

'''simple docstring'''

import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin





class 				_snake_case			(							_UpperCamelCase    ):
   # to overwrite at feature extractactor specific tests
   _A							:  int      =       None
   _A							:  Tuple      =       None
   @property
   def      __UpperCamelCase    (			self      :   Optional[int]				):
     return self.feat_extract_tester.prepare_feat_extract_dict()
   def      __UpperCamelCase    (			self      :   Optional[Any]				):
     SCREAMING_SNAKE_CASE:List[str]       =       self.feature_extraction_class(**self.feat_extract_dict				)
     self.assertTrue(hasattr(a_  ,"feature_size"				)				)
     self.assertTrue(hasattr(a_  ,"sampling_rate"				)				)
     self.assertTrue(hasattr(a_  ,"padding_value"				)				)
   def      __UpperCamelCase    (			self      :   List[str]				):
     SCREAMING_SNAKE_CASE:Any       =       self.feat_extract_tester.prepare_inputs_for_common()
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       self.feature_extraction_class(**self.feat_extract_dict				)
     SCREAMING_SNAKE_CASE:List[str]       =       feat_extract.model_input_names[0]

     SCREAMING_SNAKE_CASE:Tuple       =       BatchFeature({input_name: speech_inputs}				)

     self.assertTrue(all(len(a_				) == len(a_				) for x, y in zip(a_  ,processed_features[input_name]				)				)				)

     SCREAMING_SNAKE_CASE:Any       =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_				)
     SCREAMING_SNAKE_CASE:List[Any]       =       BatchFeature({input_name: speech_inputs}  ,tensor_type="np"				)

     SCREAMING_SNAKE_CASE:Union[str, Any]       =       processed_features[input_name]

     if len(batch_features_input.shape				) < 3:
       SCREAMING_SNAKE_CASE:List[Any]       =       batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)
   @require_torch
   def      __UpperCamelCase    (			self      :   Optional[Any]				):
     SCREAMING_SNAKE_CASE:Optional[Any]       =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_				)
     SCREAMING_SNAKE_CASE:List[Any]       =       self.feature_extraction_class(**self.feat_extract_dict				)
     SCREAMING_SNAKE_CASE:Optional[Any]       =       feat_extract.model_input_names[0]

     SCREAMING_SNAKE_CASE:Any       =       BatchFeature({input_name: speech_inputs}  ,tensor_type="pt"				)

     SCREAMING_SNAKE_CASE:Optional[int]       =       processed_features[input_name]

     if len(batch_features_input.shape				) < 3:
       SCREAMING_SNAKE_CASE:Any       =       batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)
   @require_tf
   def      __UpperCamelCase    (			self      :   Optional[Any]				):
     SCREAMING_SNAKE_CASE:Optional[int]       =       self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_				)
     SCREAMING_SNAKE_CASE:Optional[int]       =       self.feature_extraction_class(**self.feat_extract_dict				)
     SCREAMING_SNAKE_CASE:str       =       feat_extract.model_input_names[0]

     SCREAMING_SNAKE_CASE:List[str]       =       BatchFeature({input_name: speech_inputs}  ,tensor_type="tf"				)

     SCREAMING_SNAKE_CASE:Tuple       =       processed_features[input_name]

     if len(batch_features_input.shape				) < 3:
       SCREAMING_SNAKE_CASE:List[str]       =       batch_features_input[:, :, None]

     self.assertTrue(
         batch_features_input.shape
         == (self.feat_extract_tester.batch_size, len(speech_inputs[0]				), self.feat_extract_tester.feature_size)				)
   def      __UpperCamelCase    (			self      :   Dict  ,SCREAMING_SNAKE_CASE__      :   Optional[Any]=False				):



     def _inputs_have_equal_length(SCREAMING_SNAKE_CASE__      :   int				):
       SCREAMING_SNAKE_CASE:Any       =       len(input[0]				)
       for input_slice in input[1:]:
         if len(a_				) != length:
           return False
       return True

     def _inputs_are_equal(SCREAMING_SNAKE_CASE__      :   List[Any]  ,SCREAMING_SNAKE_CASE__      :   str				):
       if len(a_				) != len(a_				):
         return False

       for input_slice_a, input_slice_a in zip(a_  ,a_				):
         if not np.allclose(np.asarray(a_				)  ,np.asarray(a_				)  ,atol=1e-3				):
           return False
       return True

