



import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


a__    =	getLogger(__name__)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	int = 8				,       SCREAMING_SNAKE_CASE__       :	int = 1_024				,       SCREAMING_SNAKE_CASE__       :	Optional[int]="val"				,       SCREAMING_SNAKE_CASE__       :	Any=None				,       SCREAMING_SNAKE_CASE__       :	Tuple=False				,       SCREAMING_SNAKE_CASE__       :	List[str]="summarization"				,       SCREAMING_SNAKE_CASE__       :	List[Any]=None				,       SCREAMING_SNAKE_CASE__       :	List[str]=1				,       SCREAMING_SNAKE_CASE__       :	Dict = None				,       SCREAMING_SNAKE_CASE__       :	Tuple=""				,       **SCREAMING_SNAKE_CASE__       :	Any				,       )  ->     Dict:
 _snake_case    :  str					=					str(SCREAMING_SNAKE_CASE__				)
 assert local_rank is not None
 torch.distributed.init_process_group(backend="""nccl"""				,       rank=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  List[str]					=					Path(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Tuple					=					save_dir.joinpath(F'''rank_{local_rank}_output.json'''				)
 torch.cuda.set_device(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Any					=					AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__				).cuda()
 if fpaa:
  _snake_case    :  List[Any]					=					model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)  # update config with task specific params
 _snake_case    :  Optional[int]					=					generate_kwargs.pop("""num_beams"""				,       model.config.num_beams				)  # AttributeError risk?
 if num_return_sequences > num_beams:
  _snake_case    :  str					=					num_return_sequences

 _snake_case    :  Tuple					=					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__				)
 logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''				)  # if this is wrong, check config.model_type.

 if max_source_length is None:
  _snake_case    :  Optional[int]					=					tokenizer.model_max_length
 if prefix is None:
  _snake_case    :  List[Any]					=					prefix or getattr(model.config				,       """prefix"""				,       """"""				) or """"""
 _snake_case    :  Union[str, Any]					=					SeqaSeqDataset(
     SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       max_target_length=1_024				,       type_path=SCREAMING_SNAKE_CASE__				,       n_obs=SCREAMING_SNAKE_CASE__				,       prefix=SCREAMING_SNAKE_CASE__				,       **SCREAMING_SNAKE_CASE__				,       )
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 _snake_case    :  List[str]					=					ds.make_sortish_sampler(SCREAMING_SNAKE_CASE__				,       distributed=SCREAMING_SNAKE_CASE__				,       add_extra_examples=SCREAMING_SNAKE_CASE__				,       shuffle=SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					DataLoader(SCREAMING_SNAKE_CASE__				,       sampler=SCREAMING_SNAKE_CASE__				,       batch_size=SCREAMING_SNAKE_CASE__				,       collate_fn=ds.collate_fn				)
 _snake_case    :  Dict					=					[]
 for batch in tqdm(SCREAMING_SNAKE_CASE__				):
  _snake_case    :  Any					=					model.generate(
      input_ids=batch["""input_ids"""].to(model.device				)				,       attention_mask=batch["""attention_mask"""].to(model.device				)				,       num_return_sequences=SCREAMING_SNAKE_CASE__				,       num_beams=SCREAMING_SNAKE_CASE__				,       **SCREAMING_SNAKE_CASE__				,       )
  _snake_case    :  Dict					=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__				,       skip_special_tokens=SCREAMING_SNAKE_CASE__				,       clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Optional[int]					=					batch["""ids"""]
  if num_return_sequences > 1:
   _snake_case    :  Any					=					chunks(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(SCREAMING_SNAKE_CASE__				):
   results.append({"""pred""": pred, """id""": ids[i].item()}				)
 save_json(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 return results, sampler.num_replicas



