				)						->							List[str]:
           # Tests that all call wrap to encode_plus and batch_encode_plus
           _A							:     Union[str, Any]			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
           # create three inputs of length 800, 1000, and 1200
           _A							:     int			=						[floats_list((1, x)				)[0] for x in range(800		,     1400		,     200				)]
           _A							:     Any			=						[np.asarray(lowercase_				) for speech_input in speech_inputs]

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

           # Test batched
           _A							:     List[str]			=						feat_extract(lowercase_		,     return_tensors="""np"""				).input_values
           _A							:     List[str]			=						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.
           _A							:     Dict			=						[floats_list((1, x)				)[0] for x in (800, 800, 800)]
           _A							:     Optional[Any]			=						np.asarray(lowercase_				)
           _A							:     Optional[int]			=						feat_extract(lowercase_		,     return_tensors="""np"""				).input_values
           _A							:     List[Any]			=						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 					a__	(  self				)						->							Union[str, Any]:
           _A							:     int			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
           _A							:     List[str]			=						[floats_list((1, x)				)[0] for x in range(800		,     1400		,     200				)]

           _A							:     Optional[Any]			=						["""longest""", """max_length""", """do_not_pad"""]
           _A							:     str			=						[None, 1600, None]
           for max_length, padding in zip(lowercase_		,     lowercase_				):
                 _A							:     int			=						feat_extract(lowercase_		,     padding=lowercase_		,     max_length=lowercase_		,     return_tensors="""np"""				)
                 _A							:     List[str]			=						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 					a__	(  self				)						->							Any:
           _A							:     int			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
           _A							:     List[Any]			=						range(800		,     1400		,     200				)
           _A							:     str			=						[floats_list((1, x)				)[0] for x in lengths]

           _A							:     Optional[Any]			=						["""longest""", """max_length""", """do_not_pad"""]
           _A							:     List[str]			=						[None, 1600, None]

           for max_length, padding in zip(lowercase_		,     lowercase_				):
                 _A							:     Dict			=						feat_extract(lowercase_		,     max_length=lowercase_		,     padding=lowercase_				)
                 _A							:     Optional[int]			=						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 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[Any]			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
           _A							:     List[str]			=						[floats_list((1, x)				)[0] for x in range(800		,     1400		,     200				)]
           _A							:     Any			=						feat_extract(
               lowercase_		,     truncation=lowercase_		,     max_length=1000		,     padding="""max_length"""		,     return_tensors="""np"""				)
           _A							:     Tuple			=						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 					a__	(  self				)						->							Union[str, Any]:
           _A							:     Dict			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
           _A							:     Any			=						[floats_list((1, x)				)[0] for x in range(800		,     1400		,     200				)]
           _A							:     Tuple			=						feat_extract(
               lowercase_		,     truncation=lowercase_		,     max_length=1000		,     padding="""longest"""		,     return_tensors="""np"""				)
           _A							:     Optional[Any]			=						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)				)

           _A							:     int			=						[floats_list((1, x)				)[0] for x in range(800		,     1400		,     200				)]
           _A							:     Optional[int]			=						feat_extract(
               lowercase_		,     truncation=lowercase_		,     max_length=2000		,     padding="""longest"""		,     return_tensors="""np"""				)
           _A							:     Tuple			=						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 					a__	(  self				)						->							List[Any]:
           import torch

           _A							:     Optional[int]			=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
           _A							:     List[str]			=						np.random.rand(100				).astype(np.floataa				)
           _A							:     List[str]			=						np_speech_inputs.tolist()

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



     @slow
     @require_torch
     def 					a__	(  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:
                 _A							:     Union[str, Any]			=						WavaVecaConfig.from_pretrained(lowercase_				)
                 _A							:     Any			=						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'''

from collections import defaultdict


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				1
					snake_case_									=				True
					for v in tree[start]:
										if v not in visited:
															ret += dfs(__UpperCAmelCase	)
					if ret % 2 == 0:
										cuts.append(__UpperCAmelCase	)
					return ret


def 		__magic_name__    (	)		->					Union[str, Any]:

					'''simple docstring'''

					dfs(1	)


if __name__ == "__main__":
							a		,a  :				Dict									=  10, 9
							a  :				Dict									=  defaultdict(list)
							a  :				dict[int, bool]									=  {}
							a  :				list[int]									=  []
							a  :				Tuple									=  0
							a  :				str									=  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)