     SCREAMING_SNAKE_CASE:int       =       self.feature_extraction_class(**self.feat_extract_dict				)
     SCREAMING_SNAKE_CASE:int       =       self.feat_extract_tester.prepare_inputs_for_common(numpify=a_				)
     SCREAMING_SNAKE_CASE:Tuple       =       feat_extract.model_input_names[0]

     SCREAMING_SNAKE_CASE:List[str]       =       BatchFeature({input_name: speech_inputs}				)

     SCREAMING_SNAKE_CASE:Optional[int]       =       self.feat_extract_tester.seq_length_diff
     SCREAMING_SNAKE_CASE:Optional[Any]       =       self.feat_extract_tester.max_seq_length + pad_diff
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       self.feat_extract_tester.min_seq_length
     SCREAMING_SNAKE_CASE:Tuple       =       self.feat_extract_tester.batch_size
     SCREAMING_SNAKE_CASE:str       =       self.feat_extract_tester.feature_size

     # test padding for List[int] + numpy
     SCREAMING_SNAKE_CASE:Any       =       feat_extract.pad(a_  ,padding=a_				)
     SCREAMING_SNAKE_CASE:List[str]       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:int       =       feat_extract.pad(a_  ,padding="longest"				)
     SCREAMING_SNAKE_CASE:Optional[Any]       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:Optional[Any]       =       feat_extract.pad(a_  ,padding="max_length"  ,max_length=len(speech_inputs[-1]				)				)
     SCREAMING_SNAKE_CASE:Any       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:Tuple       =       feat_extract.pad(a_  ,padding="longest"  ,return_tensors="np"				)
     SCREAMING_SNAKE_CASE:Optional[Any]       =       input_a[input_name]

     # max_length parameter has to be provided when setting `padding="max_length"`
     with self.assertRaises(a_				):
       feat_extract.pad(a_  ,padding="max_length"				)[input_name]

     SCREAMING_SNAKE_CASE:Tuple       =       feat_extract.pad(
         a_  ,padding="max_length"  ,max_length=a_  ,return_tensors="np"				)
     SCREAMING_SNAKE_CASE:Optional[Any]       =       input_a[input_name]

     self.assertFalse(_inputs_have_equal_length(a_				)				)
     self.assertTrue(_inputs_have_equal_length(a_				)				)
     self.assertTrue(_inputs_have_equal_length(a_				)				)
     self.assertTrue(_inputs_are_equal(a_  ,a_				)				)
     self.assertTrue(len(input_a[0]				) == pad_min_length				)
     self.assertTrue(len(input_a[1]				) == pad_min_length + pad_diff				)
     self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]				))				)
     self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)				)

     if feature_size > 1:
       self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size				)

     # test padding for `pad_to_multiple_of` for List[int] + numpy
     SCREAMING_SNAKE_CASE:Optional[int]       =       feat_extract.pad(a_  ,pad_to_multiple_of=10				)
     SCREAMING_SNAKE_CASE:Optional[Any]       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:List[str]       =       feat_extract.pad(a_  ,padding="longest"  ,pad_to_multiple_of=10				)
     SCREAMING_SNAKE_CASE:Dict       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:List[Any]       =       feat_extract.pad(
         a_  ,padding="max_length"  ,pad_to_multiple_of=10  ,max_length=a_				)
     SCREAMING_SNAKE_CASE:Tuple       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:List[Any]       =       feat_extract.pad(
         a_  ,padding="max_length"  ,pad_to_multiple_of=10  ,max_length=a_  ,return_tensors="np"  ,)
     SCREAMING_SNAKE_CASE:Optional[int]       =       input_a[input_name]

     self.assertTrue(all(len(a_				) % 10 == 0 for x in input_a				)				)
     self.assertTrue(_inputs_are_equal(a_  ,a_				)				)

