





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







	'''simple docstring'''

	a      :			Optional[int]     =    KandinskyVaaInpaintPipeline
	a      :			Tuple     =    ["image_embeds", "negative_image_embeds", "image", "mask_image"]
	a      :			Tuple     =    [
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	a      :			List[str]     =    [
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	a      :			List[str]     =    False


	@property
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				return 32


	@property
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				return 32


	@property
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				return self.time_input_dim


	@property
	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				return self.time_input_dim * 4


	@property
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				return 100


	@property
	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				torch.manual_seed(0	)

				__lowercase			  =							{
				    '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,
				}

				__lowercase			  =							UNetaDConditionModel(**snake_case__	)
				return model


	@property
	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''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   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				torch.manual_seed(0	)
				__lowercase			  =							VQModel(**self.dummy_movq_kwargs	)
				return model


	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase			  =							self.dummy_unet
				__lowercase			  =							self.dummy_movq

				__lowercase			  =							DDIMScheduler(
				    num_train_timesteps=1000       ,beta_schedule='''linear'''       ,beta_start=0.0_0_0_8_5       ,beta_end=0.0_1_2       ,clip_sample=snake_case__       ,set_alpha_to_one=snake_case__       ,steps_offset=1       ,prediction_type='''epsilon'''       ,thresholding=snake_case__       ,)

				__lowercase			  =							{
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}

				return components


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=0	)							->							Optional[int]:







				'''simple docstring'''





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

				if str(snake_case__	).startswith('''mps'''	):
							__lowercase			  =							torch.manual_seed(snake_case__	)
				else:
							__lowercase			  =							torch.Generator(device=snake_case__	).manual_seed(snake_case__	)
				__lowercase			  =							{
				    '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   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





				__lowercase			  =							'cpu'

				__lowercase			  =							self.get_dummy_components()

				__lowercase			  =							self.pipeline_class(**snake_case__	)
				__lowercase			  =							pipe.to(snake_case__	)

				pipe.set_progress_bar_config(disable=snake_case__	)

				__lowercase			  =							pipe(**self.get_dummy_inputs(snake_case__	)	)
				__lowercase			  =							output.images

				__lowercase			  =							pipe(
				    **self.get_dummy_inputs(snake_case__	)       ,return_dict=snake_case__       ,)[0]

				__lowercase			  =							image[0, -3:, -3:, -1]
				__lowercase			  =							image_from_tuple[0, -3:, -3:, -1]

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

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

				__lowercase			  =							np.array(
				    [0.5_0_7_7_5_9_0_3, 0.4_9_5_2_7_1_9_5, 0.4_8_8_2_4_5_4_3, 0.5_0_1_9_2_2_3_7, 0.4_8_6_4_4_9_0_6, 0.4_9_3_7_3_8_1_4, 0.4_7_8_0_5_9_8, 0.4_7_2_3_4_8_2_7, 0.4_8_3_2_7_8_4_8]	)

				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   _UpperCAmelCase       (self	)							->							Tuple:







				'''simple docstring'''





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






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







	'''simple docstring'''

	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





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


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







				'''simple docstring'''





				__lowercase			  =							load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''	)

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

				__lowercase			  =							'a hat'

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

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

				__lowercase			  =							torch.Generator(device='''cpu'''	).manual_seed(0	)
				__lowercase			  =							pipe_prior(
				    snake_case__       ,generator=snake_case__       ,num_inference_steps=5       ,negative_prompt=''''''       ,).to_tuple()

				__lowercase			  =							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'''       ,)

				__lowercase			  =							output.images[0]

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

				assert_mean_pixel_difference(snake_case__       ,snake_case__	)












'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Dict     =    "open-llama"


	def __init__(self       ,_lowerCamelCase=100000       ,_lowerCamelCase=4096       ,_lowerCamelCase=11008       ,_lowerCamelCase=32       ,_lowerCamelCase=32       ,_lowerCamelCase="silu"       ,_lowerCamelCase=2048       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-6       ,_lowerCamelCase=True       ,_lowerCamelCase=0       ,_lowerCamelCase=1       ,_lowerCamelCase=2       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.1       ,_lowerCamelCase=True       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							vocab_size
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							hidden_size
				__lowercase			  =							intermediate_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							hidden_act
				__lowercase			  =							initializer_range
				__lowercase			  =							rms_norm_eps
				__lowercase			  =							use_cache
				__lowercase			  =							kwargs.pop(
				    '''use_memorry_efficient_attention'''       ,_lowerCamelCase	)
				__lowercase			  =							hidden_dropout_prob
				__lowercase			  =							attention_dropout_prob
				__lowercase			  =							use_stable_embedding
				__lowercase			  =							shared_input_output_embedding
				__lowercase			  =							rope_scaling
				self._rope_scaling_validation()

				super().__init__(
				    pad_token_id=_lowerCamelCase       ,bos_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,tie_word_embeddings=_lowerCamelCase       ,**_lowerCamelCase       ,)


	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				if self.rope_scaling is None:
							return

				if not isinstance(self.rope_scaling       ,_lowerCamelCase	) or len(self.rope_scaling	) != 2:
							raise ValueError(
							    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
							    f"got {self.rope_scaling}"	)
				__lowercase			  =							self.rope_scaling.get('''type'''       ,_lowerCamelCase	)
				__lowercase			  =							self.rope_scaling.get('''factor'''       ,_lowerCamelCase	)
				if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
							raise ValueError(
							    f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"	)
				if rope_scaling_factor is None or not isinstance(_lowerCamelCase       ,_lowerCamelCase	) or rope_scaling_factor <= 1.0:
							raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"	)





