"""simple docstring"""







import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 						UpperCamelCase     (       unittest.TestCase		):



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



				@property
				def    _lowercase	(self							: Tuple)       ->      Optional[Any]:
					__snake_case     :				List[Any] 						=					1
					__snake_case     :				Optional[Any] 						=					3
					__snake_case     :				Tuple 						=					(32, 32)

					__snake_case     :				Dict 						=					floats_tensor((batch_size, num_channels) + sizes      ,    rng=random.Random(0)).to(_A)
					return image



				@property
				def    _lowercase	(self							: str)       ->      Any:
					torch.manual_seed(0)
					__snake_case     :				List[str] 						=					UNetaDConditionModel(
					    block_out_channels=(32, 64)      ,    layers_per_block=2      ,    sample_size=32      ,    in_channels=4      ,    out_channels=4      ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,    cross_attention_dim=32      ,    )
					return model



				@property
				def    _lowercase	(self							: Optional[int])       ->      Tuple:
					torch.manual_seed(0)
					__snake_case     :				Union[str, Any] 						=					AutoencoderKL(
					    block_out_channels=[32, 64]      ,    in_channels=3      ,    out_channels=3      ,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,    latent_channels=4      ,    )
					return model



				@property
				def    _lowercase	(self							: List[str])       ->      str:
					torch.manual_seed(0)
					__snake_case     :				Tuple 						=					CLIPTextConfig(
					    bos_token_id=0      ,    eos_token_id=2      ,    hidden_size=32      ,    intermediate_size=37      ,    layer_norm_eps=1E-05      ,    num_attention_heads=4      ,    num_hidden_layers=5      ,    pad_token_id=1      ,    vocab_size=10_00      ,    )
					return CLIPTextModel(_A)



				@property
				def    _lowercase	(self							: List[Any])       ->      Tuple:
					def extract(*_A							: Union[str, Any]      ,    **_A							: List[Any]):

						class 						UpperCamelCase     :



										def __init__(self							: Dict)       ->      int:
											__snake_case     :				Optional[Any] 						=					torch.ones([0])






										def    _lowercase	(self							: Optional[int]      ,    _A							: Dict)       ->      str:
											self.pixel_values.to(_A)
											return self







						return Out()

					return extract



				def    _lowercase	(self							: List[Any])       ->      Dict:
					__snake_case     :				str 						=					'cpu'  # ensure determinism for the device-dependent torch.Generator
					__snake_case     :				Any 						=					self.dummy_cond_unet
					__snake_case     :				List[Any] 						=					DDIMScheduler(
					    beta_start=0.00_085      ,    beta_end=0.012      ,    beta_schedule='scaled_linear'      ,    clip_sample=_A      ,    set_alpha_to_one=_A      ,    )

					__snake_case     :				Tuple 						=					self.dummy_vae
					__snake_case     :				List[Any] 						=					self.dummy_text_encoder
					__snake_case     :				List[Any] 						=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

					# make sure here that pndm scheduler skips prk
					__snake_case     :				Optional[int] 						=					StableDiffusionPipeline(
					    unet=_A      ,    scheduler=_A      ,    vae=_A      ,    text_encoder=_A      ,    tokenizer=_A      ,    safety_checker=_A      ,    feature_extractor=self.dummy_extractor      ,    )
					__snake_case     :				Optional[Any] 						=					sd_pipe.to(_A)
					sd_pipe.set_progress_bar_config(disable=_A)

					__snake_case     :				Dict 						=					'A painting of a squirrel eating a burger'

					__snake_case     :				Any 						=					torch.Generator(device=_A).manual_seed(0)
					__snake_case     :				Any 						=					sd_pipe([prompt]      ,    generator=_A      ,    guidance_scale=6.0      ,    num_inference_steps=2      ,    output_type='np')
					__snake_case     :				str 						=					output.images

