



'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a__    =	logging.get_logger(__name__)

a__    =	{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
a__    =	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     Dict:
 for attribute in key.split("""."""				):
  _snake_case    :  Optional[int]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 if weight_type is not None:
  _snake_case    :  List[str]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				).shape
 else:
  _snake_case    :  Any					=					hf_pointer.shape

 assert hf_shape == value.shape, (
     F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
     F''' {value.shape} for {full_name}'''
 )

 if weight_type == "weight":
  _snake_case    :  Tuple					=					value
 elif weight_type == "weight_g":
  _snake_case    :  List[str]					=					value
 elif weight_type == "weight_v":
  _snake_case    :  List[str]					=					value
 elif weight_type == "bias":
  _snake_case    :  int					=					value
 else:
  _snake_case    :  Tuple					=					value

 logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				)  ->     Dict:
 _snake_case    :  Dict					=					[]
 _snake_case    :  List[str]					=					fairseq_model.state_dict()

 _snake_case    :  List[str]					=					hf_model.feature_extractor

 # if encoder has different dim to decoder -> use proj_weight
 _snake_case    :  int					=					None

 for name, value in fairseq_dict.items():
  _snake_case    :  Optional[Any]					=					False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       hf_model.config.feat_extract_norm == """group"""				,       )
   _snake_case    :  Optional[int]					=					True
  elif name.split("""."""				)[0] == "proj":
   _snake_case    :  Any					=					fairseq_model.proj
   _snake_case    :  List[Any]					=					True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split("""w2v_model."""				)[-1] == name.split("""."""				)[0]:
     _snake_case    :  List[Any]					=					True
     if "*" in mapped_key:
      _snake_case    :  Optional[Any]					=					name.split(SCREAMING_SNAKE_CASE__				)[0].split("""."""				)[-2]
      _snake_case    :  Optional[int]					=					mapped_key.replace("""*"""				,       SCREAMING_SNAKE_CASE__				)
     if "weight_g" in name:
      _snake_case    :  Dict					=					"""weight_g"""
     elif "weight_v" in name:
      _snake_case    :  int					=					"""weight_v"""
     elif "bias" in name:
      _snake_case    :  Optional[int]					=					"""bias"""
     elif "weight" in name:
      _snake_case    :  Union[str, Any]					=					"""weight"""
     else:
      _snake_case    :  Optional[int]					=					None
     set_recursively(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE__				)

 logger.warning(F'''Unused weights: {unused_weights}'''				)

 return proj_weight



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Tuple				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				)  ->     Optional[int]:
 _snake_case    :  Any					=					full_name.split("""conv_layers."""				)[-1]
 _snake_case    :  List[str]					=					name.split("""."""				)
 _snake_case    :  Tuple					=					int(items[0]				)
 _snake_case    :  List[str]					=					int(items[1]				)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
   )
   _snake_case    :  Union[str, Any]					=					value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
   )
   _snake_case    :  int					=					value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
       " found."
   )
   _snake_case    :  Any					=					value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
   )
   _snake_case    :  Dict					=					value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
 else:
  unused_weights.append(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				)  ->     Tuple:
 _snake_case    :  Any					=					emb.weight.shape
 _snake_case    :  Union[str, Any]					=					nn.Linear(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       bias=SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					emb.weight.data
 return lin_layer



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     Optional[int]:
 with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				) as f:
  _snake_case    :  Optional[int]					=					f.readlines()
  _snake_case    :  str					=					[line.split(""" """				)[0] for line in lines]

 _snake_case    :  Tuple					=					len(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  List[Any]					=					{
     """<s>""": 0,
     """<pad>""": 1,
     """</s>""": 2,
     """<unk>""": 3,
 }

 vocab_dict.update(dict(zip(SCREAMING_SNAKE_CASE__				,       range(4				,       num_words + 4				)				)				)				)
 return vocab_dict



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				,       )  ->     Optional[Any]:
 _snake_case    :  str					=					WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Union[str, Any]					=					SpeechaTextaConfig.from_pretrained(
     SCREAMING_SNAKE_CASE__				,       vocab_size=SCREAMING_SNAKE_CASE__				,       decoder_layers=SCREAMING_SNAKE_CASE__				,       do_stable_layer_norm=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Dict					=					WavaVecaFeatureExtractor(
     feature_size=1				,       sampling_rate=16_000				,       padding_value=0				,       do_normalize=SCREAMING_SNAKE_CASE__				,       return_attention_mask=SCREAMING_SNAKE_CASE__				,       )

 _snake_case    :  Union[str, Any]					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]				,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""				)[:-1]				)}				)
 _snake_case    :  Optional[int]					=					model[0].eval()

 # set weights for wav2vec2 encoder
 _snake_case    :  Union[str, Any]					=					WavaVecaModel(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  str					=					recursively_load_weights_wavaveca(model.encoder				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  str					=					SpeechaTextaForCausalLM(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[Any]					=					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,       strict=SCREAMING_SNAKE_CASE__				)

 # set output linear layer
 unexpected_keys.remove("""embed_out"""				)
 _snake_case    :  str					=					nn.Parameter(model.decoder.embed_out.detach()				)

 # layer norm is init to identity matrix so leaving it is fine
 logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''				)
 logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''				)

 _snake_case    :  List[str]					=					SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__				,       decoder=SCREAMING_SNAKE_CASE__				)
 _snake_case    :  List[Any]					=					False

 # add projection layer
 _snake_case    :  Dict					=					nn.Parameter(projection_layer.weight				)
 _snake_case    :  Any					=					nn.Parameter(projection_layer.bias				)

 _snake_case    :  Any					=					create_vocab_dict(SCREAMING_SNAKE_CASE__				)

 with open(os.path.join(SCREAMING_SNAKE_CASE__				,       """vocab.json"""				)				,       """w"""				) as fp:
  json.dump(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Dict					=					SpeechaTextaTokenizer(os.path.join(SCREAMING_SNAKE_CASE__				,       """vocab.json"""				)				)
 tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					hf_wavavec.config.to_dict()
 _snake_case    :  Optional[int]					=					tokenizer.pad_token_id
 _snake_case    :  Tuple					=					tokenizer.bos_token_id
 _snake_case    :  str					=					tokenizer.eos_token_id
 _snake_case    :  Optional[Any]					=					"""speech_to_text_2"""
 _snake_case    :  int					=					"""wav2vec2"""

 _snake_case    :  Any					=					SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__				)

 hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__				)
 feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
  parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
  parser.add_argument(
      """--encoder_config_path""",
      default="""facebook/wav2vec2-large-lv60""",
      type=str,
      help="""Path to hf encoder wav2vec2 checkpoint config""",
  )
  parser.add_argument(
      """--decoder_config_path""",
      default="""facebook/s2t-small-mustc-en-fr-st""",
      type=str,
      help="""Path to hf decoder s2t checkpoint config""",
  )
  parser.add_argument("""--vocab_size""", default=1_02_24, type=int, help="""Vocab size of decoder""")
  parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

  a__    =	parser.parse_args()
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.dict_path,
      encoder_config_path=args.encoder_config_path,
      decoder_config_path=args.decoder_config_path,
      vocab_size=args.vocab_size,
      num_decoder_layers=args.num_decoder_layers,
  )





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
