'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 					_lowercase						(   lowerCAmelCase		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Any   				= AudioLDMPipeline
      UpperCAmelCase_   :   Tuple   				= TEXT_TO_AUDIO_PARAMS
      UpperCAmelCase_   :   Union[str, Any]   				= TEXT_TO_AUDIO_BATCH_PARAMS
      UpperCAmelCase_   :   Any   				= frozenset(
          [
              '''num_inference_steps''',
              '''num_waveforms_per_prompt''',
              '''generator''',
              '''latents''',
              '''output_type''',
              '''return_dict''',
              '''callback''',
              '''callback_steps''',
          ] )




      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         torch.manual_seed(0				)
         UpperCAmelCase__				:					Any 					=	UNetaDConditionModel(
             block_out_channels=(32, 64)       ,layers_per_block=2       ,sample_size=32       ,in_channels=4       ,out_channels=4       ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,cross_attention_dim=(32, 64)       ,class_embed_type='''simple_projection'''       ,projection_class_embeddings_input_dim=32       ,class_embeddings_concat=lowerCamelCase_       ,)
         UpperCAmelCase__				:					Optional[int] 					=	DDIMScheduler(
             beta_start=0.0_0085       ,beta_end=0.012       ,beta_schedule='''scaled_linear'''       ,clip_sample=lowerCamelCase_       ,set_alpha_to_one=lowerCamelCase_       ,)
         torch.manual_seed(0				)
         UpperCAmelCase__				:					Dict 					=	AutoencoderKL(
             block_out_channels=[32, 64]       ,in_channels=1       ,out_channels=1       ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,latent_channels=4       ,)
         torch.manual_seed(0				)
         UpperCAmelCase__				:					Dict 					=	ClapTextConfig(
             bos_token_id=0       ,eos_token_id=2       ,hidden_size=32       ,intermediate_size=37       ,layer_norm_eps=1e-05       ,num_attention_heads=4       ,num_hidden_layers=5       ,pad_token_id=1       ,vocab_size=1000       ,projection_dim=32       ,)
         UpperCAmelCase__				:					List[Any] 					=	ClapTextModelWithProjection(lowerCamelCase_				)
         UpperCAmelCase__				:					List[str] 					=	RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta'''       ,model_max_length=77				)

         UpperCAmelCase__				:					int 					=	SpeechTaHifiGanConfig(
             model_in_dim=8       ,sampling_rate=16000       ,upsample_initial_channel=16       ,upsample_rates=[2, 2]       ,upsample_kernel_sizes=[4, 4]       ,resblock_kernel_sizes=[3, 7]       ,resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]       ,normalize_before=lowerCamelCase_       ,)

         UpperCAmelCase__				:					int 					=	SpeechTaHifiGan(lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''vocoder''': vocoder,
         }
         return components




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_=0				)      ->      Optional[Any]:



         '''simple docstring'''



         if str(lowerCamelCase_				).startswith('''mps'''				):
            UpperCAmelCase__				:					Any 					=	torch.manual_seed(lowerCamelCase_				)
         else:
            UpperCAmelCase__				:					int 					=	torch.Generator(device=lowerCamelCase_				).manual_seed(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	{
             '''prompt''': '''A hammer hitting a wooden surface''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
         }
         return inputs




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

         UpperCAmelCase__				:					List[str] 					=	self.get_dummy_components()
         UpperCAmelCase__				:					Union[str, Any] 					=	AudioLDMPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	audioldm_pipe.to(lowerCamelCase_				)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					str 					=	self.get_dummy_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	audioldm_pipe(**lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	output.audios[0]

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

         UpperCAmelCase__				:					str 					=	audio[:10]
         UpperCAmelCase__				:					Dict 					=	np.array(
             [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]				)

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




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	self.get_dummy_components()
         UpperCAmelCase__				:					Optional[int] 					=	AudioLDMPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	audioldm_pipe.to(lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	audioldm_pipe.to(lowerCamelCase_				)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					Tuple 					=	self.get_dummy_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	3 * [inputs['''prompt''']]

         # forward
         UpperCAmelCase__				:					List[str] 					=	audioldm_pipe(**lowerCamelCase_				)
         UpperCAmelCase__				:					Union[str, Any] 					=	output.audios[0]

         UpperCAmelCase__				:					Any 					=	self.get_dummy_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	3 * [inputs.pop('''prompt'''				)]

         UpperCAmelCase__				:					List[Any] 					=	audioldm_pipe.tokenizer(
             lowerCamelCase_       ,padding='''max_length'''       ,max_length=audioldm_pipe.tokenizer.model_max_length       ,truncation=lowerCamelCase_       ,return_tensors='''pt'''       ,)
         UpperCAmelCase__				:					str 					=	text_inputs['''input_ids'''].to(lowerCamelCase_				)

         UpperCAmelCase__				:					Optional[Any] 					=	audioldm_pipe.text_encoder(
             lowerCamelCase_       ,)
         UpperCAmelCase__				:					str 					=	prompt_embeds.text_embeds
         # additional L_2 normalization over each hidden-state
         UpperCAmelCase__				:					List[Any] 					=	F.normalize(lowerCamelCase_       ,dim=-1				)

