



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 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Optional[Any] =  AudioLDMPipeline
 a__					: Optional[Any] =  TEXT_TO_AUDIO_PARAMS
 a__					: List[str] =  TEXT_TO_AUDIO_BATCH_PARAMS
 a__					: int =  frozenset(
     [
         "num_inference_steps",
         "num_waveforms_per_prompt",
         "generator",
         "latents",
         "output_type",
         "return_dict",
         "callback",
         "callback_steps",
     ])

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Dict:
    torch.manual_seed(0      )
    _A      =       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			,						)
    _A      =       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      )
    _A      =       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      )
    _A      =       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=10_00			,						projection_dim=32			,						)
    _A      =       ClapTextModelWithProjection(__lowerCAmelCase      )
    _A      =       RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta'''			,						model_max_length=77      )

    _A      =       SpeechTaHifiGanConfig(
        model_in_dim=8			,						sampling_rate=1_60_00			,						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			,						)

    _A      =       SpeechTaHifiGan(__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					Tuple=0      )   ->	Optional[Any]:
    if str(__lowerCAmelCase      ).startswith('''mps'''      ):
       _A      =       torch.manual_seed(__lowerCAmelCase      )
    else:
       _A      =       torch.Generator(device=__lowerCAmelCase      ).manual_seed(__lowerCAmelCase      )
    _A      =       {
        '''prompt''': '''A hammer hitting a wooden surface''',
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 6.0,
    }
    return inputs

 def     snake_case_      (    self					:					List[str]      )   ->	int:
    _A      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator

    _A      =       self.get_dummy_components()
    _A      =       AudioLDMPipeline(**__lowerCAmelCase      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    audioldm_pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

    _A      =       self.get_dummy_inputs(__lowerCAmelCase      )
    _A      =       audioldm_pipe(**__lowerCAmelCase      )
    _A      =       output.audios[0]

    assert audio.ndim == 1
    assert len(__lowerCAmelCase      ) == 2_56

    _A      =       audio[:10]
    _A      =       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     snake_case_      (    self					:					Optional[int]      )   ->	str:
    _A      =       self.get_dummy_components()
    _A      =       AudioLDMPipeline(**__lowerCAmelCase      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    audioldm_pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

    _A      =       self.get_dummy_inputs(__lowerCAmelCase      )
    _A      =       3 * [inputs['''prompt''']]

    # forward
    _A      =       audioldm_pipe(**__lowerCAmelCase      )
    _A      =       output.audios[0]

    _A      =       self.get_dummy_inputs(__lowerCAmelCase      )
    _A      =       3 * [inputs.pop('''prompt'''      )]

    _A      =       audioldm_pipe.tokenizer(
        __lowerCAmelCase			,						padding='''max_length'''			,						max_length=audioldm_pipe.tokenizer.model_max_length			,						truncation=__lowerCAmelCase			,						return_tensors='''pt'''			,						)
    _A      =       text_inputs['''input_ids'''].to(__lowerCAmelCase      )

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

    _A      =       prompt_embeds

    # forward
    _A      =       audioldm_pipe(**__lowerCAmelCase      )
    _A      =       output.audios[0]

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

 def     snake_case_      (    self					:					List[Any]      )   ->	Union[str, Any]:
    _A      =       self.get_dummy_components()
    _A      =       AudioLDMPipeline(**__lowerCAmelCase      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    audioldm_pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

    _A      =       self.get_dummy_inputs(__lowerCAmelCase      )
    _A      =       3 * ['''this is a negative prompt''']
    _A      =       negative_prompt
    _A      =       3 * [inputs['''prompt''']]

    # forward
    _A      =       audioldm_pipe(**__lowerCAmelCase      )
    _A      =       output.audios[0]

    _A      =       self.get_dummy_inputs(__lowerCAmelCase      )
    _A      =       3 * [inputs.pop('''prompt'''      )]

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

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

       embeds.append(__lowerCAmelCase      )

    _A  ,    _A      =       embeds

    # forward
    _A      =       audioldm_pipe(**__lowerCAmelCase      )
    _A      =       output.audios[0]

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

 def     snake_case_      (    self					:					Tuple      )   ->	Dict:
    _A      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    _A      =       self.get_dummy_components()
    _A      =       PNDMScheduler(skip_prk_steps=__lowerCAmelCase      )
    _A      =       AudioLDMPipeline(**__lowerCAmelCase      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    audioldm_pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

    _A      =       self.get_dummy_inputs(__lowerCAmelCase      )
    _A      =       '''egg cracking'''
    _A      =       audioldm_pipe(**__lowerCAmelCase			,						negative_prompt=__lowerCAmelCase      )
    _A      =       output.audios[0]

    assert audio.ndim == 1
    assert len(__lowerCAmelCase      ) == 2_56

    _A      =       audio[:10]
    _A      =       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     snake_case_      (    self					:					Union[str, Any]      )   ->	Optional[Any]:
    _A      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    _A      =       self.get_dummy_components()
    _A      =       PNDMScheduler(skip_prk_steps=__lowerCAmelCase      )
    _A      =       AudioLDMPipeline(**__lowerCAmelCase      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    audioldm_pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

    _A      =       '''A hammer hitting a wooden surface'''

    # test num_waveforms_per_prompt=1 (default)
    _A      =       audioldm_pipe(__lowerCAmelCase			,						num_inference_steps=2      ).audios

    assert audios.shape == (1, 2_56)

    # test num_waveforms_per_prompt=1 (default) for batch of prompts
    _A      =       2
    _A      =       audioldm_pipe([prompt] * batch_size			,						num_inference_steps=2      ).audios

    assert audios.shape == (batch_size, 2_56)

