




"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class lowerCAmelCase__      (	lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     KandinskyVaaInpaintPipeline
			lowerCamelCase__ =     ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			lowerCamelCase__ =     [
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			lowerCamelCase__ =     [
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowerCamelCase__ =     False
			@property
			def    A_       (    self    ):
							return 32
			@property
			def    A_       (    self    ):
							return 32
			@property
			def    A_       (    self    ):
							return self.time_input_dim
			@property
			def    A_       (    self    ):
							return self.time_input_dim * 4
			@property
			def    A_       (    self    ):
							return 100
			@property
			def    A_       (    self    ):
							torch.manual_seed(0    )

							_lowerCamelCase	:     Optional[int]       =       {
							    'in_channels': 9,
							    # Out channels is double in channels because predicts mean and variance
							    'out_channels': 8,
							    'addition_embed_type': 'image',
							    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
							    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
							    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
							    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
							    'layers_per_block': 1,
							    'encoder_hid_dim': self.text_embedder_hidden_size,
							    'encoder_hid_dim_type': 'image_proj',
							    'cross_attention_dim': self.cross_attention_dim,
							    'attention_head_dim': 4,
							    'resnet_time_scale_shift': 'scale_shift',
							    'class_embed_type': None,
							}

							_lowerCamelCase	:     List[str]       =       UNetaDConditionModel(**lowercase    )
							return model
			@property
			def    A_       (    self    ):
							return {
							    "block_out_channels": [32, 64],
							    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": [
							        "AttnUpDecoderBlock2D",
							        "UpDecoderBlock2D",
							    ],
							    "vq_embed_dim": 4,
							}
			@property
			def    A_       (    self    ):
							torch.manual_seed(0    )
							_lowerCamelCase	:     Any       =       VQModel(**self.dummy_movq_kwargs    )
							return model
			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       self.dummy_unet
							_lowerCamelCase	:     int       =       self.dummy_movq

							_lowerCamelCase	:     Optional[int]       =       DDIMScheduler(
							    num_train_timesteps=1000    , beta_schedule='linear'    , beta_start=0.0_00_85    , beta_end=0.0_12    , clip_sample=lowercase    , set_alpha_to_one=lowercase    , steps_offset=1    , prediction_type='epsilon'    , thresholding=lowercase    , )

							_lowerCamelCase	:     Union[str, Any]       =       {
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}

							return components
			def    A_       (    self    , lowercase    , lowercase=0    ):
							_lowerCamelCase	:     Optional[Any]       =       floats_tensor((1, self.text_embedder_hidden_size)    , rng=random.Random(lowercase    )    ).to(lowercase    )
							_lowerCamelCase	:     Union[str, Any]       =       floats_tensor((1, self.text_embedder_hidden_size)    , rng=random.Random(seed + 1    )    ).to(
							    lowercase    )
							# create init_image
							_lowerCamelCase	:     Optional[Any]       =       floats_tensor((1, 3, 64, 64)    , rng=random.Random(lowercase    )    ).to(lowercase    )
							_lowerCamelCase	:     Optional[Any]       =       image.cpu().permute(0    , 2    , 3    , 1    )[0]
							_lowerCamelCase	:     int       =       Image.fromarray(np.uinta(lowercase    )    ).convert('RGB'    ).resize((256, 256)    )
							# create mask
							_lowerCamelCase	:     Union[str, Any]       =       np.ones((64, 64)    , dtype=np.floataa    )
							_lowerCamelCase	:     Any       =       0

							if str(lowercase    ).startswith('mps'    ):
											_lowerCamelCase	:     int       =       torch.manual_seed(lowercase    )
							else:
											_lowerCamelCase	:     str       =       torch.Generator(device=lowercase    ).manual_seed(lowercase    )
							_lowerCamelCase	:     str       =       {
							    'image': init_image,
							    'mask_image': mask,
							    'image_embeds': image_embeds,
							    'negative_image_embeds': negative_image_embeds,
							    'generator': generator,
							    'height': 64,
							    'width': 64,
							    'num_inference_steps': 2,
							    'guidance_scale': 4.0,
							    'output_type': 'np',
							}
							return inputs
			def    A_       (    self    ):
							_lowerCamelCase	:     int       =       'cpu'

