



"""simple docstring"""






import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			{}
  for name in unet.attn_processors.keys():
    _SCREAMING_SNAKE_CASE        =			None if name.endswith('''attn1.processor'''    ) else unet.config.cross_attention_dim
    if name.startswith('''mid_block'''    ):
      _SCREAMING_SNAKE_CASE        =			unet.config.block_out_channels[-1]
    elif name.startswith('''up_blocks'''    ):
      _SCREAMING_SNAKE_CASE        =			int(name[len('''up_blocks.'''    )]    )
      _SCREAMING_SNAKE_CASE        =			list(reversed(unet.config.block_out_channels    )    )[block_id]
    elif name.startswith('''down_blocks'''    ):
      _SCREAMING_SNAKE_CASE        =			int(name[len('''down_blocks.'''    )]    )
      _SCREAMING_SNAKE_CASE        =			unet.config.block_out_channels[block_id]
    _SCREAMING_SNAKE_CASE        =			(
        LoRAAttnProcessor2_0 if hasattr(UpperCamelCase__       ,							'''scaled_dot_product_attention'''    ) else LoRAAttnProcessor
    )
    _SCREAMING_SNAKE_CASE        =			lora_attn_processor_class(
        hidden_size=UpperCamelCase__       ,							cross_attention_dim=UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			AttnProcsLayers(UpperCamelCase__    )
  return lora_attn_procs, unet_lora_layers


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			{}
  _SCREAMING_SNAKE_CASE        =			(
      LoRAAttnProcessor2_0 if hasattr(UpperCamelCase__       ,							'''scaled_dot_product_attention'''    ) else LoRAAttnProcessor
  )
  for name, module in text_encoder.named_modules():
    if name.endswith(UpperCamelCase__    ):
      _SCREAMING_SNAKE_CASE        =			lora_attn_processor_class(
          hidden_size=module.out_proj.out_features       ,							cross_attention_dim=UpperCamelCase__    )
  return text_lora_attn_procs


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			create_text_encoder_lora_attn_procs(UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			AttnProcsLayers(UpperCamelCase__    )
  return text_encoder_lora_layers


def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__=False    ):


  '''simple docstring'''


  for _, attn_proc in text_lora_attn_procs.items():
    # set up.weights
    for layer_name, layer_module in attn_proc.named_modules():
      if layer_name.endswith('''_lora'''    ):
        _SCREAMING_SNAKE_CASE        =			(
            torch.randn_like(layer_module.up.weight    )
            if randn_weight
            else torch.zeros_like(layer_module.up.weight    )
        )
        _SCREAMING_SNAKE_CASE        =			torch.nn.Parameter(UpperCamelCase__    )







class    __snake_case(					unittest.TestCase       ):




    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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    ,							)
      _SCREAMING_SNAKE_CASE        =			DDIMScheduler(
          beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule='''scaled_linear'''    ,							clip_sample=A_    ,							set_alpha_to_one=A_    ,							steps_offset=1    ,							)
      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    ,							)
      _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=1_000    ,							)
      _SCREAMING_SNAKE_CASE        =			CLIPTextModel(A_			)
      _SCREAMING_SNAKE_CASE        =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			create_unet_lora_layers(A_			)
      _SCREAMING_SNAKE_CASE        =			create_text_encoder_lora_layers(A_			)

      _SCREAMING_SNAKE_CASE        =			{
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      _SCREAMING_SNAKE_CASE        =			{
          '''unet_lora_layers''': unet_lora_layers,
          '''text_encoder_lora_layers''': text_encoder_lora_layers,
          '''unet_lora_attn_procs''': unet_lora_attn_procs,
      }
      return pipeline_components, lora_components




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			1
      _SCREAMING_SNAKE_CASE        =			10
      _SCREAMING_SNAKE_CASE        =			4
      _SCREAMING_SNAKE_CASE        =			(32, 32)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			floats_tensor((batch_size, num_channels) + sizes			)
      _SCREAMING_SNAKE_CASE        =			torch.randint(1    ,							A_    ,							size=(batch_size, sequence_length)    ,							generator=A_			)

