"""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        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   0



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"    )
													self.assertIsInstance(_A				,    _A    )



							def 	UpperCamelCase	(						self    ):
													with tempfile.TemporaryDirectory() as tmpdirname:
																			_snake_case						:					List[str]    =   Path(_A    ) / 'preprocessor_config.json'
																			_snake_case						:					Optional[Any]    =   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						:					Optional[int]    =   AutoImageProcessor.from_pretrained(_A    )
																			self.assertIsInstance(_A				,    _A    )



							def 	UpperCamelCase	(						self    ):
													# Ensure we can load the image processor from the feature extractor config
													with tempfile.TemporaryDirectory() as tmpdirname:
																			_snake_case						:					Any    =   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						:					Tuple    =   AutoImageProcessor.from_pretrained(_A    )
																			self.assertIsInstance(_A				,    _A    )



							def 	UpperCamelCase	(						self    ):
													with tempfile.TemporaryDirectory() as tmpdirname:
																			_snake_case						:					str    =   CLIPConfig()

																			# Create a dummy config file with image_proceesor_type
																			_snake_case						:					List[Any]    =   Path(_A    ) / 'preprocessor_config.json'
																			_snake_case						:					Optional[Any]    =   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						:					List[str]    =   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[int]    =   AutoImageProcessor.from_pretrained(_A    )

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

													self.assertIsInstance(_A				,    _A    )



							def 	UpperCamelCase	(						self    ):
													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						:					List[str]    =   AutoImageProcessor.from_pretrained(_A    )
																			self.assertIsInstance(_A				,    _A    )



							def 	UpperCamelCase	(						self    ):
													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 	UpperCamelCase	(						self    ):
													with self.assertRaisesRegex(
													    _A				,    R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"    ):
																			_snake_case						:					str    =   AutoImageProcessor.from_pretrained(_A				,    revision="aaaaaa"    )



							def 	UpperCamelCase	(						self    ):
													with self.assertRaisesRegex(
													    _A				,    "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."				,    ):
																			_snake_case						:					List[Any]    =   AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"    )



							def 	UpperCamelCase	(						self    ):
													# If remote code is not set, we will time out when asking whether to load the model.
													with self.assertRaises(_A    ):
																			_snake_case						:					Any    =   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						:					Tuple    =   AutoImageProcessor.from_pretrained(
																			    "hf-internal-testing/test_dynamic_image_processor"				,    trust_remote_code=_A    )

													_snake_case						:					Union[str, 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						:					Optional[int]    =   AutoImageProcessor.from_pretrained(_A				,    trust_remote_code=_A    )
													self.assertEqual(reloaded_image_processor.__class__.__name__				,    "NewImageProcessor"    )



							def 	UpperCamelCase	(						self    ):
													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						:					Tuple    =   Path(_A    ) / 'preprocessor_config.json'
																									_snake_case						:					Dict    =   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						:					Tuple    =   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						:					Tuple    =   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 	UpperCamelCase	(						self    ):



													class        lowercase_   (					__snake_case		):
																				_lowerCamelCase						=		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						:					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(image_processor.is_local    )

																			# If remote is enabled, we load from the Hub
																			_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(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]
import os

import pytest
from attr import dataclass


__SCREAMING_SNAKE_CASE						:   int         =			'us-east-1'  # defaults region



@dataclass
class        lowercase_   :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		'arn:aws:iam::558105141721:role/sagemaker_execution_role'
							_lowerCamelCase						=		{
							    'task_name': 'mnli',
							    'per_device_train_batch_size': 16,
							    'per_device_eval_batch_size': 16,
							    'do_train': True,
							    'do_eval': True,
							    'do_predict': True,
							    'output_dir': '/opt/ml/model',
							    'overwrite_output_dir': True,
							    'max_steps': 500,
							    'save_steps': 5_500,
							}
							_lowerCamelCase						=		{**hyperparameters, 'max_steps': 1_000}



							@property
							def 	UpperCamelCase	(						self    ):
													if self.framework == "pytorch":
																			return [
																			    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
																			    {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
																			    {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
																			]
													else:
																			return [
																			    {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
																			    {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
																			    {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
																			]



							@property
							def 	UpperCamelCase	(						self    ):
													return f"""{self.framework}-transfromers-test"""



							@property
							def 	UpperCamelCase	(						self    ):
													return f"""./tests/sagemaker/scripts/{self.framework}"""



							@property
							def 	UpperCamelCase	(						self    ):
													if self.framework == "pytorch":
																			return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
													else:
																			return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"




@pytest.fixture(scope="class"						)
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					List[str]    =   SageMakerTestEnvironment(framework=request.cls.framework						)