
import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()




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

						"""simple docstring"""



						snake_case__   					=			StableDiffusionControlNetInpaintPipeline
						snake_case__   					=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						snake_case__   					=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						snake_case__   					=			frozenset({"""control_image"""}			)  # skip `image` and `mask` for now, only test for control_image
						snake_case__   					=			TEXT_TO_IMAGE_IMAGE_PARAMS



						def 					UpperCAmelCase__				(self:				Optional[int]      )						->    int:




											'''simple docstring'''



											torch.manual_seed(0      )
											__a				:	int			=				UNetaDConditionModel(
											    block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=9   ,				out_channels=4   ,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,				up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,				cross_attention_dim=32   ,				)
											torch.manual_seed(0      )
											__a				:	Union[str, Any]			=				ControlNetModel(
											    block_out_channels=(32, 64)   ,				layers_per_block=2   ,				in_channels=4   ,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,				cross_attention_dim=32   ,				conditioning_embedding_out_channels=(16, 32)   ,				)
											torch.manual_seed(0      )
											__a				:	Optional[Any]			=				DDIMScheduler(
											    beta_start=0.0_00_85   ,				beta_end=0.0_12   ,				beta_schedule="scaled_linear"   ,				clip_sample=__UpperCAmelCase   ,				set_alpha_to_one=__UpperCAmelCase   ,				)
											torch.manual_seed(0      )
											__a				:	Optional[int]			=				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   ,				)
											torch.manual_seed(0      )
											__a				:	Optional[Any]			=				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=1000   ,				)
											__a				:	int			=				CLIPTextModel(__UpperCAmelCase      )
											__a				:	Optional[int]			=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

											__a				:	List[Any]			=				{
											    "unet": unet,
											    "controlnet": controlnet,
											    "scheduler": scheduler,
											    "vae": vae,
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "safety_checker": None,
											    "feature_extractor": None,
											}
											return components



						def 					UpperCAmelCase__				(self:				Any   ,				__UpperCAmelCase:				int   ,				__UpperCAmelCase:				List[Any]=0      )						->    str:




											'''simple docstring'''



											if str(__UpperCAmelCase      ).startswith("mps"      ):
																__a				:	Optional[int]			=				torch.manual_seed(__UpperCAmelCase      )
											else:
																__a				:	str			=				torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )

											__a				:	Dict			=				2
											__a				:	List[Any]			=				randn_tensor(
											    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,				generator=__UpperCAmelCase   ,				device=torch.device(__UpperCAmelCase      )   ,				)
											__a				:	Optional[int]			=				floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )
											__a				:	str			=				init_image.cpu().permute(0   ,				2   ,				3   ,				1      )[0]

											__a				:	Union[str, Any]			=				Image.fromarray(np.uinta(__UpperCAmelCase      )      ).convert("RGB"      ).resize((64, 64)      )
											__a				:	Union[str, Any]			=				Image.fromarray(np.uinta(init_image + 4      )      ).convert("RGB"      ).resize((64, 64)      )

											__a				:	Tuple			=				{
											    "prompt": "A painting of a squirrel eating a burger",
											    "generator": generator,
											    "num_inference_steps": 2,
											    "guidance_scale": 6.0,
											    "output_type": "numpy",
											    "image": image,
											    "mask_image": mask_image,
											    "control_image": control_image,
											}

											return inputs



						def 					UpperCAmelCase__				(self:				str      )						->    Tuple:




											'''simple docstring'''



											return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3      )



						@unittest.skipIf(
						    torch_device != "cuda" or not is_xformers_available()   ,				reason="XFormers attention is only available with CUDA and `xformers` installed"   ,				)
						def 					UpperCAmelCase__				(self:				List[Any]      )						->    int:




											'''simple docstring'''



											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )



						def 					UpperCAmelCase__				(self:				int      )						->    Optional[int]:




											'''simple docstring'''



											self._test_inference_batch_single_identical(expected_max_diff=2E-3      )




class        snake_case_					(						lowerCamelCase_			):

						"""simple docstring"""



						snake_case__   					=			StableDiffusionControlNetInpaintPipeline
						snake_case__   					=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						snake_case__   					=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						snake_case__   					=			frozenset([]			)



						def 					UpperCAmelCase__				(self:				str      )						->    Optional[int]:




