



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



			def     _lowerCamelCase       (						self   :  Tuple)   ->    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=__lowerCAmelCase ,		)
						_UpperCAmelCase		       =   PNDMScheduler(skip_prk_steps=__lowerCAmelCase)
						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=1_28 ,		)
						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=10_00 ,		hidden_act='gelu' ,		projection_dim=5_12 ,		)
						_UpperCAmelCase		       =   CLIPTextModel(__lowerCAmelCase)
						_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     _lowerCamelCase       (						self   :  List[str] ,		A   :  Any ,		A   :  Optional[int]=0)   ->    Any:



						"""simple docstring"""






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



						"""simple docstring"""






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

						_UpperCAmelCase		       =   self.get_dummy_inputs(__lowerCAmelCase)
						_UpperCAmelCase		       =   sd_pipe(**__lowerCAmelCase).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     _lowerCamelCase       (						self   :  Union[str, Any])   ->    str:



						"""simple docstring"""






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







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



			def     _lowerCamelCase       (						self   :  Dict)   ->    int:



						"""simple docstring"""






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



			def     _lowerCamelCase       (						self   :  Dict)   ->    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.npy')

						_UpperCAmelCase		       =   '''stabilityai/stable-diffusion-2-inpainting'''
						_UpperCAmelCase		       =   StableDiffusionInpaintPipeline.from_pretrained(__lowerCAmelCase ,		safety_checker=__lowerCAmelCase)
						pipe.to(__lowerCAmelCase)
						pipe.set_progress_bar_config(disable=__lowerCAmelCase)
						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=__lowerCAmelCase ,		image=__lowerCAmelCase ,		mask_image=__lowerCAmelCase ,		generator=__lowerCAmelCase ,		output_type='np' ,		)
						_UpperCAmelCase		       =   output.images[0]

						assert image.shape == (5_12, 5_12, 3)
						assert np.abs(expected_image - image).max() < 9E-3



			def     _lowerCamelCase       (						self   :  str)   ->    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(
						    __lowerCAmelCase ,		torch_dtype=torch.floataa ,		safety_checker=__lowerCAmelCase ,		)
						pipe.to(__lowerCAmelCase)
						pipe.set_progress_bar_config(disable=__lowerCAmelCase)
						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=__lowerCAmelCase ,		image=__lowerCAmelCase ,		mask_image=__lowerCAmelCase ,		generator=__lowerCAmelCase ,		output_type='np' ,		)
						_UpperCAmelCase		       =   output.images[0]

						assert image.shape == (5_12, 5_12, 3)
						assert np.abs(expected_image - image).max() < 5E-1



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



						"""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(__lowerCAmelCase ,		subfolder='scheduler')
						_UpperCAmelCase		       =   StableDiffusionInpaintPipeline.from_pretrained(
						    __lowerCAmelCase ,		safety_checker=__lowerCAmelCase ,		scheduler=__lowerCAmelCase ,		torch_dtype=torch.floataa ,		)
						pipe.to(__lowerCAmelCase)
						pipe.set_progress_bar_config(disable=__lowerCAmelCase)
						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=__lowerCAmelCase ,		image=__lowerCAmelCase ,		mask_image=__lowerCAmelCase ,		generator=__lowerCAmelCase ,		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

'''simple docstring'''

import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    CTRLTokenizer
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Dict:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__				:    Optional[Any]  =					['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
        lowercase__				:    str  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )
        lowercase__				:    Tuple  =					['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
        lowercase__				:    Optional[Any]  =					{'''unk_token''': '''<unk>'''}

        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  List[str]:
        kwargs.update(self.special_tokens_map )
        return CTRLTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    List[str]  =					'''adapt react readapt apt'''
        lowercase__				:    Union[str, Any]  =					'''adapt react readapt apt'''
        return input_text, output_text


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Union[str, Any]  =					CTRLTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Optional[Any]  =					'''adapt react readapt apt'''
        lowercase__				:    Dict  =					'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    int  =					tokens + [tokenizer.unk_token]

        lowercase__				:    List[str]  =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

