


"""simple docstring"""
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    SCREAMING_SNAKE_CASE__						(      _snake_case   ,    unittest.TestCase				):
   __lowerCAmelCase						:     Tuple		=		AudioLDMPipeline
   __lowerCAmelCase						:     str		=		TEXT_TO_AUDIO_PARAMS
   __lowerCAmelCase						:     str		=		TEXT_TO_AUDIO_BATCH_PARAMS
   __lowerCAmelCase						:     Optional[int]		=		frozenset(
       [
           'num_inference_steps',
           'num_waveforms_per_prompt',
           'generator',
           'latents',
           'output_type',
           'return_dict',
           'callback',
           'callback_steps',
       ]				)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    torch.manual_seed(0		)
    UpperCAmelCase			:      Union[str, Any]			=			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=snake_case_   ,  )
    UpperCAmelCase			:      List[Any]			=			DDIMScheduler(
        beta_start=0.0_0085   ,  beta_end=0.012   ,  beta_schedule="""scaled_linear"""   ,  clip_sample=snake_case_   ,  set_alpha_to_one=snake_case_   ,  )
    torch.manual_seed(0		)
    UpperCAmelCase			:      Dict			=			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		)
    UpperCAmelCase			:      int			=			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=1000   ,  projection_dim=32   ,  )
    UpperCAmelCase			:      Tuple			=			ClapTextModelWithProjection(snake_case_		)
    UpperCAmelCase			:      Union[str, Any]			=			RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""   ,  model_max_length=77		)

    UpperCAmelCase			:      int			=			SpeechTaHifiGanConfig(
        model_in_dim=8   ,  sampling_rate=16000   ,  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=snake_case_   ,  )

    UpperCAmelCase			:      Tuple			=			SpeechTaHifiGan(snake_case_		)

    UpperCAmelCase			:      Optional[int]			=			{
        "unet": unet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "vocoder": vocoder,
    }
    return components

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=0		)						->   int:





    '''simple docstring'''




    if str(snake_case_		).startswith("""mps"""		):
     UpperCAmelCase			:      Optional[Any]			=			torch.manual_seed(snake_case_		)
    else:
     UpperCAmelCase			:      List[Any]			=			torch.Generator(device=snake_case_		).manual_seed(snake_case_		)
    UpperCAmelCase			:      Union[str, Any]			=			{
        "prompt": "A hammer hitting a wooden surface",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 6.0,
    }
    return inputs

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			"cpu"  # ensure determinism for the device-dependent torch.Generator

    UpperCAmelCase			:      Optional[int]			=			self.get_dummy_components()
    UpperCAmelCase			:      Optional[int]			=			AudioLDMPipeline(**snake_case_		)
    UpperCAmelCase			:      Any			=			audioldm_pipe.to(snake_case_		)
    audioldm_pipe.set_progress_bar_config(disable=snake_case_		)

    UpperCAmelCase			:      List[str]			=			self.get_dummy_inputs(snake_case_		)
    UpperCAmelCase			:      List[Any]			=			audioldm_pipe(**snake_case_		)
    UpperCAmelCase			:      Optional[Any]			=			output.audios[0]

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

    UpperCAmelCase			:      Union[str, Any]			=			audio[:10]
    UpperCAmelCase			:      int			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.get_dummy_components()
    UpperCAmelCase			:      Any			=			AudioLDMPipeline(**snake_case_		)
    UpperCAmelCase			:      Any			=			audioldm_pipe.to(snake_case_		)
    UpperCAmelCase			:      List[str]			=			audioldm_pipe.to(snake_case_		)
    audioldm_pipe.set_progress_bar_config(disable=snake_case_		)

    UpperCAmelCase			:      Union[str, Any]			=			self.get_dummy_inputs(snake_case_		)
    UpperCAmelCase			:      str			=			3 * [inputs["prompt"]]

    # forward
    UpperCAmelCase			:      Dict			=			audioldm_pipe(**snake_case_		)
    UpperCAmelCase			:      List[str]			=			output.audios[0]

