

from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTransfoXLForSequenceClassification,
        TFTransfoXLLMHeadModel,
        TFTransfoXLModel,
    )
class  _A		:

  def __init__(				self ,					_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     parent
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     13
        SCREAMING_SNAKE_CASE_				:  Tuple           =     7
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     30
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.seq_length + self.mem_len
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     15
        SCREAMING_SNAKE_CASE_				:  List[str]           =     True
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     True
        SCREAMING_SNAKE_CASE_				:  Tuple           =     99
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [10, 50, 80]
        SCREAMING_SNAKE_CASE_				:  Tuple           =     32
        SCREAMING_SNAKE_CASE_				:  int           =     32
        SCREAMING_SNAKE_CASE_				:  Tuple           =     4
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     8
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     128
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     2
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     2
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     None
        SCREAMING_SNAKE_CASE_				:  int           =     1
        SCREAMING_SNAKE_CASE_				:  Tuple           =     0
        SCREAMING_SNAKE_CASE_				:  Dict           =     3
        SCREAMING_SNAKE_CASE_				:  str           =     self.vocab_size - 1
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0.01

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size					)

        SCREAMING_SNAKE_CASE_				:  str           =     TransfoXLConfig(
            vocab_size=self.vocab_size ,					mem_len=self.mem_len ,					clamp_len=self.clamp_len ,					cutoffs=self.cutoffs ,					d_model=self.hidden_size ,					d_embed=self.d_embed ,					n_head=self.num_attention_heads ,					d_head=self.d_head ,					d_inner=self.d_inner ,					div_val=self.div_val ,					n_layer=self.num_hidden_layers ,					eos_token_id=self.eos_token_id ,					pad_token_id=self.vocab_size - 1 ,					init_range=self.init_range ,					num_labels=self.num_labels ,					)

        return (config, input_ids_a, input_ids_a, lm_labels)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        random.seed(self.seed					)
        tf.random.set_seed(self.seed					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     TFTransfoXLModel(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  str           =     model(_SCREAMING_SNAKE_CASE					).to_tuple()

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     {'input_ids': input_ids_a, 'mems': mems_a}

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  int           =     model(_SCREAMING_SNAKE_CASE					).to_tuple()

        self.parent.assertEqual(hidden_states_a.shape ,					(self.batch_size, self.seq_length, self.hidden_size)					)
        self.parent.assertEqual(hidden_states_a.shape ,					(self.batch_size, self.seq_length, self.hidden_size)					)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a] ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,					)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a] ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     TFTransfoXLLMHeadModel(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[str]           =     model(_SCREAMING_SNAKE_CASE					).to_tuple()

        SCREAMING_SNAKE_CASE_				:  Dict           =     {'input_ids': input_ids_a, 'labels': lm_labels}
        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     model(_SCREAMING_SNAKE_CASE					).to_tuple()

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     model([input_ids_a, mems_a]					).to_tuple()

        SCREAMING_SNAKE_CASE_				:  Tuple           =     {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Tuple           =     model(_SCREAMING_SNAKE_CASE					).to_tuple()

        self.parent.assertEqual(lm_logits_a.shape ,					(self.batch_size, self.seq_length, self.vocab_size)					)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a] ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,					)

        self.parent.assertEqual(lm_logits_a.shape ,					(self.batch_size, self.seq_length, self.vocab_size)					)
        self.parent.assertListEqual(
            [mem.shape for mem in mems_a] ,					[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     TFTransfoXLForSequenceClassification(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     model(_SCREAMING_SNAKE_CASE					)
        self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     self.prepare_config_and_inputs()
        ((SCREAMING_SNAKE_CASE_)       ,						(SCREAMING_SNAKE_CASE_)       ,						(SCREAMING_SNAKE_CASE_)       ,						(SCREAMING_SNAKE_CASE_))				:  Dict           =     config_and_inputs
        SCREAMING_SNAKE_CASE_				:  Tuple           =     {'input_ids': input_ids_a}
        return config, inputs_dict

@require_tf
class  _A		(  __magic_name__   ,     __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  (
      (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
  )
  SCREAMING_SNAKE_CASE    :      Any       =  () if is_tf_available() else ()
  SCREAMING_SNAKE_CASE    :      List[Any]       =  (
      {
          '''feature-extraction''': TFTransfoXLModel,
          '''text-classification''': TFTransfoXLForSequenceClassification,
          '''text-generation''': TFTransfoXLLMHeadModel,
          '''zero-shot''': TFTransfoXLForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
  SCREAMING_SNAKE_CASE    :      List[Any]       =  False
  SCREAMING_SNAKE_CASE    :      Dict       =  False
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  False
  SCREAMING_SNAKE_CASE    :      List[str]       =  False

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if pipeline_test_casse_name == "TextGenerationPipelineTests":
              # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
              # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
              # tokenizer.
              return True

        return False

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     TFTransfoXLModelTester(self					)
        SCREAMING_SNAKE_CASE_				:  int           =     ConfigTester(self ,					config_class=_SCREAMING_SNAKE_CASE ,					d_embed=37					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.config_tester.run_common_tests()

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.model_tester.set_seed()
        SCREAMING_SNAKE_CASE_				:  int           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        self.model_tester.set_seed()
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[str]           =     self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_				:  List[str]           =     [TFTransfoXLForSequenceClassification]

