



"""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


__snake_case							:   int      =    get_tests_dir('fixtures')
__snake_case							:   int      =    get_tests_dir('fixtures/dummy_feature_extractor_config.json')
__snake_case							:   Optional[int]      =    get_tests_dir('fixtures/dummy-config.json')



class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





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

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       0

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
				self.assertIsInstance(_lowercase       ,			_lowercase)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       AutoFeatureExtractor.from_pretrained(_lowercase)
				self.assertIsInstance(_lowercase       ,			_lowercase)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Dict:

				"""simple docstring"""

				with tempfile.TemporaryDirectory() as tmpdirname:
							__lowerCAmelCase      :  Dict												=       WavaVecaConfig()

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

							config_dict.pop("feature_extractor_type")
							__lowerCAmelCase      :  Tuple												=       WavaVecaFeatureExtractor(**_lowercase)

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

							__lowerCAmelCase      :  Tuple												=       AutoFeatureExtractor.from_pretrained(_lowercase)

							# make sure private variable is not incorrectly saved
							__lowerCAmelCase      :  str												=       json.loads(config.to_json_string())
							self.assertTrue("_processor_class" not in dict_as_saved)

				self.assertIsInstance(_lowercase       ,			_lowercase)

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       AutoFeatureExtractor.from_pretrained(_lowercase)
				self.assertIsInstance(_lowercase       ,			_lowercase)

	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Dict:

				"""simple docstring"""

				with self.assertRaisesRegex(
				    _lowercase       ,			"bert-base is not a local folder and is not a valid model identifier"):
							__lowerCAmelCase      :  Any												=       AutoFeatureExtractor.from_pretrained("bert-base")

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Optional[int]:

				"""simple docstring"""

				with self.assertRaisesRegex(
				    _lowercase       ,			r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"):
							__lowerCAmelCase      :  List[str]												=       AutoFeatureExtractor.from_pretrained(_lowercase       ,			revision="aaaaaa")

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       List[str]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Optional[int]:

				"""simple docstring"""

				with self.assertRaises(_lowercase):
							__lowerCAmelCase      :  Optional[Any]												=       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):
							__lowerCAmelCase      :  Any												=       AutoFeatureExtractor.from_pretrained(
							    "hf-internal-testing/test_dynamic_feature_extractor"       ,			trust_remote_code=_lowercase)

				__lowerCAmelCase      :  Any												=       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)
							__lowerCAmelCase      :  Optional[Any]												=       AutoFeatureExtractor.from_pretrained(_lowercase       ,			trust_remote_code=_lowercase)
				self.assertEqual(reloaded_feature_extractor.__class__.__name__       ,			"NewFeatureExtractor")

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       List[str]:

				"""simple docstring"""

				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
							__lowerCAmelCase      :  Optional[int]												=       CustomFeatureExtractor.from_pretrained(_lowercase)
							with tempfile.TemporaryDirectory() as tmp_dir:
										feature_extractor.save_pretrained(_lowercase)
										__lowerCAmelCase      :  int												=       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 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       int:

				"""simple docstring"""

				class 			A__   (  lowerCamelCase_							):




					'''simple docstring'''





					SCREAMING_SNAKE_CASE          =	True

				try:
							AutoConfig.register("custom"       ,			_lowercase)
							AutoFeatureExtractor.register(_lowercase       ,			_lowercase)
							# If remote code is not set, the default is to use local
							__lowerCAmelCase      :  Optional[Any]												=       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.
							__lowerCAmelCase      :  int												=       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
							__lowerCAmelCase      :  str												=       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]
import doctest
from collections import deque

import numpy as np







class __snake_case				:




   def __init__(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [2, 1, 2, -1]
     SCREAMING_SNAKE_CASE__										=      [1, 2, 3, 4]




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      len(self.first_signal      )
     SCREAMING_SNAKE_CASE__										=      len(self.second_signal      )

     SCREAMING_SNAKE_CASE__										=      max(_lowercase       ,	_lowercase      )

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

     # 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(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      deque(self.second_signal      )
       rotated_signal.rotate(_lowercase      )
       for j, item in enumerate(_lowercase      ):
         matrix[i][j] += item

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

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


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