      _SCREAMING_SNAKE_CASE        =			{
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''guidance_scale''': 6.0,
          '''output_type''': '''numpy''',
      }

      return noise, input_ids, pipeline_inputs

      # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			77

      _SCREAMING_SNAKE_CASE        =			torch.randint(2    ,							56    ,							size=(1, max_seq_length)    ,							generator=torch.manual_seed(0			)			)

      _SCREAMING_SNAKE_CASE        =			{}
      _SCREAMING_SNAKE_CASE        =			inputs
      return prepared_inputs




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      LoraLoaderMixin.save_lora_weights(
          save_directory=A_    ,							unet_lora_layers=lora_components['''unet_lora_layers''']    ,							text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']    ,							)
      self.assertTrue(os.path.isfile(os.path.join(A_    ,							'''pytorch_lora_weights.bin'''			)			)			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			StableDiffusionPipeline(**A_			)
      _SCREAMING_SNAKE_CASE        =			sd_pipe.to(A_			)
      sd_pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs()

      _SCREAMING_SNAKE_CASE        =			sd_pipe(**A_			).images
      _SCREAMING_SNAKE_CASE        =			original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
        LoraLoaderMixin.save_lora_weights(
            save_directory=A_    ,							unet_lora_layers=lora_components['''unet_lora_layers''']    ,							text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']    ,							)
        self.assertTrue(os.path.isfile(os.path.join(A_    ,							'''pytorch_lora_weights.bin'''			)			)			)
        sd_pipe.load_lora_weights(A_			)

      _SCREAMING_SNAKE_CASE        =			sd_pipe(**A_			).images
      _SCREAMING_SNAKE_CASE        =			lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(A_			)    ,							torch.from_numpy(A_			)			)			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			StableDiffusionPipeline(**A_			)
      _SCREAMING_SNAKE_CASE        =			sd_pipe.to(A_			)
      sd_pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs()

      _SCREAMING_SNAKE_CASE        =			sd_pipe(**A_			).images
      _SCREAMING_SNAKE_CASE        =			original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
        LoraLoaderMixin.save_lora_weights(
            save_directory=A_    ,							unet_lora_layers=lora_components['''unet_lora_layers''']    ,							text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']    ,							safe_serialization=A_    ,							)
        self.assertTrue(os.path.isfile(os.path.join(A_    ,							'''pytorch_lora_weights.safetensors'''			)			)			)
        sd_pipe.load_lora_weights(A_			)

      _SCREAMING_SNAKE_CASE        =			sd_pipe(**A_			).images
      _SCREAMING_SNAKE_CASE        =			lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(A_			)    ,							torch.from_numpy(A_			)			)			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			lora_components['''unet_lora_attn_procs''']
      _SCREAMING_SNAKE_CASE        =			StableDiffusionPipeline(**A_			)
      _SCREAMING_SNAKE_CASE        =			sd_pipe.to(A_			)
      sd_pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs()

      _SCREAMING_SNAKE_CASE        =			sd_pipe(**A_			).images
      _SCREAMING_SNAKE_CASE        =			original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
        _SCREAMING_SNAKE_CASE        =			sd_pipe.unet
        unet.set_attn_processor(A_			)
        unet.save_attn_procs(A_			)
        self.assertTrue(os.path.isfile(os.path.join(A_    ,							'''pytorch_lora_weights.bin'''			)			)			)
        sd_pipe.load_lora_weights(A_			)

      _SCREAMING_SNAKE_CASE        =			sd_pipe(**A_			).images
      _SCREAMING_SNAKE_CASE        =			lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(A_			)    ,							torch.from_numpy(A_			)			)			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			StableDiffusionPipeline(**A_			)

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_tokens()

      # inference without lora
      _SCREAMING_SNAKE_CASE        =			pipe.text_encoder(**A_			)[0]
      assert outputs_without_lora.shape == (1, 77, 32)

      # create lora_attn_procs with zeroed out up.weights
      _SCREAMING_SNAKE_CASE        =			create_text_encoder_lora_attn_procs(pipe.text_encoder			)
      set_lora_up_weights(A_    ,							randn_weight=A_			)