					__snake_case     :				Union[str, Any] 						=					torch.Generator(device=_A).manual_seed(0)
					__snake_case     :				Optional[Any] 						=					sd_pipe(
					    [prompt]      ,    generator=_A      ,    guidance_scale=6.0      ,    num_inference_steps=2      ,    output_type='np'      ,    return_dict=_A      ,    )[0]

					__snake_case     :				Any 						=					image[0, -3:, -3:, -1]
					__snake_case     :				int 						=					image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					__snake_case     :				Optional[Any] 						=					np.array([0.5_753, 0.6_114, 0.5_001, 0.5_034, 0.5_470, 0.4_729, 0.4_971, 0.4_867, 0.4_867])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2



				def    _lowercase	(self							: int)       ->      Dict:
					__snake_case     :				Union[str, Any] 						=					'cpu'  # ensure determinism for the device-dependent torch.Generator
					__snake_case     :				Union[str, Any] 						=					self.dummy_cond_unet
					__snake_case     :				Any 						=					PNDMScheduler(skip_prk_steps=_A)
					__snake_case     :				Optional[Any] 						=					self.dummy_vae
					__snake_case     :				List[str] 						=					self.dummy_text_encoder
					__snake_case     :				Optional[int] 						=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

					# make sure here that pndm scheduler skips prk
					__snake_case     :				Union[str, Any] 						=					StableDiffusionPipeline(
					    unet=_A      ,    scheduler=_A      ,    vae=_A      ,    text_encoder=_A      ,    tokenizer=_A      ,    safety_checker=_A      ,    feature_extractor=self.dummy_extractor      ,    )
					__snake_case     :				Dict 						=					sd_pipe.to(_A)
					sd_pipe.set_progress_bar_config(disable=_A)

					__snake_case     :				Union[str, Any] 						=					'A painting of a squirrel eating a burger'
					__snake_case     :				Union[str, Any] 						=					torch.Generator(device=_A).manual_seed(0)
					__snake_case     :				List[str] 						=					sd_pipe([prompt]      ,    generator=_A      ,    guidance_scale=6.0      ,    num_inference_steps=2      ,    output_type='np')

					__snake_case     :				Optional[Any] 						=					output.images

					__snake_case     :				Dict 						=					torch.Generator(device=_A).manual_seed(0)
					__snake_case     :				Any 						=					sd_pipe(
					    [prompt]      ,    generator=_A      ,    guidance_scale=6.0      ,    num_inference_steps=2      ,    output_type='np'      ,    return_dict=_A      ,    )[0]

					__snake_case     :				Any 						=					image[0, -3:, -3:, -1]
					__snake_case     :				Tuple 						=					image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					__snake_case     :				Optional[Any] 						=					np.array([0.5_122, 0.5_712, 0.4_825, 0.5_053, 0.5_646, 0.4_769, 0.5_179, 0.4_894, 0.4_994])

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2



				def    _lowercase	(self							: Dict)       ->      Optional[Any]:
					__snake_case     :				str 						=					StableDiffusionPipeline.from_pretrained(
					    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'      ,    safety_checker=_A)
					assert isinstance(_A      ,    _A)
					assert isinstance(pipe.scheduler      ,    _A)
					assert pipe.safety_checker is None

					__snake_case     :				Any 						=					pipe('example prompt'      ,    num_inference_steps=2).images[0]
					assert image is not None

					# check that there's no error when saving a pipeline with one of the models being None
					with tempfile.TemporaryDirectory() as tmpdirname:
						pipe.save_pretrained(_A)
						__snake_case     :				int 						=					StableDiffusionPipeline.from_pretrained(_A)

					# sanity check that the pipeline still works
					assert pipe.safety_checker is None
					__snake_case     :				Dict 						=					pipe('example prompt'      ,    num_inference_steps=2).images[0]
					assert image is not None






