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


_lowerCamelCase					:   Union[str, Any]							=		get_tests_dir('''fixtures''')
_lowerCamelCase					:   Dict							=		get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
_lowerCamelCase					:   int							=		get_tests_dir('''fixtures/dummy-config.json''')


class 						lowercase    (     unittest.TestCase     ):




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       0




							def 	__snake_case(						self   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"      )
												self.assertIsInstance(A__	,							A__      )




							def 	__snake_case(						self   :					List[str]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       AutoFeatureExtractor.from_pretrained(A__      )
												self.assertIsInstance(A__	,							A__      )




							def 	__snake_case(						self   :					List[str]      )					->				List[str]:



												'''simple docstring'''

												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(A__      ).to_dict()

																	config_dict.pop("feature_extractor_type"      )
																	SCREAMING_SNAKE_CASE    			=       WavaVecaFeatureExtractor(**A__      )

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

																	SCREAMING_SNAKE_CASE    			=       AutoFeatureExtractor.from_pretrained(A__      )

																	# 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(A__	,							A__      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       AutoFeatureExtractor.from_pretrained(A__      )
												self.assertIsInstance(A__	,							A__      )




							def 	__snake_case(						self   :					Dict      )					->				Optional[Any]:



												'''simple docstring'''

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




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



												'''simple docstring'''

												with self.assertRaisesRegex(
												    A__	,							R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"      ):
																	SCREAMING_SNAKE_CASE    			=       AutoFeatureExtractor.from_pretrained(A__	,							revision="aaaaaa"      )




							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												with self.assertRaisesRegex(
												    A__	,							"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 	__snake_case(						self   :					Any      )					->				int:



												'''simple docstring'''

												with self.assertRaises(A__      ):
																	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(A__      ):
																	SCREAMING_SNAKE_CASE    			=       AutoFeatureExtractor.from_pretrained(
																	    "hf-internal-testing/test_dynamic_feature_extractor"	,							trust_remote_code=A__      )

												SCREAMING_SNAKE_CASE    			=       AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"	,							trust_remote_code=A__      )
												self.assertEqual(feature_extractor.__class__.__name__	,							"NewFeatureExtractor"      )

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




							def 	__snake_case(						self   :					Optional[Any]      )					->				Optional[Any]:



												'''simple docstring'''

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

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

												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 	__snake_case(						self   :					List[str]      )					->				str:



												'''simple docstring'''

												class 						lowercase    (     _lowerCamelCase     ):
																			lowercase__						:       int								=							True

												try:
																	AutoConfig.register("custom"	,							A__      )
																	AutoFeatureExtractor.register(A__	,							A__      )
																	# 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=A__      )
																	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=A__      )
																	self.assertEqual(feature_extractor.__class__.__name__	,							"NewFeatureExtractor"      )
																	self.assertTrue(not hasattr(A__	,							"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 numpy as np





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							float = 1e-12      , UpperCAmelCase__     :							int = 1_0_0      , ):
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[1]
					# Ensure proper dimensionality.
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(UpperCAmelCase__  ) == np.iscomplexobj(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       np.iscomplexobj(UpperCAmelCase__  )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(UpperCAmelCase__      , input_matrix.conj().T  )

					# Set convergence to False. Will define convergence when we exceed max_iterations
					# or when we have small changes from one iteration to next.

					SCREAMING_SNAKE_CASE    			=       False
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       1e12

					while not convergence:
										# Multiple matrix by the vector.
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , UpperCAmelCase__  )
										# Normalize the resulting output vector.
										SCREAMING_SNAKE_CASE    			=       w / np.linalg.norm(UpperCAmelCase__  )
										# Find rayleigh quotient
										# (faster than usual b/c we know vector is normalized already)
										SCREAMING_SNAKE_CASE    			=       vector.conj().T if is_complex else vector.T
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , np.dot(UpperCAmelCase__      , UpperCAmelCase__  )  )

										# Check convergence.
										SCREAMING_SNAKE_CASE    			=       np.abs(lambda_ - lambda_previous  ) / lambda_
										iterations += 1

										if error <= error_tol or iterations >= max_iterations:
															SCREAMING_SNAKE_CASE    			=       True

										SCREAMING_SNAKE_CASE    			=       lambda_

					if is_complex:
										SCREAMING_SNAKE_CASE    			=       np.real(lambda_  )

					return lambda_, vector





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]  )
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  )
					SCREAMING_SNAKE_CASE    			=       real_input_matrix.astype(np.complexaaa  )
					SCREAMING_SNAKE_CASE    			=       np.triu(1j * complex_input_matrix      , 1  )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  ).astype(np.complexaaa  )

					for problem_type in ["real", "complex"]:
										if problem_type == "real":
															SCREAMING_SNAKE_CASE    			=       real_input_matrix
															SCREAMING_SNAKE_CASE    			=       real_vector
										elif problem_type == "complex":
															SCREAMING_SNAKE_CASE    			=       complex_input_matrix
															SCREAMING_SNAKE_CASE    			=       complex_vector

										# Our implementation.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       power_iteration(UpperCAmelCase__      , UpperCAmelCase__  )

										# Numpy implementation.

										# Get eigenvalues and eigenvectors using built-in numpy
										# eigh (eigh used for symmetric or hermetian matrices).
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       np.linalg.eigh(UpperCAmelCase__  )
										# Last eigenvalue is the maximum one.
										SCREAMING_SNAKE_CASE    			=       eigen_values[-1]
										# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
										SCREAMING_SNAKE_CASE    			=       eigen_vectors[:, -1]

										# Check our implementation and numpy gives close answers.
										assert np.abs(eigen_value - eigen_value_max  ) <= 1e-6
										# Take absolute values element wise of each eigenvector.
										# as they are only unique to a minus sign.
										assert np.linalg.norm(np.abs(UpperCAmelCase__  ) - np.abs(UpperCAmelCase__  )  ) <= 1e-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




