

"""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 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



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







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						torch.manual_seed(0		)
						lowerCAmelCase__	:						List[str]  				=	UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)   ,layers_per_block=2   ,sample_size=3_2   ,in_channels=4   ,out_channels=4   ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,cross_attention_dim=(3_2, 6_4)   ,class_embed_type='''simple_projection'''   ,projection_class_embeddings_input_dim=3_2   ,class_embeddings_concat=lowercase_   ,)
						lowerCAmelCase__	:						List[Any]  				=	DDIMScheduler(
						    beta_start=0.0_0085   ,beta_end=0.012   ,beta_schedule='''scaled_linear'''   ,clip_sample=lowercase_   ,set_alpha_to_one=lowercase_   ,)
						torch.manual_seed(0		)
						lowerCAmelCase__	:						Any  				=	AutoencoderKL(
						    block_out_channels=[3_2, 6_4]   ,in_channels=1   ,out_channels=1   ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,latent_channels=4   ,)
						torch.manual_seed(0		)
						lowerCAmelCase__	:						Dict  				=	ClapTextConfig(
						    bos_token_id=0   ,eos_token_id=2   ,hidden_size=3_2   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,projection_dim=3_2   ,)
						lowerCAmelCase__	:						int  				=	ClapTextModelWithProjection(lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta'''   ,model_max_length=7_7		)

						lowerCAmelCase__	:						Union[str, Any]  				=	SpeechTaHifiGanConfig(
						    model_in_dim=8   ,sampling_rate=1_6_0_0_0   ,upsample_initial_channel=1_6   ,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=lowercase_   ,)

						lowerCAmelCase__	:						Any  				=	SpeechTaHifiGan(lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	{
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''vocoder''': vocoder,
						}
						return components







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







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

						lowerCAmelCase__	:						Tuple  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Optional[Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						str  				=	output.audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 2_5_6

						lowerCAmelCase__	:						str  				=	audio[:1_0]
						lowerCAmelCase__	:						str  				=	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        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Any  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						int  				=	audioldm_pipe.to(lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Tuple  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	3 * [inputs['''prompt''']]

						# forward
						lowerCAmelCase__	:						int  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	output.audios[0]

						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						str  				=	3 * [inputs.pop('''prompt'''		)]

						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.tokenizer(
						    lowercase_   ,padding='''max_length'''   ,max_length=audioldm_pipe.tokenizer.model_max_length   ,truncation=lowercase_   ,return_tensors='''pt'''   ,)
						lowerCAmelCase__	:						Tuple  				=	text_inputs['''input_ids'''].to(lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.text_encoder(
						    lowercase_   ,)
						lowerCAmelCase__	:						Tuple  				=	prompt_embeds.text_embeds
						# additional L_2 normalization over each hidden-state
						lowerCAmelCase__	:						Tuple  				=	F.normalize(lowercase_   ,dim=-1		)

						lowerCAmelCase__	:						List[Any]  				=	prompt_embeds

						# forward
						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						Any  				=	output.audios[0]

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







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						List[Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe.to(lowercase_		)
						lowerCAmelCase__	:						Dict  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						str  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						int  				=	3 * ['''this is a negative prompt''']
						lowerCAmelCase__	:						Any  				=	negative_prompt
						lowerCAmelCase__	:						int  				=	3 * [inputs['''prompt''']]

						# forward
						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	output.audios[0]

						lowerCAmelCase__	:						Optional[Any]  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	3 * [inputs.pop('''prompt'''		)]

						lowerCAmelCase__	:						Optional[Any]  				=	[]
						for p in [prompt, negative_prompt]:
							lowerCAmelCase__	:						int  				=	audioldm_pipe.tokenizer(
							    lowercase_   ,padding='''max_length'''   ,max_length=audioldm_pipe.tokenizer.model_max_length   ,truncation=lowercase_   ,return_tensors='''pt'''   ,)
							lowerCAmelCase__	:						Optional[Any]  				=	text_inputs['''input_ids'''].to(lowercase_		)

							lowerCAmelCase__	:						Dict  				=	audioldm_pipe.text_encoder(
							    lowercase_   ,)
							lowerCAmelCase__	:						Dict  				=	text_embeds.text_embeds
							# additional L_2 normalization over each hidden-state
							lowerCAmelCase__	:						Any  				=	F.normalize(lowercase_   ,dim=-1		)

							embeds.append(lowercase_		)

						lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	embeds

						# forward
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	output.audios[0]

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







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Dict  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__	:						Optional[int]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Union[str, Any]  				=	PNDMScheduler(skip_prk_steps=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Dict  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	'''egg cracking'''
						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe(**lowercase_   ,negative_prompt=lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	output.audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 2_5_6

						lowerCAmelCase__	:						Any  				=	audio[:1_0]
						lowerCAmelCase__	:						List[Any]  				=	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        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						str  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__	:						Tuple  				=	self.get_dummy_components()
						lowerCAmelCase__	:						int  				=	PNDMScheduler(skip_prk_steps=lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	'''A hammer hitting a wooden surface'''