     SCREAMING_SNAKE_CASE:List[str]       =       pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
     self.assertTrue(all(len(a_				) == expected_mult_pad_length for x in input_a				)				)
     self.assertEqual(input_a.shape[:2]  ,(batch_size, expected_mult_pad_length)				)

     if feature_size > 1:
       self.assertTrue(input_a.shape[2] == feature_size				)

     # Check padding value is correct
     SCREAMING_SNAKE_CASE:Optional[int]       =       (np.ones(self.feat_extract_tester.feature_size				) * feat_extract.padding_value).sum()
     self.assertTrue(
         abs(np.asarray(input_a[0]				)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				)
         < 1e-3				)
     self.assertTrue(
         abs(
             np.asarray(input_a[1]				)[pad_min_length + pad_diff :].sum()
             - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)				)
         < 1e-3				)
     self.assertTrue(
         abs(
             np.asarray(input_a[2]				)[pad_min_length + 2 * pad_diff :].sum()
             - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)				)
         < 1e-3				)
     self.assertTrue(
         abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)				) < 1e-3				)
     self.assertTrue(
         abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)				)
         < 1e-3				)
   def      __UpperCamelCase    (			self      :   Dict  ,SCREAMING_SNAKE_CASE__      :   Optional[int]=False				):



     def _inputs_have_equal_length(SCREAMING_SNAKE_CASE__      :   List[Any]				):
       SCREAMING_SNAKE_CASE:List[str]       =       len(input[0]				)
       for input_slice in input[1:]:
         if len(a_				) != length:
           return False
       return True

     def _inputs_are_equal(SCREAMING_SNAKE_CASE__      :   Optional[int]  ,SCREAMING_SNAKE_CASE__      :   List[Any]				):
       if len(a_				) != len(a_				):
         return False

       for input_slice_a, input_slice_a in zip(a_  ,a_				):
         if not np.allclose(np.asarray(a_				)  ,np.asarray(a_				)  ,atol=1e-3				):
           return False
       return True

     SCREAMING_SNAKE_CASE:Dict       =       self.feature_extraction_class(**self.feat_extract_dict				)
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       self.feat_extract_tester.prepare_inputs_for_common(numpify=a_				)
     SCREAMING_SNAKE_CASE:List[str]       =       feat_extract.model_input_names[0]

     SCREAMING_SNAKE_CASE:str       =       BatchFeature({input_name: speech_inputs}				)

     # truncate to smallest
     SCREAMING_SNAKE_CASE:int       =       feat_extract.pad(
         a_  ,padding="max_length"  ,max_length=len(speech_inputs[0]				)  ,truncation=a_				)
     SCREAMING_SNAKE_CASE:Optional[Any]       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:int       =       feat_extract.pad(a_  ,padding="max_length"  ,max_length=len(speech_inputs[0]				)				)
     SCREAMING_SNAKE_CASE:List[Any]       =       input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(a_				)				)
     self.assertFalse(_inputs_have_equal_length(a_				)				)

     # truncate to smallest with np
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       feat_extract.pad(
         a_  ,padding="max_length"  ,max_length=len(speech_inputs[0]				)  ,return_tensors="np"  ,truncation=a_  ,)
     SCREAMING_SNAKE_CASE:Tuple       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:str       =       feat_extract.pad(
         a_  ,padding="max_length"  ,max_length=len(speech_inputs[0]				)  ,return_tensors="np"				)
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       input_a[input_name]

     self.assertTrue(_inputs_have_equal_length(a_				)				)
     self.assertTrue(input_a.shape[1] == len(speech_inputs[0]				)				)

     # since truncation forces padding to be smaller than longest input
     # function can't return `np.ndarray`, but has to return list
     self.assertFalse(_inputs_have_equal_length(a_				)				)

     # truncate to middle
     SCREAMING_SNAKE_CASE:str       =       feat_extract.pad(
         a_  ,padding="max_length"  ,max_length=len(speech_inputs[1]				)  ,truncation=a_  ,return_tensors="np"  ,)
     SCREAMING_SNAKE_CASE:List[Any]       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:List[str]       =       feat_extract.pad(
         a_  ,padding="max_length"  ,max_length=len(speech_inputs[1]				)  ,truncation=a_				)
     SCREAMING_SNAKE_CASE:str       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:Any       =       feat_extract.pad(
         a_  ,padding="max_length"  ,max_length=len(speech_inputs[1]				)  ,return_tensors="np"				)
     SCREAMING_SNAKE_CASE:Optional[int]       =       input_a[input_name]

     self.assertTrue(input_a.shape[1] == len(speech_inputs[1]				)				)
     self.assertTrue(_inputs_have_equal_length(a_				)				)
     self.assertTrue(_inputs_have_equal_length(a_				)				)
     self.assertTrue(_inputs_are_equal(a_  ,a_				)				)