         UpperCAmelCase__				:					List[Any] 					=	prompt_embeds

         # forward
         UpperCAmelCase__				:					int 					=	audioldm_pipe(**lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	output.audios[0]

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




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.get_dummy_components()
         UpperCAmelCase__				:					List[Any] 					=	AudioLDMPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	audioldm_pipe.to(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	audioldm_pipe.to(lowerCamelCase_				)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					str 					=	self.get_dummy_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[Any] 					=	3 * ['''this is a negative prompt''']
         UpperCAmelCase__				:					List[Any] 					=	negative_prompt
         UpperCAmelCase__				:					Optional[int] 					=	3 * [inputs['''prompt''']]

         # forward
         UpperCAmelCase__				:					str 					=	audioldm_pipe(**lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	output.audios[0]

         UpperCAmelCase__				:					str 					=	self.get_dummy_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					List[str] 					=	3 * [inputs.pop('''prompt'''				)]

         UpperCAmelCase__				:					Any 					=	[]
         for p in [prompt, negative_prompt]:
            UpperCAmelCase__				:					int 					=	audioldm_pipe.tokenizer(
                lowerCamelCase_       ,padding='''max_length'''       ,max_length=audioldm_pipe.tokenizer.model_max_length       ,truncation=lowerCamelCase_       ,return_tensors='''pt'''       ,)
            UpperCAmelCase__				:					List[str] 					=	text_inputs['''input_ids'''].to(lowerCamelCase_				)

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

            embeds.append(lowerCamelCase_				)

         UpperCAmelCase__					,   UpperCAmelCase__				:					Tuple 					=	embeds

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

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




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__				:					List[Any] 					=	self.get_dummy_components()
         UpperCAmelCase__				:					int 					=	PNDMScheduler(skip_prk_steps=lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	AudioLDMPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	audioldm_pipe.to(lowerCamelCase_				)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.get_dummy_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	'''egg cracking'''
         UpperCAmelCase__				:					List[Any] 					=	audioldm_pipe(**lowerCamelCase_       ,negative_prompt=lowerCamelCase_				)
         UpperCAmelCase__				:					Any 					=	output.audios[0]

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

         UpperCAmelCase__				:					Optional[int] 					=	audio[:10]
         UpperCAmelCase__				:					Any 					=	np.array(
             [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]				)

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




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



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__				:					Optional[Any] 					=	self.get_dummy_components()
         UpperCAmelCase__				:					Dict 					=	PNDMScheduler(skip_prk_steps=lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	AudioLDMPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					List[Any] 					=	audioldm_pipe.to(lowerCamelCase_				)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	'''A hammer hitting a wooden surface'''

         # test num_waveforms_per_prompt=1 (default)
         UpperCAmelCase__				:					Tuple 					=	audioldm_pipe(lowerCamelCase_       ,num_inference_steps=2				).audios

         assert audios.shape == (1, 256)

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

         assert audios.shape == (batch_size, 256)

         # test num_waveforms_per_prompt for single prompt
         UpperCAmelCase__				:					List[str] 					=	2
         UpperCAmelCase__				:					Dict 					=	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
         UpperCAmelCase__				:					Tuple 					=	2
         UpperCAmelCase__				:					List[Any] 					=	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 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__				:					Tuple 					=	self.get_dummy_components()
         UpperCAmelCase__				:					int 					=	AudioLDMPipeline(**lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	audioldm_pipe.to(lowerCamelCase_				)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_				)
         UpperCAmelCase__				:					str 					=	audioldm_pipe.vocoder.config.sampling_rate

         UpperCAmelCase__				:					Tuple 					=	self.get_dummy_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					int 					=	audioldm_pipe(audio_length_in_s=0.016       ,**lowerCamelCase_				)
         UpperCAmelCase__				:					Dict 					=	output.audios[0]

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

         UpperCAmelCase__				:					Union[str, Any] 					=	audioldm_pipe(audio_length_in_s=0.032       ,**lowerCamelCase_				)
         UpperCAmelCase__				:					List[str] 					=	output.audios[0]

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




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



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

         UpperCAmelCase__				:					List[str] 					=	['''hey''']

         UpperCAmelCase__				:					str 					=	audioldm_pipe(lowerCamelCase_       ,num_inference_steps=1				)
         UpperCAmelCase__				:					Any 					=	output.audios.shape
         assert audio_shape == (1, 256)

         UpperCAmelCase__				:					Union[str, Any] 					=	audioldm_pipe.vocoder.config
         config.model_in_dim *= 2
         UpperCAmelCase__				:					str 					=	SpeechTaHifiGan(lowerCamelCase_				).to(lowerCamelCase_				)
         UpperCAmelCase__				:					Optional[int] 					=	audioldm_pipe(lowerCamelCase_       ,num_inference_steps=1				)
         UpperCAmelCase__				:					Any 					=	output.audios.shape
         # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
         assert audio_shape == (1, 256)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase_				)




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''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 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase_				)




