





'''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


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

class  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   0




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"		)
     self.assertIsInstance(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained(_lowerCAmelCase		)
     self.assertIsInstance(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				str:
     with tempfile.TemporaryDirectory() as tmpdirname:
         _lowerCAmelCase						      =   WavaVecaConfig()

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

         config_dict.pop("feature_extractor_type"		)
         _lowerCAmelCase						      =   WavaVecaFeatureExtractor(**_lowerCAmelCase		)

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

         _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained(_lowerCAmelCase		)

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

     self.assertIsInstance(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained(_lowerCAmelCase		)
     self.assertIsInstance(_lowerCAmelCase      ,  _lowerCAmelCase		)




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




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     with self.assertRaisesRegex(
         _lowerCAmelCase      ,  r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"		):
         _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained(_lowerCAmelCase      ,  revision="aaaaaa"		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     with self.assertRaisesRegex(
         _lowerCAmelCase      ,  "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."      ,  ):
         _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model"		)




 def 				_snake_case (						self		)     ->				Any:
     # If remote code is not set, we will time out when asking whether to load the model.
     with self.assertRaises(_lowerCAmelCase		):
         _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained(
             "hf-internal-testing/test_dynamic_feature_extractor"		)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(_lowerCAmelCase		):
         _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained(
             "hf-internal-testing/test_dynamic_feature_extractor"      ,  trust_remote_code=_lowerCAmelCase		)

     _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained(
         "hf-internal-testing/test_dynamic_feature_extractor"      ,  trust_remote_code=_lowerCAmelCase		)
     self.assertEqual(feature_extractor.__class__.__name__      ,  "NewFeatureExtractor"		)

     # Test feature extractor can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
         feature_extractor.save_pretrained(_lowerCAmelCase		)
         _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained(_lowerCAmelCase      ,  trust_remote_code=_lowerCAmelCase		)
     self.assertEqual(reloaded_feature_extractor.__class__.__name__      ,  "NewFeatureExtractor"		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     try:
         AutoConfig.register("custom"      ,  _lowerCAmelCase		)
         AutoFeatureExtractor.register(_lowerCAmelCase      ,  _lowerCAmelCase		)
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(_lowerCAmelCase		):
             AutoFeatureExtractor.register(_lowerCAmelCase      ,  _lowerCAmelCase		)

         # Now that the config is registered, it can be used as any other config with the auto-API
         _lowerCAmelCase						      =   CustomFeatureExtractor.from_pretrained(_lowerCAmelCase		)
         with tempfile.TemporaryDirectory() as tmp_dir:
             feature_extractor.save_pretrained(_lowerCAmelCase		)
             _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained(_lowerCAmelCase		)
             self.assertIsInstance(_lowerCAmelCase      ,  _lowerCAmelCase		)

     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 				_snake_case (						self		)     ->				Optional[int]:

     class  lowerCAmelCase_		(		__magic_name__		):
      __lowerCamelCase    :				int								=       True

     try:
         AutoConfig.register("custom"      ,  _lowerCAmelCase		)
         AutoFeatureExtractor.register(_lowerCAmelCase      ,  _lowerCAmelCase		)
         # If remote code is not set, the default is to use local
         _lowerCAmelCase						      =   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						      =   AutoFeatureExtractor.from_pretrained(
             "hf-internal-testing/test_dynamic_feature_extractor"      ,  trust_remote_code=_lowerCAmelCase		)
         self.assertEqual(feature_extractor.__class__.__name__      ,  "NewFeatureExtractor"		)
         self.assertTrue(feature_extractor.is_local		)

         # If remote is enabled, we load from the Hub
         _lowerCAmelCase						      =   AutoFeatureExtractor.from_pretrained(
             "hf-internal-testing/test_dynamic_feature_extractor"      ,  trust_remote_code=_lowerCAmelCase		)
         self.assertEqual(feature_extractor.__class__.__name__      ,  "NewFeatureExtractor"		)
         self.assertTrue(not hasattr(_lowerCAmelCase      ,  "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 gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   StableDiffusionInpaintPipeline
							lowerCAmelCase			:       int  							=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
							lowerCAmelCase			:       Optional[Any]  							=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowerCAmelCase			:       List[str]  							=   frozenset(
							    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							lowerCAmelCase			:       Optional[Any]  							=   frozenset([]  )







