





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402



class    __lowerCAmelCase			(     unittest.TestCase	):






			def      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



							snake_case_		:       Tuple           =    0






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Dict           =    AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''       )
							self.assertIsInstance(__magic_name__   ,				__magic_name__       )






			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_		:       Dict           =    Path(__magic_name__       ) / '''preprocessor_config.json'''
											snake_case_		:       Dict           =    Path(__magic_name__       ) / '''config.json'''
											json.dump(
											    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}   ,				open(__magic_name__   ,				'''w'''       )   ,				)
											json.dump({'''model_type''': '''clip'''}   ,				open(__magic_name__   ,				'''w'''       )       )

											snake_case_		:       Optional[int]           =    AutoImageProcessor.from_pretrained(__magic_name__       )
											self.assertIsInstance(__magic_name__   ,				__magic_name__       )






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


							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_		:       Dict           =    Path(__magic_name__       ) / '''preprocessor_config.json'''
											snake_case_		:       Optional[int]           =    Path(__magic_name__       ) / '''config.json'''
											json.dump(
											    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}   ,				open(__magic_name__   ,				'''w'''       )   ,				)
											json.dump({'''model_type''': '''clip'''}   ,				open(__magic_name__   ,				'''w'''       )       )

											snake_case_		:       List[Any]           =    AutoImageProcessor.from_pretrained(__magic_name__       )
											self.assertIsInstance(__magic_name__   ,				__magic_name__       )






			def      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_		:       Dict           =    CLIPConfig()

											# Create a dummy config file with image_proceesor_type
											snake_case_		:       List[str]           =    Path(__magic_name__       ) / '''preprocessor_config.json'''
											snake_case_		:       List[str]           =    Path(__magic_name__       ) / '''config.json'''
											json.dump(
											    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}   ,				open(__magic_name__   ,				'''w'''       )   ,				)
											json.dump({'''model_type''': '''clip'''}   ,				open(__magic_name__   ,				'''w'''       )       )

											# remove image_processor_type to make sure config.json alone is enough to load image processor locally
											snake_case_		:       List[str]           =    AutoImageProcessor.from_pretrained(__magic_name__       ).to_dict()

											config_dict.pop('''image_processor_type'''       )
											snake_case_		:       List[Any]           =    CLIPImageProcessor(**__magic_name__       )

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

											snake_case_		:       str           =    AutoImageProcessor.from_pretrained(__magic_name__       )

											# make sure private variable is not incorrectly saved
											snake_case_		:       Union[str, Any]           =    json.loads(config.to_json_string()       )
											self.assertTrue('''_processor_class''' not in dict_as_saved       )

							self.assertIsInstance(__magic_name__   ,				__magic_name__       )






			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							with tempfile.TemporaryDirectory() as tmpdirname:
											snake_case_		:       str           =    Path(__magic_name__       ) / '''preprocessor_config.json'''
											json.dump(
											    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}   ,				open(__magic_name__   ,				'''w'''       )   ,				)

											snake_case_		:       str           =    AutoImageProcessor.from_pretrained(__magic_name__       )
											self.assertIsInstance(__magic_name__   ,				__magic_name__       )






			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							with self.assertRaisesRegex(
							    __magic_name__   ,				'''clip-base is not a local folder and is not a valid model identifier'''       ):
											snake_case_		:       Optional[Any]           =    AutoImageProcessor.from_pretrained('''clip-base'''       )






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


							'''simple docstring'''



							with self.assertRaisesRegex(
							    __magic_name__   ,				R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''       ):
											snake_case_		:       Union[str, Any]           =    AutoImageProcessor.from_pretrained(__magic_name__   ,				revision='''aaaaaa'''       )






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


							'''simple docstring'''



							with self.assertRaisesRegex(
							    __magic_name__   ,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''   ,				):
											snake_case_		:       int           =    AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''       )