    UpperCAmelCase			:      Optional[int]			=			self.get_dummy_inputs(snake_case_		)
    UpperCAmelCase			:      str			=			3 * [inputs.pop("""prompt"""		)]

    UpperCAmelCase			:      Optional[Any]			=			audioldm_pipe.tokenizer(
        snake_case_   ,  padding="""max_length"""   ,  max_length=audioldm_pipe.tokenizer.model_max_length   ,  truncation=snake_case_   ,  return_tensors="""pt"""   ,  )
    UpperCAmelCase			:      Dict			=			text_inputs["input_ids"].to(snake_case_		)

    UpperCAmelCase			:      Dict			=			audioldm_pipe.text_encoder(
        snake_case_   ,  )
    UpperCAmelCase			:      str			=			prompt_embeds.text_embeds
    # additional L_2 normalization over each hidden-state
    UpperCAmelCase			:      Optional[int]			=			F.normalize(snake_case_   ,  dim=-1		)

    UpperCAmelCase			:      List[Any]			=			prompt_embeds

    # forward
    UpperCAmelCase			:      Tuple			=			audioldm_pipe(**snake_case_		)
    UpperCAmelCase			:      Optional[int]			=			output.audios[0]

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			self.get_dummy_components()
    UpperCAmelCase			:      str			=			AudioLDMPipeline(**snake_case_		)
    UpperCAmelCase			:      str			=			audioldm_pipe.to(snake_case_		)
    UpperCAmelCase			:      List[Any]			=			audioldm_pipe.to(snake_case_		)
    audioldm_pipe.set_progress_bar_config(disable=snake_case_		)

    UpperCAmelCase			:      str			=			self.get_dummy_inputs(snake_case_		)
    UpperCAmelCase			:      Any			=			3 * ["this is a negative prompt"]
    UpperCAmelCase			:      int			=			negative_prompt
    UpperCAmelCase			:      Any			=			3 * [inputs["prompt"]]

    # forward
    UpperCAmelCase			:      int			=			audioldm_pipe(**snake_case_		)
    UpperCAmelCase			:      List[str]			=			output.audios[0]

    UpperCAmelCase			:      str			=			self.get_dummy_inputs(snake_case_		)
    UpperCAmelCase			:      str			=			3 * [inputs.pop("""prompt"""		)]

    UpperCAmelCase			:      Optional[int]			=			[]
    for p in [prompt, negative_prompt]:
     UpperCAmelCase			:      str			=			audioldm_pipe.tokenizer(
         snake_case_   ,  padding="""max_length"""   ,  max_length=audioldm_pipe.tokenizer.model_max_length   ,  truncation=snake_case_   ,  return_tensors="""pt"""   ,  )
     UpperCAmelCase			:      Tuple			=			text_inputs["input_ids"].to(snake_case_		)

     UpperCAmelCase			:      Union[str, Any]			=			audioldm_pipe.text_encoder(
         snake_case_   ,  )
     UpperCAmelCase			:      List[str]			=			text_embeds.text_embeds
     # additional L_2 normalization over each hidden-state
     UpperCAmelCase			:      int			=			F.normalize(snake_case_   ,  dim=-1		)

     embeds.append(snake_case_		)

    UpperCAmelCase			:      List[Any]			=			embeds

    # forward
    UpperCAmelCase			:      str			=			audioldm_pipe(**snake_case_		)
    UpperCAmelCase			:      Tuple			=			output.audios[0]

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			"cpu"  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_components()
    UpperCAmelCase			:      List[Any]			=			PNDMScheduler(skip_prk_steps=snake_case_		)
    UpperCAmelCase			:      int			=			AudioLDMPipeline(**snake_case_		)
    UpperCAmelCase			:      int			=			audioldm_pipe.to(snake_case_		)
    audioldm_pipe.set_progress_bar_config(disable=snake_case_		)

    UpperCAmelCase			:      int			=			self.get_dummy_inputs(snake_case_		)
    UpperCAmelCase			:      Tuple			=			"egg cracking"
    UpperCAmelCase			:      Tuple			=			audioldm_pipe(**snake_case_   ,  negative_prompt=snake_case_		)
    UpperCAmelCase			:      str			=			output.audios[0]

