


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


_lowercase       :      Optional[int]	=	random.Random()





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Optional[int]=1.0							,					UpperCamelCase__:     Union[str, Any]=None							,					UpperCamelCase__:     str=None						) ->   str:





	"""simple docstring"""







	if rng is None:
		A								=				global_rng

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

	return values






class       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__=7				,   a__=400				,   a__=2000				,   a__=1				,   a__=0.0				,   a__=1_6000				,   a__=True				,   a__=True				,   )	->				Optional[Any]:
					A								=				parent
					A								=				batch_size
					A								=				min_seq_length
					A								=				max_seq_length
					A								=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					A								=				feature_size
					A								=				padding_value
					A								=				sampling_rate
					A								=				return_attention_mask
					A								=				do_normalize

				def      _UpperCAmelCase					(						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      _UpperCAmelCase					(						self				,   a__=False				,   a__=False   )	->				Dict:







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

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

					return speech_inputs






class       _UpperCamelCase    (					__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= WavaVecaFeatureExtractor

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					A								=				WavaVecaFeatureExtractionTester(self   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				int:
					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      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					# create three inputs of length 800, 1000, and 1200
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				[np.asarray(a__   ) for speech_input in speech_inputs]

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

					# Test batched
					A								=				feat_extract(a__				,   return_tensors="""np"""   ).input_values
					A								=				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.
					A								=				[floats_list((1, x)   )[0] for x in (800, 800, 800)]
					A								=				np.asarray(a__   )
					A								=				feat_extract(a__				,   return_tensors="""np"""   ).input_values
					A								=				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      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]

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

					A								=				['longest', 'max_length', 'do_not_pad']
					A								=				[None, 1600, None]

					for max_length, padding in zip(a__				,   a__   ):
						A								=				feat_extract(a__				,   max_length=a__				,   padding=a__   )
						A								=				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      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feat_extract(
					    a__				,   truncation=a__				,   max_length=1000				,   padding="""max_length"""				,   return_tensors="""np"""   )
					A								=				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      _UpperCAmelCase					(						self   )	->				str:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feat_extract(
					    a__				,   truncation=a__				,   max_length=1000				,   padding="""longest"""				,   return_tensors="""np"""   )
					A								=				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								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feat_extract(
					    a__				,   truncation=a__				,   max_length=2000				,   padding="""longest"""				,   return_tensors="""np"""   )
					A								=				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      _UpperCAmelCase					(						self   )	->				Dict:
					import torch

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

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

				@slow
				@require_torch
				def      _UpperCAmelCase					(						self   )	->				Dict:







					for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
						A								=				WavaVecaConfig.from_pretrained(a__   )
						A								=				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"""   )



def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	return str(UpperCamelCase__						) == str(UpperCamelCase__						)[::-1]





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	return int(UpperCamelCase__						) + int(str(UpperCamelCase__						)[::-1]						)





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 1_00_00						) ->   int:





	"""simple docstring"""







	A								=				[]
	for num in range(1							,					UpperCamelCase__						):
		A								=				0
		A								=				num
		while iterations < 50:
			A								=				sum_reverse(UpperCamelCase__						)
			iterations += 1
			if is_palindrome(UpperCamelCase__						):
				break
		else:
			lychrel_nums.append(UpperCamelCase__						)
	return len(UpperCamelCase__						)


if __name__ == "__main__":
				print(f'''{solution() = }''')



