

"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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"""







 __UpperCAmelCase	:					Tuple   		=	KandinskyInpaintPipeline
 __UpperCAmelCase	:					Optional[int]   		=	["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
 __UpperCAmelCase	:					Optional[Any]   		=	[
     "prompt",
     "negative_prompt",
     "image_embeds",
     "negative_image_embeds",
     "image",
     "mask_image",
 ]
 __UpperCAmelCase	:					Optional[int]   		=	[
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "negative_prompt",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 __UpperCAmelCase	:					Any   		=	False


 @property
 def 					_lowercase (			self							:			List[str]	):
    return 3_2


 @property
 def 					_lowercase (			self							:			Optional[int]	):
    return 3_2


 @property
 def 					_lowercase (			self							:			Tuple	):
    return self.time_input_dim


 @property
 def 					_lowercase (			self							:			str	):
    return self.time_input_dim * 4


 @property
 def 					_lowercase (			self							:			Optional[int]	):
    return 1_0_0


 @property
 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"	)
    return tokenizer


 @property
 def 					_lowercase (			self							:			Optional[int]	):
    torch.manual_seed(0	)
    __lowercase								=						MCLIPConfig(
        numDims=self.cross_attention_dim,					transformerDimensions=self.text_embedder_hidden_size,					hidden_size=self.text_embedder_hidden_size,					intermediate_size=3_7,					num_attention_heads=4,					num_hidden_layers=5,					vocab_size=1_0_0_5,					)

    __lowercase								=						MultilingualCLIP(UpperCAmelCase__	)
    __lowercase								=						text_encoder.eval()

    return text_encoder


 @property
 def 					_lowercase (			self							:			Optional[int]	):
    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': 'text_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': 'text_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(**UpperCAmelCase__	)
    return model


 @property
 def 					_lowercase (			self							:			str	):
    return {
        "block_out_channels": [3_2, 6_4],
        "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": 1_2,
        "out_channels": 3,
        "up_block_types": [
            "AttnUpDecoderBlock2D",
            "UpDecoderBlock2D",
        ],
        "vq_embed_dim": 4,
    }


 @property
 def 					_lowercase (			self							:			int	):
    torch.manual_seed(0	)
    __lowercase								=						VQModel(**self.dummy_movq_kwargs	)
    return model


 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.dummy_text_encoder
    __lowercase								=						self.dummy_tokenizer
    __lowercase								=						self.dummy_unet
    __lowercase								=						self.dummy_movq

    __lowercase								=						DDIMScheduler(
        num_train_timesteps=1_0_0_0,					beta_schedule="linear",					beta_start=0.00_085,					beta_end=0.012,					clip_sample=UpperCAmelCase__,					set_alpha_to_one=UpperCAmelCase__,					steps_offset=1,					prediction_type="epsilon",					thresholding=UpperCAmelCase__,					)

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

    return components


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			int=0	):
    __lowercase								=						floats_tensor((1, self.cross_attention_dim),					rng=random.Random(UpperCAmelCase__	)	).to(UpperCAmelCase__	)
    __lowercase								=						floats_tensor((1, self.cross_attention_dim),					rng=random.Random(seed + 1	)	).to(UpperCAmelCase__	)
    # create init_image
    __lowercase								=						floats_tensor((1, 3, 6_4, 6_4),					rng=random.Random(UpperCAmelCase__	)	).to(UpperCAmelCase__	)
    __lowercase								=						image.cpu().permute(0,					2,					3,					1	)[0]
    __lowercase								=						Image.fromarray(np.uinta(UpperCAmelCase__	)	).convert("RGB"	).resize((2_5_6, 2_5_6)	)
    # create mask
    __lowercase								=						np.ones((6_4, 6_4),					dtype=np.floataa	)
    __lowercase								=						0

    if str(UpperCAmelCase__	).startswith("mps"	):
       __lowercase								=						torch.manual_seed(UpperCAmelCase__	)
    else:
       __lowercase								=						torch.Generator(device=UpperCAmelCase__	).manual_seed(UpperCAmelCase__	)
    __lowercase								=						{
        'prompt': 'horse',
        'image': init_image,
        'mask_image': mask,
        'image_embeds': image_embeds,
        'negative_image_embeds': negative_image_embeds,
        'generator': generator,
        'height': 6_4,
        'width': 6_4,
        'num_inference_steps': 2,
        'guidance_scale': 4.0,
        'output_type': 'np',
    }
    return inputs


 def 					_lowercase (			self							:			Any	):
    __lowercase								=						'cpu'

    __lowercase								=						self.get_dummy_components()

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

    pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    __lowercase								=						pipe(
        **self.get_dummy_inputs(UpperCAmelCase__	),					return_dict=UpperCAmelCase__,					)[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, 6_4, 6_4, 3)

    __lowercase								=						np.array(
        [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

    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 					_lowercase (			self							:			Any	):
    super().test_inference_batch_single_identical(expected_max_diff=3E-3	)







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







 """simple docstring"""







 def 					_lowercase (			self							:			List[Any]	):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/kandinsky/kandinsky_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((7_6_8, 7_6_8),					dtype=np.floataa	)
    __lowercase								=						0

    __lowercase								=						'a hat'

    __lowercase								=						KandinskyPriorPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-1-prior",					torch_dtype=torch.floataa	)
    pipe_prior.to(UpperCAmelCase__	)

    __lowercase								=						KandinskyInpaintPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-1-inpaint",					torch_dtype=torch.floataa	)
    __lowercase								=						pipeline.to(UpperCAmelCase__	)
    pipeline.set_progress_bar_config(disable=UpperCAmelCase__	)

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

    __lowercase								=						pipeline(
        UpperCAmelCase__,					image=UpperCAmelCase__,					mask_image=UpperCAmelCase__,					image_embeds=UpperCAmelCase__,					negative_image_embeds=UpperCAmelCase__,					generator=UpperCAmelCase__,					num_inference_steps=1_0_0,					height=7_6_8,					width=7_6_8,					output_type="np",					)

    __lowercase								=						output.images[0]

    assert image.shape == (7_6_8, 7_6_8, 3)

    assert_mean_pixel_difference(UpperCAmelCase__,					UpperCAmelCase__	)










"""simple docstring"""






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


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""ctrl"""
			lowerCamelCase__      =			["""past_key_values"""]
			lowerCamelCase__      =			{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}
			def __init__(  self  ,      lowercase=246534  ,      lowercase=256  ,      lowercase=1280  ,      lowercase=8192  ,      lowercase=48  ,      lowercase=16  ,      lowercase=0.1  ,      lowercase=0.1  ,      lowercase=1E-6  ,      lowercase=0.02  ,      lowercase=True  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Any    =    vocab_size
							_lowerCamelCase     :			Dict    =    n_positions
							_lowerCamelCase     :			Optional[int]    =    n_embd
							_lowerCamelCase     :			str    =    n_layer
							_lowerCamelCase     :			Union[str, Any]    =    n_head
							_lowerCamelCase     :			Any    =    dff
							_lowerCamelCase     :			int    =    resid_pdrop
							_lowerCamelCase     :			Dict    =    embd_pdrop
							_lowerCamelCase     :			Union[str, Any]    =    layer_norm_epsilon
							_lowerCamelCase     :			Tuple    =    initializer_range

							_lowerCamelCase     :			str    =    use_cache

							super().__init__(**lowercase  )