import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						AudioLDMPipeline
  lowerCamelCase__     =						TEXT_TO_AUDIO_PARAMS
  lowerCamelCase__     =						TEXT_TO_AUDIO_BATCH_PARAMS
  lowerCamelCase__     =						frozenset(
      [
          '''num_inference_steps''',
          '''num_waveforms_per_prompt''',
          '''generator''',
          '''latents''',
          '''output_type''',
          '''return_dict''',
          '''callback''',
          '''callback_steps''',
      ]					)







  def    __A     (							self :       int   )  ->	Optional[int]:
        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, 64)  ,     class_embed_type="simple_projection"  ,     projection_class_embeddings_input_dim=32  ,     class_embeddings_concat=__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  DDIMScheduler(
            beta_start=0.0_0085  ,     beta_end=0.012  ,     beta_schedule="scaled_linear"  ,     clip_sample=__magic_name__  ,     set_alpha_to_one=__magic_name__  ,     )
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  AutoencoderKL(
            block_out_channels=[32, 64]  ,     in_channels=1  ,     out_channels=1  ,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]  ,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]  ,     latent_channels=4  ,     )
        torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  ClapTextConfig(
            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  ,     projection_dim=32  ,     )
        SCREAMING_SNAKE_CASE_          =  ClapTextModelWithProjection(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"  ,     model_max_length=77   )

        SCREAMING_SNAKE_CASE_          =  SpeechTaHifiGanConfig(
            model_in_dim=8  ,     sampling_rate=16_000  ,     upsample_initial_channel=16  ,     upsample_rates=[2, 2]  ,     upsample_kernel_sizes=[4, 4]  ,     resblock_kernel_sizes=[3, 7]  ,     resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]  ,     normalize_before=__magic_name__  ,     )

        SCREAMING_SNAKE_CASE_          =  SpeechTaHifiGan(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "vocoder": vocoder,
        }
        return components







  def    __A     (							self :       str  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Optional[int]=0   )  ->	Any:
        if str(__magic_name__   ).startswith("mps"   ):
              SCREAMING_SNAKE_CASE_          =  torch.manual_seed(__magic_name__   )
        else:
              SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {
            "prompt": "A hammer hitting a wooden surface",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
        }
        return inputs







  def    __A     (							self :       Any   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  "cpu"  # ensure determinism for the device-dependent torch.Generator

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  AudioLDMPipeline(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        audioldm_pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output.audios[0]

        assert audio.ndim == 1
        assert len(__magic_name__   ) == 256

        SCREAMING_SNAKE_CASE_          =  audio[:10]
        SCREAMING_SNAKE_CASE_          =  np.array(
            [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]   )

        assert np.abs(audio_slice - expected_slice   ).max() < 1e-2







  def    __A     (							self :       List[str]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  AudioLDMPipeline(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        audioldm_pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  3 * [inputs["prompt"]]

        # forward
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output.audios[0]

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  3 * [inputs.pop("prompt"   )]

        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.tokenizer(
            __magic_name__  ,     padding="max_length"  ,     max_length=audioldm_pipe.tokenizer.model_max_length  ,     truncation=__magic_name__  ,     return_tensors="pt"  ,     )
        SCREAMING_SNAKE_CASE_          =  text_inputs["input_ids"].to(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.text_encoder(
            __magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  prompt_embeds.text_embeds
        # additional L_2 normalization over each hidden-state
        SCREAMING_SNAKE_CASE_          =  F.normalize(__magic_name__  ,     dim=-1   )

        SCREAMING_SNAKE_CASE_          =  prompt_embeds

        # forward
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output.audios[0]

        assert np.abs(audio_a - audio_a   ).max() < 1e-2







  def    __A     (							self :       Optional[int]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  AudioLDMPipeline(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        audioldm_pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  3 * ["this is a negative prompt"]
        SCREAMING_SNAKE_CASE_          =  negative_prompt
        SCREAMING_SNAKE_CASE_          =  3 * [inputs["prompt"]]

        # forward
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output.audios[0]

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  3 * [inputs.pop("prompt"   )]

        SCREAMING_SNAKE_CASE_          =  []
        for p in [prompt, negative_prompt]:
              SCREAMING_SNAKE_CASE_          =  audioldm_pipe.tokenizer(
                  __magic_name__  ,     padding="max_length"  ,     max_length=audioldm_pipe.tokenizer.model_max_length  ,     truncation=__magic_name__  ,     return_tensors="pt"  ,     )
              SCREAMING_SNAKE_CASE_          =  text_inputs["input_ids"].to(__magic_name__   )

              SCREAMING_SNAKE_CASE_          =  audioldm_pipe.text_encoder(
                  __magic_name__  ,     )
              SCREAMING_SNAKE_CASE_          =  text_embeds.text_embeds
              # additional L_2 normalization over each hidden-state
              SCREAMING_SNAKE_CASE_          =  F.normalize(__magic_name__  ,     dim=-1   )

              embeds.append(__magic_name__   )