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

    UpperCAmelCase			:      Dict			=			audio[:10]
    UpperCAmelCase			:      Dict			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			"cpu"  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase			:      Any			=			self.get_dummy_components()
    UpperCAmelCase			:      Optional[Any]			=			PNDMScheduler(skip_prk_steps=snake_case_		)
    UpperCAmelCase			:      int			=			AudioLDMPipeline(**snake_case_		)
    UpperCAmelCase			:      Dict			=			audioldm_pipe.to(snake_case_		)
    audioldm_pipe.set_progress_bar_config(disable=snake_case_		)

    UpperCAmelCase			:      List[str]			=			"A hammer hitting a wooden surface"

    # test num_waveforms_per_prompt=1 (default)
    UpperCAmelCase			:      Optional[Any]			=			audioldm_pipe(snake_case_   ,  num_inference_steps=2		).audios

    assert audios.shape == (1, 256)

    # test num_waveforms_per_prompt=1 (default) for batch of prompts
    UpperCAmelCase			:      Any			=			2
    UpperCAmelCase			:      Union[str, Any]			=			audioldm_pipe([prompt] * batch_size   ,  num_inference_steps=2		).audios

    assert audios.shape == (batch_size, 256)

    # test num_waveforms_per_prompt for single prompt
    UpperCAmelCase			:      Optional[Any]			=			2
    UpperCAmelCase			:      Optional[int]			=			audioldm_pipe(snake_case_   ,  num_inference_steps=2   ,  num_waveforms_per_prompt=snake_case_		).audios

    assert audios.shape == (num_waveforms_per_prompt, 256)

    # test num_waveforms_per_prompt for batch of prompts
    UpperCAmelCase			:      str			=			2
    UpperCAmelCase			:      Optional[Any]			=			audioldm_pipe(
        [prompt] * batch_size   ,  num_inference_steps=2   ,  num_waveforms_per_prompt=snake_case_		).audios

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			"cpu"  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase			:      List[Any]			=			self.get_dummy_components()
    UpperCAmelCase			:      Union[str, Any]			=			AudioLDMPipeline(**snake_case_		)
    UpperCAmelCase			:      Dict			=			audioldm_pipe.to(snake_case_		)
    audioldm_pipe.set_progress_bar_config(disable=snake_case_		)
    UpperCAmelCase			:      Any			=			audioldm_pipe.vocoder.config.sampling_rate

    UpperCAmelCase			:      Optional[int]			=			self.get_dummy_inputs(snake_case_		)
    UpperCAmelCase			:      List[Any]			=			audioldm_pipe(audio_length_in_s=0.016   ,  **snake_case_		)
    UpperCAmelCase			:      str			=			output.audios[0]

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

    UpperCAmelCase			:      Optional[int]			=			audioldm_pipe(audio_length_in_s=0.032   ,  **snake_case_		)
    UpperCAmelCase			:      Union[str, Any]			=			output.audios[0]

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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			self.get_dummy_components()
    UpperCAmelCase			:      Union[str, Any]			=			AudioLDMPipeline(**snake_case_		)
    UpperCAmelCase			:      Optional[int]			=			audioldm_pipe.to(snake_case_		)
    audioldm_pipe.set_progress_bar_config(disable=snake_case_		)

    UpperCAmelCase			:      Any			=			["hey"]

    UpperCAmelCase			:      Any			=			audioldm_pipe(snake_case_   ,  num_inference_steps=1		)
    UpperCAmelCase			:      Any			=			output.audios.shape
    assert audio_shape == (1, 256)

    UpperCAmelCase			:      Optional[Any]			=			audioldm_pipe.vocoder.config
    config.model_in_dim *= 2
    UpperCAmelCase			:      List[str]			=			SpeechTaHifiGan(snake_case_		).to(snake_case_		)
    UpperCAmelCase			:      List[Any]			=			audioldm_pipe(snake_case_   ,  num_inference_steps=1		)
    UpperCAmelCase			:      List[Any]			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case_		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    self._test_inference_batch_single_identical(test_mean_pixel_difference=snake_case_		)