@slow
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_="cpu"       ,lowerCamelCase_=torch.floataa       ,lowerCamelCase_=0				)      ->      Union[str, Any]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''				)
         UpperCAmelCase__				:					Optional[int] 					=	audioldm_pipe.to(lowerCamelCase_				)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					Union[str, Any] 					=	self.get_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	25
         UpperCAmelCase__				:					List[Any] 					=	audioldm_pipe(**lowerCamelCase_				).audios[0]

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

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




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''				)
         UpperCAmelCase__				:					List[str] 					=	LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config				)
         UpperCAmelCase__				:					Union[str, Any] 					=	audioldm_pipe.to(lowerCamelCase_				)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_				)

         UpperCAmelCase__				:					List[Any] 					=	self.get_inputs(lowerCamelCase_				)
         UpperCAmelCase__				:					Tuple 					=	audioldm_pipe(**lowerCamelCase_				).audios[0]

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

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





'''simple docstring'''

import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment







def __UpperCamelCase( _A       :					Any	,      _A       :					List[str]=()	,      _A       :					List[str]=None	,      _A       :					Dict="no"	,      _A       :					List[str]="29500"     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	False
   UpperCAmelCase__				:					List[str] 					=	False
   if any(key.startswith('''KAGGLE'''     ) for key in os.environ.keys()     ):
      UpperCAmelCase__				:					List[str] 					=	True
   elif "IPython" in sys.modules:
      UpperCAmelCase__				:					List[Any] 					=	'''google.colab''' in str(sys.modules['''IPython'''].get_ipython()     )

   try:
      UpperCAmelCase__				:					Tuple 					=	PrecisionType(mixed_precision.lower()     )
   except ValueError:
      raise ValueError(
          F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''     )

   if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME'''	,      _A     ) is not None):
      # TPU launch
      import torch_xla.distributed.xla_multiprocessing as xmp

      if len(AcceleratorState._shared_state     ) > 0:
         raise ValueError(
             '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
             '''your training function. Restart your notebook and make sure no cells initializes an '''
             '''`Accelerator`.'''     )
      if num_processes is None:
         UpperCAmelCase__				:					Tuple 					=	8

      UpperCAmelCase__				:					Optional[Any] 					=	PrepareForLaunch(_A	,      distributed_type='''TPU'''     )
      print(F'''Launching a training on {num_processes} TPU cores.'''     )
      xmp.spawn(_A	,      args=_A	,      nprocs=_A	,      start_method='''fork'''     )
   elif in_colab:
      # No need for a distributed launch otherwise as it's either CPU or one GPU.
      if torch.cuda.is_available():
         print('''Launching training on one GPU.'''     )
      else:
         print('''Launching training on one CPU.'''     )
      function(*_A     )
   else:
      if num_processes is None:
         raise ValueError(
             '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'''     )

      if num_processes > 1:
         # Multi-GPU launch
         from torch.multiprocessing import start_processes
         from torch.multiprocessing.spawn import ProcessRaisedException

         if len(AcceleratorState._shared_state     ) > 0:
            raise ValueError(
                '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
                '''inside your training function. Restart your notebook and make sure no cells initializes an '''
                '''`Accelerator`.'''     )

         if torch.cuda.is_initialized():
            raise ValueError(
                '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
                '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
                '''function.'''     )

         # torch.distributed will expect a few environment variable to be here. We set the ones common to each
         # process here (the other ones will be set be the launcher).
         with patch_environment(
             world_size=_A	,      master_addr='''127.0.01'''	,      master_port=_A	,      mixed_precision=_A     ):
            UpperCAmelCase__				:					str 					=	PrepareForLaunch(_A	,      distributed_type='''MULTI_GPU'''     )
            print(F'''Launching training on {num_processes} GPUs.'''     )
            try:
               start_processes(_A	,      args=_A	,      nprocs=_A	,      start_method='''fork'''     )
            except ProcessRaisedException as e:
               if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                  raise RuntimeError(
                      '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
                      '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
                      '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
                      '''which one is problematic.'''     ) from e

      else:
         # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
         if is_mps_available():
            UpperCAmelCase__				:					Union[str, Any] 					=	'''1'''
            print('''Launching training on MPS.'''     )
         elif torch.cuda.is_available():
            print('''Launching training on one GPU.'''     )
         else:
            print('''Launching training on CPU.'''     )
         function(*_A     )







def __UpperCamelCase( _A       :					List[str]	,      _A       :					Optional[Any]=()	,      _A       :					str=2     ):
   '''simple docstring'''
   from torch.multiprocessing import start_processes

   with tempfile.NamedTemporaryFile() as tmp_file:
      # torch.distributed will expect a few environment variable to be here. We set the ones common to each
      # process here (the other ones will be set be the launcher).
      with patch_environment(
          world_size=_A	,      master_addr='''127.0.01'''	,      master_port='''29500'''	,      accelerate_mixed_precision='''no'''	,      accelerate_debug_rdv_file=tmp_file.name	,      accelerate_use_cpu='''yes'''	,      ):
         UpperCAmelCase__				:					Optional[int] 					=	PrepareForLaunch(_A	,      debug=_A     )
         start_processes(_A	,      args=_A	,      nprocs=_A	,      start_method='''fork'''     )




