



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__  (						__lowercase       ,    unittest.TestCase):
     """simple docstring"""



     _A       =			AudioLDMPipeline
     _A       =			TEXT_TO_AUDIO_PARAMS
     _A       =			TEXT_TO_AUDIO_BATCH_PARAMS
     _A       =			frozenset(
         [
             'num_inference_steps',
             'num_waveforms_per_prompt',
             'generator',
             'latents',
             'output_type',
             'return_dict',
             'callback',
             'callback_steps',
         ])







     def 						_a      (self    ):






           '''simple docstring'''



           torch.manual_seed(0    )
           lowerCamelCase								=		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				,			)
           lowerCamelCase								=		DDIMScheduler(
               beta_start=0.0_0085				,			beta_end=0.012				,			beta_schedule="scaled_linear"				,			clip_sample=_A				,			set_alpha_to_one=_A				,			)
           torch.manual_seed(0    )
           lowerCamelCase								=		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    )
           lowerCamelCase								=		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				,			)
           lowerCamelCase								=		ClapTextModelWithProjection(_A    )
           lowerCamelCase								=		RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"				,			model_max_length=77    )

           lowerCamelCase								=		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=_A				,			)

           lowerCamelCase								=		SpeechTaHifiGan(_A    )

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







     def 						_a      (self				,			__a				,			__a=0    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



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

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

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

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

           lowerCamelCase								=		audio[:10]
           lowerCamelCase								=		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 						_a      (self    ):






           '''simple docstring'''



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

           lowerCamelCase								=		self.get_dummy_inputs(_A    )
           lowerCamelCase								=		3 * [inputs["prompt"]]

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

           lowerCamelCase								=		self.get_dummy_inputs(_A    )
           lowerCamelCase								=		3 * [inputs.pop("prompt"    )]

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

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

           lowerCamelCase								=		prompt_embeds

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

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







     def 						_a      (self    ):






           '''simple docstring'''



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

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

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

           lowerCamelCase								=		self.get_dummy_inputs(_A    )
           lowerCamelCase								=		3 * [inputs.pop("prompt"    )]

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

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

                 embeds.append(_A    )

           lowerCamelCase			,							lowerCamelCase								=		embeds

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

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







     def 						_a      (self    ):






           '''simple docstring'''



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

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

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

           lowerCamelCase								=		audio[:10]
           lowerCamelCase								=		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 						_a      (self    ):






           '''simple docstring'''



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

           lowerCamelCase								=		"A hammer hitting a wooden surface"

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

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

           # test num_waveforms_per_prompt=1 (default) for batch of prompts
           lowerCamelCase								=		2
           lowerCamelCase								=		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
           lowerCamelCase								=		2
           lowerCamelCase								=		audioldm_pipe(_A				,			num_inference_steps=2				,			num_waveforms_per_prompt=_A    ).audios

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

           # test num_waveforms_per_prompt for batch of prompts
           lowerCamelCase								=		2
           lowerCamelCase								=		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_56)







     def 						_a      (self    ):






           '''simple docstring'''



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

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

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

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

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







     def 						_a      (self    ):






           '''simple docstring'''



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

           lowerCamelCase								=		["hey"]

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

           lowerCamelCase								=		audioldm_pipe.vocoder.config
           config.model_in_dim *= 2
           lowerCamelCase								=		SpeechTaHifiGan(_A    ).to(_A    )
           lowerCamelCase								=		audioldm_pipe(_A				,			num_inference_steps=1    )
           lowerCamelCase								=		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 						_a      (self    ):






           '''simple docstring'''



           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_A    )







     def 						_a      (self    ):






           '''simple docstring'''



           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 						_a      (self    ):






           '''simple docstring'''



           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_A    )





@slow
class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def 						_a      (self    ):






           '''simple docstring'''



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







     def 						_a      (self				,			__a				,			__a="cpu"				,			__a=torch.floataa				,			__a=0    ):






           '''simple docstring'''



           lowerCamelCase								=		torch.Generator(device=_A    ).manual_seed(_A    )
           lowerCamelCase								=		np.random.RandomState(_A    ).standard_normal((1, 8, 1_28, 16)    )
           lowerCamelCase								=		torch.from_numpy(_A    ).to(device=_A				,			dtype=_A    )
           lowerCamelCase								=		{
               "prompt": "A hammer hitting a wooden surface",
               "latents": latents,
               "generator": generator,
               "num_inference_steps": 3,
               "guidance_scale": 2.5,
           }
           return inputs







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		AudioLDMPipeline.from_pretrained("cvssp/audioldm"    )
           lowerCamelCase								=		audioldm_pipe.to(_A    )
           audioldm_pipe.set_progress_bar_config(disable=_A    )

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

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

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







     def 						_a      (self    ):






           '''simple docstring'''



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

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

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

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




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_						:     Optional[int]	     =		logging.get_logger(__name__)

a_						:     Any	     =		{'vocab_file': 'spiece.model'}

a_						:     List[Any]	     =		{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

a_						:     List[Any]	     =		{'bert_for_seq_generation': 5_1_2}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			VOCAB_FILES_NAMES
     _A       =			PRETRAINED_VOCAB_FILES_MAP
     _A       =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A       =			[]
     _A       =			['input_ids', 'attention_mask']







     def __init__(self				,			__a				,			__a="<s>"				,			__a="</s>"				,			__a="<unk>"				,			__a="<pad>"				,			__a="<::::>"				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		{} if sp_model_kwargs is None else sp_model_kwargs

           # Add extra_ids to the special token list
           super().__init__(
               bos_token=__a				,			eos_token=__a				,			unk_token=__a				,			pad_token=__a				,			sep_token=__a				,			sp_model_kwargs=self.sp_model_kwargs				,			**__a				,			)

           lowerCamelCase								=		vocab_file

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(__a    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self.sp_model.get_piece_size()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		{self.convert_ids_to_tokens(__a    ): i for i in range(self.vocab_size    )}
           vocab.update(self.added_tokens_encoder    )
           return vocab







     def __getstate__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.__dict__.copy()
           lowerCamelCase								=		None
           return state







     def __setstate__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		d

           # for backward compatibility
           if not hasattr(self				,			"sp_model_kwargs"    ):
                 lowerCamelCase								=		{}

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(self.vocab_file    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.encode(__a				,			out_type=__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.piece_to_id(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.sp_model.IdToPiece(__a    )
           return token







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[]
           lowerCamelCase								=		""
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       out_string += self.sp_model.decode(__a    ) + token
                       lowerCamelCase								=		[]
                 else:
                       current_sub_tokens.append(__a    )
           out_string += self.sp_model.decode(__a    )
           return out_string.strip()







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           if not os.path.isdir(__a    ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
           lowerCamelCase								=		os.path.join(
               __a				,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

           if os.path.abspath(self.vocab_file    ) != os.path.abspath(__a    ) and os.path.isfile(self.vocab_file    ):
                 copyfile(self.vocab_file				,			__a    )
           elif not os.path.isfile(self.vocab_file    ):
                 with open(__a				,			"wb"    ) as fi:
                       lowerCamelCase								=		self.sp_model.serialized_model_proto()
                       fi.write(__a    )

           return (out_vocab_file,)