											'''simple docstring'''



											torch.manual_seed(0      )
											__a				:	Optional[int]			=				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   ,				)
											torch.manual_seed(0      )
											__a				:	Optional[int]			=				ControlNetModel(
											    block_out_channels=(32, 64)   ,				layers_per_block=2   ,				in_channels=4   ,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,				cross_attention_dim=32   ,				conditioning_embedding_out_channels=(16, 32)   ,				)
											torch.manual_seed(0      )
											__a				:	Optional[int]			=				DDIMScheduler(
											    beta_start=0.0_00_85   ,				beta_end=0.0_12   ,				beta_schedule="scaled_linear"   ,				clip_sample=__UpperCAmelCase   ,				set_alpha_to_one=__UpperCAmelCase   ,				)
											torch.manual_seed(0      )
											__a				:	Dict			=				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   ,				)
											torch.manual_seed(0      )
											__a				:	int			=				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=1000   ,				)
											__a				:	Any			=				CLIPTextModel(__UpperCAmelCase      )
											__a				:	str			=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

											__a				:	int			=				{
											    "unet": unet,
											    "controlnet": controlnet,
											    "scheduler": scheduler,
											    "vae": vae,
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "safety_checker": None,
											    "feature_extractor": None,
											}
											return components




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

						"""simple docstring"""



						snake_case__   					=			StableDiffusionControlNetInpaintPipeline
						snake_case__   					=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						snake_case__   					=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



						def 					UpperCAmelCase__				(self:				Union[str, Any]      )						->    str:




											'''simple docstring'''



											torch.manual_seed(0      )
											__a				:	Any			=				UNetaDConditionModel(
											    block_out_channels=(32, 64)   ,				layers_per_block=2   ,				sample_size=32   ,				in_channels=9   ,				out_channels=4   ,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,				up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,				cross_attention_dim=32   ,				)
											torch.manual_seed(0      )

											def init_weights(__UpperCAmelCase:				List[str]      ):
																if isinstance(__UpperCAmelCase   ,				torch.nn.Convad      ):
																					torch.nn.init.normal(m.weight      )
																					m.bias.data.fill_(1.0      )

											__a				:	Tuple			=				ControlNetModel(
											    block_out_channels=(32, 64)   ,				layers_per_block=2   ,				in_channels=4   ,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,				cross_attention_dim=32   ,				conditioning_embedding_out_channels=(16, 32)   ,				)
											controlneta.controlnet_down_blocks.apply(__UpperCAmelCase      )

											torch.manual_seed(0      )
											__a				:	Any			=				ControlNetModel(
											    block_out_channels=(32, 64)   ,				layers_per_block=2   ,				in_channels=4   ,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,				cross_attention_dim=32   ,				conditioning_embedding_out_channels=(16, 32)   ,				)
											controlneta.controlnet_down_blocks.apply(__UpperCAmelCase      )

											torch.manual_seed(0      )
											__a				:	str			=				DDIMScheduler(
											    beta_start=0.0_00_85   ,				beta_end=0.0_12   ,				beta_schedule="scaled_linear"   ,				clip_sample=__UpperCAmelCase   ,				set_alpha_to_one=__UpperCAmelCase   ,				)
											torch.manual_seed(0      )
											__a				:	int			=				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   ,				)
											torch.manual_seed(0      )
											__a				:	Optional[Any]			=				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=1000   ,				)
											__a				:	List[Any]			=				CLIPTextModel(__UpperCAmelCase      )
											__a				:	int			=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

											__a				:	Tuple			=				MultiControlNetModel([controlneta, controlneta]      )

											__a				:	List[str]			=				{
											    "unet": unet,
											    "controlnet": controlnet,
											    "scheduler": scheduler,
											    "vae": vae,
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "safety_checker": None,
											    "feature_extractor": None,
											}
											return components



						def 					UpperCAmelCase__				(self:				Tuple   ,				__UpperCAmelCase:				List[Any]   ,				__UpperCAmelCase:				Union[str, Any]=0      )						->    Optional[int]:




											'''simple docstring'''



											if str(__UpperCAmelCase      ).startswith("mps"      ):
																__a				:	int			=				torch.manual_seed(__UpperCAmelCase      )
											else:
																__a				:	List[str]			=				torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )

											__a				:	Optional[Any]			=				2

											__a				:	Optional[Any]			=				[
											    randn_tensor(
											        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,				generator=__UpperCAmelCase   ,				device=torch.device(__UpperCAmelCase      )   ,				),
											    randn_tensor(
											        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,				generator=__UpperCAmelCase   ,				device=torch.device(__UpperCAmelCase      )   ,				),
											]
											__a				:	Optional[int]			=				floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )
											__a				:	List[Any]			=				init_image.cpu().permute(0   ,				2   ,				3   ,				1      )[0]