   @unittest.skipIf(
       torch_device != """cuda""" or not is_xformers_available()   ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,  )
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case_		)



@slow
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




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

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="cpu"   ,  _SCREAMING_SNAKE_CASE=torch.floataa   ,  _SCREAMING_SNAKE_CASE=0		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			torch.Generator(device=snake_case_		).manual_seed(snake_case_		)
    UpperCAmelCase			:      int			=			np.random.RandomState(snake_case_		).standard_normal((1, 8, 128, 16)		)
    UpperCAmelCase			:      str			=			torch.from_numpy(snake_case_		).to(device=snake_case_   ,  dtype=snake_case_		)
    UpperCAmelCase			:      List[str]			=			{
        "prompt": "A hammer hitting a wooden surface",
        "latents": latents,
        "generator": generator,
        "num_inference_steps": 3,
        "guidance_scale": 2.5,
    }
    return inputs

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""		)
    UpperCAmelCase			:      Any			=			audioldm_pipe.to(snake_case_		)
    audioldm_pipe.set_progress_bar_config(disable=snake_case_		)

    UpperCAmelCase			:      Any			=			self.get_inputs(snake_case_		)
    UpperCAmelCase			:      Tuple			=			25
    UpperCAmelCase			:      Any			=			audioldm_pipe(**snake_case_		).audios[0]

    assert audio.ndim == 1
    assert len(snake_case_		) == 81920

    UpperCAmelCase			:      Tuple			=			audio[77230:77240]
    UpperCAmelCase			:      Any			=			np.array(
        [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]		)
    UpperCAmelCase			:      int			=			np.abs(expected_slice - audio_slice		).max()
    assert max_diff < 1E-2




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""		)
    UpperCAmelCase			:      Dict			=			LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config		)
    UpperCAmelCase			:      Tuple			=			audioldm_pipe.to(snake_case_		)
    audioldm_pipe.set_progress_bar_config(disable=snake_case_		)

    UpperCAmelCase			:      Any			=			self.get_inputs(snake_case_		)
    UpperCAmelCase			:      str			=			audioldm_pipe(**snake_case_		).audios[0]

    assert audio.ndim == 1
    assert len(snake_case_		) == 81920

    UpperCAmelCase			:      Any			=			audio[27780:27790]
    UpperCAmelCase			:      Dict			=			np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]		)
    UpperCAmelCase			:      List[str]			=			np.abs(expected_slice - audio_slice		).max()
    assert max_diff < 3E-2





"""simple docstring"""
import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    UpperCAmelCase__				):

   @register_to_config
   def __init__(     self   ,  _SCREAMING_SNAKE_CASE = 128   ,  _SCREAMING_SNAKE_CASE = 256   ,  _SCREAMING_SNAKE_CASE = 2000.0   ,  _SCREAMING_SNAKE_CASE = 768   ,  _SCREAMING_SNAKE_CASE = 12   ,  _SCREAMING_SNAKE_CASE = 12   ,  _SCREAMING_SNAKE_CASE = 64   ,  _SCREAMING_SNAKE_CASE = 2048   ,  _SCREAMING_SNAKE_CASE = 0.1   ,  )						->   int:





    '''simple docstring'''




    super().__init__()

    UpperCAmelCase			:      Tuple			=			nn.Sequential(
        nn.Linear(_SCREAMING_SNAKE_CASE   ,  d_model * 4   ,  bias=_SCREAMING_SNAKE_CASE		)   ,  nn.SiLU()   ,  nn.Linear(d_model * 4   ,  d_model * 4   ,  bias=_SCREAMING_SNAKE_CASE		)   ,  nn.SiLU()   ,  )

    UpperCAmelCase			:      Tuple			=			nn.Embedding(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			False

