






"""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    lowerCamelCase__   (			__snake_case       ,	unittest.TestCase					):
    SCREAMING_SNAKE_CASE						=     AudioLDMPipeline
    SCREAMING_SNAKE_CASE						=     TEXT_TO_AUDIO_PARAMS
    SCREAMING_SNAKE_CASE						=     TEXT_TO_AUDIO_BATCH_PARAMS
    SCREAMING_SNAKE_CASE						=     frozenset(
        [
            '''num_inference_steps''',
            '''num_waveforms_per_prompt''',
            '''generator''',
            '''latents''',
            '''output_type''',
            '''return_dict''',
            '''callback''',
            '''callback_steps''',
        ]					)






    def 							_UpperCamelCase				(					self   ):
          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=__UpperCamelCase      ,)
          UpperCAmelCase							     =			DDIMScheduler(
              beta_start=0.00085      ,beta_end=0.012      ,beta_schedule="""scaled_linear"""      ,clip_sample=__UpperCamelCase      ,set_alpha_to_one=__UpperCamelCase      ,)
          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-0_5      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1_000      ,projection_dim=32      ,)
          UpperCAmelCase							     =			ClapTextModelWithProjection(__UpperCamelCase   )
          UpperCAmelCase							     =			RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta"""      ,model_max_length=77   )

          UpperCAmelCase							     =			SpeechTaHifiGanConfig(
              model_in_dim=8      ,sampling_rate=16_000      ,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=__UpperCamelCase      ,)

          UpperCAmelCase							     =			SpeechTaHifiGan(__UpperCamelCase   )

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






    def 							_UpperCamelCase				(					self      ,A      ,A=0   ):


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






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator

          UpperCAmelCase							     =			self.get_dummy_components()
          UpperCAmelCase							     =			AudioLDMPipeline(**__UpperCamelCase   )
          UpperCAmelCase							     =			audioldm_pipe.to(__UpperCamelCase   )
          audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase   )

          UpperCAmelCase							     =			self.get_dummy_inputs(__UpperCamelCase   )
          UpperCAmelCase							     =			audioldm_pipe(**__UpperCamelCase   )
          UpperCAmelCase							     =			output.audios[0]

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

          UpperCAmelCase							     =			audio[:10]
          UpperCAmelCase							     =			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 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			self.get_dummy_components()
          UpperCAmelCase							     =			AudioLDMPipeline(**__UpperCamelCase   )
          UpperCAmelCase							     =			audioldm_pipe.to(__UpperCamelCase   )
          UpperCAmelCase							     =			audioldm_pipe.to(__UpperCamelCase   )
          audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase   )

          UpperCAmelCase							     =			self.get_dummy_inputs(__UpperCamelCase   )
          UpperCAmelCase							     =			3 * [inputs["""prompt"""]]

          # forward
          UpperCAmelCase							     =			audioldm_pipe(**__UpperCamelCase   )
          UpperCAmelCase							     =			output.audios[0]

          UpperCAmelCase							     =			self.get_dummy_inputs(__UpperCamelCase   )
          UpperCAmelCase							     =			3 * [inputs.pop("""prompt"""   )]

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

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

          UpperCAmelCase							     =			prompt_embeds

          # forward
          UpperCAmelCase							     =			audioldm_pipe(**__UpperCamelCase   )
          UpperCAmelCase							     =			output.audios[0]

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






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			self.get_dummy_components()
          UpperCAmelCase							     =			AudioLDMPipeline(**__UpperCamelCase   )
          UpperCAmelCase							     =			audioldm_pipe.to(__UpperCamelCase   )
          UpperCAmelCase							     =			audioldm_pipe.to(__UpperCamelCase   )
          audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase   )

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

          # forward
          UpperCAmelCase							     =			audioldm_pipe(**__UpperCamelCase   )
          UpperCAmelCase							     =			output.audios[0]

          UpperCAmelCase							     =			self.get_dummy_inputs(__UpperCamelCase   )
          UpperCAmelCase							     =			3 * [inputs.pop("""prompt"""   )]

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

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

                embeds.append(__UpperCamelCase   )

          UpperCAmelCase     ,   UpperCAmelCase							     =			embeds

          # forward
          UpperCAmelCase							     =			audioldm_pipe(**__UpperCamelCase   )
          UpperCAmelCase							     =			output.audios[0]

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






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

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

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

          UpperCAmelCase							     =			audio[:10]
          UpperCAmelCase							     =			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 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
          UpperCAmelCase							     =			self.get_dummy_components()
          UpperCAmelCase							     =			PNDMScheduler(skip_prk_steps=__UpperCamelCase   )
          UpperCAmelCase							     =			AudioLDMPipeline(**__UpperCamelCase   )
          UpperCAmelCase							     =			audioldm_pipe.to(__UpperCamelCase   )
          audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase   )

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

          # test num_waveforms_per_prompt=1 (default)
          UpperCAmelCase							     =			audioldm_pipe(__UpperCamelCase      ,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(__UpperCamelCase      ,num_inference_steps=2      ,num_waveforms_per_prompt=__UpperCamelCase   ).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=__UpperCamelCase   ).audios

