






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   (   _A						,				unittest.TestCase						):
      _a							:						List[str]										=							AudioLDMPipeline
      _a							:						Tuple										=							TEXT_TO_AUDIO_PARAMS
      _a							:						int										=							TEXT_TO_AUDIO_BATCH_PARAMS
      _a							:						str										=							frozenset(
          [
              'num_inference_steps',
              'num_waveforms_per_prompt',
              'generator',
              'latents',
              'output_type',
              'return_dict',
              'callback',
              'callback_steps',
          ]						)
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    str							       =UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)				,			layers_per_block=2				,			sample_size=3_2				,			in_channels=4				,			out_channels=4				,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,			cross_attention_dim=(3_2, 6_4)				,			class_embed_type="""simple_projection"""				,			projection_class_embeddings_input_dim=3_2				,			class_embeddings_concat=a				,			)
       snake_case__  :    Tuple							       =DDIMScheduler(
           beta_start=0.00085				,			beta_end=0.012				,			beta_schedule="""scaled_linear"""				,			clip_sample=a				,			set_alpha_to_one=a				,			)
       torch.manual_seed(0   )
       snake_case__  :    str							       =AutoencoderKL(
           block_out_channels=[3_2, 6_4]				,			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__  :    List[Any]							       =ClapTextConfig(
           bos_token_id=0				,			eos_token_id=2				,			hidden_size=3_2				,			intermediate_size=3_7				,			layer_norm_eps=1e-05				,			num_attention_heads=4				,			num_hidden_layers=5				,			pad_token_id=1				,			vocab_size=1_0_0_0				,			projection_dim=3_2				,			)
       snake_case__  :    Union[str, Any]							       =ClapTextModelWithProjection(a   )
       snake_case__  :    Dict							       =RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""				,			model_max_length=7_7   )

       snake_case__  :    Dict							       =SpeechTaHifiGanConfig(
           model_in_dim=8				,			sampling_rate=1_6_0_0_0				,			upsample_initial_channel=1_6				,			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=a				,			)

       snake_case__  :    List[Any]							       =SpeechTaHifiGan(a   )

       snake_case__  :    Union[str, Any]							       ={
           """unet""": unet,
           """scheduler""": scheduler,
           """vae""": vae,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """vocoder""": vocoder,
       }
       return components
      def 						lowercase__					(     self				,			a				,			a=0   ):
       if str(a   ).startswith("""mps"""   ):
        snake_case__  :    Dict							       =torch.manual_seed(a   )
       else:
        snake_case__  :    Union[str, Any]							       =torch.Generator(device=a   ).manual_seed(a   )
       snake_case__  :    Any							       ={
           """prompt""": """A hammer hitting a wooden surface""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """guidance_scale""": 6.0,
       }
       return inputs
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       ="""cpu"""  # ensure determinism for the device-dependent torch.Generator

       snake_case__  :    Union[str, Any]							       =self.get_dummy_components()
       snake_case__  :    Tuple							       =AudioLDMPipeline(**a   )
       snake_case__  :    List[Any]							       =audioldm_pipe.to(a   )
       audioldm_pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    List[str]							       =self.get_dummy_inputs(a   )
       snake_case__  :    Optional[Any]							       =audioldm_pipe(**a   )
       snake_case__  :    Dict							       =output.audios[0]

       assert audio.ndim == 1
       assert len(a   ) == 2_5_6

       snake_case__  :    str							       =audio[:1_0]
       snake_case__  :    Tuple							       =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 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =self.get_dummy_components()
       snake_case__  :    Dict							       =AudioLDMPipeline(**a   )
       snake_case__  :    str							       =audioldm_pipe.to(a   )
       snake_case__  :    Any							       =audioldm_pipe.to(a   )
       audioldm_pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    str							       =self.get_dummy_inputs(a   )
       snake_case__  :    Optional[Any]							       =3 * [inputs["""prompt"""]]

       # forward
       snake_case__  :    Any							       =audioldm_pipe(**a   )
       snake_case__  :    int							       =output.audios[0]

       snake_case__  :    List[Any]							       =self.get_dummy_inputs(a   )
       snake_case__  :    Tuple							       =3 * [inputs.pop("""prompt"""   )]

       snake_case__  :    Tuple							       =audioldm_pipe.tokenizer(
           a				,			padding="""max_length"""				,			max_length=audioldm_pipe.tokenizer.model_max_length				,			truncation=a				,			return_tensors="""pt"""				,			)
       snake_case__  :    Optional[int]							       =text_inputs["""input_ids"""].to(a   )