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


							'''simple docstring'''



							with self.assertRaises(__magic_name__       ):
											snake_case_		:       Tuple           =    AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''       )
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(__magic_name__       ):
											snake_case_		:       List[Any]           =    AutoImageProcessor.from_pretrained(
											    '''hf-internal-testing/test_dynamic_image_processor'''   ,				trust_remote_code=__magic_name__       )

							snake_case_		:       List[Any]           =    AutoImageProcessor.from_pretrained(
							    '''hf-internal-testing/test_dynamic_image_processor'''   ,				trust_remote_code=__magic_name__       )
							self.assertEqual(image_processor.__class__.__name__   ,				'''NewImageProcessor'''       )

							# Test image processor can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
											image_processor.save_pretrained(__magic_name__       )
											snake_case_		:       Tuple           =    AutoImageProcessor.from_pretrained(__magic_name__   ,				trust_remote_code=__magic_name__       )
							self.assertEqual(reloaded_image_processor.__class__.__name__   ,				'''NewImageProcessor'''       )






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


							'''simple docstring'''



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

											with tempfile.TemporaryDirectory() as tmpdirname:
															snake_case_		:       Tuple           =    Path(__magic_name__       ) / '''preprocessor_config.json'''
															snake_case_		:       List[str]           =    Path(__magic_name__       ) / '''config.json'''
															json.dump(
															    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}   ,				open(__magic_name__   ,				'''w'''       )   ,				)
															json.dump({'''model_type''': '''clip'''}   ,				open(__magic_name__   ,				'''w'''       )       )

															snake_case_		:       Tuple           =    CustomImageProcessor.from_pretrained(__magic_name__       )

											# Now that the config is registered, it can be used as any other config with the auto-API
											with tempfile.TemporaryDirectory() as tmp_dir:
															image_processor.save_pretrained(__magic_name__       )
															snake_case_		:       Dict           =    AutoImageProcessor.from_pretrained(__magic_name__       )
															self.assertIsInstance(__magic_name__   ,				__magic_name__       )

							finally:
											if "custom" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["custom"]
											if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
															del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






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


							'''simple docstring'''



							class    __lowerCAmelCase			(     _a	):
										lowerCamelCase_	:	Union[str, Any]			     = True

							try:
											AutoConfig.register('''custom'''   ,				__magic_name__       )
											AutoImageProcessor.register(__magic_name__   ,				__magic_name__       )
											# If remote code is not set, the default is to use local
											snake_case_		:       Dict           =    AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''       )
											self.assertEqual(image_processor.__class__.__name__   ,				'''NewImageProcessor'''       )
											self.assertTrue(image_processor.is_local       )

											# If remote code is disabled, we load the local one.
											snake_case_		:       Optional[Any]           =    AutoImageProcessor.from_pretrained(
											    '''hf-internal-testing/test_dynamic_image_processor'''   ,				trust_remote_code=__magic_name__       )
											self.assertEqual(image_processor.__class__.__name__   ,				'''NewImageProcessor'''       )
											self.assertTrue(image_processor.is_local       )

											# If remote is enabled, we load from the Hub
											snake_case_		:       int           =    AutoImageProcessor.from_pretrained(
											    '''hf-internal-testing/test_dynamic_image_processor'''   ,				trust_remote_code=__magic_name__       )
											self.assertEqual(image_processor.__class__.__name__   ,				'''NewImageProcessor'''       )
											self.assertTrue(not hasattr(__magic_name__   ,				'''is_local'''       )       )

							finally:
											if "custom" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["custom"]
											if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
															del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]


"""simple docstring"""




import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')



def 							_lowerCamelCase			(			__a     ):
		return input_array.reshape((input_array.size, 1)     )



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			np.nan
		for i in range(__a     ):
				SCREAMING_SNAKE_CASE_    			=			features[:, labels == i]
				SCREAMING_SNAKE_CASE_    			=			data.mean(1     )
				# Centralize the data of class i
				SCREAMING_SNAKE_CASE_    			=			data - column_reshape(__a     )
				if i > 0:
						# If covariance_sum is not None
						covariance_sum += np.dot(__a,				centered_data.T     )
				else:
						# If covariance_sum is np.nan (i.e. first loop)
						SCREAMING_SNAKE_CASE_    			=			np.dot(__a,				centered_data.T     )

		return covariance_sum / features.shape[1]