											__a				:	List[Any]			=				Image.fromarray(np.uinta(__UpperCAmelCase      )      ).convert("RGB"      ).resize((64, 64)      )
											__a				:	str			=				Image.fromarray(np.uinta(init_image + 4      )      ).convert("RGB"      ).resize((64, 64)      )

											__a				:	str			=				{
											    "prompt": "A painting of a squirrel eating a burger",
											    "generator": generator,
											    "num_inference_steps": 2,
											    "guidance_scale": 6.0,
											    "output_type": "numpy",
											    "image": image,
											    "mask_image": mask_image,
											    "control_image": control_image,
											}

											return inputs



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Dict:




											'''simple docstring'''



											__a				:	Optional[Any]			=				self.get_dummy_components()
											__a				:	int			=				self.pipeline_class(**__UpperCAmelCase      )
											pipe.to(__UpperCAmelCase      )

											__a				:	List[Any]			=				10.0
											__a				:	str			=				4

											__a				:	Tuple			=				self.get_dummy_inputs(__UpperCAmelCase      )
											__a				:	Optional[int]			=				steps
											__a				:	Any			=				scale
											__a				:	Union[str, Any]			=				pipe(**__UpperCAmelCase      )[0]

											__a				:	str			=				self.get_dummy_inputs(__UpperCAmelCase      )
											__a				:	Optional[Any]			=				steps
											__a				:	Optional[Any]			=				scale
											__a				:	Dict			=				pipe(**__UpperCAmelCase   ,				control_guidance_start=0.1   ,				control_guidance_end=0.2      )[0]

											__a				:	Union[str, Any]			=				self.get_dummy_inputs(__UpperCAmelCase      )
											__a				:	Optional[int]			=				steps
											__a				:	Optional[int]			=				scale
											__a				:	List[str]			=				pipe(**__UpperCAmelCase   ,				control_guidance_start=[0.1, 0.3]   ,				control_guidance_end=[0.2, 0.7]      )[0]

											__a				:	Union[str, Any]			=				self.get_dummy_inputs(__UpperCAmelCase      )
											__a				:	Dict			=				steps
											__a				:	Optional[int]			=				scale
											__a				:	Union[str, Any]			=				pipe(**__UpperCAmelCase   ,				control_guidance_start=0.4   ,				control_guidance_end=[0.5, 0.8]      )[0]

											# make sure that all outputs are different
											assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3
											assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3
											assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3



						def 					UpperCAmelCase__				(self:				List[Any]      )						->    int:




											'''simple docstring'''



											return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3      )



						@unittest.skipIf(
						    torch_device != "cuda" or not is_xformers_available()   ,				reason="XFormers attention is only available with CUDA and `xformers` installed"   ,				)
						def 					UpperCAmelCase__				(self:				List[str]      )						->    Tuple:




											'''simple docstring'''



											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )



						def 					UpperCAmelCase__				(self:				List[Any]      )						->    List[str]:




											'''simple docstring'''



											self._test_inference_batch_single_identical(expected_max_diff=2E-3      )



						def 					UpperCAmelCase__				(self:				Dict      )						->    int:




											'''simple docstring'''



											__a				:	List[str]			=				self.get_dummy_components()
											__a				:	Union[str, Any]			=				self.pipeline_class(**__UpperCAmelCase      )
											pipe.to(__UpperCAmelCase      )
											pipe.set_progress_bar_config(disable=__UpperCAmelCase      )
											with tempfile.TemporaryDirectory() as tmpdir:
																try:
																					# save_pretrained is not implemented for Multi-ControlNet
																					pipe.save_pretrained(__UpperCAmelCase      )
																except NotImplementedError:
																					pass




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

						"""simple docstring"""



						def 					UpperCAmelCase__				(self:				Union[str, Any]      )						->    Any:




											'''simple docstring'''



											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()



						def 					UpperCAmelCase__				(self:				Tuple      )						->    Optional[Any]:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"      )

											__a				:	List[Any]			=				StableDiffusionControlNetInpaintPipeline.from_pretrained(
											    "runwayml/stable-diffusion-inpainting"   ,				safety_checker=__UpperCAmelCase   ,				controlnet=__UpperCAmelCase      )
											pipe.enable_model_cpu_offload()
											pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