							_lowerCamelCase	:     int       =       self.get_dummy_components()

							_lowerCamelCase	:     Optional[Any]       =       self.pipeline_class(**lowercase    )
							_lowerCamelCase	:     List[Any]       =       pipe.to(lowercase    )

							pipe.set_progress_bar_config(disable=lowercase    )

							_lowerCamelCase	:     str       =       pipe(**self.get_dummy_inputs(lowercase    )    )
							_lowerCamelCase	:     Any       =       output.images

							_lowerCamelCase	:     List[Any]       =       pipe(
							    **self.get_dummy_inputs(lowercase    )    , return_dict=lowercase    , )[0]

							_lowerCamelCase	:     Optional[int]       =       image[0, -3:, -3:, -1]
							_lowerCamelCase	:     Union[str, Any]       =       image_from_tuple[0, -3:, -3:, -1]

							print(F'''image.shape {image.shape}'''    )

							assert image.shape == (1, 64, 64, 3)

							_lowerCamelCase	:     Dict       =       np.array(
							    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]    )

							assert (
							    np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
							), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2
							), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






			def    A_       (    self    ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3    )






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






			'''simple docstring'''



			def    A_       (    self    ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[int]       =       load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'    )

							_lowerCamelCase	:     Any       =       load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'    )
							_lowerCamelCase	:     Any       =       np.ones((768, 768)    , dtype=np.floataa    )
							_lowerCamelCase	:     Union[str, Any]       =       0

							_lowerCamelCase	:     List[str]       =       'a hat'

							_lowerCamelCase	:     Optional[int]       =       KandinskyVaaPriorPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-prior'    , torch_dtype=torch.floataa    )
							pipe_prior.to(lowercase    )

							_lowerCamelCase	:     int       =       KandinskyVaaInpaintPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-decoder-inpaint'    , torch_dtype=torch.floataa    )
							_lowerCamelCase	:     int       =       pipeline.to(lowercase    )
							pipeline.set_progress_bar_config(disable=lowercase    )

							_lowerCamelCase	:     Optional[int]       =       torch.Generator(device='cpu'    ).manual_seed(0    )
							_lowerCamelCase,					_lowerCamelCase	:     Tuple       =       pipe_prior(
							    lowercase    , generator=lowercase    , num_inference_steps=5    , negative_prompt=''    , ).to_tuple()

							_lowerCamelCase	:     Tuple       =       pipeline(
							    image=lowercase    , mask_image=lowercase    , image_embeds=lowercase    , negative_image_embeds=lowercase    , generator=lowercase    , num_inference_steps=100    , height=768    , width=768    , output_type='np'    , )

							_lowerCamelCase	:     Any       =       output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(lowercase    , lowercase    )





"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			=		logging.get_logger(__name__)

lowercase__			=		{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """openai-gpt"""
			lowerCamelCase__ =     {
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}
			def __init__(    self    , lowercase=40478    , lowercase=512    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=0.1    , lowercase=1E-5    , lowercase=0.02    , lowercase="cls_index"    , lowercase=True    , lowercase=None    , lowercase=True    , lowercase=0.1    , **lowercase    , ):
							_lowerCamelCase	:     Tuple       =       vocab_size
							_lowerCamelCase	:     List[str]       =       n_positions
							_lowerCamelCase	:     Tuple       =       n_embd
							_lowerCamelCase	:     Optional[int]       =       n_layer
							_lowerCamelCase	:     List[Any]       =       n_head
							_lowerCamelCase	:     Dict       =       afn
							_lowerCamelCase	:     Union[str, Any]       =       resid_pdrop
							_lowerCamelCase	:     Dict       =       embd_pdrop
							_lowerCamelCase	:     List[Any]       =       attn_pdrop
							_lowerCamelCase	:     Any       =       layer_norm_epsilon
							_lowerCamelCase	:     List[Any]       =       initializer_range
							_lowerCamelCase	:     List[Any]       =       summary_type
							_lowerCamelCase	:     List[str]       =       summary_use_proj
							_lowerCamelCase	:     Tuple       =       summary_activation
							_lowerCamelCase	:     int       =       summary_first_dropout
							_lowerCamelCase	:     str       =       summary_proj_to_labels
							super().__init__(**lowercase    )