				@unittest.skipIf(torch_device != 'cuda'      ,    'This test requires a GPU')
				def    _lowercase	(self							: int)       ->      Any:
					__snake_case     :				List[str] 						=					self.dummy_cond_unet
					__snake_case     :				Tuple 						=					PNDMScheduler(skip_prk_steps=_A)
					__snake_case     :				Any 						=					self.dummy_vae
					__snake_case     :				Tuple 						=					self.dummy_text_encoder
					__snake_case     :				Optional[Any] 						=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

					# put models in fp16
					__snake_case     :				Dict 						=					unet.half()
					__snake_case     :				List[Any] 						=					vae.half()
					__snake_case     :				Optional[Any] 						=					bert.half()

					# make sure here that pndm scheduler skips prk
					__snake_case     :				Dict 						=					StableDiffusionPipeline(
					    unet=_A      ,    scheduler=_A      ,    vae=_A      ,    text_encoder=_A      ,    tokenizer=_A      ,    safety_checker=_A      ,    feature_extractor=self.dummy_extractor      ,    )
					__snake_case     :				Union[str, Any] 						=					sd_pipe.to(_A)
					sd_pipe.set_progress_bar_config(disable=_A)

					__snake_case     :				int 						=					'A painting of a squirrel eating a burger'
					__snake_case     :				int 						=					sd_pipe([prompt]      ,    num_inference_steps=2      ,    output_type='np').images

					assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 						UpperCamelCase     (       unittest.TestCase		):



				def    _lowercase	(self							: Any)       ->      Tuple:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



				def    _lowercase	(self							: Union[str, Any])       ->      List[Any]:
					__snake_case     :				int 						=					'cuda'
					__snake_case     :				List[str] 						=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5')
					__snake_case     :				Union[str, Any] 						=					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case     :				str 						=					'a photo of a cat'
					__snake_case     :				Any 						=					{
					    'editing_prompt': ['sunglasses'],
					    'reverse_editing_direction': [False],
					    'edit_warmup_steps': 10,
					    'edit_guidance_scale': 6,
					    'edit_threshold': 0.95,
					    'edit_momentum_scale': 0.5,
					    'edit_mom_beta': 0.6,
					}

					__snake_case     :				Optional[int] 						=					3
					__snake_case     :				int 						=					7

					# no sega enabled
					__snake_case     :				Optional[int] 						=					torch.Generator(_A)
					generator.manual_seed(_A)
					__snake_case     :				str 						=					pipe(
					    [prompt]      ,    generator=_A      ,    guidance_scale=_A      ,    num_inference_steps=50      ,    output_type='np'      ,    width=5_12      ,    height=5_12      ,    )

					__snake_case     :				Optional[Any] 						=					output.images
					__snake_case     :				Optional[Any] 						=					image[0, -3:, -3:, -1]
					__snake_case     :				Union[str, Any] 						=					[
					    0.34_673_113,
					    0.38_492_733,
					    0.37_597_352,
					    0.34_086_335,
					    0.35_650_748,
					    0.35_579_205,
					    0.3_384_763,
					    0.34_340_236,
					    0.3_573_271,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

					# with sega enabled
					# generator = torch.manual_seed(seed)
					generator.manual_seed(_A)
					__snake_case     :				Optional[Any] 						=					pipe(
					    [prompt]      ,    generator=_A      ,    guidance_scale=_A      ,    num_inference_steps=50      ,    output_type='np'      ,    width=5_12      ,    height=5_12      ,    **_A      ,    )

