






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


UpperCamelCase        =				get_tests_dir("fixtures")
UpperCamelCase        =				get_tests_dir("fixtures/dummy_feature_extractor_config.json")
UpperCamelCase        =				get_tests_dir("fixtures/dummy-config.json")

class        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




				def 				__a   ( self     :Optional[int]     ):
					UpperCamelCase__ :Optional[int]						=      0







				def 				__a   ( self     :str     ):
					UpperCamelCase__ :Optional[Any]						=      AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""     )
					self.assertIsInstance(lowerCamelCase__  ,     lowerCamelCase__     )







				def 				__a   ( self     :Dict     ):
					UpperCamelCase__ :Dict						=      AutoFeatureExtractor.from_pretrained(lowerCamelCase__     )
					self.assertIsInstance(lowerCamelCase__  ,     lowerCamelCase__     )







				def 				__a   ( self     :Optional[int]     ):
					with tempfile.TemporaryDirectory() as tmpdirname:
						UpperCamelCase__ :List[str]						=      WavaVecaConfig()

						# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
						UpperCamelCase__ :Tuple						=      AutoFeatureExtractor.from_pretrained(lowerCamelCase__     ).to_dict()

						config_dict.pop("""feature_extractor_type"""     )
						UpperCamelCase__ :Union[str, Any]						=      WavaVecaFeatureExtractor(**lowerCamelCase__     )

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

						UpperCamelCase__ :Optional[Any]						=      AutoFeatureExtractor.from_pretrained(lowerCamelCase__     )

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

					self.assertIsInstance(lowerCamelCase__  ,     lowerCamelCase__     )







				def 				__a   ( self     :Union[str, Any]     ):
					UpperCamelCase__ :Dict						=      AutoFeatureExtractor.from_pretrained(lowerCamelCase__     )
					self.assertIsInstance(lowerCamelCase__  ,     lowerCamelCase__     )







				def 				__a   ( self     :Dict     ):
					with self.assertRaisesRegex(
					    lowerCamelCase__  ,     """bert-base is not a local folder and is not a valid model identifier"""     ):
						UpperCamelCase__ :Dict						=      AutoFeatureExtractor.from_pretrained("""bert-base"""     )







				def 				__a   ( self     :List[Any]     ):
					with self.assertRaisesRegex(
					    lowerCamelCase__  ,     r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""     ):
						UpperCamelCase__ :Optional[int]						=      AutoFeatureExtractor.from_pretrained(lowerCamelCase__  ,     revision="""aaaaaa"""     )







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







				def 				__a   ( self     :Optional[int]     ):
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(lowerCamelCase__     ):
						UpperCamelCase__ :List[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(lowerCamelCase__     ):
						UpperCamelCase__ :Optional[Any]						=      AutoFeatureExtractor.from_pretrained(
						    """hf-internal-testing/test_dynamic_feature_extractor"""  ,     trust_remote_code=lowerCamelCase__     )

					UpperCamelCase__ :str						=      AutoFeatureExtractor.from_pretrained(
					    """hf-internal-testing/test_dynamic_feature_extractor"""  ,     trust_remote_code=lowerCamelCase__     )
					self.assertEqual(feature_extractor.__class__.__name__  ,     """NewFeatureExtractor"""     )

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







				def 				__a   ( self     :Dict     ):
					try:
						AutoConfig.register("""custom"""  ,     lowerCamelCase__     )
						AutoFeatureExtractor.register(lowerCamelCase__  ,     lowerCamelCase__     )
						# Trying to register something existing in the Transformers library will raise an error
						with self.assertRaises(lowerCamelCase__     ):
							AutoFeatureExtractor.register(lowerCamelCase__  ,     lowerCamelCase__     )

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

					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     :Optional[int]     ):

					class        lowerCAmelCase_				(      lowercase	):







									"""simple docstring"""




									_snake_case				:   Optional[int]        =			True

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

						# If remote is enabled, we load from the Hub
						UpperCamelCase__ :Optional[int]						=      AutoFeatureExtractor.from_pretrained(
						    """hf-internal-testing/test_dynamic_feature_extractor"""  ,     trust_remote_code=lowerCamelCase__     )
						self.assertEqual(feature_extractor.__class__.__name__  ,     """NewFeatureExtractor"""     )
						self.assertTrue(not hasattr(lowerCamelCase__  ,     """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]







from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_SCREAMING_SNAKE_CASE   =  2_99_79_24_58

# Symbols
_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   =  symbols("ct x y z")





def        _snake_case   (_snake_case				:       float)					->	float:
   if velocity > c:
      raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!')
   elif velocity < 1:
      # Usually the speed should be much higher than 1 (c order of magnitude)
      raise ValueError('Speed must be greater than or equal to 1!')

   return velocity / c





def        _snake_case   (_snake_case				:       float)					->	float:
   return 1 / sqrt(1 - beta(_snake_case) ** 2)





def        _snake_case   (_snake_case				:       float)					->	np.ndarray:
   return np.array(
       [
           [gamma(_snake_case), -gamma(_snake_case) * beta(_snake_case), 0, 0],
           [-gamma(_snake_case) * beta(_snake_case), gamma(_snake_case), 0, 0],
           [0, 0, 1, 0],
           [0, 0, 0, 1],
       ])





def        _snake_case   (_snake_case				:       float      ,    _snake_case				:       np.ndarray | None = None)					->	np.ndarray:
   # Ensure event is not empty
   if event is None:
      _lowercase              =np.array([ct, x, y, z])  # Symbolic four vector
   else:
      event[0] *= c  # x0 is ct (speed of light * time)

   return transformation_matrix(_snake_case) @ event


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Example of symbolic vector:
    _SCREAMING_SNAKE_CASE   =  transform(29_97_92_45)
    print("Example of four vector: ")
    print(f'''ct\' = {four_vector[0]}''')
    print(f'''x\' = {four_vector[1]}''')
    print(f'''y\' = {four_vector[2]}''')
    print(f'''z\' = {four_vector[3]}''')

    # Substitute symbols with numerical values
    _SCREAMING_SNAKE_CASE   =  {ct: c, x: 1, y: 1, z: 1}
    _SCREAMING_SNAKE_CASE   =  [four_vector[i].subs(sub_dict) for i in range(4)]

    print(f'''\n{numerical_vector}''')