    UpperCAmelCase			:      Optional[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			nn.Dropout(p=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			nn.ModuleList()
    for lyr_num in range(_SCREAMING_SNAKE_CASE		):
     # FiLM conditional T5 decoder
     UpperCAmelCase			:      List[Any]			=			DecoderLayer(d_model=_SCREAMING_SNAKE_CASE   ,  d_kv=_SCREAMING_SNAKE_CASE   ,  num_heads=_SCREAMING_SNAKE_CASE   ,  d_ff=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE		)
     self.decoders.append(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			TaLayerNorm(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			nn.Dropout(p=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			torch.mul(query_input.unsqueeze(-1		)   ,  key_input.unsqueeze(-2		)		)
    return mask.unsqueeze(-3		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      str			=			decoder_input_tokens.shape
    assert decoder_noise_time.shape == (batch,)

    # decoder_noise_time is in [0, 1), so rescale to expected timing range.
    UpperCAmelCase			:      Union[str, Any]			=			get_timestep_embedding(
        decoder_noise_time * self.config.max_decoder_noise_time   ,  embedding_dim=self.config.d_model   ,  max_period=self.config.max_decoder_noise_time   ,  ).to(dtype=self.dtype		)

    UpperCAmelCase			:      Any			=			self.conditioning_emb(_SCREAMING_SNAKE_CASE		).unsqueeze(1		)

    assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

    UpperCAmelCase			:      Dict			=			decoder_input_tokens.shape[1]

    # If we want to use relative positions for audio context, we can just offset
    # this sequence by the length of encodings_and_masks.
    UpperCAmelCase			:      int			=			torch.broadcast_to(
        torch.arange(_SCREAMING_SNAKE_CASE   ,  device=decoder_input_tokens.device		)   ,  (batch, seq_length)   ,  )

    UpperCAmelCase			:      List[str]			=			self.position_encoding(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			self.continuous_inputs_projection(_SCREAMING_SNAKE_CASE		)
    inputs += position_encodings
    UpperCAmelCase			:      List[str]			=			self.dropout(_SCREAMING_SNAKE_CASE		)

    # decoder: No padding present.
    UpperCAmelCase			:      Dict			=			torch.ones(
        decoder_input_tokens.shape[:2]   ,  device=decoder_input_tokens.device   ,  dtype=inputs.dtype		)

    # Translate encoding masks to encoder-decoder masks.
    UpperCAmelCase			:      Optional[int]			=			[(x, self.encoder_decoder_mask(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)) for x, y in encodings_and_masks]

    # cross attend style: concat encodings
    UpperCAmelCase			:      Optional[Any]			=			torch.cat([x[0] for x in encodings_and_encdec_masks]   ,  dim=1		)
    UpperCAmelCase			:      Union[str, Any]			=			torch.cat([x[1] for x in encodings_and_encdec_masks]   ,  dim=-1		)

    for lyr in self.decoders:
     UpperCAmelCase			:      List[Any]			=			lyr(
         _SCREAMING_SNAKE_CASE   ,  conditioning_emb=_SCREAMING_SNAKE_CASE   ,  encoder_hidden_states=_SCREAMING_SNAKE_CASE   ,  encoder_attention_mask=_SCREAMING_SNAKE_CASE   ,  )[0]

    UpperCAmelCase			:      List[Any]			=			self.decoder_norm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			self.post_dropout(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			self.spec_out(_SCREAMING_SNAKE_CASE		)
    return spec_out



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=1E-6		)						->   List[str]:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[Any]			=			nn.ModuleList()

    # cond self attention: layer 0
    self.layer.append(
        TaLayerSelfAttentionCond(d_model=_SCREAMING_SNAKE_CASE   ,  d_kv=_SCREAMING_SNAKE_CASE   ,  num_heads=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE		)		)

    # cross attention: layer 1
    self.layer.append(
        TaLayerCrossAttention(
            d_model=_SCREAMING_SNAKE_CASE   ,  d_kv=_SCREAMING_SNAKE_CASE   ,  num_heads=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE   ,  layer_norm_epsilon=_SCREAMING_SNAKE_CASE   ,  )		)

    # Film Cond MLP + dropout: last layer
    self.layer.append(
        TaLayerFFCond(d_model=_SCREAMING_SNAKE_CASE   ,  d_ff=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE   ,  layer_norm_epsilon=_SCREAMING_SNAKE_CASE		)		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			self.layer[0](
        _SCREAMING_SNAKE_CASE   ,  conditioning_emb=_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  )

    if encoder_hidden_states is not None:
     UpperCAmelCase			:      Tuple			=			torch.where(encoder_attention_mask > 0   ,  0   ,  -1E10		).to(
         encoder_hidden_states.dtype		)