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  embeds

        # forward
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output.audios[0]

        assert np.abs(audio_a - audio_a   ).max() < 1e-2







  def    __A     (							self :       Dict   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  "cpu"  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  PNDMScheduler(skip_prk_steps=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  AudioLDMPipeline(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        audioldm_pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  "egg cracking"
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(**__magic_name__  ,     negative_prompt=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output.audios[0]

        assert audio.ndim == 1
        assert len(__magic_name__   ) == 256

        SCREAMING_SNAKE_CASE_          =  audio[:10]
        SCREAMING_SNAKE_CASE_          =  np.array(
            [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]   )

        assert np.abs(audio_slice - expected_slice   ).max() < 1e-2







  def    __A     (							self :       List[str]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  "cpu"  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  PNDMScheduler(skip_prk_steps=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  AudioLDMPipeline(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        audioldm_pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A hammer hitting a wooden surface"

        # test num_waveforms_per_prompt=1 (default)
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(__magic_name__  ,     num_inference_steps=2   ).audios

        assert audios.shape == (1, 256)

        # test num_waveforms_per_prompt=1 (default) for batch of prompts
        SCREAMING_SNAKE_CASE_          =  2
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe([prompt] * batch_size  ,     num_inference_steps=2   ).audios

        assert audios.shape == (batch_size, 256)

        # test num_waveforms_per_prompt for single prompt
        SCREAMING_SNAKE_CASE_          =  2
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(__magic_name__  ,     num_inference_steps=2  ,     num_waveforms_per_prompt=__magic_name__   ).audios

        assert audios.shape == (num_waveforms_per_prompt, 256)

        # test num_waveforms_per_prompt for batch of prompts
        SCREAMING_SNAKE_CASE_          =  2
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(
            [prompt] * batch_size  ,     num_inference_steps=2  ,     num_waveforms_per_prompt=__magic_name__   ).audios

        assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)







  def    __A     (							self :       Optional[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  "cpu"  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  AudioLDMPipeline(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        audioldm_pipe.set_progress_bar_config(disable=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.vocoder.config.sampling_rate

        SCREAMING_SNAKE_CASE_          =  self.get_dummy_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(audio_length_in_s=0.016  ,     **__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output.audios[0]

        assert audio.ndim == 1
        assert len(__magic_name__   ) / vocoder_sampling_rate == 0.016

        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(audio_length_in_s=0.032  ,     **__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output.audios[0]

        assert audio.ndim == 1
        assert len(__magic_name__   ) / vocoder_sampling_rate == 0.032







  def    __A     (							self :       Any   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.get_dummy_components()
        SCREAMING_SNAKE_CASE_          =  AudioLDMPipeline(**__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        audioldm_pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  ["hey"]

        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(__magic_name__  ,     num_inference_steps=1   )
        SCREAMING_SNAKE_CASE_          =  output.audios.shape
        assert audio_shape == (1, 256)

        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.vocoder.config
        config.model_in_dim *= 2
        SCREAMING_SNAKE_CASE_          =  SpeechTaHifiGan(__magic_name__   ).to(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(__magic_name__  ,     num_inference_steps=1   )
        SCREAMING_SNAKE_CASE_          =  output.audios.shape
        # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
        assert audio_shape == (1, 256)







  def    __A     (							self :       str   )  ->	List[str]:
        self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__magic_name__   )







  def    __A     (							self :       Dict   )  ->	Union[str, Any]:
        self._test_inference_batch_single_identical(test_mean_pixel_difference=__magic_name__   )







  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()  ,     reason="XFormers attention is only available with CUDA and `xformers` installed"  ,     )
  def    __A     (							self :       str   )  ->	Optional[Any]:
        self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__magic_name__   )



@slow
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Union[str, Any]   )  ->	Dict:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def    __A     (							self :       str  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       str="cpu"  ,     __magic_name__ :       Any=torch.floataa  ,     __magic_name__ :       Tuple=0   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  torch.Generator(device=__magic_name__   ).manual_seed(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  np.random.RandomState(__magic_name__   ).standard_normal((1, 8, 128, 16)   )
        SCREAMING_SNAKE_CASE_          =  torch.from_numpy(__magic_name__   ).to(device=__magic_name__  ,     dtype=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {
            "prompt": "A hammer hitting a wooden surface",
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 2.5,
        }
        return inputs







  def    __A     (							self :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  AudioLDMPipeline.from_pretrained("cvssp/audioldm"   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        audioldm_pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.get_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  25
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(**__magic_name__   ).audios[0]

        assert audio.ndim == 1
        assert len(__magic_name__   ) == 81_920

        SCREAMING_SNAKE_CASE_          =  audio[77_230:77_240]
        SCREAMING_SNAKE_CASE_          =  np.array(
            [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]   )
        SCREAMING_SNAKE_CASE_          =  np.abs(expected_slice - audio_slice   ).max()
        assert max_diff < 1e-2







  def    __A     (							self :       Union[str, Any]   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  AudioLDMPipeline.from_pretrained("cvssp/audioldm"   )
        SCREAMING_SNAKE_CASE_          =  LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe.to(__magic_name__   )
        audioldm_pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.get_inputs(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  audioldm_pipe(**__magic_name__   ).audios[0]

        assert audio.ndim == 1
        assert len(__magic_name__   ) == 81_920

        SCREAMING_SNAKE_CASE_          =  audio[27_780:27_790]
        SCREAMING_SNAKE_CASE_          =  np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]   )
        SCREAMING_SNAKE_CASE_          =  np.abs(expected_slice - audio_slice   ).max()
        assert max_diff < 3e-2



from typing import TYPE_CHECKING

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


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