     # since truncation forces padding to be smaller than longest input
     # function can't return `np.ndarray`, but has to return list
     self.assertFalse(_inputs_have_equal_length(a_				)				)
     self.assertTrue(len(input_a[-1]				) == len(speech_inputs[-1]				)				)

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(a_				):
       feat_extract.pad(a_  ,truncation=a_				)[input_name]

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(a_				):
       feat_extract.pad(a_  ,padding="longest"  ,truncation=a_				)[input_name]

     # padding has to be max_length when setting `truncation=True`
     with self.assertRaises(a_				):
       feat_extract.pad(a_  ,padding="longest"  ,truncation=a_				)[input_name]

     # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
     with self.assertRaises(a_				):
       feat_extract.pad(a_  ,padding="max_length"  ,truncation=a_				)[input_name]

     # test truncation for `pad_to_multiple_of` for List[int] + numpy
     SCREAMING_SNAKE_CASE:Tuple       =       12
     SCREAMING_SNAKE_CASE:Dict       =       feat_extract.pad(
         a_  ,padding="max_length"  ,max_length=len(speech_inputs[0]				)  ,pad_to_multiple_of=a_  ,truncation=a_  ,)
     SCREAMING_SNAKE_CASE:Optional[int]       =       input_a[input_name]

     SCREAMING_SNAKE_CASE:List[Any]       =       feat_extract.pad(
         a_  ,padding="max_length"  ,max_length=len(speech_inputs[0]				)  ,pad_to_multiple_of=a_  ,)
     SCREAMING_SNAKE_CASE:str       =       input_a[input_name]

     # retrieve expected_length as multiple of pad_to_multiple_of
     SCREAMING_SNAKE_CASE:Dict       =       len(speech_inputs[0]				)
     if expected_length % pad_to_multiple_of != 0:
       SCREAMING_SNAKE_CASE:Tuple       =       ((len(speech_inputs[0]				) // pad_to_multiple_of) + 1) * pad_to_multiple_of

     self.assertTrue(len(input_a[0]				) == expected_length				)
     self.assertTrue(_inputs_have_equal_length(a_				)				)
     self.assertFalse(_inputs_have_equal_length(a_				)				)
   def      __UpperCamelCase    (			self      :   List[str]				):
     self._check_padding(numpify=a_				)
   def      __UpperCamelCase    (			self      :   Tuple				):
     self._check_padding(numpify=a_				)
   def      __UpperCamelCase    (			self      :   int				):
     self._check_truncation(numpify=a_				)
   def      __UpperCamelCase    (			self      :   str				):
     self._check_truncation(numpify=a_				)
   @require_torch
   def      __UpperCamelCase    (			self      :   List[Any]				):
     SCREAMING_SNAKE_CASE:Tuple       =       self.feature_extraction_class(**self.feat_extract_dict				)
     SCREAMING_SNAKE_CASE:Tuple       =       self.feat_extract_tester.prepare_inputs_for_common()
     SCREAMING_SNAKE_CASE:int       =       feat_extract.model_input_names[0]

     SCREAMING_SNAKE_CASE:str       =       BatchFeature({input_name: speech_inputs}				)

     SCREAMING_SNAKE_CASE:int       =       feat_extract.pad(a_  ,padding="longest"  ,return_tensors="np"				)[input_name]
     SCREAMING_SNAKE_CASE:str       =       feat_extract.pad(a_  ,padding="longest"  ,return_tensors="pt"				)[input_name]

     self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_pt.numpy().astype(np.floataa				).sum()				) < 1e-2				)
   @require_tf
   def      __UpperCamelCase    (			self      :   Optional[Any]				):
     SCREAMING_SNAKE_CASE:int       =       self.feature_extraction_class(**self.feat_extract_dict				)
     SCREAMING_SNAKE_CASE:Optional[int]       =       self.feat_extract_tester.prepare_inputs_for_common()
     SCREAMING_SNAKE_CASE:Optional[Any]       =       feat_extract.model_input_names[0]

