'''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 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						Union[str, Any]	       =  AudioLDMPipeline
    a__	:						Optional[int]	       =  TEXT_TO_AUDIO_PARAMS
    a__	:						Any	       =  TEXT_TO_AUDIO_BATCH_PARAMS
    a__	:						Union[str, Any]	       =  frozenset(
        [
            """num_inference_steps""",
            """num_waveforms_per_prompt""",
            """generator""",
            """latents""",
            """output_type""",
            """return_dict""",
            """callback""",
            """callback_steps""",
        ]     )



    def  _lowercase						(self   :   List[str]			):
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       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=__a       ,      )
         UpperCAmelCase_	   =       DDIMScheduler(
             beta_start=0.0_00_85       ,      beta_end=0.0_12       ,      beta_schedule="scaled_linear"       ,      clip_sample=__a       ,      set_alpha_to_one=__a       ,      )
         torch.manual_seed(0			)
         UpperCAmelCase_	   =       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_	   =       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_	   =       ClapTextModelWithProjection(__a			)
         UpperCAmelCase_	   =       RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"       ,      model_max_length=77			)

         UpperCAmelCase_	   =       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=__a       ,      )

         UpperCAmelCase_	   =       SpeechTaHifiGan(__a			)

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



    def  _lowercase						(self   :   Dict       ,      __a   :   Any       ,      __a   :   Any=0			):
         if str(__a			).startswith("mps"			):
              UpperCAmelCase_	   =       torch.manual_seed(__a			)
         else:
              UpperCAmelCase_	   =       torch.Generator(device=__a			).manual_seed(__a			)
         UpperCAmelCase_	   =       {
             "prompt": "A hammer hitting a wooden surface",
             "generator": generator,
             "num_inference_steps": 2,
             "guidance_scale": 6.0,
         }
         return inputs



    def  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       "cpu"  # ensure determinism for the device-dependent torch.Generator

         UpperCAmelCase_	   =       self.get_dummy_components()
         UpperCAmelCase_	   =       AudioLDMPipeline(**__a			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         audioldm_pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       self.get_dummy_inputs(__a			)
         UpperCAmelCase_	   =       audioldm_pipe(**__a			)
         UpperCAmelCase_	   =       output.audios[0]

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

         UpperCAmelCase_	   =       audio[:10]
         UpperCAmelCase_	   =       np.array(
             [-0.00_50, 0.00_50, -0.00_60, 0.00_33, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_33]			)

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



    def  _lowercase						(self   :   List[Any]			):
         UpperCAmelCase_	   =       self.get_dummy_components()
         UpperCAmelCase_	   =       AudioLDMPipeline(**__a			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         audioldm_pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       self.get_dummy_inputs(__a			)
         UpperCAmelCase_	   =       3 * [inputs["prompt"]]

         # forward
         UpperCAmelCase_	   =       audioldm_pipe(**__a			)
         UpperCAmelCase_	   =       output.audios[0]

         UpperCAmelCase_	   =       self.get_dummy_inputs(__a			)
         UpperCAmelCase_	   =       3 * [inputs.pop("prompt"			)]

         UpperCAmelCase_	   =       audioldm_pipe.tokenizer(
             __a       ,      padding="max_length"       ,      max_length=audioldm_pipe.tokenizer.model_max_length       ,      truncation=__a       ,      return_tensors="pt"       ,      )
         UpperCAmelCase_	   =       text_inputs["input_ids"].to(__a			)

         UpperCAmelCase_	   =       audioldm_pipe.text_encoder(
             __a       ,      )
         UpperCAmelCase_	   =       prompt_embeds.text_embeds
         # additional L_2 normalization over each hidden-state
         UpperCAmelCase_	   =       F.normalize(__a       ,      dim=-1			)

         UpperCAmelCase_	   =       prompt_embeds

         # forward
         UpperCAmelCase_	   =       audioldm_pipe(**__a			)
         UpperCAmelCase_	   =       output.audios[0]

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



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.get_dummy_components()
         UpperCAmelCase_	   =       AudioLDMPipeline(**__a			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         audioldm_pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       self.get_dummy_inputs(__a			)
         UpperCAmelCase_	   =       3 * ["this is a negative prompt"]
         UpperCAmelCase_	   =       negative_prompt
         UpperCAmelCase_	   =       3 * [inputs["prompt"]]

         # forward
         UpperCAmelCase_	   =       audioldm_pipe(**__a			)
         UpperCAmelCase_	   =       output.audios[0]

         UpperCAmelCase_	   =       self.get_dummy_inputs(__a			)
         UpperCAmelCase_	   =       3 * [inputs.pop("prompt"			)]