    # test num_waveforms_per_prompt for single prompt
    _A      =       2
    _A      =       audioldm_pipe(__lowerCAmelCase			,						num_inference_steps=2			,						num_waveforms_per_prompt=__lowerCAmelCase      ).audios

    assert audios.shape == (num_waveforms_per_prompt, 2_56)

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

    assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)

 def     snake_case_      (    self					:					Tuple      )   ->	Any:
    _A      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
    _A      =       self.get_dummy_components()
    _A      =       AudioLDMPipeline(**__lowerCAmelCase      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    audioldm_pipe.set_progress_bar_config(disable=__lowerCAmelCase      )
    _A      =       audioldm_pipe.vocoder.config.sampling_rate

    _A      =       self.get_dummy_inputs(__lowerCAmelCase      )
    _A      =       audioldm_pipe(audio_length_in_s=0.016			,						**__lowerCAmelCase      )
    _A      =       output.audios[0]

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

    _A      =       audioldm_pipe(audio_length_in_s=0.032			,						**__lowerCAmelCase      )
    _A      =       output.audios[0]

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

 def     snake_case_      (    self					:					str      )   ->	List[Any]:
    _A      =       self.get_dummy_components()
    _A      =       AudioLDMPipeline(**__lowerCAmelCase      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    audioldm_pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

    _A      =       ['''hey''']

    _A      =       audioldm_pipe(__lowerCAmelCase			,						num_inference_steps=1      )
    _A      =       output.audios.shape
    assert audio_shape == (1, 2_56)

    _A      =       audioldm_pipe.vocoder.config
    config.model_in_dim *= 2
    _A      =       SpeechTaHifiGan(__lowerCAmelCase      ).to(__lowerCAmelCase      )
    _A      =       audioldm_pipe(__lowerCAmelCase			,						num_inference_steps=1      )
    _A      =       output.audios.shape
    # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
    assert audio_shape == (1, 2_56)

 def     snake_case_      (    self					:					Optional[int]      )   ->	int:
    self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict      )   ->	Optional[Any]:
    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     snake_case_      (    self					:					int      )   ->	List[str]:
    self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__lowerCAmelCase      )


@slow
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def     snake_case_      (    self					:					Optional[int]      )   ->	Any:
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					List[Any]="cpu"			,						__lowerCAmelCase					:					List[Any]=torch.floataa			,						__lowerCAmelCase					:					List[Any]=0      )   ->	int:
    _A      =       torch.Generator(device=__lowerCAmelCase      ).manual_seed(__lowerCAmelCase      )
    _A      =       np.random.RandomState(__lowerCAmelCase      ).standard_normal((1, 8, 1_28, 16)      )
    _A      =       torch.from_numpy(__lowerCAmelCase      ).to(device=__lowerCAmelCase			,						dtype=__lowerCAmelCase      )
    _A      =       {
        '''prompt''': '''A hammer hitting a wooden surface''',
        '''latents''': latents,
        '''generator''': generator,
        '''num_inference_steps''': 3,
        '''guidance_scale''': 2.5,
    }
    return inputs

 def     snake_case_      (    self					:					Optional[Any]      )   ->	List[Any]:
    _A      =       AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    audioldm_pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

    _A      =       self.get_inputs(__lowerCAmelCase      )
    _A      =       25
    _A      =       audioldm_pipe(**__lowerCAmelCase      ).audios[0]

    assert audio.ndim == 1
    assert len(__lowerCAmelCase      ) == 8_19_20

    _A      =       audio[7_72_30:7_72_40]
    _A      =       np.array(
        [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]      )
    _A      =       np.abs(expected_slice - audio_slice      ).max()
    assert max_diff < 1E-2

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Optional[Any]:
    _A      =       AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''      )
    _A      =       LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config      )
    _A      =       audioldm_pipe.to(__lowerCAmelCase      )
    audioldm_pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

    _A      =       self.get_inputs(__lowerCAmelCase      )
    _A      =       audioldm_pipe(**__lowerCAmelCase      ).audios[0]

    assert audio.ndim == 1
    assert len(__lowerCAmelCase      ) == 8_19_20

    _A      =       audio[2_77_80:2_77_90]
    _A      =       np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]      )
    _A      =       np.abs(expected_slice - audio_slice      ).max()
    assert max_diff < 3E-2








UpperCAmelCase_		 =					0  # The first color of the flag.
UpperCAmelCase_		 =					1  # The second color of the flag.
UpperCAmelCase_		 =					2  # The third color of the flag.
UpperCAmelCase_		 =					(red, white, blue)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     list:
   if not sequence:
      return []
   if len(_snake_case				) == 1:
      return list(_snake_case				)
   _A      =       0
   _A      =       len(_snake_case				) - 1
   _A      =       0
   while mid <= high:
      if sequence[mid] == colors[0]:
         _A  ,    _A      =       sequence[mid], sequence[low]
         low += 1
         mid += 1
      elif sequence[mid] == colors[1]:
         mid += 1
      elif sequence[mid] == colors[2]:
         _A  ,    _A      =       sequence[high], sequence[mid]
         high -= 1
      else:
         _A      =       F'''The elements inside the sequence must contains only {colors} values'''
         raise ValueError(_snake_case				)
   return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase_		 =					input("""Enter numbers separated by commas:\n""").strip()
   UpperCAmelCase_		 =					[int(item.strip()) for item in user_input.split(""",""")]
   print(f'{dutch_national_flag_sort(unsorted)}')



