


# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


A	:		Optional[Any]    =       'pytorch_model.bin'
A	:		List[str]    =       'pytorch_model.bin.index.json'
A	:		List[Any]    =       'adapter_config.json'
A	:		List[Any]    =       'adapter_model.bin'
A	:		Dict    =       'adapter_model.safetensors'
A	:		Dict    =       'tf_model.h5'
A	:		List[Any]    =       'tf_model.h5.index.json'
A	:		Optional[int]    =       'model.ckpt'
A	:		Union[str, Any]    =       'flax_model.msgpack'
A	:		Dict    =       'flax_model.msgpack.index.json'
A	:		List[Any]    =       'model.safetensors'
A	:		int    =       'model.safetensors.index.json'
A	:		Optional[Any]    =       'config.json'
A	:		Any    =       'preprocessor_config.json'
A	:		Union[str, Any]    =       FEATURE_EXTRACTOR_NAME
A	:		Any    =       'generation_config.json'
A	:		List[str]    =       'modelcard.json'

A	:		Tuple    =       '▁'
A	:		Optional[Any]    =       SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

A	:		Optional[Any]    =       [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
A	:		Optional[int]    =       [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
A	:		List[Any]    =       [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]






def 			UpperCamelCase (     __magic_name__					:							Dict					)       ->  int:
		"""simple docstring"""




		if version.parse(__magic_name__					) < version.parse(__magic_name__					):
				if "dev" in min_version:
						lowercase__					      =		(
						    """This example requires a source install from HuggingFace Transformers (see """
						    """`https://huggingface.co/docs/transformers/installation#install-from-source`),"""
						)
				else:
						lowercase__					      =		f'''This example requires a minimum version of {min_version},'''
				error_message += f''' but the version found is {__version__}.\n'''
				raise ImportError(
				    error_message
				    + """Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other """
				    """versions of HuggingFace Transformers."""					)





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 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			StableDiffusionInpaintPipeline
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					A__      =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					A__      =			frozenset(
					    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A__      =			frozenset([]							)

					def   lowerCamelCase__					(self   :						Tuple		)						->       List[Any]:




							"""simple docstring"""


							torch.manual_seed(0		)
							lowercase__					      =		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				, )
							lowercase__					      =		PNDMScheduler(skip_prk_steps=_UpperCAmelCase		)
							torch.manual_seed(0		)
							lowercase__					      =		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		)
							lowercase__					      =		CLIPTextConfig(
							    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-0_5				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, hidden_act="""gelu"""				, projection_dim=512				, )
							lowercase__					      =		CLIPTextModel(_UpperCAmelCase		)
							lowercase__					      =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

							lowercase__					      =		{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """vae""": vae,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """safety_checker""": None,
							    """feature_extractor""": None,
							}
							return components

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Optional[Any]=0		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		floats_tensor((1, 3, 32, 32)				, rng=random.Random(_UpperCAmelCase		)		).to(_UpperCAmelCase		)
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		)[0]
							lowercase__					      =		Image.fromarray(np.uinta(_UpperCAmelCase		)		).convert("""RGB"""		).resize((64, 64)		)
							lowercase__					      =		Image.fromarray(np.uinta(image + 4		)		).convert("""RGB"""		).resize((64, 64)		)
							if str(_UpperCAmelCase		).startswith("""mps"""		):
									lowercase__					      =		torch.manual_seed(_UpperCAmelCase		)
							else:
									lowercase__					      =		torch.Generator(device=_UpperCAmelCase		).manual_seed(_UpperCAmelCase		)
							lowercase__					      =		{
							    """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   lowerCamelCase__					(self   :						Optional[int]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
							lowercase__					      =		self.get_dummy_components()
							lowercase__					      =		StableDiffusionInpaintPipeline(**_UpperCAmelCase		)
							lowercase__					      =		sd_pipe.to(_UpperCAmelCase		)
							sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase		)

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

							assert image.shape == (1, 64, 64, 3)
							lowercase__					      =		np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]		)

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


					def   lowerCamelCase__					(self   :						str		)						->       int:




							"""simple docstring"""


							super().test_inference_batch_single_identical(expected_max_diff=3E-3		)






@slow
@require_torch_gpu
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						str		)						->       Optional[Any]:




							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

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




							"""simple docstring"""


							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
							    """/yellow_cat_sitting_on_a_park_bench.npy"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase				, safety_checker=_UpperCAmelCase		)
							pipe.to(_UpperCAmelCase		)
							pipe.set_progress_bar_config(disable=_UpperCAmelCase		)
							pipe.enable_attention_slicing()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

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

					def   lowerCamelCase__					(self   :						Optional[int]		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)
							lowercase__					      =		load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
							    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		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()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, output_type="""np"""				, )
							lowercase__					      =		output.images[0]

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


					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[Any]:




							"""simple docstring"""


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/sd2-inpaint/init_image.png"""		)
							lowercase__					      =		load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""		)

							lowercase__					      =		"""stabilityai/stable-diffusion-2-inpainting"""
							lowercase__					      =		PNDMScheduler.from_pretrained(_UpperCAmelCase				, subfolder="""scheduler"""		)
							lowercase__					      =		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()

							lowercase__					      =		"""Face of a yellow cat, high resolution, sitting on a park bench"""

							lowercase__					      =		torch.manual_seed(0		)
							lowercase__					      =		pipe(
							    prompt=_UpperCAmelCase				, image=_UpperCAmelCase				, mask_image=_UpperCAmelCase				, generator=_UpperCAmelCase				, num_inference_steps=2				, output_type="""np"""				, )

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

