



"""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.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class 					a	(  unittest.TestCase       ):



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Tuple:


     '''simple docstring'''




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



  @property
  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=1
     SCREAMING_SNAKE_CASE_: Dict     			=3
     SCREAMING_SNAKE_CASE_: List[Any]     			=(32, 32)

     SCREAMING_SNAKE_CASE_: List[str]     			=floats_tensor((batch_size, num_channels) + sizes       ,		rng=random.Random(0		)		).to(lowerCAmelCase		)
     return image



  @property
  def 	lowerCamelCase__       (						self :					Dict		)     ->					Optional[int]:


     '''simple docstring'''




     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: 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 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Optional[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 	lowerCamelCase__       (						self :					Tuple		)     ->					Optional[int]:


     '''simple docstring'''




     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: str     			=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       ,		)
     return CLIPTextModel(lowerCAmelCase		)



  @property
  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Optional[int]:


     '''simple docstring'''




     def extract(*lowerCAmelCase :					Union[str, Any]       ,		**lowerCAmelCase :					Any		):
        class 					a	:



          def __init__(						self :					List[str]		)     ->					str:


             '''simple docstring'''




             SCREAMING_SNAKE_CASE_: Dict     			=torch.ones([0]		)





          def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					Optional[Any]		)     ->					List[Any]:


             '''simple docstring'''




             self.pixel_values.to(lowerCAmelCase		)
             return self







        return Out()

     return extract



  def 	lowerCamelCase__       (						self :					int		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			="""cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.dummy_cond_unet
     SCREAMING_SNAKE_CASE_: Tuple     			=DDIMScheduler(
         beta_start=0.0_0_0_8_5       ,		beta_end=0.0_1_2       ,		beta_schedule="""scaled_linear"""       ,		clip_sample=lowerCAmelCase       ,		set_alpha_to_one=lowerCAmelCase       ,		)

     SCREAMING_SNAKE_CASE_: int     			=self.dummy_vae
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.dummy_text_encoder
     SCREAMING_SNAKE_CASE_: int     			=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

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

     SCREAMING_SNAKE_CASE_: Any     			="""A painting of a squirrel eating a burger"""

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.Generator(device=lowerCAmelCase		).manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=sd_pipe([prompt]       ,		generator=lowerCAmelCase       ,		guidance_scale=6.0       ,		num_inference_steps=2       ,		output_type="""np"""		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=output.images

     SCREAMING_SNAKE_CASE_: Tuple     			=torch.Generator(device=lowerCAmelCase		).manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Dict     			=sd_pipe(
         [prompt]       ,		generator=lowerCAmelCase       ,		guidance_scale=6.0       ,		num_inference_steps=2       ,		output_type="""np"""       ,		return_dict=lowerCAmelCase       ,		)[0]

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE_: Tuple     			=np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]		)

     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 	lowerCamelCase__       (						self :					str		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			="""cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE_: int     			=self.dummy_cond_unet
     SCREAMING_SNAKE_CASE_: str     			=PNDMScheduler(skip_prk_steps=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=self.dummy_vae
     SCREAMING_SNAKE_CASE_: int     			=self.dummy_text_encoder
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

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

     SCREAMING_SNAKE_CASE_: Any     			="""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE_: Any     			=torch.Generator(device=lowerCAmelCase		).manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Tuple     			=sd_pipe([prompt]       ,		generator=lowerCAmelCase       ,		guidance_scale=6.0       ,		num_inference_steps=2       ,		output_type="""np"""		)

     SCREAMING_SNAKE_CASE_: Dict     			=output.images

     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.Generator(device=lowerCAmelCase		).manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Tuple     			=sd_pipe(
         [prompt]       ,		generator=lowerCAmelCase       ,		guidance_scale=6.0       ,		num_inference_steps=2       ,		output_type="""np"""       ,		return_dict=lowerCAmelCase       ,		)[0]

     SCREAMING_SNAKE_CASE_: Tuple     			=image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE_: str     			=image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     SCREAMING_SNAKE_CASE_: Dict     			=np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]		)

     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 	lowerCamelCase__       (						self :					Tuple		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=StableDiffusionPipeline.from_pretrained(
         """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""       ,		safety_checker=lowerCAmelCase		)
     assert isinstance(lowerCAmelCase       ,		lowerCAmelCase		)
     assert isinstance(pipe.scheduler       ,		lowerCAmelCase		)
     assert pipe.safety_checker is None

     SCREAMING_SNAKE_CASE_: Union[str, 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(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Dict     			=StableDiffusionPipeline.from_pretrained(lowerCAmelCase		)

     # sanity check that the pipeline still works
     assert pipe.safety_checker is None
     SCREAMING_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 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=self.dummy_cond_unet
     SCREAMING_SNAKE_CASE_: List[str]     			=PNDMScheduler(skip_prk_steps=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=self.dummy_vae
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.dummy_text_encoder
     SCREAMING_SNAKE_CASE_: Tuple     			=CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

     # put models in fp16
     SCREAMING_SNAKE_CASE_: str     			=unet.half()
     SCREAMING_SNAKE_CASE_: Dict     			=vae.half()
     SCREAMING_SNAKE_CASE_: int     			=bert.half()

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

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=sd_pipe([prompt]       ,		num_inference_steps=2       ,		output_type="""np"""		).images

