


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 		__a				(    UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Any           =     StableDiffusionInstructPixaPixPipeline
		_a     :  Tuple           =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
		_a     :  Union[str, Any]           =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_a     :  Tuple           =     IMAGE_TO_IMAGE_IMAGE_PARAMS
		_a     :  Tuple           =     IMAGE_TO_IMAGE_IMAGE_PARAMS
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									torch.manual_seed(0						)
									_UpperCAmelCase					=      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      ,   )
									_UpperCAmelCase					=      PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE						)
									torch.manual_seed(0						)
									_UpperCAmelCase					=      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						)
									_UpperCAmelCase					=      CLIPTextConfig(
									    bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-0_5      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1000      ,   )
									_UpperCAmelCase					=      CLIPTextModel(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

									_UpperCAmelCase					=      {
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'safety_checker': None,
									    'feature_extractor': None,
									}
									return components
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=0						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(_SCREAMING_SNAKE_CASE						)						).to(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      image.cpu().permute(0      ,   2      ,   3      ,   1						)[0]
									_UpperCAmelCase					=      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE						)						).convert('RGB'						)
									if str(_SCREAMING_SNAKE_CASE						).startswith('mps'						):
																_UpperCAmelCase					=      torch.manual_seed(_SCREAMING_SNAKE_CASE						)
									else:
																_UpperCAmelCase					=      torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      {
									    '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      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      'cpu'  # ensure determinism for the device-dependent torch.Generator
									_UpperCAmelCase					=      self.get_dummy_components()
									_UpperCAmelCase					=      StableDiffusionInstructPixaPixPipeline(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      sd_pipe.to(_SCREAMING_SNAKE_CASE						)
									sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      sd_pipe(**_SCREAMING_SNAKE_CASE						).images
									_UpperCAmelCase					=      image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      'cpu'  # ensure determinism for the device-dependent torch.Generator
									_UpperCAmelCase					=      self.get_dummy_components()
									_UpperCAmelCase					=      StableDiffusionInstructPixaPixPipeline(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      sd_pipe.to(_SCREAMING_SNAKE_CASE						)
									sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

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

									assert image.shape == (1, 32, 32, 3)
									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      'cpu'  # ensure determinism for the device-dependent torch.Generator
									_UpperCAmelCase					=      self.get_dummy_components()
									_UpperCAmelCase					=      StableDiffusionInstructPixaPixPipeline(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      sd_pipe.to(_SCREAMING_SNAKE_CASE						)
									sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      [inputs['prompt']] * 2

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

									_UpperCAmelCase					=      sd_pipe(**_SCREAMING_SNAKE_CASE						).images
									_UpperCAmelCase					=      image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      'cpu'  # ensure determinism for the device-dependent torch.Generator
									_UpperCAmelCase					=      self.get_dummy_components()
									_UpperCAmelCase					=      EulerAncestralDiscreteScheduler(
									    beta_start=0.00085      ,   beta_end=0.012      ,   beta_schedule='scaled_linear'						)
									_UpperCAmelCase					=      StableDiffusionInstructPixaPixPipeline(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      sd_pipe.to(_SCREAMING_SNAKE_CASE						)
									sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

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

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

									assert image.shape == (1, 32, 32, 3)
									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									super().test_inference_batch_single_identical(expected_max_diff=3e-3						)






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




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_dummy_components()
									_UpperCAmelCase					=      StableDiffusionInstructPixaPixPipeline(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      VaeImageProcessor(do_resize=_SCREAMING_SNAKE_CASE      ,   do_normalize=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      pipe.to(_SCREAMING_SNAKE_CASE						)
									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      pipe(**self.get_dummy_inputs_by_type(_SCREAMING_SNAKE_CASE      ,   input_image_type='pt'						)						)[0]

									_UpperCAmelCase					=      components['vae']
									_UpperCAmelCase					=      self.get_dummy_inputs_by_type(_SCREAMING_SNAKE_CASE      ,   input_image_type='pt'						)

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

									_UpperCAmelCase					=      pipe(**_SCREAMING_SNAKE_CASE						)[0]

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



@slow
@require_torch_gpu
class 		__a				(    unittest.TestCase      ):
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE=0						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      torch.manual_seed(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      load_image(
									    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'						)
									_UpperCAmelCase					=      {
									    '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      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





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

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

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    'timbrooks/instruct-pix2pix'      ,   safety_checker=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
									pipe.to(_SCREAMING_SNAKE_CASE						)
									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    'timbrooks/instruct-pix2pix'      ,   safety_checker=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      DDIMScheduler.from_config(pipe.scheduler.config						)
									pipe.to(_SCREAMING_SNAKE_CASE						)
									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 512, 512, 3)
									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      0

									def callback_fn(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						) -> None:
																_UpperCAmelCase					=      True
																nonlocal number_of_steps
																number_of_steps += 1
																if step == 1:
																							_UpperCAmelCase					=      latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 64, 64)
																							_UpperCAmelCase					=      latents[0, -3:, -3:, -1]
																							_UpperCAmelCase					=      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:
																							_UpperCAmelCase					=      latents.detach().cpu().numpy()
																							assert latents.shape == (1, 4, 64, 64)
																							_UpperCAmelCase					=      latents[0, -3:, -3:, -1]
																							_UpperCAmelCase					=      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

									_UpperCAmelCase					=      False

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

									_UpperCAmelCase					=      self.get_inputs()
									pipe(**_SCREAMING_SNAKE_CASE      ,   callback=_SCREAMING_SNAKE_CASE      ,   callback_steps=1						)
									assert callback_fn.has_been_called
									assert number_of_steps == 3
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

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

									_UpperCAmelCase					=      self.get_inputs()
									_UpperCAmelCase					=      pipe(**_SCREAMING_SNAKE_CASE						)

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






		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									_UpperCAmelCase					=      inputs['image'].resize((504, 504)						)

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

									_UpperCAmelCase					=      pipe(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      output.images[0]

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

									assert image.shape == (504, 504, 3)
									_UpperCAmelCase					=      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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__  :Any             =							{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Optional[Any]             =							['''XGLMTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :str             =							['''XGLMTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[str]             =							[
		    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XGLMForCausalLM''',
		    '''XGLMModel''',
		    '''XGLMPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :str             =							[
		    '''FlaxXGLMForCausalLM''',
		    '''FlaxXGLMModel''',
		    '''FlaxXGLMPreTrainedModel''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							[
		    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFXGLMForCausalLM''',
		    '''TFXGLMModel''',
		    '''TFXGLMPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xglm import XGLMTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xglm_fast import XGLMTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xglm import (
				    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXGLMForCausalLM,
				    TFXGLMModel,
				    TFXGLMPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__  :Any             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)
