



'''simple docstring'''





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Tuple=False				)  ->     Tuple:
 _snake_case    :  str					=					[]
 for i in range(config.num_hidden_layers				):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')				)
  rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')				)
  rename_keys.append(
      (F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')				)
  rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')				)
  rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')				)
  rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')				)
  rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')				)
  rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')				)
  rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')				)
  rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')				)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ("""module.cls_token""", """vit.embeddings.cls_token"""),
         ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
         ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
         ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
     ]				)

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("""module.norm.weight""", """layernorm.weight"""),
          ("""module.norm.bias""", """layernorm.bias"""),
      ]				)

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  _snake_case    :  Dict					=					[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""				) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("""norm.weight""", """vit.layernorm.weight"""),
          ("""norm.bias""", """vit.layernorm.bias"""),
          ("""head.weight""", """classifier.weight"""),
          ("""head.bias""", """classifier.bias"""),
      ]				)

 return rename_keys



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Tuple				,       SCREAMING_SNAKE_CASE__       :	str=False				)  ->     int:
 for i in range(config.num_hidden_layers				):
  if base_model:
   _snake_case    :  List[str]					=					""""""
  else:
   _snake_case    :  Optional[Any]					=					"""vit."""
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  _snake_case    :  int					=					state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight'''				)
  _snake_case    :  List[Any]					=					state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias'''				)
  # next, add query, keys and values (in that order) to the state dict
  _snake_case    :  List[Any]					=					in_proj_weight[
      : config.hidden_size, :
  ]
  _snake_case    :  List[str]					=					in_proj_bias[: config.hidden_size]
  _snake_case    :  List[Any]					=					in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  _snake_case    :  Optional[Any]					=					in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  _snake_case    :  Dict					=					in_proj_weight[
      -config.hidden_size :, :
  ]
  _snake_case    :  Union[str, Any]					=					in_proj_bias[-config.hidden_size :]



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				)  ->     List[str]:
 _snake_case    :  Optional[int]					=					["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     List[Any]:
 # projection head is used in the self-supervised pre-training in MSN,
 # for downstream task it's not needed.
 _snake_case    :  str					=					[
     """module.fc.fc1.weight""",
     """module.fc.fc1.bias""",
     """module.fc.bn1.weight""",
     """module.fc.bn1.bias""",
     """module.fc.bn1.running_mean""",
     """module.fc.bn1.running_var""",
     """module.fc.bn1.num_batches_tracked""",
     """module.fc.fc2.weight""",
     """module.fc.fc2.bias""",
     """module.fc.bn2.weight""",
     """module.fc.bn2.bias""",
     """module.fc.bn2.running_mean""",
     """module.fc.bn2.running_var""",
     """module.fc.bn2.num_batches_tracked""",
     """module.fc.fc3.weight""",
     """module.fc.fc3.bias""",
 ]
 for k in ignore_keys:
  state_dict.pop(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[int]:
 _snake_case    :  Any					=					dct.pop(_SCREAMING_SNAKE_CASE				)
 _snake_case    :  Optional[int]					=					val



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Any				)  ->     List[str]:
 _snake_case    :  Optional[Any]					=					ViTMSNConfig()
 _snake_case    :  Tuple					=					1_000

 _snake_case    :  Dict					=					"""datasets/huggingface/label-files"""
 _snake_case    :  List[str]					=					"""imagenet-1k-id2label.json"""
 _snake_case    :  int					=					json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				)				,       """r"""				)				)
 _snake_case    :  Dict					=					{int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
 _snake_case    :  Optional[Any]					=					idalabel
 _snake_case    :  int					=					{v: k for k, v in idalabel.items()}

 if "s16" in checkpoint_url:
  _snake_case    :  str					=					384
  _snake_case    :  Dict					=					1_536
  _snake_case    :  Any					=					6
 elif "l16" in checkpoint_url:
  _snake_case    :  str					=					1_024
  _snake_case    :  List[Any]					=					4_096
  _snake_case    :  List[str]					=					24
  _snake_case    :  Any					=					16
  _snake_case    :  List[Any]					=					0.1
 elif "b4" in checkpoint_url:
  _snake_case    :  List[Any]					=					4
 elif "l7" in checkpoint_url:
  _snake_case    :  Any					=					7
  _snake_case    :  int					=					1_024
  _snake_case    :  Optional[int]					=					4_096
  _snake_case    :  str					=					24
  _snake_case    :  List[str]					=					16
  _snake_case    :  int					=					0.1

