






"""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


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


class 		__lowerCamelCase				( unittest.TestCase			):





		'''simple docstring'''






		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							Dict									=			0





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							Optional[int]									=			AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"	)
				self.assertIsInstance(a_						,			a_	)





		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_   :							Optional[int]									=			AutoFeatureExtractor.from_pretrained(a_	)
				self.assertIsInstance(a_						,			a_	)





		def    lowerCamelCase       (   self							:	Dict	):
				with tempfile.TemporaryDirectory() as tmpdirname:
						lowerCAmelCase_   :							List[Any]									=			WavaVecaConfig()

						# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
						lowerCAmelCase_   :							Tuple									=			AutoFeatureExtractor.from_pretrained(a_	).to_dict()

						config_dict.pop("feature_extractor_type"	)
						lowerCAmelCase_   :							Tuple									=			WavaVecaFeatureExtractor(**a_	)

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

						lowerCAmelCase_   :							int									=			AutoFeatureExtractor.from_pretrained(a_	)

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

				self.assertIsInstance(a_						,			a_	)





		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							List[Any]									=			AutoFeatureExtractor.from_pretrained(a_	)
				self.assertIsInstance(a_						,			a_	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				with self.assertRaisesRegex(
				    a_						,			"bert-base is not a local folder and is not a valid model identifier"	):
						lowerCAmelCase_   :							int									=			AutoFeatureExtractor.from_pretrained("bert-base"	)





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				with self.assertRaisesRegex(
				    a_						,			R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"	):
						lowerCAmelCase_   :							List[Any]									=			AutoFeatureExtractor.from_pretrained(a_						,			revision="aaaaaa"	)





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





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				# If remote code is not set, we will time out when asking whether to load the model.
				with self.assertRaises(a_	):
						lowerCAmelCase_   :							Optional[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_   :							Dict									=			AutoFeatureExtractor.from_pretrained(
						    "hf-internal-testing/test_dynamic_feature_extractor"						,			trust_remote_code=a_	)

				lowerCAmelCase_   :							Tuple									=			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_   :							Union[str, Any]									=			AutoFeatureExtractor.from_pretrained(a_						,			trust_remote_code=a_	)
				self.assertEqual(reloaded_feature_extractor.__class__.__name__						,			"NewFeatureExtractor"	)





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				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_   :							List[Any]									=			CustomFeatureExtractor.from_pretrained(a_	)
						with tempfile.TemporaryDirectory() as tmp_dir:
								feature_extractor.save_pretrained(a_	)
								lowerCAmelCase_   :							List[str]									=			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    lowerCamelCase       (   self							:	Any	):


				class 		__lowerCamelCase				( A__			):





						'''simple docstring'''






						a_	:				List[Any]            =							True

				try:
						AutoConfig.register("custom"						,			a_	)
						AutoFeatureExtractor.register(a_						,			a_	)
						# If remote code is not set, the default is to use local
						lowerCAmelCase_   :							Tuple									=			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_   :							Dict									=			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_   :							List[Any]									=			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"""

import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 		__lowerCamelCase				( A__ ,							A__ ,							unittest.TestCase			):





		'''simple docstring'''






		a_	:				Optional[Any]            =							IFInpaintingSuperResolutionPipeline
		a_	:				int            =							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
		a_	:				List[str]            =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}			)
		a_	:				int            =							PipelineTesterMixin.required_optional_params - {"""latents"""}





		def    lowerCamelCase       (   self							:	Optional[Any]	):
				return self._get_superresolution_dummy_components()





		def    lowerCamelCase       (   self							:	Optional[Any]						,			a_							:	List[str]						,			a_							:	Union[str, Any]=0	):
				if str(a_	).startswith("mps"	):
						lowerCAmelCase_   :							List[Any]									=			torch.manual_seed(a_	)
				else:
						lowerCAmelCase_   :							str									=			torch.Generator(device=a_	).manual_seed(a_	)

				lowerCAmelCase_   :							List[str]									=			floats_tensor((1, 3, 16, 16)						,			rng=random.Random(a_	)	).to(a_	)
				lowerCAmelCase_   :							Tuple									=			floats_tensor((1, 3, 32, 32)						,			rng=random.Random(a_	)	).to(a_	)
				lowerCAmelCase_   :							int									=			floats_tensor((1, 3, 32, 32)						,			rng=random.Random(a_	)	).to(a_	)

				lowerCAmelCase_   :							Any									=			{
				    "prompt": "A painting of a squirrel eating a burger",
				    "image": image,
				    "original_image": original_image,
				    "mask_image": mask_image,
				    "generator": generator,
				    "num_inference_steps": 2,
				    "output_type": "numpy",
				}

				return inputs





		@unittest.skipIf(
		    torch_device != "cuda" or not is_xformers_available()						,			reason="XFormers attention is only available with CUDA and `xformers` installed"						,			)
		def    lowerCamelCase       (   self							:	List[Any]	):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				self._test_save_load_optional_components()





		@unittest.skipIf(torch_device != "cuda"						,			reason="float16 requires CUDA"	)
		def    lowerCamelCase       (   self							:	Optional[Any]	):
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1	)





		def    lowerCamelCase       (   self							:	Tuple	):
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)





		def    lowerCamelCase       (   self							:	List[str]	):
				self._test_save_load_local()





		def    lowerCamelCase       (   self							:	Optional[int]	):
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2						,			)
