





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 		UpperCamelCase__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		int:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						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__						,)
           SCREAMING_SNAKE_CASE		=						DDIMScheduler(
               beta_start=0.00085						,beta_end=0.012						,beta_schedule="""scaled_linear"""						,clip_sample=lowerCamelCase__						,set_alpha_to_one=lowerCamelCase__						,)
           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						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       )
           SCREAMING_SNAKE_CASE		=						ClapTextConfig(
               bos_token_id=0						,eos_token_id=2						,hidden_size=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=1000						,projection_dim=32						,)
           SCREAMING_SNAKE_CASE		=						ClapTextModelWithProjection(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""						,model_max_length=77       )

           SCREAMING_SNAKE_CASE		=						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__						,)

           SCREAMING_SNAKE_CASE		=						SpeechTaHifiGan(lowerCamelCase__       )

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict						,lowerCamelCase__						:    Tuple						,lowerCamelCase__						:    Optional[Any]=0       )     ->		str:




           '''simple docstring'''


           if str(lowerCamelCase__       ).startswith("""mps"""       ):
                 SCREAMING_SNAKE_CASE		=						torch.manual_seed(lowerCamelCase__       )
           else:
                 SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						{
               """prompt""": """A hammer hitting a wooden surface""",
               """generator""": generator,
               """num_inference_steps""": 2,
               """guidance_scale""": 6.0,
           }
           return inputs






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						AudioLDMPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						output.audios[0]

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

           SCREAMING_SNAKE_CASE		=						audio[:10]
           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    int       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						AudioLDMPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						3 * [inputs["""prompt"""]]

           # forward
           SCREAMING_SNAKE_CASE		=						audioldm_pipe(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						output.audios[0]

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						3 * [inputs.pop("""prompt"""       )]

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

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

           SCREAMING_SNAKE_CASE		=						prompt_embeds

           # forward
           SCREAMING_SNAKE_CASE		=						audioldm_pipe(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						output.audios[0]

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						AudioLDMPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

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

           # forward
           SCREAMING_SNAKE_CASE		=						audioldm_pipe(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						output.audios[0]

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						3 * [inputs.pop("""prompt"""       )]

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

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

                 embeds.append(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						embeds

           # forward
           SCREAMING_SNAKE_CASE		=						audioldm_pipe(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						output.audios[0]

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]       )     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						PNDMScheduler(skip_prk_steps=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						AudioLDMPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

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

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

           SCREAMING_SNAKE_CASE		=						audio[:10]
           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						PNDMScheduler(skip_prk_steps=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						AudioLDMPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						"""A hammer hitting a wooden surface"""

           # test num_waveforms_per_prompt=1 (default)
           SCREAMING_SNAKE_CASE		=						audioldm_pipe(lowerCamelCase__						,num_inference_steps=2       ).audios

           assert audios.shape == (1, 256)

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

           assert audios.shape == (batch_size, 256)

           # test num_waveforms_per_prompt for single prompt
           SCREAMING_SNAKE_CASE		=						2
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						2
           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    int       )     ->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						AudioLDMPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.vocoder.config.sampling_rate

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

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

           SCREAMING_SNAKE_CASE		=						audioldm_pipe(audio_length_in_s=0.032						,**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						output.audios[0]

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						AudioLDMPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						["""hey"""]

           SCREAMING_SNAKE_CASE		=						audioldm_pipe(lowerCamelCase__						,num_inference_steps=1       )
           SCREAMING_SNAKE_CASE		=						output.audios.shape
           assert audio_shape == (1, 256)

           SCREAMING_SNAKE_CASE		=						audioldm_pipe.vocoder.config
           config.model_in_dim *= 2
           SCREAMING_SNAKE_CASE		=						SpeechTaHifiGan(lowerCamelCase__       ).to(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe(lowerCamelCase__						,num_inference_steps=1       )
           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		str:




           '''simple docstring'''


           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		List[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 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Optional[int]:




           '''simple docstring'''


           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__       )

@slow
class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Any:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    List[Any]						,lowerCamelCase__						:    Any="cpu"						,lowerCamelCase__						:    Optional[int]=torch.floataa						,lowerCamelCase__						:    List[str]=0       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						np.random.RandomState(lowerCamelCase__       ).standard_normal((1, 8, 128, 16)       )
           SCREAMING_SNAKE_CASE		=						torch.from_numpy(lowerCamelCase__       ).to(device=lowerCamelCase__						,dtype=lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						{
               """prompt""": """A hammer hitting a wooden surface""",
               """latents""": latents,
               """generator""": generator,
               """num_inference_steps""": 3,
               """guidance_scale""": 2.5,
           }
           return inputs






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						self.get_inputs(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						25
           SCREAMING_SNAKE_CASE		=						audioldm_pipe(**lowerCamelCase__       ).audios[0]

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

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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""       )
           SCREAMING_SNAKE_CASE		=						LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe.to(lowerCamelCase__       )
           audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						self.get_inputs(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						audioldm_pipe(**lowerCamelCase__       ).audios[0]

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

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









from collections import defaultdict
from math import gcd



def    __lowercase   (      _SCREAMING_SNAKE_CASE = 1_50_00_00	) ->   int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						defaultdict(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						2
      while 2 * euclid_m * (euclid_m + 1) <= limit:
            for euclid_n in range((euclid_m % 2) + 1 ,						_SCREAMING_SNAKE_CASE ,						2	):
                  if gcd(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) > 1:
                        continue
                  SCREAMING_SNAKE_CASE		=						2 * euclid_m * (euclid_m + euclid_n)
                  for perimeter in range(_SCREAMING_SNAKE_CASE ,						limit + 1 ,						_SCREAMING_SNAKE_CASE	):
                        frequencies[perimeter] += 1
            euclid_m += 1
      return sum(1 for frequency in frequencies.values() if frequency == 1	)


if __name__ == "__main__":
      print(F'''{solution() = }''')


