



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


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

enable_full_determinism()



class 	lowerCamelCase      (						lowercase__,     lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Union[str, Any]									=  UNetaDModel
					lowerCAmelCase_				:    str									=  'sample'



					@property
					def    A__		(			self     ):
							UpperCAmelCase_						=				4
							UpperCAmelCase_						=				3
							UpperCAmelCase_						=				(32, 32)

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

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



					@property
					def    A__		(			self     ):
							return (3, 32, 32)



					@property
					def    A__		(			self     ):
							return (3, 32, 32)



					def    A__		(			self     ):
							UpperCAmelCase_						=				{
							    "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,
							}
							UpperCAmelCase_						=				self.dummy_input
							return init_dict, inputs_dict





class 	lowerCamelCase      (						lowercase__,     lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    str									=  UNetaDModel
					lowerCAmelCase_				:    Tuple									=  'sample'



					@property
					def    A__		(			self     ):
							UpperCAmelCase_						=				4
							UpperCAmelCase_						=				4
							UpperCAmelCase_						=				(32, 32)

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

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



					@property
					def    A__		(			self     ):
							return (4, 32, 32)



					@property
					def    A__		(			self     ):
							return (4, 32, 32)



					def    A__		(			self     ):
							UpperCAmelCase_						=				{
							    "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"),
							}
							UpperCAmelCase_						=				self.dummy_input
							return init_dict, inputs_dict



					def    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"    ,	output_loading_info=lowerCAmelCase     )

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

							model.to(lowerCAmelCase     )
							UpperCAmelCase_						=				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    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"    ,	output_loading_info=lowerCAmelCase     )
							model.to(lowerCAmelCase     )
							UpperCAmelCase_						=				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    A__		(			self     ):
							# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
							UpperCAmelCase_					,						UpperCAmelCase_						=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"    ,	output_loading_info=lowerCAmelCase     )
							model_accelerate.to(lowerCAmelCase     )
							model_accelerate.eval()

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

							UpperCAmelCase_						=				model_accelerate(lowerCAmelCase    ,	lowerCAmelCase     )["sample"]

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

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

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



					def    A__		(			self     ):
							UpperCAmelCase_						=				UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"     )
							model.eval()
							model.to(lowerCAmelCase     )

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

							with torch.no_grad():
									UpperCAmelCase_						=				model(lowerCAmelCase    ,	lowerCAmelCase     ).sample

							UpperCAmelCase_						=				output[0, -1, -3:, -3:].flatten().cpu()
							# fmt: off
							UpperCAmelCase_						=				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(lowerCAmelCase    ,	lowerCAmelCase    ,	rtol=1e-3     )     )





class 	lowerCamelCase      (						lowercase__,     lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Optional[int]									=  UNetaDModel
					lowerCAmelCase_				:    str									=  'sample'



					@property
					def    A__		(			self    ,	lowerCAmelCase=(32, 32)     ):
							UpperCAmelCase_						=				4
							UpperCAmelCase_						=				3

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

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



					@property
					def    A__		(			self     ):
							return (3, 32, 32)



					@property
					def    A__		(			self     ):
							return (3, 32, 32)



					def    A__		(			self     ):
							UpperCAmelCase_						=				{
							    "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",
							    ],
							}
							UpperCAmelCase_						=				self.dummy_input
							return init_dict, inputs_dict



					@slow
					def    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"    ,	output_loading_info=lowerCAmelCase     )
							self.assertIsNotNone(lowerCAmelCase     )
							self.assertEqual(len(loading_info["missing_keys"]     )    ,	0     )

							model.to(lowerCAmelCase     )
							UpperCAmelCase_						=				self.dummy_input
							UpperCAmelCase_						=				floats_tensor((4, 3) + (256, 256)     ).to(lowerCAmelCase     )
							UpperCAmelCase_						=				noise
							UpperCAmelCase_						=				model(**lowerCAmelCase     )

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



					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"     )
							model.to(lowerCAmelCase     )

							UpperCAmelCase_						=				4
							UpperCAmelCase_						=				3
							UpperCAmelCase_						=				(256, 256)

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

							with torch.no_grad():
									UpperCAmelCase_						=				model(lowerCAmelCase    ,	lowerCAmelCase     ).sample

							UpperCAmelCase_						=				output[0, -3:, -3:, -1].flatten().cpu()
							# fmt: off
							UpperCAmelCase_						=				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(lowerCAmelCase    ,	lowerCAmelCase    ,	rtol=1e-2     )     )



					def    A__		(			self     ):
							UpperCAmelCase_						=				UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"     )
							model.to(lowerCAmelCase     )

							UpperCAmelCase_						=				4
							UpperCAmelCase_						=				3
							UpperCAmelCase_						=				(32, 32)

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

							with torch.no_grad():
									UpperCAmelCase_						=				model(lowerCAmelCase    ,	lowerCAmelCase     ).sample

							UpperCAmelCase_						=				output[0, -3:, -3:, -1].flatten().cpu()
							# fmt: off
							UpperCAmelCase_						=				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(lowerCAmelCase    ,	lowerCAmelCase    ,	rtol=1e-2     )     )



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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FNetForMaskedLM",
			    "FNetForMultipleChoice",
			    "FNetForNextSentencePrediction",
			    "FNetForPreTraining",
			    "FNetForQuestionAnswering",
			    "FNetForSequenceClassification",
			    "FNetForTokenClassification",
			    "FNetLayer",
			    "FNetModel",
			    "FNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