											__a				:	Optional[int]			=				torch.Generator(device="cpu"      ).manual_seed(0      )
											__a				:	int			=				load_image(
											    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"      ).resize((512, 512)      )

											__a				:	int			=				load_image(
											    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
											    "/stable_diffusion_inpaint/input_bench_mask.png"      ).resize((512, 512)      )

											__a				:	Tuple			=				"pitch black hole"

											__a				:	Optional[int]			=				load_image(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"      ).resize((512, 512)      )

											__a				:	Optional[Any]			=				pipe(
											    __UpperCAmelCase   ,				image=__UpperCAmelCase   ,				mask_image=__UpperCAmelCase   ,				control_image=__UpperCAmelCase   ,				generator=__UpperCAmelCase   ,				output_type="np"   ,				num_inference_steps=3   ,				)

											__a				:	Optional[Any]			=				output.images[0]

											assert image.shape == (512, 512, 3)

											__a				:	Union[str, Any]			=				load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"      )

											assert np.abs(expected_image - image      ).max() < 9E-2



						def 					UpperCAmelCase__				(self:				List[Any]      )						->    int:




											'''simple docstring'''



											__a				:	int			=				ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"      )

											__a				:	Tuple			=				StableDiffusionControlNetInpaintPipeline.from_pretrained(
											    "runwayml/stable-diffusion-v1-5"   ,				safety_checker=__UpperCAmelCase   ,				controlnet=__UpperCAmelCase      )
											__a				:	Tuple			=				DDIMScheduler.from_config(pipe.scheduler.config      )
											pipe.enable_model_cpu_offload()
											pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

											__a				:	List[str]			=				torch.Generator(device="cpu"      ).manual_seed(33      )

											__a				:	Any			=				load_image(
											    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"      )
											__a				:	List[str]			=				init_image.resize((512, 512)      )

											__a				:	Optional[Any]			=				load_image(
											    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"      )
											__a				:	Any			=				mask_image.resize((512, 512)      )

											__a				:	int			=				"a handsome man with ray-ban sunglasses"

											def make_inpaint_condition(__UpperCAmelCase:				List[Any]   ,				__UpperCAmelCase:				Optional[int]      ):
																__a				:	Optional[int]			=				np.array(image.convert("RGB"      )      ).astype(np.floataa      ) / 255.0
																__a				:	int			=				np.array(image_mask.convert("L"      )      ).astype(np.floataa      ) / 255.0

																assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
																__a				:	Union[str, Any]			=				-1.0  # set as masked pixel
																__a				:	List[str]			=				np.expand_dims(__UpperCAmelCase   ,				0      ).transpose(0   ,				3   ,				1   ,				2      )
																__a				:	Optional[Any]			=				torch.from_numpy(__UpperCAmelCase      )
																return image

											__a				:	Any			=				make_inpaint_condition(__UpperCAmelCase   ,				__UpperCAmelCase      )

											__a				:	List[Any]			=				pipe(
											    __UpperCAmelCase   ,				image=__UpperCAmelCase   ,				mask_image=__UpperCAmelCase   ,				control_image=__UpperCAmelCase   ,				guidance_scale=9.0   ,				eta=1.0   ,				generator=__UpperCAmelCase   ,				num_inference_steps=20   ,				output_type="np"   ,				)
											__a				:	Optional[Any]			=				output.images[0]

											assert image.shape == (512, 512, 3)

											__a				:	Tuple			=				load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"      )

											assert np.abs(expected_image - image      ).max() < 9E-2













from typing import TYPE_CHECKING

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


UpperCAmelCase__			 =	{'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	['''PegasusTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	['''PegasusTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	[
			    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''PegasusForCausalLM''',
			    '''PegasusForConditionalGeneration''',
			    '''PegasusModel''',
			    '''PegasusPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	[
			    '''TFPegasusForConditionalGeneration''',
			    '''TFPegasusModel''',
			    '''TFPegasusPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__			 =	[
			    '''FlaxPegasusForConditionalGeneration''',
			    '''FlaxPegasusModel''',
			    '''FlaxPegasusPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_pegasus import PegasusTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_pegasus_fast import PegasusTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_pegasus import (
						    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PegasusForCausalLM,
						    PegasusForConditionalGeneration,
						    PegasusModel,
						    PegasusPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_pegasus import (
						    FlaxPegasusForConditionalGeneration,
						    FlaxPegasusModel,
						    FlaxPegasusPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase__			 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