					__snake_case     :				Tuple 						=					output.images
					__snake_case     :				Dict 						=					image[0, -3:, -3:, -1]
					__snake_case     :				List[Any] 						=					[
					    0.41_887_826,
					    0.37_728_766,
					    0.30_138_272,
					    0.41_416_335,
					    0.41_664_985,
					    0.36_283_392,
					    0.36_191_246,
					    0.43_364_465,
					    0.43_001_732,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2



				def    _lowercase	(self							: int)       ->      str:
					__snake_case     :				Union[str, Any] 						=					'cuda'
					__snake_case     :				Optional[int] 						=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5')
					__snake_case     :				Optional[int] 						=					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case     :				Dict 						=					'an image of a crowded boulevard, realistic, 4k'
					__snake_case     :				Optional[int] 						=					{
					    'editing_prompt': 'crowd, crowded, people',
					    'reverse_editing_direction': True,
					    'edit_warmup_steps': 10,
					    'edit_guidance_scale': 8.3,
					    'edit_threshold': 0.9,
					    'edit_momentum_scale': 0.5,
					    'edit_mom_beta': 0.6,
					}

					__snake_case     :				int 						=					9
					__snake_case     :				Union[str, Any] 						=					7

					# no sega enabled
					__snake_case     :				Any 						=					torch.Generator(_A)
					generator.manual_seed(_A)
					__snake_case     :				List[Any] 						=					pipe(
					    [prompt]      ,    generator=_A      ,    guidance_scale=_A      ,    num_inference_steps=50      ,    output_type='np'      ,    width=5_12      ,    height=5_12      ,    )

					__snake_case     :				Union[str, Any] 						=					output.images
					__snake_case     :				List[str] 						=					image[0, -3:, -3:, -1]
					__snake_case     :				Union[str, Any] 						=					[
					    0.43_497_998,
					    0.91_814_065,
					    0.7_540_739,
					    0.55_580_205,
					    0.8_467_265,
					    0.5_389_691,
					    0.62_574_506,
					    0.58_897_763,
					    0.50_926_757,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

					# with sega enabled
					# generator = torch.manual_seed(seed)
					generator.manual_seed(_A)
					__snake_case     :				Any 						=					pipe(
					    [prompt]      ,    generator=_A      ,    guidance_scale=_A      ,    num_inference_steps=50      ,    output_type='np'      ,    width=5_12      ,    height=5_12      ,    **_A      ,    )

					__snake_case     :				List[Any] 						=					output.images
					__snake_case     :				Optional[Any] 						=					image[0, -3:, -3:, -1]
					__snake_case     :				List[str] 						=					[
					    0.3_089_719,
					    0.30_500_144,
					    0.29_016_042,
					    0.30_630_964,
					    0.325_687,
					    0.29_419_225,
					    0.2_908_091,
					    0.28_723_598,
					    0.27_696_294,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2



				def    _lowercase	(self							: int)       ->      Dict:
					__snake_case     :				List[Any] 						=					'cuda'
					__snake_case     :				List[Any] 						=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5')
					__snake_case     :				Any 						=					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case     :				int 						=					'a castle next to a river'
					__snake_case     :				Optional[Any] 						=					{
					    'editing_prompt': ['boat on a river, boat', 'monet, impression, sunrise'],
					    'reverse_editing_direction': False,
					    'edit_warmup_steps': [15, 18],
					    'edit_guidance_scale': 6,
					    'edit_threshold': [0.9, 0.8],
					    'edit_momentum_scale': 0.5,
					    'edit_mom_beta': 0.6,
					}

					__snake_case     :				List[str] 						=					48
					__snake_case     :				Tuple 						=					7

					# no sega enabled
					__snake_case     :				Optional[Any] 						=					torch.Generator(_A)
					generator.manual_seed(_A)
					__snake_case     :				int 						=					pipe(
					    [prompt]      ,    generator=_A      ,    guidance_scale=_A      ,    num_inference_steps=50      ,    output_type='np'      ,    width=5_12      ,    height=5_12      ,    )

					__snake_case     :				str 						=					output.images
					__snake_case     :				List[str] 						=					image[0, -3:, -3:, -1]
					__snake_case     :				Tuple 						=					[
					    0.75_163_555,
					    0.76_037_145,
					    0.61_785,
					    0.9_189_673,
					    0.8_627_701,
					    0.85_189_694,
					    0.8_512_813,
					    0.87_012_076,
					    0.8_312_857,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