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

       snake_case__  :    Union[str, Any]							       =prompt_embeds

       # forward
       snake_case__  :    Union[str, Any]							       =audioldm_pipe(**a   )
       snake_case__  :    Tuple							       =output.audios[0]

       assert np.abs(audio_a - audio_a   ).max() < 1e-2
      def 						lowercase__					(     self   ):
       snake_case__  :    Any							       =self.get_dummy_components()
       snake_case__  :    Optional[int]							       =AudioLDMPipeline(**a   )
       snake_case__  :    Any							       =audioldm_pipe.to(a   )
       snake_case__  :    List[Any]							       =audioldm_pipe.to(a   )
       audioldm_pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    List[str]							       =self.get_dummy_inputs(a   )
       snake_case__  :    Dict							       =3 * ["""this is a negative prompt"""]
       snake_case__  :    Optional[Any]							       =negative_prompt
       snake_case__  :    Union[str, Any]							       =3 * [inputs["""prompt"""]]

       # forward
       snake_case__  :    Tuple							       =audioldm_pipe(**a   )
       snake_case__  :    List[str]							       =output.audios[0]

       snake_case__  :    Dict							       =self.get_dummy_inputs(a   )
       snake_case__  :    Tuple							       =3 * [inputs.pop("""prompt"""   )]

       snake_case__  :    List[Any]							       =[]
       for p in [prompt, negative_prompt]:
        snake_case__  :    Union[str, Any]							       =audioldm_pipe.tokenizer(
            a				,			padding="""max_length"""				,			max_length=audioldm_pipe.tokenizer.model_max_length				,			truncation=a				,			return_tensors="""pt"""				,			)
        snake_case__  :    Dict							       =text_inputs["""input_ids"""].to(a   )

        snake_case__  :    List[Any]							       =audioldm_pipe.text_encoder(
            a				,			)
        snake_case__  :    Tuple							       =text_embeds.text_embeds
        # additional L_2 normalization over each hidden-state
        snake_case__  :    int							       =F.normalize(a				,			dim=-1   )

        embeds.append(a   )

       snake_case__       ,   snake_case__  :    Union[str, Any]							       =embeds

       # forward
       snake_case__  :    Dict							       =audioldm_pipe(**a   )
       snake_case__  :    Tuple							       =output.audios[0]

       assert np.abs(audio_a - audio_a   ).max() < 1e-2
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
       snake_case__  :    Dict							       =self.get_dummy_components()
       snake_case__  :    Any							       =PNDMScheduler(skip_prk_steps=a   )
       snake_case__  :    List[Any]							       =AudioLDMPipeline(**a   )
       snake_case__  :    List[str]							       =audioldm_pipe.to(a   )
       audioldm_pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    str							       =self.get_dummy_inputs(a   )
       snake_case__  :    Dict							       ="""egg cracking"""
       snake_case__  :    Dict							       =audioldm_pipe(**a				,			negative_prompt=a   )
       snake_case__  :    Union[str, Any]							       =output.audios[0]

       assert audio.ndim == 1
       assert len(a   ) == 2_5_6

       snake_case__  :    int							       =audio[:1_0]
       snake_case__  :    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 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
       snake_case__  :    str							       =self.get_dummy_components()
       snake_case__  :    Any							       =PNDMScheduler(skip_prk_steps=a   )
       snake_case__  :    List[Any]							       =AudioLDMPipeline(**a   )
       snake_case__  :    Optional[Any]							       =audioldm_pipe.to(a   )
       audioldm_pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    int							       ="""A hammer hitting a wooden surface"""

       # test num_waveforms_per_prompt=1 (default)
       snake_case__  :    int							       =audioldm_pipe(a				,			num_inference_steps=2   ).audios

       assert audios.shape == (1, 2_5_6)

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

       assert audios.shape == (batch_size, 2_5_6)

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

       assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

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

       assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
       snake_case__  :    Tuple							       =self.get_dummy_components()
       snake_case__  :    str							       =AudioLDMPipeline(**a   )
       snake_case__  :    Optional[Any]							       =audioldm_pipe.to(a   )
       audioldm_pipe.set_progress_bar_config(disable=a   )
       snake_case__  :    int							       =audioldm_pipe.vocoder.config.sampling_rate

       snake_case__  :    str							       =self.get_dummy_inputs(a   )
       snake_case__  :    Optional[Any]							       =audioldm_pipe(audio_length_in_s=0.016				,			**a   )
       snake_case__  :    Optional[Any]							       =output.audios[0]

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

       snake_case__  :    Tuple							       =audioldm_pipe(audio_length_in_s=0.032				,			**a   )
       snake_case__  :    Tuple							       =output.audios[0]

       assert audio.ndim == 1
       assert len(a   ) / vocoder_sampling_rate == 0.032
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =self.get_dummy_components()
       snake_case__  :    Dict							       =AudioLDMPipeline(**a   )
       snake_case__  :    Union[str, Any]							       =audioldm_pipe.to(a   )
       audioldm_pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    Optional[int]							       =["""hey"""]

       snake_case__  :    Any							       =audioldm_pipe(a				,			num_inference_steps=1   )
       snake_case__  :    Tuple							       =output.audios.shape
       assert audio_shape == (1, 2_5_6)

       snake_case__  :    int							       =audioldm_pipe.vocoder.config
       config.model_in_dim *= 2
       snake_case__  :    int							       =SpeechTaHifiGan(a   ).to(a   )
       snake_case__  :    Union[str, Any]							       =audioldm_pipe(a				,			num_inference_steps=1   )
       snake_case__  :    Any							       =output.audios.shape
       # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
       assert audio_shape == (1, 2_5_6)
      def 						lowercase__					(     self   ):
       self._test_attention_slicing_forward_pass(test_mean_pixel_difference=a   )
      def 						lowercase__					(     self   ):
       self._test_inference_batch_single_identical(test_mean_pixel_difference=a   )