     SCREAMING_SNAKE_CASE:Dict       =       BatchFeature({input_name: speech_inputs}				)

     SCREAMING_SNAKE_CASE:List[Any]       =       feat_extract.pad(a_  ,padding="longest"  ,return_tensors="np"				)[input_name]
     SCREAMING_SNAKE_CASE:List[str]       =       feat_extract.pad(a_  ,padding="longest"  ,return_tensors="tf"				)[input_name]

     self.assertTrue(abs(input_np.astype(np.floataa				).sum() - input_tf.numpy().astype(np.floataa				).sum()				) < 1e-2				)
   def      __UpperCamelCase    (			self      :   Optional[Any]				):
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       self.feat_extract_dict
     SCREAMING_SNAKE_CASE:Dict       =       True
     SCREAMING_SNAKE_CASE:Dict       =       self.feature_extraction_class(**a_				)
     SCREAMING_SNAKE_CASE:List[Any]       =       self.feat_extract_tester.prepare_inputs_for_common()
     SCREAMING_SNAKE_CASE:List[Any]       =       [len(a_				) for x in speech_inputs]
     SCREAMING_SNAKE_CASE:List[str]       =       feat_extract.model_input_names[0]

     SCREAMING_SNAKE_CASE:int       =       BatchFeature({input_name: speech_inputs}				)

     SCREAMING_SNAKE_CASE:Tuple       =       feat_extract.pad(a_  ,padding="longest"  ,return_tensors="np"				)
     self.assertIn("attention_mask"  ,a_				)
     self.assertListEqual(list(processed.attention_mask.shape				)  ,list(processed[input_name].shape[:2]				)				)
     self.assertListEqual(processed.attention_mask.sum(-1				).tolist()  ,a_				)







   def      __UpperCamelCase    (			self      :   List[Any]				):
     SCREAMING_SNAKE_CASE:List[str]       =       self.feat_extract_dict
     SCREAMING_SNAKE_CASE:Any       =       True
     SCREAMING_SNAKE_CASE:Optional[Any]       =       self.feature_extraction_class(**a_				)
     SCREAMING_SNAKE_CASE:List[Any]       =       self.feat_extract_tester.prepare_inputs_for_common()
     SCREAMING_SNAKE_CASE:Optional[int]       =       [len(a_				) for x in speech_inputs]
     SCREAMING_SNAKE_CASE:Tuple       =       feat_extract.model_input_names[0]

     SCREAMING_SNAKE_CASE:int       =       BatchFeature({input_name: speech_inputs}				)
     SCREAMING_SNAKE_CASE:Tuple       =       min(a_				)

     SCREAMING_SNAKE_CASE:int       =       feat_extract.pad(
         a_  ,padding="max_length"  ,max_length=a_  ,truncation=a_  ,return_tensors="np"				)
     self.assertIn("attention_mask"  ,a_				)
     self.assertListEqual(
         list(processed_pad.attention_mask.shape				)  ,[processed_pad[input_name].shape[0], max_length]				)
     self.assertListEqual(
         processed_pad.attention_mask[:, :max_length].sum(-1				).tolist()  ,[max_length for x in speech_inputs]				)













"""simple docstring"""






import os






def      lowercase__ (					)    ->			Optional[Any]:



			'''simple docstring'''
			with open(os.path.dirname(lowerCAmelCase__			) + "/p022_names.txt"			) as file:
						a__  :	Optional[int]		 =			str(file.readlines()[0]			)
						a__  :	Optional[int]		 =			names.replace("\""							,			""			).split(","			)

			names.sort()

			a__  :	int		 =			0
			a__  :	Dict		 =			0

			for i, name in enumerate(lowerCAmelCase__			):
						for letter in name:
									name_score += ord(lowerCAmelCase__			) - 6_4

						total_score += (i + 1) * name_score
						a__  :	Tuple		 =			0
			return total_score


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