         UpperCAmelCase_	   =       []
         for p in [prompt, negative_prompt]:
              UpperCAmelCase_	   =       audioldm_pipe.tokenizer(
                  __a       ,      padding="max_length"       ,      max_length=audioldm_pipe.tokenizer.model_max_length       ,      truncation=__a       ,      return_tensors="pt"       ,      )
              UpperCAmelCase_	   =       text_inputs["input_ids"].to(__a			)

              UpperCAmelCase_	   =       audioldm_pipe.text_encoder(
                  __a       ,      )
              UpperCAmelCase_	   =       text_embeds.text_embeds
              # additional L_2 normalization over each hidden-state
              UpperCAmelCase_	   =       F.normalize(__a       ,      dim=-1			)

              embeds.append(__a			)

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       embeds

         # forward
         UpperCAmelCase_	   =       audioldm_pipe(**__a			)
         UpperCAmelCase_	   =       output.audios[0]

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



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_	   =       self.get_dummy_components()
         UpperCAmelCase_	   =       PNDMScheduler(skip_prk_steps=__a			)
         UpperCAmelCase_	   =       AudioLDMPipeline(**__a			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         audioldm_pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       self.get_dummy_inputs(__a			)
         UpperCAmelCase_	   =       "egg cracking"
         UpperCAmelCase_	   =       audioldm_pipe(**__a       ,      negative_prompt=__a			)
         UpperCAmelCase_	   =       output.audios[0]

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

         UpperCAmelCase_	   =       audio[:10]
         UpperCAmelCase_	   =       np.array(
             [-0.00_51, 0.00_50, -0.00_60, 0.00_34, -0.00_26, 0.00_33, -0.00_27, 0.00_33, -0.00_28, 0.00_32]			)

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



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_	   =       self.get_dummy_components()
         UpperCAmelCase_	   =       PNDMScheduler(skip_prk_steps=__a			)
         UpperCAmelCase_	   =       AudioLDMPipeline(**__a			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         audioldm_pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       "A hammer hitting a wooden surface"

         # test num_waveforms_per_prompt=1 (default)
         UpperCAmelCase_	   =       audioldm_pipe(__a       ,      num_inference_steps=2			).audios

         assert audios.shape == (1, 256)

         # test num_waveforms_per_prompt=1 (default) for batch of prompts
         UpperCAmelCase_	   =       2
         UpperCAmelCase_	   =       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_	   =       2
         UpperCAmelCase_	   =       audioldm_pipe(__a       ,      num_inference_steps=2       ,      num_waveforms_per_prompt=__a			).audios

         assert audios.shape == (num_waveforms_per_prompt, 256)

         # test num_waveforms_per_prompt for batch of prompts
         UpperCAmelCase_	   =       2
         UpperCAmelCase_	   =       audioldm_pipe(
             [prompt] * batch_size       ,      num_inference_steps=2       ,      num_waveforms_per_prompt=__a			).audios

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



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       "cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_	   =       self.get_dummy_components()
         UpperCAmelCase_	   =       AudioLDMPipeline(**__a			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         audioldm_pipe.set_progress_bar_config(disable=__a			)
         UpperCAmelCase_	   =       audioldm_pipe.vocoder.config.sampling_rate

         UpperCAmelCase_	   =       self.get_dummy_inputs(__a			)
         UpperCAmelCase_	   =       audioldm_pipe(audio_length_in_s=0.0_16       ,      **__a			)
         UpperCAmelCase_	   =       output.audios[0]

         assert audio.ndim == 1
         assert len(__a			) / vocoder_sampling_rate == 0.0_16

         UpperCAmelCase_	   =       audioldm_pipe(audio_length_in_s=0.0_32       ,      **__a			)
         UpperCAmelCase_	   =       output.audios[0]

         assert audio.ndim == 1
         assert len(__a			) / vocoder_sampling_rate == 0.0_32



    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       self.get_dummy_components()
         UpperCAmelCase_	   =       AudioLDMPipeline(**__a			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         audioldm_pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       ["hey"]

         UpperCAmelCase_	   =       audioldm_pipe(__a       ,      num_inference_steps=1			)
         UpperCAmelCase_	   =       output.audios.shape
         assert audio_shape == (1, 256)

         UpperCAmelCase_	   =       audioldm_pipe.vocoder.config
         config.model_in_dim *= 2
         UpperCAmelCase_	   =       SpeechTaHifiGan(__a			).to(__a			)
         UpperCAmelCase_	   =       audioldm_pipe(__a       ,      num_inference_steps=1			)
         UpperCAmelCase_	   =       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  _lowercase						(self   :   Union[str, Any]			):
         self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__a			)



    def  _lowercase						(self   :   Optional[Any]			):
         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   :   str			):
         self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__a			)