      @unittest.skipIf(
          torch_device != """cuda""" or not is_xformers_available()				,			reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,			)
      def 						lowercase__					(     self   ):
       self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a   )







@slow
class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self   ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
      def 						lowercase__					(     self				,			a				,			a="cpu"				,			a=torch.floataa				,			a=0   ):
       snake_case__  :    Optional[int]							       =torch.Generator(device=a   ).manual_seed(a   )
       snake_case__  :    str							       =np.random.RandomState(a   ).standard_normal((1, 8, 1_2_8, 1_6)   )
       snake_case__  :    Optional[Any]							       =torch.from_numpy(a   ).to(device=a				,			dtype=a   )
       snake_case__  :    Any							       ={
           """prompt""": """A hammer hitting a wooden surface""",
           """latents""": latents,
           """generator""": generator,
           """num_inference_steps""": 3,
           """guidance_scale""": 2.5,
       }
       return inputs
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""   )
       snake_case__  :    List[str]							       =audioldm_pipe.to(a   )
       audioldm_pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    List[Any]							       =self.get_inputs(a   )
       snake_case__  :    Any							       =2_5
       snake_case__  :    Dict							       =audioldm_pipe(**a   ).audios[0]

       assert audio.ndim == 1
       assert len(a   ) == 8_1_9_2_0

       snake_case__  :    Union[str, Any]							       =audio[7_7_2_3_0:7_7_2_4_0]
       snake_case__  :    str							       =np.array(
           [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]   )
       snake_case__  :    List[Any]							       =np.abs(expected_slice - audio_slice   ).max()
       assert max_diff < 1e-2



      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""   )
       snake_case__  :    Tuple							       =LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config   )
       snake_case__  :    Tuple							       =audioldm_pipe.to(a   )
       audioldm_pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    Optional[Any]							       =self.get_inputs(a   )
       snake_case__  :    List[str]							       =audioldm_pipe(**a   ).audios[0]

       assert audio.ndim == 1
       assert len(a   ) == 8_1_9_2_0

       snake_case__  :    Any							       =audio[2_7_7_8_0:2_7_7_9_0]
       snake_case__  :    List[Any]							       =np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]   )
       snake_case__  :    Tuple							       =np.abs(expected_slice - audio_slice   ).max()
       assert max_diff < 3e-2













import math







def 			A__							(      _a  :					list		,  _a  :					int = 0		,  _a  :					int = 0	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =end or len(_a	)
 for i in range(_a		,  _a	):
  snake_case__  :    int							       =i
  snake_case__  :    int							       =array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   snake_case__  :    Dict							       =array[temp_index - 1]
   temp_index -= 1
  snake_case__  :    Dict							       =temp_index_value
 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int	):  # Max Heap





 '''simple docstring'''







 snake_case__  :    Tuple							       =index
 snake_case__  :    Dict							       =2 * index + 1  # Left Node
 snake_case__  :    Union[str, Any]							       =2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  snake_case__  :    Dict							       =left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  snake_case__  :    List[Any]							       =right_index

 if largest != index:
  snake_case__       ,   snake_case__  :    List[Any]							       =array[largest], array[index]
  heapify(_a		,  _a		,  _a	)







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =len(_a	)

 for i in range(n // 2		,  -1		,  -1	):
  heapify(_a		,  _a		,  _a	)

 for i in range(n - 1		,  0		,  -1	):
  snake_case__       ,   snake_case__  :    Tuple							       =array[0], array[i]
  heapify(_a		,  0		,  _a	)

 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =low
 snake_case__  :    Union[str, Any]							       =high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  snake_case__       ,   snake_case__  :    List[Any]							       =array[j], array[i]
  i += 1







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) == 0:
  return array
 snake_case__  :    Optional[int]							       =2 * math.ceil(math.loga(len(_a	)	)	)
 snake_case__  :    Tuple							       =16
 return intro_sort(_a		,  0		,  len(_a	)		,  _a		,  _a	)







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(_a	)
  max_depth -= 1
  snake_case__  :    Dict							       =median_of_a(_a		,  _a		,  start + ((end - start) // 2) + 1		,  end - 1	)
  snake_case__  :    List[str]							       =partition(_a		,  _a		,  _a		,  _a	)
  intro_sort(_a		,  _a		,  _a		,  _a		,  _a	)
  snake_case__  :    Union[str, Any]							       =p
 return insertion_sort(_a		,  _a		,  _a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma : """).strip()
       __lowerCamelCase	:		Dict										=   [float(item) for item in user_input.split(""",""")]
       print(sort(unsorted))