 _snake_case    :  Tuple					=					ViTMSNModel(_SCREAMING_SNAKE_CASE				)

 _snake_case    :  Optional[Any]					=					torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE				,       map_location="""cpu"""				)["""target_encoder"""]

 _snake_case    :  Tuple					=					ViTImageProcessor(size=config.image_size				)

 remove_projection_head(_SCREAMING_SNAKE_CASE				)
 _snake_case    :  List[str]					=					create_rename_keys(_SCREAMING_SNAKE_CASE				,       base_model=_SCREAMING_SNAKE_CASE				)

 for src, dest in rename_keys:
  rename_key(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				)
 read_in_q_k_v(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       base_model=_SCREAMING_SNAKE_CASE				)

 model.load_state_dict(_SCREAMING_SNAKE_CASE				)
 model.eval()

 _snake_case    :  Optional[Any]					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""

 _snake_case    :  int					=					Image.open(requests.get(_SCREAMING_SNAKE_CASE				,       stream=_SCREAMING_SNAKE_CASE				).raw				)
 _snake_case    :  List[Any]					=					ViTImageProcessor(
     size=config.image_size				,       image_mean=_SCREAMING_SNAKE_CASE				,       image_std=_SCREAMING_SNAKE_CASE				)
 _snake_case    :  Any					=					image_processor(images=_SCREAMING_SNAKE_CASE				,       return_tensors="""pt"""				)

 # forward pass
 torch.manual_seed(2				)
 _snake_case    :  List[Any]					=					model(**_SCREAMING_SNAKE_CASE				)
 _snake_case    :  str					=					outputs.last_hidden_state

 # The following Colab Notebook was used to generate these outputs:
 # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
 if "s16" in checkpoint_url:
  _snake_case    :  Tuple					=					torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]				)
 elif "b16" in checkpoint_url:
  _snake_case    :  Optional[int]					=					torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]				)
 elif "l16" in checkpoint_url:
  _snake_case    :  Optional[int]					=					torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]				)
 elif "b4" in checkpoint_url:
  _snake_case    :  List[Any]					=					torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]				)
 else:
  _snake_case    :  Optional[Any]					=					torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]				)

 # verify logits
 assert torch.allclose(last_hidden_state[:, 0, :3]				,       _SCREAMING_SNAKE_CASE				,       atol=1e-4				)

 print(F'''Saving model to {pytorch_dump_folder_path}'''				)
 model.save_pretrained(_SCREAMING_SNAKE_CASE				)

 print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
 image_processor.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
      type=str,
      help="""URL of the checkpoint you\'d like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  a__    =	parser.parse_args()
  convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





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 		snake_case						(	SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: str          =		AudioLDMPipeline
   snake_case_					: Any          =		TEXT_TO_AUDIO_PARAMS
   snake_case_					: Tuple          =		TEXT_TO_AUDIO_BATCH_PARAMS
   snake_case_					: Dict          =		frozenset(
       [
           """num_inference_steps""",
           """num_waveforms_per_prompt""",
           """generator""",
           """latents""",
           """output_type""",
           """return_dict""",
           """callback""",
           """callback_steps""",
       ]  )

   def      UpperCamelCase_  (					self							:			Any)				->				Any:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case    :  List[str]					=					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=lowerCAmelCase				,		)
    _snake_case    :  List[Any]					=					DDIMScheduler(
        beta_start=0.00_085				,		beta_end=0.012				,		beta_schedule="""scaled_linear"""				,		clip_sample=lowerCAmelCase				,		set_alpha_to_one=lowerCAmelCase				,		)
    torch.manual_seed(0)
    _snake_case    :  str					=					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)
    _snake_case    :  Any					=					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				,		)
    _snake_case    :  Any					=					ClapTextModelWithProjection(lowerCAmelCase)
    _snake_case    :  List[str]					=					RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""				,		model_max_length=77)

    _snake_case    :  List[Any]					=					SpeechTaHifiGanConfig(
        model_in_dim=8				,		sampling_rate=1_6000				,		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=lowerCAmelCase				,		)

    _snake_case    :  Tuple					=					SpeechTaHifiGan(lowerCAmelCase)

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

   def      UpperCamelCase_  (					self							:			Optional[Any]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Dict=0)				->				int:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			List[Any])				->				str:

    """simple docstring"""







