



"""simple docstring"""
from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


__a      =				logging.get_logger(__name__)


__a      =				OrderedDict(
    [
        # Base model mapping
        ("albert", "FlaxAlbertModel"),
        ("bart", "FlaxBartModel"),
        ("beit", "FlaxBeitModel"),
        ("bert", "FlaxBertModel"),
        ("big_bird", "FlaxBigBirdModel"),
        ("blenderbot", "FlaxBlenderbotModel"),
        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
        ("clip", "FlaxCLIPModel"),
        ("distilbert", "FlaxDistilBertModel"),
        ("electra", "FlaxElectraModel"),
        ("gpt-sw3", "FlaxGPT2Model"),
        ("gpt2", "FlaxGPT2Model"),
        ("gpt_neo", "FlaxGPTNeoModel"),
        ("gptj", "FlaxGPTJModel"),
        ("longt5", "FlaxLongT5Model"),
        ("marian", "FlaxMarianModel"),
        ("mbart", "FlaxMBartModel"),
        ("mt5", "FlaxMT5Model"),
        ("opt", "FlaxOPTModel"),
        ("pegasus", "FlaxPegasusModel"),
        ("regnet", "FlaxRegNetModel"),
        ("resnet", "FlaxResNetModel"),
        ("roberta", "FlaxRobertaModel"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
        ("roformer", "FlaxRoFormerModel"),
        ("t5", "FlaxT5Model"),
        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
        ("vit", "FlaxViTModel"),
        ("wav2vec2", "FlaxWav2Vec2Model"),
        ("whisper", "FlaxWhisperModel"),
        ("xglm", "FlaxXGLMModel"),
        ("xlm-roberta", "FlaxXLMRobertaModel"),
    ]
)

__a      =				OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "FlaxAlbertForPreTraining"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForPreTraining"),
        ("big_bird", "FlaxBigBirdForPreTraining"),
        ("electra", "FlaxElectraForPreTraining"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("t5", "FlaxT5ForConditionalGeneration"),
        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

__a      =				OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "FlaxAlbertForMaskedLM"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForMaskedLM"),
        ("big_bird", "FlaxBigBirdForMaskedLM"),
        ("distilbert", "FlaxDistilBertForMaskedLM"),
        ("electra", "FlaxElectraForMaskedLM"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

__a      =				OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "FlaxBartForConditionalGeneration"),
        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "FlaxEncoderDecoderModel"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("marian", "FlaxMarianMTModel"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("pegasus", "FlaxPegasusForConditionalGeneration"),
        ("t5", "FlaxT5ForConditionalGeneration"),
    ]
)

__a      =				OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

__a      =				OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

__a      =				OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "FlaxBartForCausalLM"),
        ("bert", "FlaxBertForCausalLM"),
        ("big_bird", "FlaxBigBirdForCausalLM"),
        ("electra", "FlaxElectraForCausalLM"),
        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
        ("gpt2", "FlaxGPT2LMHeadModel"),
        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
        ("gptj", "FlaxGPTJForCausalLM"),
        ("opt", "FlaxOPTForCausalLM"),
        ("roberta", "FlaxRobertaForCausalLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
        ("xglm", "FlaxXGLMForCausalLM"),
        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
    ]
)

__a      =				OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

__a      =				OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "FlaxAlbertForQuestionAnswering"),
        ("bart", "FlaxBartForQuestionAnswering"),
        ("bert", "FlaxBertForQuestionAnswering"),
        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
        ("electra", "FlaxElectraForQuestionAnswering"),
        ("mbart", "FlaxMBartForQuestionAnswering"),
        ("roberta", "FlaxRobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
        ("roformer", "FlaxRoFormerForQuestionAnswering"),
        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
    ]
)

__a      =				OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "FlaxAlbertForTokenClassification"),
        ("bert", "FlaxBertForTokenClassification"),
        ("big_bird", "FlaxBigBirdForTokenClassification"),
        ("distilbert", "FlaxDistilBertForTokenClassification"),
        ("electra", "FlaxElectraForTokenClassification"),
        ("roberta", "FlaxRobertaForTokenClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
        ("roformer", "FlaxRoFormerForTokenClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
    ]
)

