





'''simple docstring'''







import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowerCAmelCase_					:    List[Any]      	=			logging.get_logger(__name__)

enable_full_determinism()
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =UNetaDModel
				__a            ='sample'




				@property
				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= 4
								_a								= 3
								_a								= (32, 32)

								_a								= floats_tensor((batch_size, num_channels) + sizes					).to(__a					)
								_a								= torch.tensor([10]					).to(__a					)

								return {"sample": noise, "timestep": time_step}




				@property
				def   UpperCamelCase__   (  self     :					List[Any]					):
								return (3, 32, 32)




				@property
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								return (3, 32, 32)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= {
								    "block_out_channels": (32, 64),
								    "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
								    "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
								    "attention_head_dim": 3,
								    "out_channels": 3,
								    "in_channels": 3,
								    "layers_per_block": 2,
								    "sample_size": 32,
								}
								_a								= self.dummy_input
								return init_dict, inputs_dict


class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =UNetaDModel
				__a            ='sample'




				@property
				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= 4
								_a								= 4
								_a								= (32, 32)

								_a								= floats_tensor((batch_size, num_channels) + sizes					).to(__a					)
								_a								= torch.tensor([10]					).to(__a					)

								return {"sample": noise, "timestep": time_step}




				@property
				def   UpperCamelCase__   (  self     :					List[str]					):
								return (4, 32, 32)




				@property
				def   UpperCamelCase__   (  self     :					List[Any]					):
								return (4, 32, 32)




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= {
								    "sample_size": 32,
								    "in_channels": 4,
								    "out_channels": 4,
								    "layers_per_block": 2,
								    "block_out_channels": (32, 64),
								    "attention_head_dim": 32,
								    "down_block_types": ("DownBlock2D", "DownBlock2D"),
								    "up_block_types": ("UpBlock2D", "UpBlock2D"),
								}
								_a								= self.dummy_input
								return init_dict, inputs_dict




				def   UpperCamelCase__   (  self     :					str					):
								_a	,					_a								= UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"					,				output_loading_info=__a					)

								self.assertIsNotNone(__a					)
								self.assertEqual(len(loading_info["missing_keys"]					)					,				0					)

								model.to(__a					)
								_a								= model(**self.dummy_input					).sample

								assert image is not None, "Make sure output is not None"




				@unittest.skipIf(torch_device != "cuda"					,				"This test is supposed to run on GPU"					)
				def   UpperCamelCase__   (  self     :					int					):
								_a	,					_a								= UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"					,				output_loading_info=__a					)
								model.to(__a					)
								_a								= model(**self.dummy_input					).sample

								assert image is not None, "Make sure output is not None"




				@unittest.skipIf(torch_device != "cuda"					,				"This test is supposed to run on GPU"					)
				def   UpperCamelCase__   (  self     :					Any					):
								# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
								_a	,					_a								= UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"					,				output_loading_info=__a					)
								model_accelerate.to(__a					)
								model_accelerate.eval()

								_a								= torch.randn(
								    1					,				model_accelerate.config.in_channels					,				model_accelerate.config.sample_size					,				model_accelerate.config.sample_size					,				generator=torch.manual_seed(0					)					,				)
								_a								= noise.to(__a					)
								_a								= torch.tensor([10] * noise.shape[0]					).to(__a					)

								_a								= model_accelerate(__a					,				__a					)["sample"]

								# two models don't need to stay in the device at the same time
								del model_accelerate
								torch.cuda.empty_cache()
								gc.collect()

								_a	,					_a								= UNetaDModel.from_pretrained(
								    "fusing/unet-ldm-dummy-update"					,				output_loading_info=__a					,				low_cpu_mem_usage=__a					)
								model_normal_load.to(__a					)
								model_normal_load.eval()
								_a								= model_normal_load(__a					,				__a					)["sample"]

								assert torch_all_close(__a					,				__a					,				rtol=1e-3					)




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"					)
								model.eval()
								model.to(__a					)

