"""simple docstring"""







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 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   Any)					->			Any:
					__snake_case    :							List[Any]   	=				0



				def       _lowercase  (self       :   Any)					->			Optional[int]:
					__snake_case    :							Tuple   	=				AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
					self.assertIsInstance(A_			,						A_)



				def       _lowercase  (self       :   Any)					->			Any:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case    :							Tuple   	=				Path(A_) / 'preprocessor_config.json'
						__snake_case    :							int   	=				Path(A_) / 'config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}			,						open(A_			,						'w')			,						)
						json.dump({'model_type': 'clip'}			,						open(A_			,						'w'))

						__snake_case    :							List[str]   	=				AutoImageProcessor.from_pretrained(A_)
						self.assertIsInstance(A_			,						A_)



				def       _lowercase  (self       :   Optional[Any])					->			Union[str, Any]:
					# Ensure we can load the image processor from the feature extractor config
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case    :							str   	=				Path(A_) / 'preprocessor_config.json'
						__snake_case    :							List[Any]   	=				Path(A_) / 'config.json'
						json.dump(
						    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}			,						open(A_			,						'w')			,						)
						json.dump({'model_type': 'clip'}			,						open(A_			,						'w'))

						__snake_case    :							int   	=				AutoImageProcessor.from_pretrained(A_)
						self.assertIsInstance(A_			,						A_)



				def       _lowercase  (self       :   List[str])					->			Union[str, Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case    :							Tuple   	=				CLIPConfig()

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

						# remove image_processor_type to make sure config.json alone is enough to load image processor locally
						__snake_case    :							Union[str, Any]   	=				AutoImageProcessor.from_pretrained(A_).to_dict()

						config_dict.pop('image_processor_type')
						__snake_case    :							Optional[int]   	=				CLIPImageProcessor(**A_)

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

						__snake_case    :							Optional[Any]   	=				AutoImageProcessor.from_pretrained(A_)

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

					self.assertIsInstance(A_			,						A_)



				def       _lowercase  (self       :   Dict)					->			Union[str, Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case    :							int   	=				Path(A_) / 'preprocessor_config.json'
						json.dump(
						    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}			,						open(A_			,						'w')			,						)

						__snake_case    :							Optional[int]   	=				AutoImageProcessor.from_pretrained(A_)
						self.assertIsInstance(A_			,						A_)



				def       _lowercase  (self       :   Tuple)					->			Optional[Any]:
					with self.assertRaisesRegex(
					    A_			,						'clip-base is not a local folder and is not a valid model identifier'):
						__snake_case    :							Tuple   	=				AutoImageProcessor.from_pretrained('clip-base')



				def       _lowercase  (self       :   List[Any])					->			Dict:
					with self.assertRaisesRegex(
					    A_			,						r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
						__snake_case    :							Tuple   	=				AutoImageProcessor.from_pretrained(A_			,						revision='aaaaaa')



				def       _lowercase  (self       :   Optional[Any])					->			int:
					with self.assertRaisesRegex(
					    A_			,						'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'			,						):
						__snake_case    :							Tuple   	=				AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model')



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

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

					# Test image processor can be reloaded.
					with tempfile.TemporaryDirectory() as tmp_dir:
						image_processor.save_pretrained(A_)
						__snake_case    :							List[str]   	=				AutoImageProcessor.from_pretrained(A_			,						trust_remote_code=A_)
					self.assertEqual(reloaded_image_processor.__class__.__name__			,						'NewImageProcessor')



				def       _lowercase  (self       :   int)					->			List[str]:
					try:
						AutoConfig.register('custom'			,						A_)
						AutoImageProcessor.register(A_			,						A_)
						# Trying to register something existing in the Transformers library will raise an error
						with self.assertRaises(A_):
							AutoImageProcessor.register(A_			,						A_)

						with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case    :							Any   	=				Path(A_) / 'preprocessor_config.json'
							__snake_case    :							int   	=				Path(A_) / 'config.json'
							json.dump(
							    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}			,						open(A_			,						'w')			,						)
							json.dump({'model_type': 'clip'}			,						open(A_			,						'w'))

							__snake_case    :							Any   	=				CustomImageProcessor.from_pretrained(A_)

						# 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(A_)
							__snake_case    :							str   	=				AutoImageProcessor.from_pretrained(A_)
							self.assertIsInstance(A_			,						A_)

					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       _lowercase  (self       :   Dict)					->			Optional[Any]:

					class 	UpperCamelCase    (					_lowercase		):
									UpperCAmelCase       :   Optional[Any]					=	True

					try:
						AutoConfig.register('custom'			,						A_)
						AutoImageProcessor.register(A_			,						A_)
						# If remote code is not set, the default is to use local
						__snake_case    :							Tuple   	=				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    :							List[Any]   	=				AutoImageProcessor.from_pretrained(
						    'hf-internal-testing/test_dynamic_image_processor'			,						trust_remote_code=A_)
						self.assertEqual(image_processor.__class__.__name__			,						'NewImageProcessor')
						self.assertTrue(image_processor.is_local)

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_a  :  Any=				{"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Union[str, Any]=				["SpeechEncoderDecoderModel"]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Tuple=				["FlaxSpeechEncoderDecoderModel"]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	_a  :  str=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