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

    _snake_case    :  Dict					=					self.get_dummy_components()
    _snake_case    :  Optional[Any]					=					AudioLDMPipeline(**lowerCAmelCase)
    _snake_case    :  Optional[int]					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  str					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  Tuple					=					audioldm_pipe(**lowerCAmelCase)
    _snake_case    :  int					=					output.audios[0]

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

    _snake_case    :  Union[str, Any]					=					audio[:10]
    _snake_case    :  Union[str, Any]					=					np.array(
        [-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033])

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

   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Dict					=					self.get_dummy_components()
    _snake_case    :  Any					=					AudioLDMPipeline(**lowerCAmelCase)
    _snake_case    :  int					=					audioldm_pipe.to(lowerCAmelCase)
    _snake_case    :  str					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  str					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  List[Any]					=					3 * [inputs["""prompt"""]]

    # forward
    _snake_case    :  str					=					audioldm_pipe(**lowerCAmelCase)
    _snake_case    :  Dict					=					output.audios[0]

    _snake_case    :  Dict					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  List[str]					=					3 * [inputs.pop("""prompt""")]

    _snake_case    :  List[Any]					=					audioldm_pipe.tokenizer(
        lowerCAmelCase				,		padding="""max_length"""				,		max_length=audioldm_pipe.tokenizer.model_max_length				,		truncation=lowerCAmelCase				,		return_tensors="""pt"""				,		)
    _snake_case    :  List[Any]					=					text_inputs["""input_ids"""].to(lowerCAmelCase)

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

    _snake_case    :  List[Any]					=					prompt_embeds

    # forward
    _snake_case    :  Union[str, Any]					=					audioldm_pipe(**lowerCAmelCase)
    _snake_case    :  str					=					output.audios[0]

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

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				Dict:

    """simple docstring"""







    _snake_case    :  List[Any]					=					self.get_dummy_components()
    _snake_case    :  Optional[Any]					=					AudioLDMPipeline(**lowerCAmelCase)
    _snake_case    :  Tuple					=					audioldm_pipe.to(lowerCAmelCase)
    _snake_case    :  List[Any]					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Optional[int]					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					3 * ["""this is a negative prompt"""]
    _snake_case    :  int					=					negative_prompt
    _snake_case    :  Dict					=					3 * [inputs["""prompt"""]]

    # forward
    _snake_case    :  Dict					=					audioldm_pipe(**lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					output.audios[0]

    _snake_case    :  str					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					3 * [inputs.pop("""prompt""")]

    _snake_case    :  Tuple					=					[]
    for p in [prompt, negative_prompt]:
     _snake_case    :  Optional[Any]					=					audioldm_pipe.tokenizer(
         lowerCAmelCase				,		padding="""max_length"""				,		max_length=audioldm_pipe.tokenizer.model_max_length				,		truncation=lowerCAmelCase				,		return_tensors="""pt"""				,		)
     _snake_case    :  Any					=					text_inputs["""input_ids"""].to(lowerCAmelCase)

     _snake_case    :  int					=					audioldm_pipe.text_encoder(
         lowerCAmelCase				,		)
     _snake_case    :  Optional[Any]					=					text_embeds.text_embeds
     # additional L_2 normalization over each hidden-state
     _snake_case    :  str					=					F.normalize(lowerCAmelCase				,		dim=-1)

     embeds.append(lowerCAmelCase)

    _snake_case	,    _snake_case    :  int					=					embeds

    # forward
    _snake_case    :  List[str]					=					audioldm_pipe(**lowerCAmelCase)
    _snake_case    :  Optional[Any]					=					output.audios[0]

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

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				List[Any]:

    """simple docstring"""







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

    _snake_case    :  List[str]					=					self.get_dummy_inputs(lowerCAmelCase)
    _snake_case    :  List[Any]					=					"""egg cracking"""
    _snake_case    :  Optional[Any]					=					audioldm_pipe(**lowerCAmelCase				,		negative_prompt=lowerCAmelCase)
    _snake_case    :  List[str]					=					output.audios[0]

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

    _snake_case    :  int					=					audio[:10]
    _snake_case    :  Any					=					np.array(
        [-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032])

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

   def      UpperCamelCase_  (					self							:			Optional[int])				->				Optional[int]:

    """simple docstring"""







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

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

    # test num_waveforms_per_prompt=1 (default)
    _snake_case    :  List[Any]					=					audioldm_pipe(lowerCAmelCase				,		num_inference_steps=2).audios

    assert audios.shape == (1, 256)