def       lowercase       (   )  ->     Union[str, Any]:
 _snake_case    :  Any					=					argparse.ArgumentParser(
     epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"""				)
 parser.add_argument("""--data_dir"""				,       type=SCREAMING_SNAKE_CASE__				,       help="""like cnn_dm/test.source"""				)
 parser.add_argument(
     """--model_name"""				,       type=SCREAMING_SNAKE_CASE__				,       help="""like facebook/bart-large-cnn,t5-base, etc."""				,       default="""sshleifer/distilbart-xsum-12-3"""				,       )
 parser.add_argument("""--save_dir"""				,       type=SCREAMING_SNAKE_CASE__				,       help="""where to save"""				,       default="""tmp_gen"""				)
 parser.add_argument("""--max_source_length"""				,       type=SCREAMING_SNAKE_CASE__				,       default=SCREAMING_SNAKE_CASE__				)
 parser.add_argument(
     """--type_path"""				,       type=SCREAMING_SNAKE_CASE__				,       default="""test"""				,       help="""which subset to evaluate typically train/val/test"""				)
 parser.add_argument("""--task"""				,       type=SCREAMING_SNAKE_CASE__				,       default="""summarization"""				,       help="""used for task_specific_params + metrics"""				)
 parser.add_argument("""--bs"""				,       type=SCREAMING_SNAKE_CASE__				,       default=8				,       required=SCREAMING_SNAKE_CASE__				,       help="""batch size"""				)
 parser.add_argument(
     """--local_rank"""				,       type=SCREAMING_SNAKE_CASE__				,       default=-1				,       required=SCREAMING_SNAKE_CASE__				,       help="""should be passed by distributed.launch"""				)

 parser.add_argument(
     """--n_obs"""				,       type=SCREAMING_SNAKE_CASE__				,       default=SCREAMING_SNAKE_CASE__				,       required=SCREAMING_SNAKE_CASE__				,       help="""How many observations. Defaults to all."""				)
 parser.add_argument(
     """--num_return_sequences"""				,       type=SCREAMING_SNAKE_CASE__				,       default=1				,       required=SCREAMING_SNAKE_CASE__				,       help="""How many sequences to return"""				)
 parser.add_argument(
     """--sync_timeout"""				,       type=SCREAMING_SNAKE_CASE__				,       default=600				,       required=SCREAMING_SNAKE_CASE__				,       help="""How long should master process wait for other processes to finish."""				,       )
 parser.add_argument("""--src_lang"""				,       type=SCREAMING_SNAKE_CASE__				,       default=SCREAMING_SNAKE_CASE__				,       required=SCREAMING_SNAKE_CASE__				)
 parser.add_argument("""--tgt_lang"""				,       type=SCREAMING_SNAKE_CASE__				,       default=SCREAMING_SNAKE_CASE__				,       required=SCREAMING_SNAKE_CASE__				)
 parser.add_argument(
     """--prefix"""				,       type=SCREAMING_SNAKE_CASE__				,       required=SCREAMING_SNAKE_CASE__				,       default=SCREAMING_SNAKE_CASE__				,       help="""will be added to the begininng of src examples"""				)
 parser.add_argument("""--fp16"""				,       action="""store_true"""				)
 parser.add_argument("""--debug"""				,       action="""store_true"""				)
 _snake_case    :  Tuple					=					time.time()
 _snake_case	,    _snake_case    :  List[str]					=					parser.parse_known_args()
 _snake_case    :  Tuple					=					parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__				)
 if generate_kwargs and args.local_rank <= 0:
  print(F'''parsed the following generate kwargs: {generate_kwargs}'''				)
 _snake_case    :  Dict					=					Path(args.save_dir + """_tmp"""				)
 Path(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)  # this handles locking.
 _snake_case    :  Tuple					=					list(json_save_dir.glob("""rank_*.json"""				)				)
 if intermediate_files:
  raise ValueError(F'''Found files at {json_save_dir} please move or remove them.'''				)
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 _snake_case    :  Any					=					{}
 if args.src_lang is not None:
  _snake_case    :  List[str]					=					args.src_lang
 if args.tgt_lang is not None:
  _snake_case    :  Dict					=					args.tgt_lang

