






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


A        =			get_tests_dir('fixtures')
A        =			get_tests_dir('fixtures/dummy_feature_extractor_config.json')
A        =			get_tests_dir('fixtures/dummy-config.json')


class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   0


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'       )
						self.assertIsInstance(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Any													=   AutoFeatureExtractor.from_pretrained(UpperCamelCase__       )
						self.assertIsInstance(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						with tempfile.TemporaryDirectory() as tmpdirname:
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   WavaVecaConfig()

										# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   AutoFeatureExtractor.from_pretrained(UpperCamelCase__       ).to_dict()

										config_dict.pop('feature_extractor_type'       )
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   WavaVecaFeatureExtractor(**UpperCamelCase__       )

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

										SCREAMING_SNAKE_CASE_   :	List[Any]													=   AutoFeatureExtractor.from_pretrained(UpperCamelCase__       )

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

						self.assertIsInstance(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   AutoFeatureExtractor.from_pretrained(UpperCamelCase__       )
						self.assertIsInstance(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self       ):
						with self.assertRaisesRegex(
						    UpperCamelCase__ ,    'bert-base is not a local folder and is not a valid model identifier'       ):
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AutoFeatureExtractor.from_pretrained('bert-base'       )


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


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


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

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   AutoFeatureExtractor.from_pretrained(
						    'hf-internal-testing/test_dynamic_feature_extractor' ,    trust_remote_code=UpperCamelCase__       )
						self.assertEqual(feature_extractor.__class__.__name__ ,    'NewFeatureExtractor'       )

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


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

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

						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       ):


						class 							__a							(  __A						):





								'''simple docstring'''



								UpperCAmelCase__      :				int				    =						True

						try:
										AutoConfig.register('custom' ,    UpperCamelCase__       )
										AutoFeatureExtractor.register(UpperCamelCase__ ,    UpperCamelCase__       )
										# If remote code is not set, the default is to use local
										SCREAMING_SNAKE_CASE_   :	Dict													=   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_   :	Optional[Any]													=   AutoFeatureExtractor.from_pretrained(
										    'hf-internal-testing/test_dynamic_feature_extractor' ,    trust_remote_code=UpperCamelCase__       )
										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_   :	Union[str, Any]													=   AutoFeatureExtractor.from_pretrained(
										    'hf-internal-testing/test_dynamic_feature_extractor' ,    trust_remote_code=UpperCamelCase__       )
										self.assertEqual(feature_extractor.__class__.__name__ ,    'NewFeatureExtractor'       )
										self.assertTrue(not hasattr(UpperCamelCase__ ,    '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 colorsys

from PIL import Image  # type: ignore



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Any													=   x
				SCREAMING_SNAKE_CASE_   :	Tuple													=   y
				for step in range(lowerCAmelCase__      ):  # noqa: B007
								SCREAMING_SNAKE_CASE_   :	str													=   a * a - b * b + x
								SCREAMING_SNAKE_CASE_   :	Any													=   2 * a * b + y
								SCREAMING_SNAKE_CASE_   :	int													=   a_new

								# divergence happens for all complex number with an absolute value
								# greater than 4
								if a * a + b * b > 4:
												break
				return step / (max_step - 1)



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				if distance == 1:
								return (0, 0, 0)
				else:
								return (255, 255, 255)



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				if distance == 1:
								return (0, 0, 0)
				else:
								return tuple(round(i * 255      ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__    ,    1    ,    1      )      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int = 800    ,    lowerCAmelCase__				:		int = 600    ,    lowerCAmelCase__				:		float = -0.6    ,    lowerCAmelCase__				:		float = 0    ,    lowerCAmelCase__				:		float = 3.2    ,    lowerCAmelCase__				:		int = 50    ,    lowerCAmelCase__				:		bool = True    ,    ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   Image.new('RGB'    ,    (image_width, image_height)      )
				SCREAMING_SNAKE_CASE_   :	List[str]													=   img.load()

				# loop through the image-coordinates
				for image_x in range(lowerCAmelCase__      ):
								for image_y in range(lowerCAmelCase__      ):
												# determine the figure-coordinates based on the image-coordinates
												SCREAMING_SNAKE_CASE_   :	List[str]													=   figure_width / image_width * image_height
												SCREAMING_SNAKE_CASE_   :	List[Any]													=   figure_center_x + (image_x / image_width - 0.5) * figure_width
												SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   figure_center_y + (image_y / image_height - 0.5) * figure_height

												SCREAMING_SNAKE_CASE_   :	str													=   get_distance(lowerCAmelCase__    ,    lowerCAmelCase__    ,    lowerCAmelCase__      )

												# color the corresponding pixel based on the selected coloring-function
												if use_distance_color_coding:
																SCREAMING_SNAKE_CASE_   :	int													=   get_color_coded_rgb(lowerCAmelCase__      )
												else:
																SCREAMING_SNAKE_CASE_   :	Dict													=   get_black_and_white_rgb(lowerCAmelCase__      )

				return img


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# colored version, full figure
				A        =			get_image()

				# uncomment for colored version, different section, zoomed in
				# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
				# figure_width = 0.8)

				# uncomment for black and white version, full figure
				# img = get_image(use_distance_color_coding = False)

				# uncomment to save the image
				# img.save("mandelbrot.png")

				img.show()