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







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



  def 	lowerCamelCase__       (						self :					Any		)     ->					Dict:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					Any		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       ,		safety_checker=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=sd_pipe.to(lowerCAmelCase		)
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Tuple     			=(
         """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
         """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
         """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
         """ children from bahnhof zoo, detailed """
     )
     SCREAMING_SNAKE_CASE_: str     			=40_0366_0346
     SCREAMING_SNAKE_CASE_: Dict     			=7

     # without safety guidance (sld_guidance_scale = 0)
     SCREAMING_SNAKE_CASE_: Tuple     			=torch.manual_seed(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=sd_pipe(
         [prompt]       ,		generator=lowerCAmelCase       ,		guidance_scale=lowerCAmelCase       ,		num_inference_steps=50       ,		output_type="""np"""       ,		width=512       ,		height=512       ,		sld_guidance_scale=0       ,		)

     SCREAMING_SNAKE_CASE_: Tuple     			=output.images
     SCREAMING_SNAKE_CASE_: int     			=image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE_: Tuple     			=[0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

     assert image.shape == (1, 512, 512, 3)

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

     # without safety guidance (strong configuration)
     SCREAMING_SNAKE_CASE_: Dict     			=torch.manual_seed(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=sd_pipe(
         [prompt]       ,		generator=lowerCAmelCase       ,		guidance_scale=lowerCAmelCase       ,		num_inference_steps=50       ,		output_type="""np"""       ,		width=512       ,		height=512       ,		sld_guidance_scale=2000       ,		sld_warmup_steps=7       ,		sld_threshold=0.0_2_5       ,		sld_momentum_scale=0.5       ,		sld_mom_beta=0.7       ,		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=output.images
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=[0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

     assert image.shape == (1, 512, 512, 3)

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



  def 	lowerCamelCase__       (						self :					str		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       ,		safety_checker=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
     SCREAMING_SNAKE_CASE_: Any     			=sd_pipe.to(lowerCAmelCase		)
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: str     			="""padme amidala taking a bath artwork, safe for work, no nudity"""
     SCREAMING_SNAKE_CASE_: int     			=27_3497_1755
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=7

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.manual_seed(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=sd_pipe(
         [prompt]       ,		generator=lowerCAmelCase       ,		guidance_scale=lowerCAmelCase       ,		num_inference_steps=50       ,		output_type="""np"""       ,		width=512       ,		height=512       ,		sld_guidance_scale=0       ,		)

     SCREAMING_SNAKE_CASE_: List[str]     			=output.images
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE_: List[str]     			=[0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

     assert image.shape == (1, 512, 512, 3)

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

     SCREAMING_SNAKE_CASE_: Dict     			=torch.manual_seed(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=sd_pipe(
         [prompt]       ,		generator=lowerCAmelCase       ,		guidance_scale=lowerCAmelCase       ,		num_inference_steps=50       ,		output_type="""np"""       ,		width=512       ,		height=512       ,		sld_guidance_scale=2000       ,		sld_warmup_steps=7       ,		sld_threshold=0.0_2_5       ,		sld_momentum_scale=0.5       ,		sld_mom_beta=0.7       ,		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=output.images
     SCREAMING_SNAKE_CASE_: List[str]     			=image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE_: Dict     			=[0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

     assert image.shape == (1, 512, 512, 3)

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





  def 	lowerCamelCase__       (						self :					List[str]		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""		)
     SCREAMING_SNAKE_CASE_: Any     			=sd_pipe.to(lowerCAmelCase		)
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=(
         """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
         """ leyendecker"""
     )
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=10_4435_5234
     SCREAMING_SNAKE_CASE_: Tuple     			=12

     SCREAMING_SNAKE_CASE_: Any     			=torch.manual_seed(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=sd_pipe(
         [prompt]       ,		generator=lowerCAmelCase       ,		guidance_scale=lowerCAmelCase       ,		num_inference_steps=50       ,		output_type="""np"""       ,		width=512       ,		height=512       ,		sld_guidance_scale=0       ,		)

     SCREAMING_SNAKE_CASE_: List[Any]     			=output.images
     SCREAMING_SNAKE_CASE_: str     			=image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE_: Dict     			=np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]		)

     assert image.shape == (1, 512, 512, 3)

     assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-7

     SCREAMING_SNAKE_CASE_: Any     			=torch.manual_seed(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=sd_pipe(
         [prompt]       ,		generator=lowerCAmelCase       ,		guidance_scale=lowerCAmelCase       ,		num_inference_steps=50       ,		output_type="""np"""       ,		width=512       ,		height=512       ,		sld_guidance_scale=2000       ,		sld_warmup_steps=7       ,		sld_threshold=0.0_2_5       ,		sld_momentum_scale=0.5       ,		sld_mom_beta=0.7       ,		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=output.images
     SCREAMING_SNAKE_CASE_: int     			=image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE_: List[str]     			=np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]		)
     assert image.shape == (1, 512, 512, 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_tf_available, is_torch_available


_UpperCAmelCase     =			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMForMultipleChoice""",
       """XLMForQuestionAnswering""",
       """XLMForQuestionAnsweringSimple""",
       """XLMForSequenceClassification""",
       """XLMForTokenClassification""",
       """XLMModel""",
       """XLMPreTrainedModel""",
       """XLMWithLMHeadModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXLMForMultipleChoice""",
       """TFXLMForQuestionAnsweringSimple""",
       """TFXLMForSequenceClassification""",
       """TFXLMForTokenClassification""",
       """TFXLMMainLayer""",
       """TFXLMModel""",
       """TFXLMPreTrainedModel""",
       """TFXLMWithLMHeadModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