@slow
class 	__A				(			unittest.TestCase     ):



    def  _lowercase						(self   :   Tuple			):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   Optional[Any]       ,      __a   :   List[Any]="cpu"       ,      __a   :   int=torch.floataa       ,      __a   :   Union[str, Any]=0			):
         UpperCAmelCase_	   =       torch.Generator(device=__a			).manual_seed(__a			)
         UpperCAmelCase_	   =       np.random.RandomState(__a			).standard_normal((1, 8, 128, 16)			)
         UpperCAmelCase_	   =       torch.from_numpy(__a			).to(device=__a       ,      dtype=__a			)
         UpperCAmelCase_	   =       {
             "prompt": "A hammer hitting a wooden surface",
             "latents": latents,
             "generator": generator,
             "num_inference_steps": 3,
             "guidance_scale": 2.5,
         }
         return inputs



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       AudioLDMPipeline.from_pretrained("cvssp/audioldm"			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         audioldm_pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       self.get_inputs(__a			)
         UpperCAmelCase_	   =       25
         UpperCAmelCase_	   =       audioldm_pipe(**__a			).audios[0]

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

         UpperCAmelCase_	   =       audio[77230:77240]
         UpperCAmelCase_	   =       np.array(
             [-0.48_84, -0.46_07, 0.00_23, 0.50_07, 0.58_96, 0.51_51, 0.38_13, -0.02_08, -0.36_87, -0.43_15]			)
         UpperCAmelCase_	   =       np.abs(expected_slice - audio_slice			).max()
         assert max_diff < 1E-2






    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       AudioLDMPipeline.from_pretrained("cvssp/audioldm"			)
         UpperCAmelCase_	   =       LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config			)
         UpperCAmelCase_	   =       audioldm_pipe.to(__a			)
         audioldm_pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       self.get_inputs(__a			)
         UpperCAmelCase_	   =       audioldm_pipe(**__a			).audios[0]

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

         UpperCAmelCase_	   =       audio[27780:27790]
         UpperCAmelCase_	   =       np.array([-0.21_31, -0.08_73, -0.01_24, -0.01_89, 0.05_69, 0.13_73, 0.18_83, 0.28_86, 0.32_97, 0.22_12]			)
         UpperCAmelCase_	   =       np.abs(expected_slice - audio_slice			).max()
         assert max_diff < 3E-2





'''simple docstring'''





from random import randint
from tempfile import TemporaryFile

import numpy as np





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Any ,   snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       0
     if start < end:
          UpperCAmelCase_	   =       randint(snake_case_ ,   snake_case_      )
          UpperCAmelCase_	   =       a[end]
          UpperCAmelCase_	   =       a[pivot]
          UpperCAmelCase_	   =       temp

          UpperCAmelCase_   ,			UpperCAmelCase_	   =       _in_place_partition(snake_case_ ,   snake_case_ ,   snake_case_      )
          count += _in_place_quick_sort(snake_case_ ,   snake_case_ ,   p - 1      )
          count += _in_place_quick_sort(snake_case_ ,   p + 1 ,   snake_case_      )
     return count





def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			Dict ,   snake_case_	:			List[str]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       randint(snake_case_ ,   snake_case_      )
     UpperCAmelCase_	   =       a[end]
     UpperCAmelCase_	   =       a[pivot]
     UpperCAmelCase_	   =       temp
     UpperCAmelCase_	   =       start - 1
     for index in range(snake_case_ ,   snake_case_      ):
          count += 1
          if a[index] < a[end]:  # check if current val is less than pivot value
               UpperCAmelCase_	   =       new_pivot_index + 1
               UpperCAmelCase_	   =       a[new_pivot_index]
               UpperCAmelCase_	   =       a[index]
               UpperCAmelCase_	   =       temp

     UpperCAmelCase_	   =       a[new_pivot_index + 1]
     UpperCAmelCase_	   =       a[end]
     UpperCAmelCase_	   =       temp
     return new_pivot_index + 1, count


SCREAMING_SNAKE_CASE_:					List[str]  =TemporaryFile()
SCREAMING_SNAKE_CASE_:					int  =1_00  # 1000 elements are to be sorted


SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_:					str  =0, 1  # mean and standard deviation
SCREAMING_SNAKE_CASE_:					List[str]  =np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
SCREAMING_SNAKE_CASE_:					str  =np.load(outfile)
SCREAMING_SNAKE_CASE_:					List[Any]  =len(M) - 1
SCREAMING_SNAKE_CASE_:					Dict  =_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)






print(z)