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			features.mean(1     )
		SCREAMING_SNAKE_CASE_    			=			np.nan
		for i in range(__a     ):
				SCREAMING_SNAKE_CASE_    			=			features[:, labels == i]
				SCREAMING_SNAKE_CASE_    			=			data.shape[1]
				SCREAMING_SNAKE_CASE_    			=			data.mean(1     )
				if i > 0:
						# If covariance_sum is not None
						covariance_sum += device_data * np.dot(
						    column_reshape(__a     ) - column_reshape(__a     ),				(column_reshape(__a     ) - column_reshape(__a     )).T,				)
				else:
						# If covariance_sum is np.nan (i.e. first loop)
						SCREAMING_SNAKE_CASE_    			=			device_data * np.dot(
						    column_reshape(__a     ) - column_reshape(__a     ),				(column_reshape(__a     ) - column_reshape(__a     )).T,				)

		return covariance_sum / features.shape[1]



def 							_lowerCamelCase			(			__a,				__a     ):

		# Check if the features have been loaded
		if features.any():
				SCREAMING_SNAKE_CASE_    			=			features.mean(1     )
				# Center the dataset
				SCREAMING_SNAKE_CASE_    			=			features - np.reshape(__a,				(data_mean.size, 1)     )
				SCREAMING_SNAKE_CASE_    			=			np.dot(__a,				centered_data.T     ) / features.shape[1]
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			np.linalg.eigh(__a     )
				# Take all the columns in the reverse order (-1), and then takes only the first
				SCREAMING_SNAKE_CASE_    			=			eigenvectors[:, ::-1][:, 0:dimensions]
				# Project the database on the new space
				SCREAMING_SNAKE_CASE_    			=			np.dot(filtered_eigenvectors.T,				__a     )
				logging.info('''Principal Component Analysis computed'''     )

				return projected_data
		else:
				logging.basicConfig(level=logging.ERROR,				format='''%(message)s''',				force=__a     )
				logging.error('''Dataset empty'''     )
				raise AssertionError



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a     ):
		assert classes > dimensions

		# Check if features have been already loaded
		if features.any:
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			eigh(
				    covariance_between_classes(__a,				__a,				__a     ),				covariance_within_classes(__a,				__a,				__a     ),				)
				SCREAMING_SNAKE_CASE_    			=			eigenvectors[:, ::-1][:, :dimensions]
				SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			np.linalg.svd(__a     )
				SCREAMING_SNAKE_CASE_    			=			svd_matrix[:, 0:dimensions]
				SCREAMING_SNAKE_CASE_    			=			np.dot(filtered_svd_matrix.T,				__a     )
				logging.info('''Linear Discriminant Analysis computed'''     )

				return projected_data
		else:
				logging.basicConfig(level=logging.ERROR,				format='''%(message)s''',				force=__a     )
				logging.error('''Dataset empty'''     )
				raise AssertionError



def 							_lowerCamelCase			(			):
		# Create dummy dataset with 2 classes and 3 features
		SCREAMING_SNAKE_CASE_    			=			np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
		SCREAMING_SNAKE_CASE_    			=			np.array([0, 0, 0, 1, 1]     )
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			2

		# Assert that the function raises an AssertionError if dimensions > classes
		with pytest.raises(__a     ) as error_info:
				SCREAMING_SNAKE_CASE_    			=			linear_discriminant_analysis(
				    __a,				__a,				__a,				__a     )
				if isinstance(__a,				np.ndarray     ):
						raise AssertionError(
						    '''Did not raise AssertionError for dimensions > classes'''     )
				assert error_info.type is AssertionError



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
		SCREAMING_SNAKE_CASE_    			=			2
		SCREAMING_SNAKE_CASE_    			=			np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]     )

		with pytest.raises(__a     ) as error_info:
				SCREAMING_SNAKE_CASE_    			=			principal_component_analysis(__a,				__a     )
				if not np.allclose(__a,				__a     ):
						raise AssertionError
				assert error_info.type is AssertionError


if __name__ == "__main__":
						import doctest

						doctest.testmod()