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

    assert audios.shape == (batch_size, 256)

    # test num_waveforms_per_prompt for single prompt
    _snake_case    :  List[Any]					=					2
    _snake_case    :  Tuple					=					audioldm_pipe(lowerCAmelCase				,		num_inference_steps=2				,		num_waveforms_per_prompt=lowerCAmelCase).audios

    assert audios.shape == (num_waveforms_per_prompt, 256)

    # test num_waveforms_per_prompt for batch of prompts
    _snake_case    :  Dict					=					2
    _snake_case    :  int					=					audioldm_pipe(
        [prompt] * batch_size				,		num_inference_steps=2				,		num_waveforms_per_prompt=lowerCAmelCase).audios

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

   def      UpperCamelCase_  (					self							:			Optional[int])				->				int:

    """simple docstring"""







    _snake_case    :  Dict					=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    _snake_case    :  Optional[int]					=					self.get_dummy_components()
    _snake_case    :  Any					=					AudioLDMPipeline(**lowerCAmelCase)
    _snake_case    :  Any					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)
    _snake_case    :  str					=					audioldm_pipe.vocoder.config.sampling_rate

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

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

    _snake_case    :  int					=					audioldm_pipe(audio_length_in_s=0.032				,		**lowerCAmelCase)
    _snake_case    :  List[Any]					=					output.audios[0]

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

   def      UpperCamelCase_  (					self							:			int)				->				List[Any]:

    """simple docstring"""







    _snake_case    :  str					=					self.get_dummy_components()
    _snake_case    :  List[str]					=					AudioLDMPipeline(**lowerCAmelCase)
    _snake_case    :  str					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  List[str]					=					["""hey"""]

    _snake_case    :  int					=					audioldm_pipe(lowerCAmelCase				,		num_inference_steps=1)
    _snake_case    :  Optional[int]					=					output.audios.shape
    assert audio_shape == (1, 256)

    _snake_case    :  Dict					=					audioldm_pipe.vocoder.config
    config.model_in_dim *= 2
    _snake_case    :  str					=					SpeechTaHifiGan(lowerCAmelCase).to(lowerCAmelCase)
    _snake_case    :  List[str]					=					audioldm_pipe(lowerCAmelCase				,		num_inference_steps=1)
    _snake_case    :  Dict					=					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      UpperCamelCase_  (					self							:			Dict)				->				Dict:

    """simple docstring"""







    self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			str)				->				Optional[int]:

    """simple docstring"""







    self._test_inference_batch_single_identical(test_mean_pixel_difference=lowerCAmelCase)


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

    """simple docstring"""







    self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCAmelCase)







@slow
class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






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

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Any				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Optional[Any]="cpu"				,		lowerCAmelCase							:			Any=torch.floataa				,		lowerCAmelCase							:			Optional[Any]=0)				->				Tuple:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Any)				->				List[str]:

    """simple docstring"""







    _snake_case    :  Dict					=					AudioLDMPipeline.from_pretrained("""cvssp/audioldm""")
    _snake_case    :  Optional[int]					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Tuple					=					self.get_inputs(lowerCAmelCase)
    _snake_case    :  int					=					25
    _snake_case    :  Optional[Any]					=					audioldm_pipe(**lowerCAmelCase).audios[0]

    assert audio.ndim == 1
    assert len(lowerCAmelCase) == 8_1920

    _snake_case    :  Any					=					audio[7_7230:7_7240]
    _snake_case    :  str					=					np.array(
        [-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315])
    _snake_case    :  List[str]					=					np.abs(expected_slice - audio_slice).max()
    assert max_diff < 1E-2


   def      UpperCamelCase_  (					self							:			Dict)				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Dict					=					AudioLDMPipeline.from_pretrained("""cvssp/audioldm""")
    _snake_case    :  Any					=					LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
    _snake_case    :  Optional[int]					=					audioldm_pipe.to(lowerCAmelCase)
    audioldm_pipe.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case    :  Union[str, Any]					=					self.get_inputs(lowerCAmelCase)
    _snake_case    :  List[str]					=					audioldm_pipe(**lowerCAmelCase).audios[0]

    assert audio.ndim == 1
    assert len(lowerCAmelCase) == 8_1920

    _snake_case    :  Optional[Any]					=					audio[2_7780:2_7790]
    _snake_case    :  int					=					np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212])
    _snake_case    :  List[Any]					=					np.abs(expected_slice - audio_slice).max()
    assert max_diff < 3E-2