								_a								= torch.randn(
								    1					,				model.config.in_channels					,				model.config.sample_size					,				model.config.sample_size					,				generator=torch.manual_seed(0					)					,				)
								_a								= noise.to(__a					)
								_a								= torch.tensor([10] * noise.shape[0]					).to(__a					)

								with torch.no_grad():
												_a								= model(__a					,				__a					).sample

								_a								= output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								_a								= torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]					)
								# fmt: on

								self.assertTrue(torch_all_close(__a					,				__a					,				rtol=1e-3					)					)


class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =UNetaDModel
				__a            ='sample'




				@property
				def   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					List[Any]=(32, 32)					):
								_a								= 4
								_a								= 3

								_a								= floats_tensor((batch_size, num_channels) + sizes					).to(__a					)
								_a								= torch.tensor(batch_size * [10]					).to(dtype=torch.intaa					,				device=__a					)

								return {"sample": noise, "timestep": time_step}




				@property
				def   UpperCamelCase__   (  self     :					List[Any]					):
								return (3, 32, 32)




				@property
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								return (3, 32, 32)




				def   UpperCamelCase__   (  self     :					List[str]					):
								_a								= {
								    "block_out_channels": [32, 64, 64, 64],
								    "in_channels": 3,
								    "layers_per_block": 1,
								    "out_channels": 3,
								    "time_embedding_type": "fourier",
								    "norm_eps": 1e-6,
								    "mid_block_scale_factor": math.sqrt(2.0					),
								    "norm_num_groups": None,
								    "down_block_types": [
								        "SkipDownBlock2D",
								        "AttnSkipDownBlock2D",
								        "SkipDownBlock2D",
								        "SkipDownBlock2D",
								    ],
								    "up_block_types": [
								        "SkipUpBlock2D",
								        "SkipUpBlock2D",
								        "AttnSkipUpBlock2D",
								        "SkipUpBlock2D",
								    ],
								}
								_a								= self.dummy_input
								return init_dict, inputs_dict




				@slow
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a	,					_a								= UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"					,				output_loading_info=__a					)
								self.assertIsNotNone(__a					)
								self.assertEqual(len(loading_info["missing_keys"]					)					,				0					)

								model.to(__a					)
								_a								= self.dummy_input
								_a								= floats_tensor((4, 3) + (2_56, 2_56)					).to(__a					)
								_a								= noise
								_a								= model(**__a					)

								assert image is not None, "Make sure output is not None"




				@slow
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"					)
								model.to(__a					)

								_a								= 4
								_a								= 3
								_a								= (2_56, 2_56)

								_a								= torch.ones((batch_size, num_channels) + sizes					).to(__a					)
								_a								= torch.tensor(batch_size * [1e-4]					).to(__a					)

								with torch.no_grad():
												_a								= model(__a					,				__a					).sample

								_a								= output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								_a								= torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]					)
								# fmt: on

								self.assertTrue(torch_all_close(__a					,				__a					,				rtol=1e-2					)					)




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"					)
								model.to(__a					)

								_a								= 4
								_a								= 3
								_a								= (32, 32)

								_a								= torch.ones((batch_size, num_channels) + sizes					).to(__a					)
								_a								= torch.tensor(batch_size * [1e-4]					).to(__a					)

								with torch.no_grad():
												_a								= model(__a					,				__a					).sample

								_a								= output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								_a								= torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]					)
								# fmt: on

								self.assertTrue(torch_all_close(__a					,				__a					,				rtol=1e-2					)					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								# not required for this model
								pass




'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__snake_case    						=logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	bool		=     field(default=__lowercase       ,	metadata={'''help''': '''Whether to use SortishSampler or not.'''}		)
     lowerCamelCase				:	bool		=     field(
         default=__lowercase       ,	metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}		)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[int]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }       ,	)
     lowerCamelCase				:	Optional[Union[str, Path, GenerationConfig]]		=     field(
         default=__lowercase       ,	metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }       ,	)





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