							def 				__lowercase     (				self   :			Dict						):
									torch.manual_seed(0						)
									_a	: Optional[int]								=				UNetaDConditionModel(
									    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,cross_attention_dim=32      ,attention_head_dim=(2, 4)      ,use_linear_projection=_UpperCAmelCase      ,)
									_a	: Optional[int]								=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase						)
									torch.manual_seed(0						)
									_a	: Dict								=				AutoencoderKL(
									    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,latent_channels=4      ,sample_size=128      ,)
									torch.manual_seed(0						)
									_a	: Union[str, Any]								=				CLIPTextConfig(
									    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1000      ,hidden_act='gelu'      ,projection_dim=512      ,)
									_a	: str								=				CLIPTextModel(_UpperCAmelCase						)
									_a	: Any								=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_a	: Optional[Any]								=				{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			int=0						):
									# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
									_a	: Any								=				floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
									_a	: Any								=				image.cpu().permute(0      ,2      ,3      ,1						)[0]
									_a	: Optional[int]								=				Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert('RGB'						).resize((64, 64)						)
									_a	: Tuple								=				Image.fromarray(np.uinta(image + 4						)						).convert('RGB'						).resize((64, 64)						)
									if str(_UpperCAmelCase						).startswith('mps'						):
											_a	: str								=				torch.manual_seed(_UpperCAmelCase						)
									else:
											_a	: Dict								=				torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
									_a	: Any								=				{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': init_image,
									    'mask_image': mask_image,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'numpy',
									}
									return inputs







							def 				__lowercase     (				self   :			List[str]						):
									_a	: Optional[int]								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
									_a	: Dict								=				self.get_dummy_components()
									_a	: int								=				StableDiffusionInpaintPipeline(**_UpperCAmelCase						)
									_a	: Any								=				sd_pipe.to(_UpperCAmelCase						)
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

									_a	: Dict								=				self.get_dummy_inputs(_UpperCAmelCase						)
									_a	: Tuple								=				sd_pipe(**_UpperCAmelCase						).images
									_a	: Dict								=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_a	: List[Any]								=				np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2







							def 				__lowercase     (				self   :			List[Any]						):
									super().test_inference_batch_single_identical(expected_max_diff=3E-3						)

@slow
@require_torch_gpu
class       __magic_name__		(       unittest.TestCase  ):







							def 				__lowercase     (				self   :			List[str]						):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def 				__lowercase     (				self   :			int						):
									_a	: Tuple								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Union[str, Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench.npy'						)

									_a	: List[Any]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Tuple								=				StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase      ,safety_checker=_UpperCAmelCase						)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: int								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Dict								=				torch.manual_seed(0						)
									_a	: Any								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: List[str]								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 9E-3







							def 				__lowercase     (				self   :			Tuple						):
									_a	: str								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[Any]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)
									_a	: int								=				load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
									    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'						)

									_a	: int								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: List[str]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,torch_dtype=torch.floataa      ,safety_checker=_UpperCAmelCase      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing()

									_a	: Dict								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: int								=				torch.manual_seed(0						)
									_a	: Dict								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,output_type='np'      ,)
									_a	: str								=				output.images[0]

									assert image.shape == (512, 512, 3)
									assert np.abs(expected_image - image						).max() < 5E-1







							def 				__lowercase     (				self   :			Tuple						):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_a	: Dict								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/sd2-inpaint/init_image.png'						)
									_a	: Optional[int]								=				load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'						)

									_a	: List[str]								=				'stabilityai/stable-diffusion-2-inpainting'
									_a	: Any								=				PNDMScheduler.from_pretrained(_UpperCAmelCase      ,subfolder='scheduler'						)
									_a	: Optional[Any]								=				StableDiffusionInpaintPipeline.from_pretrained(
									    _UpperCAmelCase      ,safety_checker=_UpperCAmelCase      ,scheduler=_UpperCAmelCase      ,torch_dtype=torch.floataa      ,)
									pipe.to(_UpperCAmelCase						)
									pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									_a	: Any								=				'Face of a yellow cat, high resolution, sitting on a park bench'

									_a	: Optional[Any]								=				torch.manual_seed(0						)
									_a	: int								=				pipe(
									    prompt=_UpperCAmelCase      ,image=_UpperCAmelCase      ,mask_image=_UpperCAmelCase      ,generator=_UpperCAmelCase      ,num_inference_steps=2      ,output_type='np'      ,)

									_a	: int								=				torch.cuda.max_memory_allocated()
									# make sure that less than 2.65 GB is allocated
									assert mem_bytes < 2.65 * 10**9




