




"""simple docstring"""

import doctest
from collections import deque

import numpy as np





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			Dict						):
       _A     =  [2, 1, 2, -1]
       _A     =  [1, 2, 3, 4]



 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  len(self.first_signal						)
       _A     =  len(self.second_signal						)

       _A     =  max(_UpperCAmelCase       ,				_UpperCAmelCase						)

       # create a zero matrix of max_length x max_length
       _A     =  [[0] * max_length for i in range(_UpperCAmelCase						)]

       # fills the smaller signal with zeros to make both signals of same length
       if length_first_signal < length_second_signal:
             self.first_signal += [0] * (max_length - length_first_signal)
       elif length_first_signal > length_second_signal:
             self.second_signal += [0] * (max_length - length_second_signal)
       for i in range(_UpperCAmelCase						):
             _A     =  deque(self.second_signal						)
             rotated_signal.rotate(_UpperCAmelCase						)
             for j, item in enumerate(_UpperCAmelCase						):
                   matrix[i][j] += item

        # multiply the matrix with the first signal
       _A     =  np.matmul(np.transpose(_UpperCAmelCase						)       ,				np.transpose(self.first_signal						)						)

       # rounding-off to two decimal places
       return [round(_UpperCAmelCase       ,				2						) for i in final_signal]


if __name__ == "__main__":
       doctest.testmod()




'''simple docstring'''

import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


SCREAMING_SNAKE_CASE							:				int     	=			get_tests_dir("fixtures")
SCREAMING_SNAKE_CASE							:				str     	=			get_tests_dir("fixtures/dummy_feature_extractor_config.json")
SCREAMING_SNAKE_CASE							:				str     	=			get_tests_dir("fixtures/dummy-config.json")






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				0



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							str:
											with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE_				=				WavaVecaConfig()

																		# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       ).to_dict()

																		config_dict.pop('feature_extractor_type'       )
																		SCREAMING_SNAKE_CASE_				=				WavaVecaFeatureExtractor(**_lowercase       )

																		# save in new folder
																		model_config.save_pretrained(_lowercase       )
																		config.save_pretrained(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )

																		# make sure private variable is not incorrectly saved
																		SCREAMING_SNAKE_CASE_				=				json.loads(config.to_json_string()       )
																		self.assertTrue('_processor_class' not in dict_as_saved       )

											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							Any:
											with self.assertRaisesRegex(
											    _lowercase,     'bert-base is not a local folder and is not a valid model identifier'       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('bert-base'       )



				def   a__					(    self       )     ->							List[Any]:
											with self.assertRaisesRegex(
											    _lowercase,     R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase,     revision='aaaaaa'       )



				def   a__					(    self       )     ->							List[Any]:
											with self.assertRaisesRegex(
											    _lowercase,     'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.',     ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'       )



				def   a__					(    self       )     ->							List[str]:
											# If remote code is not set, we will time out when asking whether to load the model.
											with self.assertRaises(_lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       )
											# If remote code is disabled, we can't load this config.
											with self.assertRaises(_lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
											    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
											self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )

											# Test feature extractor can be reloaded.
											with tempfile.TemporaryDirectory() as tmp_dir:
																		feature_extractor.save_pretrained(_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase,     trust_remote_code=_lowercase       )
											self.assertEqual(reloaded_feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )



				def   a__					(    self       )     ->							int:
											try:
																		AutoConfig.register('custom',     _lowercase       )
																		AutoFeatureExtractor.register(_lowercase,     _lowercase       )
																		# Trying to register something existing in the Transformers library will raise an error
																		with self.assertRaises(_lowercase       ):
																									AutoFeatureExtractor.register(_lowercase,     _lowercase       )

																		# Now that the config is registered, it can be used as any other config with the auto-API
																		SCREAMING_SNAKE_CASE_				=				CustomFeatureExtractor.from_pretrained(_lowercase       )
																		with tempfile.TemporaryDirectory() as tmp_dir:
																									feature_extractor.save_pretrained(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
																									self.assertIsInstance(_lowercase,     _lowercase       )

											finally:
																		if "custom" in CONFIG_MAPPING._extra_content:
																									del CONFIG_MAPPING._extra_content["custom"]
																		if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





				def   a__					(    self       )     ->							str:






											class    snake_case     (      lowercase_		):






															"""simple docstring"""
															_a    	=     True

											try:
																		AutoConfig.register('custom',     _lowercase       )
																		AutoFeatureExtractor.register(_lowercase,     _lowercase       )
																		# If remote code is not set, the default is to use local
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(feature_extractor.is_local       )

																		# If remote code is disabled, we load the local one.
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(feature_extractor.is_local       )

																		# If remote is enabled, we load from the Hub
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(not hasattr(_lowercase,     'is_local'       )       )

											finally:
																		if "custom" in CONFIG_MAPPING._extra_content:
																									del CONFIG_MAPPING._extra_content["custom"]
																		if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]





