

"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Dict       		=				KandinskyVaaControlnetImgaImgPipeline
			lowerCamelCase__ :		str       		=				['image_embeds', 'negative_image_embeds', 'image', 'hint']
			lowerCamelCase__ :		int       		=				['image_embeds', 'negative_image_embeds', 'image', 'hint']
			lowerCamelCase__ :		List[Any]       		=				[
			    'generator',
			    'height',
			    'width',
			    'strength',
			    'guidance_scale',
			    'num_inference_steps',
			    'return_dict',
			    'guidance_scale',
			    'num_images_per_prompt',
			    'output_type',
			    'return_dict',
			]
			lowerCamelCase__ :		int       		=				False

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Tuple:
						return 3_2

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     int:
						return 3_2

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     int:
						return self.time_input_dim

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Optional[int]:
						return self.time_input_dim * 4

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Any:
						return 1_0_0

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Dict:
						torch.manual_seed(0							)

						SCREAMING_SNAKE_CASE__										=   {
						    """in_channels""": 8,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """image_hint""",
						    """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,
						}

						SCREAMING_SNAKE_CASE__										=   UNetaDConditionModel(**__UpperCAmelCase							)
						return model

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Optional[Any]:
						return {
						    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
						    "down_block_types": [
						        "DownEncoderBlock2D",
						        "DownEncoderBlock2D",
						        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "vq_embed_dim": 4,
						}

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     Optional[int]:
						torch.manual_seed(0							)
						SCREAMING_SNAKE_CASE__										=   VQModel(**self.dummy_movq_kwargs							)
						return model

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.dummy_unet
						SCREAMING_SNAKE_CASE__										=   self.dummy_movq

						SCREAMING_SNAKE_CASE__										=   {
						    """num_train_timesteps""": 1_0_0_0,
						    """beta_schedule""": """linear""",
						    """beta_start""": 0.00_085,
						    """beta_end""": 0.012,
						    """clip_sample""": False,
						    """set_alpha_to_one""": False,
						    """steps_offset""": 0,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						}

						SCREAMING_SNAKE_CASE__										=   DDIMScheduler(**__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   {
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components

			def        SCREAMING_SNAKE_CASE					(       self					:    str     ,					__UpperCAmelCase					:    Any     ,					__UpperCAmelCase					:    Any=0							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   floats_tensor((1, self.text_embedder_hidden_size)     ,					rng=random.Random(__UpperCAmelCase							)							).to(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   floats_tensor((1, self.text_embedder_hidden_size)     ,					rng=random.Random(seed + 1							)							).to(
						    __UpperCAmelCase							)
						# create init_image
						SCREAMING_SNAKE_CASE__										=   floats_tensor((1, 3, 6_4, 6_4)     ,					rng=random.Random(__UpperCAmelCase							)							).to(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   image.cpu().permute(0     ,					2     ,					3     ,					1							)[0]
						SCREAMING_SNAKE_CASE__										=   Image.fromarray(np.uinta(__UpperCAmelCase							)							).convert("""RGB"""							).resize((2_5_6, 2_5_6)							)
						# create hint
						SCREAMING_SNAKE_CASE__										=   floats_tensor((1, 3, 6_4, 6_4)     ,					rng=random.Random(__UpperCAmelCase							)							).to(__UpperCAmelCase							)

						if str(__UpperCAmelCase							).startswith("""mps"""							):
									SCREAMING_SNAKE_CASE__										=   torch.manual_seed(__UpperCAmelCase							)
						else:
									SCREAMING_SNAKE_CASE__										=   torch.Generator(device=__UpperCAmelCase							).manual_seed(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   {
						    """image""": init_image,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """hint""": hint,
						    """generator""": generator,
						    """height""": 6_4,
						    """width""": 6_4,
						    """num_inference_steps""": 1_0,
						    """guidance_scale""": 7.0,
						    """strength""": 0.2,
						    """output_type""": """np""",
						}
						return inputs





			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   """cpu"""

						SCREAMING_SNAKE_CASE__										=   self.get_dummy_components()

						SCREAMING_SNAKE_CASE__										=   self.pipeline_class(**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   pipe.to(__UpperCAmelCase							)

						pipe.set_progress_bar_config(disable=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   pipe(**self.get_dummy_inputs(__UpperCAmelCase							)							)
						SCREAMING_SNAKE_CASE__										=   output.images

						SCREAMING_SNAKE_CASE__										=   pipe(
						    **self.get_dummy_inputs(__UpperCAmelCase							)     ,					return_dict=__UpperCAmelCase     ,					)[0]

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

						assert image.shape == (1, 6_4, 6_4, 3)

						SCREAMING_SNAKE_CASE__										=   np.array(
						    [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]							)
						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()}"""





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

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





			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""							)

						SCREAMING_SNAKE_CASE__										=   load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
						SCREAMING_SNAKE_CASE__										=   init_image.resize((5_1_2, 5_1_2)							)

						SCREAMING_SNAKE_CASE__										=   load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/hint_image_cat.png"""							)
						SCREAMING_SNAKE_CASE__										=   torch.from_numpy(np.array(__UpperCAmelCase							)							).float() / 255.0
						SCREAMING_SNAKE_CASE__										=   hint.permute(2     ,					0     ,					1							).unsqueeze(0							)

						SCREAMING_SNAKE_CASE__										=   """A robot, 4k photo"""

						SCREAMING_SNAKE_CASE__										=   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-prior"""     ,					torch_dtype=torch.floataa							)
						pipe_prior.to(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-controlnet-depth"""     ,					torch_dtype=torch.floataa							)
						SCREAMING_SNAKE_CASE__										=   pipeline.to(__UpperCAmelCase							)

						pipeline.set_progress_bar_config(disable=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   torch.Generator(device="""cpu"""							).manual_seed(0							)

						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   pipe_prior(
						    __UpperCAmelCase     ,					image=__UpperCAmelCase     ,					strength=0.85     ,					generator=__UpperCAmelCase     ,					negative_prompt=""""""     ,					).to_tuple()

						SCREAMING_SNAKE_CASE__										=   pipeline(
						    image=__UpperCAmelCase     ,					image_embeds=__UpperCAmelCase     ,					negative_image_embeds=__UpperCAmelCase     ,					hint=__UpperCAmelCase     ,					generator=__UpperCAmelCase     ,					num_inference_steps=1_0_0     ,					height=5_1_2     ,					width=5_1_2     ,					strength=0.5     ,					output_type="""np"""     ,					)

						SCREAMING_SNAKE_CASE__										=   output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)

						assert_mean_pixel_difference(__UpperCAmelCase     ,					__UpperCAmelCase							)





"""simple docstring"""




import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A_				:      Optional[Any]           =			logging.getLogger(__name__)





@dataclass
class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=0.0      ,metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'Whether to SortishSamler or not.'}      )
			lowerCamelCase__ :		bool       		=				field(
			    default=A__      ,metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'whether to use adafactor'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(default=A__      ,metadata={'help': 'Dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Attention dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default='linear'      ,metadata={'help': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()      )}"}      ,)


