


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      __lowerCamelCase       (a_							,	a_							,	a_							,	unittest.TestCase		):
       _lowercase											=			StableDiffusionInpaintPipeline
       _lowercase											=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       _lowercase											=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       _lowercase											=			frozenset(
           []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       _lowercase											=			frozenset([]		)







       def 						snake_case_     (       self:      Tuple ):






            '''simple docstring'''




            torch.manual_seed(0 )
            __UpperCamelCase      =       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=_A,)
            __UpperCamelCase      =       PNDMScheduler(skip_prk_steps=_A )
            torch.manual_seed(0 )
            __UpperCamelCase      =       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 )
            __UpperCamelCase      =       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,)
            __UpperCamelCase      =       CLIPTextModel(_A )
            __UpperCamelCase      =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

            __UpperCamelCase      =       {
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'safety_checker': None,
                'feature_extractor': None,
            }
            return components







       def 						snake_case_     (       self:      List[Any],A_:      str,A_:      Optional[Any]=0 ):






            '''simple docstring'''




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






            '''simple docstring'''




            __UpperCamelCase      =       'cpu'  # ensure determinism for the device-dependent torch.Generator
            __UpperCamelCase      =       self.get_dummy_components()
            __UpperCamelCase      =       StableDiffusionInpaintPipeline(**_A )
            __UpperCamelCase      =       sd_pipe.to(_A )
            sd_pipe.set_progress_bar_config(disable=_A )

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

            assert image.shape == (1, 64, 64, 3)
            __UpperCamelCase      =       np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6] )

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







       def 						snake_case_     (       self:      List[Any] ):






            '''simple docstring'''




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



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







       def 						snake_case_     (       self:      Optional[int] ):






            '''simple docstring'''




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







       def 						snake_case_     (       self:      int ):






            '''simple docstring'''




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

            __UpperCamelCase      =       'stabilityai/stable-diffusion-2-inpainting'
            __UpperCamelCase      =       StableDiffusionInpaintPipeline.from_pretrained(_A,safety_checker=_A )
            pipe.to(_A )
            pipe.set_progress_bar_config(disable=_A )
            pipe.enable_attention_slicing()

            __UpperCamelCase      =       'Face of a yellow cat, high resolution, sitting on a park bench'

            __UpperCamelCase      =       torch.manual_seed(0 )
            __UpperCamelCase      =       pipe(
                prompt=_A,image=_A,mask_image=_A,generator=_A,output_type='np',)
            __UpperCamelCase      =       output.images[0]

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







       def 						snake_case_     (       self:      List[str] ):






            '''simple docstring'''




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

            __UpperCamelCase      =       'stabilityai/stable-diffusion-2-inpainting'
            __UpperCamelCase      =       StableDiffusionInpaintPipeline.from_pretrained(
                _A,torch_dtype=torch.floataa,safety_checker=_A,)
            pipe.to(_A )
            pipe.set_progress_bar_config(disable=_A )
            pipe.enable_attention_slicing()

            __UpperCamelCase      =       'Face of a yellow cat, high resolution, sitting on a park bench'

            __UpperCamelCase      =       torch.manual_seed(0 )
            __UpperCamelCase      =       pipe(
                prompt=_A,image=_A,mask_image=_A,generator=_A,output_type='np',)
            __UpperCamelCase      =       output.images[0]

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







       def 						snake_case_     (       self:      List[Any] ):






            '''simple docstring'''




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

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

            __UpperCamelCase      =       'stabilityai/stable-diffusion-2-inpainting'
            __UpperCamelCase      =       PNDMScheduler.from_pretrained(_A,subfolder='scheduler' )
            __UpperCamelCase      =       StableDiffusionInpaintPipeline.from_pretrained(
                _A,safety_checker=_A,scheduler=_A,torch_dtype=torch.floataa,)
            pipe.to(_A )
            pipe.set_progress_bar_config(disable=_A )
            pipe.enable_attention_slicing(1 )
            pipe.enable_sequential_cpu_offload()

            __UpperCamelCase      =       'Face of a yellow cat, high resolution, sitting on a park bench'

            __UpperCamelCase      =       torch.manual_seed(0 )
            __UpperCamelCase      =       pipe(
                prompt=_A,image=_A,mask_image=_A,generator=_A,num_inference_steps=2,output_type='np',)

            __UpperCamelCase      =       torch.cuda.max_memory_allocated()
            # make sure that less than 2.65 GB is allocated
            assert mem_bytes < 2.6_5 * 10**9









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__A				:    Union[str, Any]       =   {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextTokenizer"]

try:
				if not is_speech_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    List[Any]       =   ["Speech2TextFeatureExtractor"]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    int       =   [
				    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFSpeech2TextForConditionalGeneration",
				    "TFSpeech2TextModel",
				    "TFSpeech2TextPreTrainedModel",
				]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A				:    Tuple       =   [
				    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "Speech2TextForConditionalGeneration",
				    "Speech2TextModel",
				    "Speech2TextPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
				from .processing_speech_to_text import SpeechaTextProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speech_to_text import SpeechaTextTokenizer

				try:
								if not is_speech_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_speech_to_text import (
								    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSpeechaTextForConditionalGeneration,
								    TFSpeechaTextModel,
								    TFSpeechaTextPreTrainedModel,
								)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speech_to_text import (
								    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechaTextForConditionalGeneration,
								    SpeechaTextModel,
								    SpeechaTextPreTrainedModel,
								)

else:
				import sys

				__A				:    Dict       =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