__a      =				OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "FlaxAlbertForMultipleChoice"),
        ("bert", "FlaxBertForMultipleChoice"),
        ("big_bird", "FlaxBigBirdForMultipleChoice"),
        ("distilbert", "FlaxDistilBertForMultipleChoice"),
        ("electra", "FlaxElectraForMultipleChoice"),
        ("roberta", "FlaxRobertaForMultipleChoice"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
        ("roformer", "FlaxRoFormerForMultipleChoice"),
        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
    ]
)

__a      =				OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

__a      =				OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

__a      =				OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

__a      =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__a      =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__a      =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__a      =				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__a      =				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__a      =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__a      =				_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__a      =				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__a      =				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__a      =				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__a      =				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__a      =				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__a      =				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__a      =				_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			Optional[int]						=     FLAX_MODEL_MAPPING

__a      =				auto_class_update(FlaxAutoModel)
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     FLAX_MODEL_FOR_PRETRAINING_MAPPING

__a      =				auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			Dict						=     FLAX_MODEL_FOR_CAUSAL_LM_MAPPING

__a      =				auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			Optional[int]						=     FLAX_MODEL_FOR_MASKED_LM_MAPPING

__a      =				auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__a      =				auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

__a      =				auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			str						=     FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING

__a      =				auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			int						=     FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__a      =				auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			List[Any]						=     FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING

__a      =				auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			Optional[int]						=     FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING

__a      =				auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			Optional[Any]						=     FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

__a      =				auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			Dict						=     FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING

__a      =				auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class 				lowerCamelCase  (			_BaseAutoModelClass				):






					'''simple docstring'''





					_A		:			Optional[int]						=     FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING

__a      =				auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)





"""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


__a      =				logging.get_logger(__name__)

enable_full_determinism()
class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			List[Any]						=     UNetaDModel
					_A		:			Union[str, Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	Tuple:
											snake_case_ :List[str]						=			4
											snake_case_ :Tuple						=			3
											snake_case_ :Optional[Any]						=			(32, 32)

											snake_case_ :str						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Union[str, Any]						=			torch.tensor([10]						).to(snake_case						)

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






					@property
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Dict:
											return (3, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											return (3, 32, 32)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Dict:
											snake_case_ :Any						=			{
											    """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,
											}
											snake_case_ :Tuple						=			self.dummy_input
											return init_dict, inputs_dict

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






					'''simple docstring'''





					_A		:			List[str]						=     UNetaDModel
					_A		:			Union[str, Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							str						)   ->	str:
											snake_case_ :List[str]						=			4
											snake_case_ :Optional[int]						=			4
											snake_case_ :int						=			(32, 32)

											snake_case_ :Any						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :List[Any]						=			torch.tensor([10]						).to(snake_case						)

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






					@property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											return (4, 32, 32)






					@property
					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	int:
											return (4, 32, 32)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	List[Any]:
											snake_case_ :Dict						=			{
											    """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"""),
											}
											snake_case_ :List[str]						=			self.dummy_input
											return init_dict, inputs_dict






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[Any]:
											snake_case_,       snake_case_ :List[str]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)

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

											model.to(snake_case						)
											snake_case_ :List[str]						=			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    lowerCAmelCase_				(       self:							Tuple						)   ->	Dict:
											snake_case_,       snake_case_ :Union[str, Any]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)
											model.to(snake_case						)
											snake_case_ :Union[str, Any]						=			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    lowerCAmelCase_				(       self:							str						)   ->	Any:
											# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
											snake_case_,       snake_case_ :List[str]						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						)
											model_accelerate.to(snake_case						)
											model_accelerate.eval()

											snake_case_ :List[Any]						=			torch.randn(
											    1						,     model_accelerate.config.in_channels						,     model_accelerate.config.sample_size						,     model_accelerate.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
											snake_case_ :int						=			noise.to(snake_case						)
											snake_case_ :str						=			torch.tensor([10] * noise.shape[0]						).to(snake_case						)

											snake_case_ :Optional[int]						=			model_accelerate(snake_case						,     snake_case						)["""sample"""]