					# with sega enabled
					# generator = torch.manual_seed(seed)
					generator.manual_seed(_A)
					__snake_case     :				Dict 						=					pipe(
					    [prompt]      ,    generator=_A      ,    guidance_scale=_A      ,    num_inference_steps=50      ,    output_type='np'      ,    width=5_12      ,    height=5_12      ,    **_A      ,    )

					__snake_case     :				str 						=					output.images
					__snake_case     :				Dict 						=					image[0, -3:, -3:, -1]
					__snake_case     :				Optional[Any] 						=					[
					    0.73_553_365,
					    0.7_537_271,
					    0.74_341_905,
					    0.66_480_356,
					    0.6_472_925,
					    0.63_039_416,
					    0.64_812_905,
					    0.6_749_717,
					    0.6_517_102,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2






				def    _lowercase	(self							: List[Any])       ->      Any:
					__snake_case     :				Union[str, Any] 						=					'cuda'
					__snake_case     :				Tuple 						=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'      ,    torch_dtype=torch.floataa)
					__snake_case     :				List[Any] 						=					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case     :				Any 						=					'a photo of a cat'
					__snake_case     :				Union[str, Any] 						=					{
					    'editing_prompt': ['sunglasses'],
					    'reverse_editing_direction': [False],
					    'edit_warmup_steps': 10,
					    'edit_guidance_scale': 6,
					    'edit_threshold': 0.95,
					    'edit_momentum_scale': 0.5,
					    'edit_mom_beta': 0.6,
					}

					__snake_case     :				Union[str, Any] 						=					3
					__snake_case     :				Optional[Any] 						=					7

					# no sega enabled
					__snake_case     :				Dict 						=					torch.Generator(_A)
					generator.manual_seed(_A)
					__snake_case     :				str 						=					pipe(
					    [prompt]      ,    generator=_A      ,    guidance_scale=_A      ,    num_inference_steps=50      ,    output_type='np'      ,    width=5_12      ,    height=5_12      ,    )

					__snake_case     :				str 						=					output.images
					__snake_case     :				Tuple 						=					image[0, -3:, -3:, -1]
					__snake_case     :				Tuple 						=					[
					    0.34_887_695,
					    0.3_876_953,
					    0.375,
					    0.34_423_828,
					    0.3_581_543,
					    0.35_717_773,
					    0.3_383_789,
					    0.34_570_312,
					    0.359_375,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2

					# with sega enabled
					# generator = torch.manual_seed(seed)
					generator.manual_seed(_A)
					__snake_case     :				List[Any] 						=					pipe(
					    [prompt]      ,    generator=_A      ,    guidance_scale=_A      ,    num_inference_steps=50      ,    output_type='np'      ,    width=5_12      ,    height=5_12      ,    **_A      ,    )

					__snake_case     :				Optional[Any] 						=					output.images
					__snake_case     :				List[Any] 						=					image[0, -3:, -3:, -1]
					__snake_case     :				Dict 						=					[
					    0.42_285_156,
					    0.36_914_062,
					    0.29_077_148,
					    0.42_041_016,
					    0.41_918_945,
					    0.35_498_047,
					    0.3_618_164,
					    0.4_423_828,
					    0.43_115_234,
					]

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_a	:	Optional[Any]=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Dict=					[
	    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTJForCausalLM",
	    "GPTJForQuestionAnswering",
	    "GPTJForSequenceClassification",
	    "GPTJModel",
	    "GPTJPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Optional[Any]=					[
	    "TFGPTJForCausalLM",
	    "TFGPTJForQuestionAnswering",
	    "TFGPTJForSequenceClassification",
	    "TFGPTJModel",
	    "TFGPTJPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	List[str]=					[
	    "FlaxGPTJForCausalLM",
	    "FlaxGPTJModel",
	    "FlaxGPTJPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	_a	:	Any=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





