


'''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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    KandinskyVaaInpaintPipeline
     __magic_name__    			=    ["image_embeds", "negative_image_embeds", "image", "mask_image"]
     __magic_name__    			=    [
         "image_embeds",
         "negative_image_embeds",
         "image",
         "mask_image",
     ]
     __magic_name__    			=    [
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     __magic_name__    			=    False







     @property
     def a	( self   ):

            '''simple docstring'''

            return 32







     @property
     def a	( self   ):

            '''simple docstring'''

            return 32







     @property
     def a	( self   ):

            '''simple docstring'''

            return self.time_input_dim







     @property
     def a	( self   ):

            '''simple docstring'''

            return self.time_input_dim * 4







     @property
     def a	( self   ):

            '''simple docstring'''

            return 100







     @property
     def a	( self   ):

            '''simple docstring'''

            torch.manual_seed(0   )

            _lowerCAmelCase    :							Union[str, Any]   			=       {
                '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    :							str   			=       UNetaDConditionModel(**snake_case__   )
            return model







     @property
     def a	( self   ):

            '''simple docstring'''

            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   ):

            '''simple docstring'''

            torch.manual_seed(0   )
            _lowerCAmelCase    :							Tuple   			=       VQModel(**self.dummy_movq_kwargs   )
            return model







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.dummy_unet
            _lowerCAmelCase    :							Any   			=       self.dummy_movq

            _lowerCAmelCase    :							int   			=       DDIMScheduler(
                num_train_timesteps=1000      ,	beta_schedule='linear'      ,	beta_start=0.0_0085      ,	beta_end=0.012      ,	clip_sample=snake_case__      ,	set_alpha_to_one=snake_case__      ,	steps_offset=1      ,	prediction_type='epsilon'      ,	thresholding=snake_case__      ,	)

            _lowerCAmelCase    :							Optional[int]   			=       {
                'unet': unet,
                'scheduler': scheduler,
                'movq': movq,
            }

            return components







     def a	( self      ,	snake_case__      ,	snake_case__=0   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       floats_tensor((1, self.text_embedder_hidden_size)      ,	rng=random.Random(snake_case__   )   ).to(snake_case__   )
            _lowerCAmelCase    :							Dict   			=       floats_tensor((1, self.text_embedder_hidden_size)      ,	rng=random.Random(seed + 1   )   ).to(
                snake_case__   )
            # create init_image
            _lowerCAmelCase    :							List[str]   			=       floats_tensor((1, 3, 64, 64)      ,	rng=random.Random(snake_case__   )   ).to(snake_case__   )
            _lowerCAmelCase    :							Any   			=       image.cpu().permute(0      ,	2      ,	3      ,	1   )[0]
            _lowerCAmelCase    :							List[Any]   			=       Image.fromarray(np.uinta(snake_case__   )   ).convert('RGB'   ).resize((256, 256)   )
            # create mask
            _lowerCAmelCase    :							List[str]   			=       np.ones((64, 64)      ,	dtype=np.floataa   )
            _lowerCAmelCase    :							List[str]   			=       0

            if str(snake_case__   ).startswith('mps'   ):
                   _lowerCAmelCase    :							Union[str, Any]   			=       torch.manual_seed(snake_case__   )
            else:
                   _lowerCAmelCase    :							Dict   			=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )
            _lowerCAmelCase    :							int   			=       {
                '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   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       'cpu'

            _lowerCAmelCase    :							List[Any]   			=       self.get_dummy_components()

            _lowerCAmelCase    :							int   			=       self.pipeline_class(**snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       pipe.to(snake_case__   )

            pipe.set_progress_bar_config(disable=snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       pipe(**self.get_dummy_inputs(snake_case__   )   )
            _lowerCAmelCase    :							str   			=       output.images

            _lowerCAmelCase    :							int   			=       pipe(
                **self.get_dummy_inputs(snake_case__   )      ,	return_dict=snake_case__      ,	)[0]

            _lowerCAmelCase    :							Any   			=       image[0, -3:, -3:, -1]
            _lowerCAmelCase    :							Tuple   			=       image_from_tuple[0, -3:, -3:, -1]

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

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

            _lowerCAmelCase    :							Optional[Any]   			=       np.array(
                [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]   )

            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   ):

            '''simple docstring'''

            super().test_inference_batch_single_identical(expected_max_diff=3E-3   )
@slow
@require_torch_gpu
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'   )

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

            _lowerCAmelCase    :							Union[str, Any]   			=       'a hat'

            _lowerCAmelCase    :							Any   			=       KandinskyVaaPriorPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-2-prior'      ,	torch_dtype=torch.floataa   )
            pipe_prior.to(snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       KandinskyVaaInpaintPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-2-decoder-inpaint'      ,	torch_dtype=torch.floataa   )
            _lowerCAmelCase    :							Any   			=       pipeline.to(snake_case__   )
            pipeline.set_progress_bar_config(disable=snake_case__   )

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

            _lowerCAmelCase    :							Dict   			=       pipeline(
                image=snake_case__      ,	mask_image=snake_case__      ,	image_embeds=snake_case__      ,	negative_image_embeds=snake_case__      ,	generator=snake_case__      ,	num_inference_steps=100      ,	height=768      ,	width=768      ,	output_type='np'      ,	)

            _lowerCAmelCase    :							Union[str, Any]   			=       output.images[0]

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

            assert_mean_pixel_difference(snake_case__      ,	snake_case__   )





'''simple docstring'''







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None   ):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase    :							Union[str, Any]   			=       pad_token_id
            _lowerCAmelCase    :							List[Any]   			=       max_length
            _lowerCAmelCase    :							Tuple   			=       vocab
            _lowerCAmelCase    :							str   			=       merges
            _lowerCAmelCase    :							List[str]   			=       BytePairTokenizer(snake_case__      ,	snake_case__      ,	sequence_length=snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [' '.join(snake_case__   ) for m in tokenizer.bpe_ranks.keys()]
            _lowerCAmelCase    :							Any   			=       tokenizer.get_vocab()
            return cls(snake_case__      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       GPTaTokenizer.from_pretrained(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            return cls.from_tokenizer(snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            return cls(**snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            return {
                "vocab": self.vocab,
                "merges": self.merges,
                "max_length": self.max_length,
                "pad_token_id": self.pad_token_id,
            }







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tf_tokenizer(snake_case__   )
            _lowerCAmelCase    :							str   			=       tf.ones_like(snake_case__   )

            if self.pad_token_id is not None:
                   # pad the tokens up to max length
                   _lowerCAmelCase    :							Optional[int]   			=       max_length if max_length is not None else self.max_length

                   if max_length is not None:
                          _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       pad_model_inputs(
                              snake_case__      ,	max_seq_length=snake_case__      ,	pad_value=self.pad_token_id   )

            return {"attention_mask": attention_mask, "input_ids": input_ids}

