

import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class  UpperCAmelCase__	( A__		,  A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      StableDiffusionInstructPixaPixPipeline
  a     =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
  a     =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  a     =      IMAGE_TO_IMAGE_IMAGE_PARAMS
  a     =      IMAGE_TO_IMAGE_IMAGE_PARAMS






  def    lowercase_	(							self		:	Optional[Any]      )	->							Tuple:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					UNetaDConditionModel(
           block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=8			,     out_channels=4			,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,     cross_attention_dim=32			,     )
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler(skip_prk_steps=__lowerCamelCase      )
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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      )
       SCREAMING_SNAKE_CASE__					=					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			,     )
       SCREAMING_SNAKE_CASE__					=					CLIPTextModel(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

       SCREAMING_SNAKE_CASE__					=					{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Optional[int]=0      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, 3, 32, 32)			,     rng=random.Random(__lowerCamelCase      )      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					image.cpu().permute(0			,     2			,     3			,     1      )[0]
       SCREAMING_SNAKE_CASE__					=					Image.fromarray(np.uinta(__lowerCamelCase      )      ).convert('''RGB'''      )
       if str(__lowerCamelCase      ).startswith('''mps'''      ):
            SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''image_guidance_scale''': 1,
           '''output_type''': '''numpy''',
       }
       return inputs






  def    lowercase_	(							self		:	Optional[Any]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3






  def    lowercase_	(							self		:	Dict      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					'''french fries'''
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase			,     negative_prompt=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3






  def    lowercase_	(							self		:	str      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					[inputs['''prompt''']] * 2

       SCREAMING_SNAKE_CASE__					=					np.array(inputs['''image''']      ).astype(np.floataa      ) / 255.0
       SCREAMING_SNAKE_CASE__					=					torch.from_numpy(__lowerCamelCase      ).unsqueeze(0      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					image / 2 + 0.5
       SCREAMING_SNAKE_CASE__					=					image.permute(0			,     3			,     1			,     2      )
       SCREAMING_SNAKE_CASE__					=					image.repeat(2			,     1			,     1			,     1      )

       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3






  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					EulerAncestralDiscreteScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''      )
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe.to(__lowerCamelCase      )
       sd_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					sd_pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       SCREAMING_SNAKE_CASE__					=					[round(__lowerCamelCase			,     4      ) for x in image_slice.flatten().tolist()]
       print(''','''.join([str(__lowerCamelCase      ) for x in slice]      )      )

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3






  def    lowercase_	(							self		:	Any      )	->							Optional[int]:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3      )







  def    lowercase_	(							self		:	List[Any]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					VaeImageProcessor(do_resize=__lowerCamelCase			,     do_normalize=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					pipe(**self.get_dummy_inputs_by_type(__lowerCamelCase			,     input_image_type='''pt'''      )      )[0]

       SCREAMING_SNAKE_CASE__					=					components['''vae''']
       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs_by_type(__lowerCamelCase			,     input_image_type='''pt'''      )

       for image_param in self.image_latents_params:
            if image_param in inputs.keys():
                 SCREAMING_SNAKE_CASE__					=					vae.encode(inputs[image_param]      ).latent_dist.mode()

       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      )[0]

       SCREAMING_SNAKE_CASE__					=					np.abs(out - out_latents_inputs      ).max()
       self.assertLess(__lowerCamelCase			,     1e-4			,     '''passing latents as image input generate different result from passing image'''      )






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






  """simple docstring"""


  def    lowercase_	(							self		:	List[str]      )	->							Optional[Any]:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]=0      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					load_image(
           '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''      )
       SCREAMING_SNAKE_CASE__					=					{
           '''prompt''': '''turn him into a cyborg''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''image_guidance_scale''': 1.0,
           '''output_type''': '''numpy''',
       }
       return inputs






  def    lowercase_	(							self		:	Tuple      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''			,     safety_checker=__lowerCamelCase      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1e-3






  def    lowercase_	(							self		:	Dict      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1e-3






  def    lowercase_	(							self		:	Union[str, Any]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_config(pipe.scheduler.config      )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      ).images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1e-3






  def    lowercase_	(							self		:	Any      )	->							int:
       SCREAMING_SNAKE_CASE__					=					0

       def callback_fn(__lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	torch.FloatTensor      ) -> None:
            SCREAMING_SNAKE_CASE__					=					True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
                 SCREAMING_SNAKE_CASE__					=					latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 64)
                 SCREAMING_SNAKE_CASE__					=					latents[0, -3:, -3:, -1]
                 SCREAMING_SNAKE_CASE__					=					np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]      )

                 assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
            elif step == 2:
                 SCREAMING_SNAKE_CASE__					=					latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 64)
                 SCREAMING_SNAKE_CASE__					=					latents[0, -3:, -3:, -1]
                 SCREAMING_SNAKE_CASE__					=					np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]      )

                 assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2

       SCREAMING_SNAKE_CASE__					=					False

       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''			,     safety_checker=__lowerCamelCase			,     torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       pipe(**__lowerCamelCase			,     callback=__lowerCamelCase			,     callback_steps=1      )
       assert callback_fn.has_been_called
       assert number_of_steps == 3






  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''			,     safety_checker=__lowerCamelCase			,     torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing(1      )
       pipe.enable_sequential_cpu_offload()

       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.cuda.max_memory_allocated()
       # make sure that less than 2.2 GB is allocated
       assert mem_bytes < 2.2 * 10**9







  def    lowercase_	(							self		:	List[str]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.get_inputs()
       # resize to resolution that is divisible by 8 but not 16 or 32
       SCREAMING_SNAKE_CASE__					=					inputs['''image'''].resize((504, 504)      )

       SCREAMING_SNAKE_CASE__					=					'''timbrooks/instruct-pix2pix'''
       SCREAMING_SNAKE_CASE__					=					StableDiffusionInstructPixaPixPipeline.from_pretrained(
           __lowerCamelCase			,     safety_checker=__lowerCamelCase			,     )
       pipe.to(__lowerCamelCase      )
       pipe.set_progress_bar_config(disable=__lowerCamelCase      )
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE__					=					pipe(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					output.images[0]

       SCREAMING_SNAKE_CASE__					=					image[255:258, 383:386, -1]

       assert image.shape == (504, 504, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE  :						Dict         =    {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    ['''LongformerTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LongformerForMaskedLM''',
           '''LongformerForMultipleChoice''',
           '''LongformerForQuestionAnswering''',
           '''LongformerForSequenceClassification''',
           '''LongformerForTokenClassification''',
           '''LongformerModel''',
           '''LongformerPreTrainedModel''',
           '''LongformerSelfAttention''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFLongformerForMaskedLM''',
           '''TFLongformerForMultipleChoice''',
           '''TFLongformerForQuestionAnswering''',
           '''TFLongformerForSequenceClassification''',
           '''TFLongformerForTokenClassification''',
           '''TFLongformerModel''',
           '''TFLongformerPreTrainedModel''',
           '''TFLongformerSelfAttention''',
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       _SCREAMING_SNAKE_CASE  :						int         =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