      # monkey patch
      pipe._modify_text_encoder(A_			)

      # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
      del text_attn_procs
      gc.collect()

      # inference with lora
      _SCREAMING_SNAKE_CASE        =			pipe.text_encoder(**A_			)[0]
      assert outputs_with_lora.shape == (1, 77, 32)

      assert torch.allclose(
          A_    ,							A_			), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

      # create lora_attn_procs with randn up.weights
      _SCREAMING_SNAKE_CASE        =			create_text_encoder_lora_attn_procs(pipe.text_encoder			)
      set_lora_up_weights(A_    ,							randn_weight=A_			)

      # monkey patch
      pipe._modify_text_encoder(A_			)

      # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
      del text_attn_procs
      gc.collect()

      # inference with lora
      _SCREAMING_SNAKE_CASE        =			pipe.text_encoder(**A_			)[0]
      assert outputs_with_lora.shape == (1, 77, 32)

      assert not torch.allclose(
          A_    ,							A_			), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			StableDiffusionPipeline(**A_			)

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_tokens()

      # inference without lora
      _SCREAMING_SNAKE_CASE        =			pipe.text_encoder(**A_			)[0]
      assert outputs_without_lora.shape == (1, 77, 32)

      # create lora_attn_procs with randn up.weights
      _SCREAMING_SNAKE_CASE        =			create_text_encoder_lora_attn_procs(pipe.text_encoder			)
      set_lora_up_weights(A_    ,							randn_weight=A_			)

      # monkey patch
      pipe._modify_text_encoder(A_			)

      # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
      del text_attn_procs
      gc.collect()

      # inference with lora
      _SCREAMING_SNAKE_CASE        =			pipe.text_encoder(**A_			)[0]
      assert outputs_with_lora.shape == (1, 77, 32)

      assert not torch.allclose(
          A_    ,							A_			), "lora outputs should be different to without lora outputs"

      # remove monkey patch
      pipe._remove_text_encoder_monkey_patch()

      # inference with removed lora
      _SCREAMING_SNAKE_CASE        =			pipe.text_encoder(**A_			)[0]
      assert outputs_without_lora_removed.shape == (1, 77, 32)

      assert torch.allclose(
          A_    ,							A_			), "remove lora monkey patch should restore the original outputs"




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			StableDiffusionPipeline(**A_			)
      _SCREAMING_SNAKE_CASE        =			sd_pipe.to(A_			)
      sd_pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs()

      with tempfile.TemporaryDirectory() as tmpdirname:
        LoraLoaderMixin.save_lora_weights(
            save_directory=A_    ,							unet_lora_layers=lora_components['''unet_lora_layers''']    ,							text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']    ,							)
        self.assertTrue(os.path.isfile(os.path.join(A_    ,							'''pytorch_lora_weights.bin'''			)			)			)
        sd_pipe.load_lora_weights(A_			)

      _SCREAMING_SNAKE_CASE        =			sd_pipe(**A_			).images
      _SCREAMING_SNAKE_CASE        =			lora_images[0, -3:, -3:, -1]

      _SCREAMING_SNAKE_CASE        =			sd_pipe(**A_    ,							cross_attention_kwargs={'''scale''': 0.5}			).images
      _SCREAMING_SNAKE_CASE        =			lora_images_with_scale[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(
          torch.allclose(torch.from_numpy(A_			)    ,							torch.from_numpy(A_			)			)			)




    def   A   (   self			):





      '''simple docstring'''



      with tempfile.TemporaryDirectory() as tmpdirname:
        self.create_lora_weight_file(A_			)

        _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
        _SCREAMING_SNAKE_CASE        =			StableDiffusionPipeline(**A_			)
        _SCREAMING_SNAKE_CASE        =			sd_pipe.to(A_			)
        sd_pipe.set_progress_bar_config(disable=A_			)

        # check if vanilla attention processors are used
        for _, module in sd_pipe.unet.named_modules():
          if isinstance(A_    ,							A_			):
            self.assertIsInstance(module.processor    ,							(AttnProcessor, AttnProcessor2_0)			)