     UpperCAmelCase			:      List[Any]			=			self.layer[1](
         _SCREAMING_SNAKE_CASE   ,  key_value_states=_SCREAMING_SNAKE_CASE   ,  attention_mask=_SCREAMING_SNAKE_CASE   ,  )

    # Apply Film Conditional Feed Forward layer
    UpperCAmelCase			:      Dict			=			self.layer[-1](_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    return (hidden_states,)



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Union[str, Any]			=			TaLayerNorm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			TaFiLMLayer(in_features=d_model * 4   ,  out_features=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			Attention(query_dim=_SCREAMING_SNAKE_CASE   ,  heads=_SCREAMING_SNAKE_CASE   ,  dim_head=_SCREAMING_SNAKE_CASE   ,  out_bias=_SCREAMING_SNAKE_CASE   ,  scale_qk=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)

    if conditioning_emb is not None:
     UpperCAmelCase			:      str			=			self.FiLMLayer(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    # Self-attention block
    UpperCAmelCase			:      List[Any]			=			self.attention(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)

    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[str]			=			Attention(query_dim=_SCREAMING_SNAKE_CASE   ,  heads=_SCREAMING_SNAKE_CASE   ,  dim_head=_SCREAMING_SNAKE_CASE   ,  out_bias=_SCREAMING_SNAKE_CASE   ,  scale_qk=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			TaLayerNorm(_SCREAMING_SNAKE_CASE   ,  eps=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			self.attention(
        _SCREAMING_SNAKE_CASE   ,  encoder_hidden_states=_SCREAMING_SNAKE_CASE   ,  attention_mask=attention_mask.squeeze(1		)   ,  )
    UpperCAmelCase			:      Dict			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)
    return layer_output



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      int			=			TaDenseGatedActDense(d_model=_SCREAMING_SNAKE_CASE   ,  d_ff=_SCREAMING_SNAKE_CASE   ,  dropout_rate=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			TaFiLMLayer(in_features=d_model * 4   ,  out_features=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			TaLayerNorm(_SCREAMING_SNAKE_CASE   ,  eps=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			self.layer_norm(_SCREAMING_SNAKE_CASE		)
    if conditioning_emb is not None:
     UpperCAmelCase			:      Union[str, Any]			=			self.film(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			self.DenseReluDense(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[str]			=			hidden_states + self.dropout(_SCREAMING_SNAKE_CASE		)
    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Any			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  bias=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Tuple			=			nn.Dropout(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			NewGELUActivation()




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.act(self.wi_a(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Optional[Any]			=			self.wi_a(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Union[str, Any]			=			hidden_gelu * hidden_linear
    UpperCAmelCase			:      int			=			self.dropout(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			self.wo(_SCREAMING_SNAKE_CASE		)
    return hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=1E-6		)						->   List[str]:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      List[str]			=			nn.Parameter(torch.ones(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Optional[Any]			=			eps




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			hidden_states.to(torch.floataa		).pow(2		).mean(-1   ,  keepdim=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Dict			=			hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

    # convert into half-precision if necessary
    if self.weight.dtype in [torch.floataa, torch.bfloataa]:
     UpperCAmelCase			:      str			=			hidden_states.to(self.weight.dtype		)

    return self.weight * hidden_states



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   torch.Tensor:





    '''simple docstring'''




    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_4715 * torch.pow(_SCREAMING_SNAKE_CASE   ,  3.0		))		))



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Union[str, Any]			=			nn.Linear(_SCREAMING_SNAKE_CASE   ,  out_features * 2   ,  bias=_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.scale_bias(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			,	UpperCAmelCase			:      Optional[int]			=			torch.chunk(_SCREAMING_SNAKE_CASE   ,  2   ,  -1		)
    UpperCAmelCase			:      Optional[int]			=			x * (1 + scale) + shift
    return x