 Path(args.save_dir				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
 _snake_case	,    _snake_case    :  Optional[Any]					=					eval_data_dir(
     args.data_dir				,       SCREAMING_SNAKE_CASE__				,       args.model_name				,       type_path=args.type_path				,       bs=args.bs				,       fpaa=args.fpaa				,       task=args.task				,       local_rank=args.local_rank				,       n_obs=args.n_obs				,       max_source_length=args.max_source_length				,       num_return_sequences=args.num_return_sequences				,       prefix=args.prefix				,       dataset_kwargs=SCREAMING_SNAKE_CASE__				,       **SCREAMING_SNAKE_CASE__				,       )

 if args.local_rank <= 0:
  _snake_case    :  List[Any]					=					Path(args.save_dir				)
  save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
  _snake_case    :  int					=					gather_results_from_each_node(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       args.sync_timeout				)
  _snake_case    :  Union[str, Any]					=					combine_partial_results(SCREAMING_SNAKE_CASE__				)
  if args.num_return_sequences > 1:
   _snake_case    :  Optional[int]					=					save_dir.joinpath("""pseudolabel_results.json"""				)
   print(F'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''				)
   save_json(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   return
  _snake_case    :  Tuple					=					Path(args.data_dir				).joinpath(args.type_path + """.target"""				)
  with open(SCREAMING_SNAKE_CASE__				) as f:
   _snake_case    :  Tuple					=					[x.rstrip() for x in f.readlines()][: len(SCREAMING_SNAKE_CASE__				)]

  # Calculate metrics, save metrics,  and save _generations.txt
  _snake_case    :  Union[str, Any]					=					"""translation""" in args.task
  _snake_case    :  Any					=					calculate_bleu if calc_bleu else calculate_rouge
  _snake_case    :  Tuple					=					"""bleu""" if calc_bleu else """rouge"""
  _snake_case    :  Dict					=					score_fn(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  _snake_case    :  int					=					len(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  int					=					time.time() - start_time
  _snake_case    :  int					=					round(runtime / metrics["""n_obs"""]				,       4				)
  _snake_case    :  Optional[int]					=					num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  _snake_case    :  Optional[int]					=					save_dir.joinpath(F'''{args.type_path}_{metric_name}.json'''				)
  save_json(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       indent=SCREAMING_SNAKE_CASE__				)
  print(SCREAMING_SNAKE_CASE__				)
  write_txt_file(SCREAMING_SNAKE_CASE__				,       save_dir.joinpath(F'''{args.type_path}_generations.txt'''				)				)
  if args.debug:
   write_txt_file(SCREAMING_SNAKE_CASE__				,       save_dir.joinpath(F'''{args.type_path}.target'''				)				)
  else:
   shutil.rmtree(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     List:
 _snake_case    :  Tuple					=					[]
 for partial_result in partial_results:
  records.extend(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Tuple					=					sorted(SCREAMING_SNAKE_CASE__				,       key=lambda SCREAMING_SNAKE_CASE__				: x["id"]				)
 _snake_case    :  int					=					[x["""pred"""] for x in records]
 return preds



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     List[Dict[str, List]]:
 # WAIT FOR lots of .json files
 _snake_case    :  int					=					time.time()
 logger.info("""waiting for all nodes to finish"""				)
 _snake_case    :  Tuple					=					None
 while (time.time() - start_wait) < timeout:
  _snake_case    :  Tuple					=					list(save_dir.glob("""rank_*.json"""				)				)
  if len(SCREAMING_SNAKE_CASE__				) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   _snake_case    :  str					=					lmap(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError("""Rank 0 gave up on waiting for other processes"""				)
 # Unreachable


if __name__ == "__main__":
  # Usage for MT:
  run_generate()





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