            # load LoRA weight file
        sd_pipe.load_lora_weights(A_			)

        # check if lora attention processors are used
        for _, module in sd_pipe.unet.named_modules():
          if isinstance(A_    ,							A_			):
            _SCREAMING_SNAKE_CASE        =			(
                LoRAAttnProcessor2_0 if hasattr(A_    ,							'''scaled_dot_product_attention'''			) else LoRAAttnProcessor
            )
            self.assertIsInstance(module.processor    ,							A_			)




    @unittest.skipIf(torch_device != '''cuda'''    ,							'''This test is supposed to run on GPU'''			)
    def   A   (   self			):





      '''simple docstring'''



      with tempfile.TemporaryDirectory() as tmpdirname:
        self.create_lora_weight_file(A_			)

        _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
        _SCREAMING_SNAKE_CASE        =			StableDiffusionPipeline(**A_			)
        _SCREAMING_SNAKE_CASE        =			sd_pipe.to(A_			)
        sd_pipe.set_progress_bar_config(disable=A_			)

        # enable XFormers
        sd_pipe.enable_xformers_memory_efficient_attention()

        # check if xFormers attention processors are used
        for _, module in sd_pipe.unet.named_modules():
          if isinstance(A_    ,							A_			):
            self.assertIsInstance(module.processor    ,							A_			)

            # load LoRA weight file
        sd_pipe.load_lora_weights(A_			)

        # check if lora attention processors are used
        for _, module in sd_pipe.unet.named_modules():
          if isinstance(A_    ,							A_			):
            self.assertIsInstance(module.processor    ,							A_			)




    @unittest.skipIf(torch_device != '''cuda'''    ,							'''This test is supposed to run on GPU'''			)
    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			StableDiffusionPipeline(**A_			)
      _SCREAMING_SNAKE_CASE        =			sd_pipe.to(A_			)
      sd_pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs()

      # enable XFormers
      sd_pipe.enable_xformers_memory_efficient_attention()

      _SCREAMING_SNAKE_CASE        =			sd_pipe(**A_			).images
      _SCREAMING_SNAKE_CASE        =			original_images[0, -3:, -3:, -1]

      with tempfile.TemporaryDirectory() as tmpdirname:
        LoraLoaderMixin.save_lora_weights(
            save_directory=A_    ,							unet_lora_layers=lora_components['''unet_lora_layers''']    ,							text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']    ,							)
        self.assertTrue(os.path.isfile(os.path.join(A_    ,							'''pytorch_lora_weights.bin'''			)			)			)
        sd_pipe.load_lora_weights(A_			)

      _SCREAMING_SNAKE_CASE        =			sd_pipe(**A_			).images
      _SCREAMING_SNAKE_CASE        =			lora_images[0, -3:, -3:, -1]

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(A_			)    ,							torch.from_numpy(A_			)			)			)





"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCamelCase				:     Dict								=  {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     int								=  [
        """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CamembertForCausalLM""",
        """CamembertForMaskedLM""",
        """CamembertForMultipleChoice""",
        """CamembertForQuestionAnswering""",
        """CamembertForSequenceClassification""",
        """CamembertForTokenClassification""",
        """CamembertModel""",
        """CamembertPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     str								=  [
        """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCamembertForCausalLM""",
        """TFCamembertForMaskedLM""",
        """TFCamembertForMultipleChoice""",
        """TFCamembertForQuestionAnswering""",
        """TFCamembertForSequenceClassification""",
        """TFCamembertForTokenClassification""",
        """TFCamembertModel""",
        """TFCamembertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert import CamembertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert_fast import CamembertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_camembert import (
            CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CamembertForCausalLM,
            CamembertForMaskedLM,
            CamembertForMultipleChoice,
            CamembertForQuestionAnswering,
            CamembertForSequenceClassification,
            CamembertForTokenClassification,
            CamembertModel,
            CamembertPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_camembert import (
            TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCamembertForCausalLM,
            TFCamembertForMaskedLM,
            TFCamembertForMultipleChoice,
            TFCamembertForQuestionAnswering,
            TFCamembertForSequenceClassification,
            TFCamembertForTokenClassification,
            TFCamembertModel,
            TFCamembertPreTrainedModel,
        )

else:
    import sys

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