






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__ (							__snake_case     ,				unittest.TestCase							):
		__snake_case      :       Union[str, Any]     =   KandinskyVaaInpaintPipeline
		__snake_case      :       List[Any]     =   ["image_embeds", "negative_image_embeds", "image", "mask_image"]
		__snake_case      :       Any     =   [
		    "image_embeds",
		    "negative_image_embeds",
		    "image",
		    "mask_image",
		]
		__snake_case      :       int     =   [
		    "generator",
		    "height",
		    "width",
		    "latents",
		    "guidance_scale",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		__snake_case      :       Dict     =   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		)

						_A	:				Dict   =      {
						    '''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,
						}

						_A	:				int   =      UNetaDConditionModel(**A__		)
						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		)
						_A	:				Any   =      VQModel(**self.dummy_movq_kwargs		)
						return model







		def  A__      (					self		):
						_A	:				List[str]   =      self.dummy_unet
						_A	:				Any   =      self.dummy_movq

						_A	:				str   =      DDIMScheduler(
						    num_train_timesteps=1000       ,beta_schedule='''linear'''       ,beta_start=0.0_00_85       ,beta_end=0.0_12       ,clip_sample=A__       ,set_alpha_to_one=A__       ,steps_offset=1       ,prediction_type='''epsilon'''       ,thresholding=A__       ,)

						_A	:				Dict   =      {
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''movq''': movq,
						}

						return components







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

						if str(A__		).startswith('''mps'''		):
										_A	:				Optional[int]   =      torch.manual_seed(A__		)
						else:
										_A	:				Union[str, Any]   =      torch.Generator(device=A__		).manual_seed(A__		)
						_A	:				Optional[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		):
						_A	:				List[Any]   =      '''cpu'''

						_A	:				Any   =      self.get_dummy_components()

						_A	:				Optional[Any]   =      self.pipeline_class(**A__		)
						_A	:				Optional[int]   =      pipe.to(A__		)

						pipe.set_progress_bar_config(disable=A__		)

						_A	:				Dict   =      pipe(**self.get_dummy_inputs(A__		)		)
						_A	:				Optional[Any]   =      output.images

						_A	:				List[str]   =      pipe(
						    **self.get_dummy_inputs(A__		)       ,return_dict=A__       ,)[0]

						_A	:				Optional[int]   =      image[0, -3:, -3:, -1]
						_A	:				Optional[Any]   =      image_from_tuple[0, -3:, -3:, -1]

						print(f"""image.shape {image.shape}"""		)

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

						_A	:				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 						UpperCAmelCase__ (							unittest.TestCase							):







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







		def  A__      (					self		):
						_A	:				Any   =      load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''		)

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

						_A	:				List[Any]   =      '''a hat'''

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

						_A	:				Dict   =      KandinskyVaaInpaintPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''       ,torch_dtype=torch.floataa		)
						_A	:				Optional[Any]   =      pipeline.to(A__		)
						pipeline.set_progress_bar_config(disable=A__		)

						_A	:				Any   =      torch.Generator(device='''cpu'''		).manual_seed(0		)
						_A  ,				_A	:				str   =      pipe_prior(
						    A__       ,generator=A__       ,num_inference_steps=5       ,negative_prompt=''''''       ,).to_tuple()

						_A	:				Optional[Any]   =      pipeline(
						    image=A__       ,mask_image=A__       ,image_embeds=A__       ,negative_image_embeds=A__       ,generator=A__       ,num_inference_steps=100       ,height=768       ,width=768       ,output_type='''np'''       ,)

						_A	:				int   =      output.images[0]

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

						assert_mean_pixel_difference(A__       ,A__		)











from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_UpperCamelCase			:	List[str]								=HfArgumentParser(InitializationArguments)
_UpperCamelCase			:	Dict								=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase			:	List[Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase			:	str								={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
_UpperCamelCase			:	int								=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase			:	List[str]								=AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)



