

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[Any]   =     random.Random()






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_=1.0     ,				lowerCamelCase_=None     ,				lowerCamelCase_=None		):
							if rng is None:
														A      :					Dict      		=					global_rng

							A      :					Any      		=					[]
							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    ,						__UpperCAmelCase    ,						__UpperCAmelCase=7    ,						__UpperCAmelCase=4_00    ,						__UpperCAmelCase=20_00    ,						__UpperCAmelCase=1    ,						__UpperCAmelCase=0.0    ,						__UpperCAmelCase=1_60_00    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=True    ,						)		->       int:
														A      :					List[str]      		=					parent
														A      :					int      		=					batch_size
														A      :					Optional[Any]      		=					min_seq_length
														A      :					str      		=					max_seq_length
														A      :					Union[str, Any]      		=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
														A      :					Any      		=					feature_size
														A      :					Any      		=					padding_value
														A      :					Union[str, Any]      		=					sampling_rate
														A      :					Any      		=					return_attention_mask
														A      :					Optional[Any]      		=					do_normalize




							def       snake_case			(					self						)		->       Union[str, 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       snake_case			(					self    ,						__UpperCAmelCase=False    ,						__UpperCAmelCase=False						)		->       List[str]:





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

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

														return speech_inputs







class 					__lowercase  (						lowerCamelCase__		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		List[Any]								=	WavaVecaFeatureExtractor




							def       snake_case			(					self						)		->       Tuple:
														A      :					Dict      		=					WavaVecaFeatureExtractionTester(self						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       str:
														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       snake_case			(					self						)		->       List[str]:
														A      :					Tuple      		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
														# create three inputs of length 800, 1000, and 1200
														A      :					Tuple      		=					[floats_list((1, x)						)[0] for x in range(8_00    ,						14_00    ,						2_00						)]
														A      :					int      		=					[np.asarray(a__						) for speech_input in speech_inputs]

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

														# Test batched
														A      :					Dict      		=					feat_extract(a__    ,						return_tensors='''np'''						).input_values
														A      :					Optional[Any]      		=					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      :					Optional[int]      		=					[floats_list((1, x)						)[0] for x in (8_00, 8_00, 8_00)]
														A      :					Any      		=					np.asarray(a__						)
														A      :					Tuple      		=					feat_extract(a__    ,						return_tensors='''np'''						).input_values
														A      :					Any      		=					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       snake_case			(					self						)		->       Tuple:
														A      :					str      		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
														A      :					List[str]      		=					[floats_list((1, x)						)[0] for x in range(8_00    ,						14_00    ,						2_00						)]

														A      :					Dict      		=					['''longest''', '''max_length''', '''do_not_pad''']
														A      :					Optional[int]      		=					[None, 16_00, None]
														for max_length, padding in zip(a__    ,						a__						):
																					A      :					Optional[int]      		=					feat_extract(a__    ,						padding=a__    ,						max_length=a__    ,						return_tensors='''np'''						)
																					A      :					Optional[Any]      		=					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       snake_case			(					self						)		->       Optional[int]:
														A      :					List[str]      		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
														A      :					str      		=					range(8_00    ,						14_00    ,						2_00						)
														A      :					Union[str, Any]      		=					[floats_list((1, x)						)[0] for x in lengths]

														A      :					List[Any]      		=					['''longest''', '''max_length''', '''do_not_pad''']
														A      :					List[Any]      		=					[None, 16_00, None]

														for max_length, padding in zip(a__    ,						a__						):
																					A      :					List[Any]      		=					feat_extract(a__    ,						max_length=a__    ,						padding=a__						)
																					A      :					Optional[int]      		=					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       snake_case			(					self						)		->       Optional[Any]:
														A      :					List[Any]      		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
														A      :					int      		=					[floats_list((1, x)						)[0] for x in range(8_00    ,						14_00    ,						2_00						)]
														A      :					Any      		=					feat_extract(
														    a__    ,						truncation=a__    ,						max_length=10_00    ,						padding='''max_length'''    ,						return_tensors='''np'''						)
														A      :					Optional[Any]      		=					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       snake_case			(					self						)		->       str:
														A      :					Tuple      		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
														A      :					Optional[Any]      		=					[floats_list((1, x)						)[0] for x in range(8_00    ,						14_00    ,						2_00						)]
														A      :					str      		=					feat_extract(
														    a__    ,						truncation=a__    ,						max_length=10_00    ,						padding='''longest'''    ,						return_tensors='''np'''						)
														A      :					Dict      		=					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)						)

														A      :					Optional[int]      		=					[floats_list((1, x)						)[0] for x in range(8_00    ,						14_00    ,						2_00						)]
														A      :					int      		=					feat_extract(
														    a__    ,						truncation=a__    ,						max_length=20_00    ,						padding='''longest'''    ,						return_tensors='''np'''						)
														A      :					List[Any]      		=					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       snake_case			(					self						)		->       Optional[int]:
														import torch

														A      :					Tuple      		=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
														A      :					Optional[Any]      		=					np.random.rand(1_00						).astype(np.floataa						)
														A      :					List[str]      		=					np_speech_inputs.tolist()

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






							@slow
							@require_torch
							def       snake_case			(					self						)		->       Optional[Any]:





														for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																					A      :					List[Any]      		=					WavaVecaConfig.from_pretrained(a__						)
																					A      :					str      		=					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




def  __UpperCAmelCase(   ):
       with open(os.path.dirname(lowercase_   ) + '''/p022_names.txt'''   ) as file:
              _lowerCamelCase				:  Optional[int]						=		str(file.readlines()[0]   )
              _lowerCamelCase				:  List[Any]						=		names.replace('''"'''			,     ''''''   ).split(''','''   )

       names.sort()

       _lowerCamelCase				:  Optional[int]						=		0
       _lowerCamelCase				:  Tuple						=		0

       for i, name in enumerate(lowercase_   ):
              for letter in name:
                     name_score += ord(lowercase_   ) - 64

              total_score += (i + 1) * name_score
              _lowerCamelCase				:  Optional[int]						=		0
       return total_score


if __name__ == "__main__":
       print(solution())