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_				:  str           =     model_class(_SCREAMING_SNAKE_CASE					)
              assert isinstance(model.get_input_embeddings() ,					tf.keras.layers.Layer					)
              if model_class in list_other_models_with_output_ebd:
                    SCREAMING_SNAKE_CASE_				:  int           =     model.get_output_embeddings()
                    assert isinstance(_SCREAMING_SNAKE_CASE ,					tf.keras.layers.Layer					)
                    SCREAMING_SNAKE_CASE_				:  Any           =     model.get_bias()
                    assert name is None
              else:
                    SCREAMING_SNAKE_CASE_				:  Dict           =     model.get_output_embeddings()
                    assert x is None
                    SCREAMING_SNAKE_CASE_				:  List[str]           =     model.get_bias()
                    assert name is None

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_				:  str           =     TFTransfoXLModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)



  @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'					)
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

@require_tf
class  _A		(  unittest.TestCase):

  @unittest.skip('Skip test until #12651 is resolved.'					)
  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'					)
        # fmt: off
        SCREAMING_SNAKE_CASE_				:  Dict           =     tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]] ,					dtype=tf.intaa					)  # noqa: E231
        # fmt: on
        #  In 1991 , the remains of Russian Tsar Nicholas II and his family
        #  ( except for Alexei and Maria ) are discovered .
        #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
        #  remainder of the story . 1883 Western Siberia ,
        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
        #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
        #  father initially slaps him for making such an accusation , Rasputin watches as the
        #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
        #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
        #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

        # fmt: off
        SCREAMING_SNAKE_CASE_				:  int           =     [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
        # fmt: on
        #  In 1991, the remains of Russian Tsar Nicholas II and his family (
        #  except for Alexei and Maria ) are discovered. The voice of young son,
        #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
        #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
        #  and a group of men to perform magic. Rasputin has a vision and
        #  denounces one of the men as a horse thief. Although his father initially
        #  slaps him for making such an accusation, Rasputin watches as the man
        #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
        #  of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for
        #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
        # Nicholas II and his family were discovered. The voice of <unk> young son,
        # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     model.generate(_SCREAMING_SNAKE_CASE ,					max_length=200 ,					do_sample=_SCREAMING_SNAKE_CASE					)
        self.assertListEqual(output_ids[0].numpy().tolist() ,					_SCREAMING_SNAKE_CASE					)









import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     UNetaDModel(
            sample_size=(32, 64) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     UNetaDConditionModel(
            sample_size=(64, 32) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D') ,					cross_attention_dim=10 ,					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     AutoencoderKL(
            sample_size=(128, 64) ,					in_channels=1 ,					out_channels=1 ,					latent_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D') ,					up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D') ,					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     UNetaDModel(
            sample_size=(64, 32) ,					in_channels=1 ,					out_channels=1 ,					layers_per_block=2 ,					block_out_channels=(128, 128) ,					down_block_types=('AttnDownBlock2D', 'DownBlock2D') ,					up_block_types=('UpBlock2D', 'AttnUpBlock2D') ,					)
        return vqvae, unet



  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'cpu'  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_				:  Any           =     Mel(
            x_res=self.dummy_unet.config.sample_size[1] ,					y_res=self.dummy_unet.config.sample_size[0] ,					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     DDPMScheduler()
        SCREAMING_SNAKE_CASE_				:  str           =     AudioDiffusionPipeline(vqvae=_SCREAMING_SNAKE_CASE ,					unet=self.dummy_unet ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					steps=4					)
        SCREAMING_SNAKE_CASE_				:  Any           =     output.audios[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     output.images[0]

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					steps=4 ,					return_dict=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     output[0][0]

        assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
        assert (
            image.height == self.dummy_unet.config.sample_size[0]
            and image.width == self.dummy_unet.config.sample_size[1]
        )
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.frombuffer(image_from_tuple.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     Mel(
            x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] ,					y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] ,					)

        SCREAMING_SNAKE_CASE_				:  str           =     DDIMScheduler()
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_vqvae_and_unet
        SCREAMING_SNAKE_CASE_				:  int           =     AudioDiffusionPipeline(
            vqvae=self.dummy_vqvae_and_unet[0] ,					unet=dummy_vqvae_and_unet[1] ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        np.random.seed(0					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.random.uniform(-1 ,					1 ,					((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     pipe(raw_audio=_SCREAMING_SNAKE_CASE ,					generator=_SCREAMING_SNAKE_CASE ,					start_step=5 ,					steps=10					)
        SCREAMING_SNAKE_CASE_				:  int           =     output.images[0]

        assert (
            image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
            and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
        )
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  int           =     np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_unet_condition
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     AudioDiffusionPipeline(
            vqvae=self.dummy_vqvae_and_unet[0] ,					unet=_SCREAMING_SNAKE_CASE ,					mel=_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        np.random.seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.rand((1, 1, 10)					)
        SCREAMING_SNAKE_CASE_				:  Any           =     pipe(generator=_SCREAMING_SNAKE_CASE ,					encoding=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     output.images[0]
        SCREAMING_SNAKE_CASE_				:  Dict           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







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



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch_device

        SCREAMING_SNAKE_CASE_				:  str           =     DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(42					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(generator=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     output.audios[0]
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     output.images[0]

        assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
        assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.frombuffer(image.tobytes() ,					dtype='uint8'					)[:10]
        SCREAMING_SNAKE_CASE_				:  Any           =     np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

        assert np.abs(image_slice.flatten() - expected_slice					).max() == 0






