

"""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


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





class    snake_case     (    unittest.TestCase  ):

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



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      0

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



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Optional[int])->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      AutoFeatureExtractor.from_pretrained(a__)
						self.assertIsInstance(a__					,  a__)

				def 							lowercase_					(     self							:    Optional[Any])->       Tuple:



						'''simple docstring'''


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

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

								config_dict.pop("feature_extractor_type")
								__lowerCAmelCase:	List[str]   				=      WavaVecaFeatureExtractor(**a__)

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

								__lowerCAmelCase:	Dict   				=      AutoFeatureExtractor.from_pretrained(a__)

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

						self.assertIsInstance(a__					,  a__)

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



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      AutoFeatureExtractor.from_pretrained(a__)
						self.assertIsInstance(a__					,  a__)

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



						'''simple docstring'''


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

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



						'''simple docstring'''


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

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



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Dict)->       str:



						'''simple docstring'''


						with self.assertRaises(a__):
								__lowerCAmelCase:	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(a__):
								__lowerCAmelCase:	List[str]   				=      AutoFeatureExtractor.from_pretrained(
								    "hf-internal-testing/test_dynamic_feature_extractor"					,  trust_remote_code=a__)

						__lowerCAmelCase:	Dict   				=      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__)
								__lowerCAmelCase:	Any   				=      AutoFeatureExtractor.from_pretrained(a__					,  trust_remote_code=a__)
						self.assertEqual(reloaded_feature_extractor.__class__.__name__					,  "NewFeatureExtractor")

				def 							lowercase_					(     self							:    int)->       Dict:



						'''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
								__lowerCAmelCase:	Dict   				=      CustomFeatureExtractor.from_pretrained(a__)
								with tempfile.TemporaryDirectory() as tmp_dir:
										feature_extractor.save_pretrained(a__)
										__lowerCAmelCase:	Optional[Any]   				=      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 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						class    snake_case     (    __a  ):
										SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  True

						try:
								AutoConfig.register("custom"					,  a__)
								AutoFeatureExtractor.register(a__					,  a__)
								# If remote code is not set, the default is to use local
								__lowerCAmelCase:	Optional[int]   				=      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:	Any   				=      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
								__lowerCAmelCase:	Optional[int]   				=      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]





"""simple docstring"""


from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      cva.getAffineTransform(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		return cva.warpAffine(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			(rows, cols)			)


if __name__ == "__main__":
			# read original image
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

			# set different points to rotate image
			__A						       =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			__A						       =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			__A						       =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			__A						       =      plt.figure(1)
			__A						       =      ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
						plt.title(titles[i])
						plt.axis("off")
						plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
			plt.show()