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






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

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

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

          UpperCAmelCase							     =			audioldm_pipe(audio_length_in_s=0.032      ,**__UpperCamelCase   )
          UpperCAmelCase							     =			output.audios[0]

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






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			self.get_dummy_components()
          UpperCAmelCase							     =			AudioLDMPipeline(**__UpperCamelCase   )
          UpperCAmelCase							     =			audioldm_pipe.to(__UpperCamelCase   )
          audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase   )

          UpperCAmelCase							     =			["""hey"""]

          UpperCAmelCase							     =			audioldm_pipe(__UpperCamelCase      ,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(__UpperCamelCase   ).to(__UpperCamelCase   )
          UpperCAmelCase							     =			audioldm_pipe(__UpperCamelCase      ,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 							_UpperCamelCase				(					self   ):
          self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__UpperCamelCase   )






    def 							_UpperCamelCase				(					self   ):
          self._test_inference_batch_single_identical(test_mean_pixel_difference=__UpperCamelCase   )






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







@slow
class    lowerCamelCase__   (			unittest.TestCase					):






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






    def 							_UpperCamelCase				(					self      ,A      ,A="cpu"      ,A=torch.floataa      ,A=0   ):
          UpperCAmelCase							     =			torch.Generator(device=__UpperCamelCase   ).manual_seed(__UpperCamelCase   )
          UpperCAmelCase							     =			np.random.RandomState(__UpperCamelCase   ).standard_normal((1, 8, 128, 16)   )
          UpperCAmelCase							     =			torch.from_numpy(__UpperCamelCase   ).to(device=__UpperCamelCase      ,dtype=__UpperCamelCase   )
          UpperCAmelCase							     =			{
              """prompt""": """A hammer hitting a wooden surface""",
              """latents""": latents,
              """generator""": generator,
              """num_inference_steps""": 3,
              """guidance_scale""": 2.5,
          }
          return inputs






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""   )
          UpperCAmelCase							     =			audioldm_pipe.to(__UpperCamelCase   )
          audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase   )

          UpperCAmelCase							     =			self.get_inputs(__UpperCamelCase   )
          UpperCAmelCase							     =			25
          UpperCAmelCase							     =			audioldm_pipe(**__UpperCamelCase   ).audios[0]

          assert audio.ndim == 1
          assert len(__UpperCamelCase   ) == 81_920

          UpperCAmelCase							     =			audio[77_230:77_240]
          UpperCAmelCase							     =			np.array(
              [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]   )
          UpperCAmelCase							     =			np.abs(expected_slice - audio_slice   ).max()
          assert max_diff < 1e-2






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			AudioLDMPipeline.from_pretrained("""cvssp/audioldm"""   )
          UpperCAmelCase							     =			LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config   )
          UpperCAmelCase							     =			audioldm_pipe.to(__UpperCamelCase   )
          audioldm_pipe.set_progress_bar_config(disable=__UpperCamelCase   )

          UpperCAmelCase							     =			self.get_inputs(__UpperCamelCase   )
          UpperCAmelCase							     =			audioldm_pipe(**__UpperCamelCase   ).audios[0]

          assert audio.ndim == 1
          assert len(__UpperCamelCase   ) == 81_920

          UpperCAmelCase							     =			audio[27_780:27_790]
          UpperCAmelCase							     =			np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]   )
          UpperCAmelCase							     =			np.abs(expected_slice - audio_slice   ).max()
          assert max_diff < 3e-2






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A									=  logging.get_logger(__name__)

A									=  {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				):






			"""simple docstring"""




			__A    	=	"""bit"""
			__A    	=	["""preactivation""", """bottleneck"""]
			__A    	=	["""SAME""", """VALID"""]


			def __init__(     self			,					__UpperCamelCase=3			,					__UpperCamelCase=64			,					__UpperCamelCase=[2_56, 5_12, 10_24, 20_48]			,					__UpperCamelCase=[3, 4, 6, 3]			,					__UpperCamelCase="preactivation"			,					__UpperCamelCase="relu"			,					__UpperCamelCase=None			,					__UpperCamelCase=32			,					__UpperCamelCase=0.0			,					__UpperCamelCase=False			,					__UpperCamelCase=32			,					__UpperCamelCase=1			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase				)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types				)}"""				)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_   		=			global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""				)
				snake_case_   		=			num_channels
				snake_case_   		=			embedding_size
				snake_case_   		=			hidden_sizes
				snake_case_   		=			depths
				snake_case_   		=			layer_type
				snake_case_   		=			hidden_act
				snake_case_   		=			global_padding
				snake_case_   		=			num_groups
				snake_case_   		=			drop_path_rate
				snake_case_   		=			embedding_dynamic_padding
				snake_case_   		=			output_stride
				snake_case_   		=			width_factor

				snake_case_   		=			['stem'] + [f"""stage{idx}""" for idx in range(1			,					len(__UpperCamelCase				) + 1				)]
				snake_case_		,							snake_case_   		=			get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase			,					out_indices=__UpperCamelCase			,					stage_names=self.stage_names				)