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

											snake_case_,       snake_case_ :str						=			UNetaDModel.from_pretrained(
											    """fusing/unet-ldm-dummy-update"""						,     output_loading_info=snake_case						,     low_cpu_mem_usage=snake_case						)
											model_normal_load.to(snake_case						)
											model_normal_load.eval()
											snake_case_ :int						=			model_normal_load(snake_case						,     snake_case						)["""sample"""]

											assert torch_all_close(snake_case						,     snake_case						,     rtol=1E-3						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_ :Tuple						=			UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						)
											model.eval()
											model.to(snake_case						)

											snake_case_ :Optional[int]						=			torch.randn(
											    1						,     model.config.in_channels						,     model.config.sample_size						,     model.config.sample_size						,     generator=torch.manual_seed(0						)						,     )
											snake_case_ :int						=			noise.to(snake_case						)
											snake_case_ :List[Any]						=			torch.tensor([10] * noise.shape[0]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :Union[str, Any]						=			model(snake_case						,     snake_case						).sample

											snake_case_ :Optional[int]						=			output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case_ :Dict						=			torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-3						)						)

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






					'''simple docstring'''





					_A		:			List[Any]						=     UNetaDModel
					_A		:			List[Any]						=     """sample"""






					@property
					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							int=(32, 32)						)   ->	Tuple:
											snake_case_ :Union[str, Any]						=			4
											snake_case_ :Any						=			3

											snake_case_ :int						=			floats_tensor((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Any						=			torch.tensor(batch_size * [10]						).to(dtype=torch.intaa						,     device=snake_case						)

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






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






					@property
					def    lowerCAmelCase_				(       self:							int						)   ->	Tuple:
											return (3, 32, 32)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Tuple:
											snake_case_ :List[Any]						=			{
											    """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""",
											    ],
											}
											snake_case_ :int						=			self.dummy_input
											return init_dict, inputs_dict






					@slow
					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	List[Any]:
											snake_case_,       snake_case_ :List[Any]						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						,     output_loading_info=snake_case						)
											self.assertIsNotNone(snake_case						)
											self.assertEqual(len(loading_info["""missing_keys"""]						)						,     0						)

											model.to(snake_case						)
											snake_case_ :Any						=			self.dummy_input
											snake_case_ :int						=			floats_tensor((4, 3) + (256, 256)						).to(snake_case						)
											snake_case_ :int						=			noise
											snake_case_ :int						=			model(**snake_case						)

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






					@slow
					def    lowerCAmelCase_				(       self:							str						)   ->	Dict:
											snake_case_ :Dict						=			UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						)
											model.to(snake_case						)

											snake_case_ :List[str]						=			4
											snake_case_ :Optional[int]						=			3
											snake_case_ :List[str]						=			(256, 256)

											snake_case_ :Tuple						=			torch.ones((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :str						=			torch.tensor(batch_size * [1E-4]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :Dict						=			model(snake_case						,     snake_case						).sample

											snake_case_ :int						=			output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											snake_case_ :Optional[Any]						=			torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-2						)						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[Any]:
											snake_case_ :Optional[Any]						=			UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""						)
											model.to(snake_case						)

											snake_case_ :Optional[int]						=			4
											snake_case_ :Optional[Any]						=			3
											snake_case_ :Optional[Any]						=			(32, 32)

											snake_case_ :Dict						=			torch.ones((batch_size, num_channels) + sizes						).to(snake_case						)
											snake_case_ :Any						=			torch.tensor(batch_size * [1E-4]						).to(snake_case						)

											with torch.no_grad():
																	snake_case_ :str						=			model(snake_case						,     snake_case						).sample

											snake_case_ :int						=			output[0, -3:, -3:, -1].flatten().cpu()
											# fmt: off
											snake_case_ :int						=			torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]						)
											# fmt: on

											self.assertTrue(torch_all_close(snake_case						,     snake_case						,     rtol=1E-2						)						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Optional[Any]:
											# not required for this model
											pass
