

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase     :   Dict           =  logging.get_logger(__name__)

lowerCAmelCase     :   List[Any]           =  {
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  '''t5'''
  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  ['''past_key_values''']
  SCREAMING_SNAKE_CASE    :      int       =  {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

  def __init__(				self ,					_SCREAMING_SNAKE_CASE=3_2128 ,					_SCREAMING_SNAKE_CASE=512 ,					_SCREAMING_SNAKE_CASE=64 ,					_SCREAMING_SNAKE_CASE=2048 ,					_SCREAMING_SNAKE_CASE=6 ,					_SCREAMING_SNAKE_CASE=None ,					_SCREAMING_SNAKE_CASE=8 ,					_SCREAMING_SNAKE_CASE=32 ,					_SCREAMING_SNAKE_CASE=128 ,					_SCREAMING_SNAKE_CASE=0.1 ,					_SCREAMING_SNAKE_CASE=1e-6 ,					_SCREAMING_SNAKE_CASE=1.0 ,					_SCREAMING_SNAKE_CASE="relu" ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=True ,					_SCREAMING_SNAKE_CASE=0 ,					_SCREAMING_SNAKE_CASE=1 ,					**_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     vocab_size
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     d_model
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     d_kv
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     d_ff
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     num_layers
        SCREAMING_SNAKE_CASE_				:  int           =     (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        SCREAMING_SNAKE_CASE_				:  Tuple           =     num_heads
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     relative_attention_num_buckets
        SCREAMING_SNAKE_CASE_				:  int           =     relative_attention_max_distance
        SCREAMING_SNAKE_CASE_				:  str           =     dropout_rate
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     layer_norm_epsilon
        SCREAMING_SNAKE_CASE_				:  str           =     initializer_factor
        SCREAMING_SNAKE_CASE_				:  List[str]           =     feed_forward_proj
        SCREAMING_SNAKE_CASE_				:  int           =     use_cache

        SCREAMING_SNAKE_CASE_				:  Any           =     self.feed_forward_proj.split('-'					)
        SCREAMING_SNAKE_CASE_				:  str           =     act_info[-1]
        SCREAMING_SNAKE_CASE_				:  int           =     act_info[0] == 'gated'

        if len(lowercase_					) > 1 and act_info[0] != "gated" or len(lowercase_					) > 2:
              raise ValueError(
                  f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                  'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                  '\'gated-gelu\' or \'relu\''					)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
              SCREAMING_SNAKE_CASE_				:  Dict           =     'gelu_new'

        super().__init__(
            pad_token_id=lowercase_ ,					eos_token_id=lowercase_ ,					is_encoder_decoder=lowercase_ ,					**lowercase_ ,					)

class  _A		(  __magic_name__):

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     'past_encoder_sequence + sequence'
              SCREAMING_SNAKE_CASE_				:  Dict           =     {0: 'batch'}
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     {0: 'batch', 1: 'decoder_sequence'}
              SCREAMING_SNAKE_CASE_				:  List[str]           =     {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
              self.fill_with_past_key_values_(lowercase_ ,					direction='inputs'					)

        return common_inputs



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 13









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