						# test num_waveforms_per_prompt=1 (default)
						lowerCAmelCase__	:						Dict  				=	audioldm_pipe(lowercase_   ,num_inference_steps=2		).audios

						assert audios.shape == (1, 2_5_6)

						# test num_waveforms_per_prompt=1 (default) for batch of prompts
						lowerCAmelCase__	:						List[Any]  				=	2
						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe([prompt] * batch_size   ,num_inference_steps=2		).audios

						assert audios.shape == (batch_size, 2_5_6)

						# test num_waveforms_per_prompt for single prompt
						lowerCAmelCase__	:						Dict  				=	2
						lowerCAmelCase__	:						int  				=	audioldm_pipe(lowercase_   ,num_inference_steps=2   ,num_waveforms_per_prompt=lowercase_		).audios

						assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

						# test num_waveforms_per_prompt for batch of prompts
						lowerCAmelCase__	:						Optional[int]  				=	2
						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe(
						    [prompt] * batch_size   ,num_inference_steps=2   ,num_waveforms_per_prompt=lowercase_		).audios

						assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__	:						Optional[int]  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Optional[Any]  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)
						lowerCAmelCase__	:						Any  				=	audioldm_pipe.vocoder.config.sampling_rate

						lowerCAmelCase__	:						Any  				=	self.get_dummy_inputs(lowercase_		)
						lowerCAmelCase__	:						Dict  				=	audioldm_pipe(audio_length_in_s=0.016   ,**lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	output.audios[0]

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

						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe(audio_length_in_s=0.032   ,**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	output.audios[0]

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







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						int  				=	self.get_dummy_components()
						lowerCAmelCase__	:						Tuple  				=	AudioLDMPipeline(**lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						str  				=	['''hey''']

						lowerCAmelCase__	:						str  				=	audioldm_pipe(lowercase_   ,num_inference_steps=1		)
						lowerCAmelCase__	:						str  				=	output.audios.shape
						assert audio_shape == (1, 2_5_6)

						lowerCAmelCase__	:						Tuple  				=	audioldm_pipe.vocoder.config
						config.model_in_dim *= 2
						lowerCAmelCase__	:						int  				=	SpeechTaHifiGan(lowercase_		).to(lowercase_		)
						lowerCAmelCase__	:						str  				=	audioldm_pipe(lowercase_   ,num_inference_steps=1		)
						lowerCAmelCase__	:						Dict  				=	output.audios.shape
						# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
						assert audio_shape == (1, 2_5_6)







					def        __lowerCAmelCase (       self			:				Any		):
						self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict		):
						self._test_inference_batch_single_identical(test_mean_pixel_difference=lowercase_		)







					@unittest.skipIf(
					    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowercase_		)







@slow
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



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







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Any   ,lowercase_			:				Union[str, Any]="cpu"   ,lowercase_			:				Any=torch.floataa   ,lowercase_			:				Tuple=0		):
						lowerCAmelCase__	:						str  				=	torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
						lowerCAmelCase__	:						str  				=	np.random.RandomState(lowercase_		).standard_normal((1, 8, 1_2_8, 1_6)		)
						lowerCAmelCase__	:						str  				=	torch.from_numpy(lowercase_		).to(device=lowercase_   ,dtype=lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	{
						    '''prompt''': '''A hammer hitting a wooden surface''',
						    '''latents''': latents,
						    '''generator''': generator,
						    '''num_inference_steps''': 3,
						    '''guidance_scale''': 2.5,
						}
						return inputs







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Union[str, Any]  				=	AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''		)
						lowerCAmelCase__	:						List[Any]  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	self.get_inputs(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	2_5
						lowerCAmelCase__	:						Optional[Any]  				=	audioldm_pipe(**lowercase_		).audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 8_1_9_2_0

						lowerCAmelCase__	:						Optional[Any]  				=	audio[7_7_2_3_0:7_7_2_4_0]
						lowerCAmelCase__	:						Optional[Any]  				=	np.array(
						    [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]		)
						lowerCAmelCase__	:						List[str]  				=	np.abs(expected_slice - audio_slice		).max()
						assert max_diff < 1E-2







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Optional[int]  				=	AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''		)
						lowerCAmelCase__	:						Any  				=	LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config		)
						lowerCAmelCase__	:						str  				=	audioldm_pipe.to(lowercase_		)
						audioldm_pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	self.get_inputs(lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	audioldm_pipe(**lowercase_		).audios[0]

						assert audio.ndim == 1
						assert len(lowercase_		) == 8_1_9_2_0

						lowerCAmelCase__	:						List[Any]  				=	audio[2_7_7_8_0:2_7_7_9_0]
						lowerCAmelCase__	:						Any  				=	np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]		)
						lowerCAmelCase__	:						Tuple  				=	np.abs(expected_slice - audio_slice		).max()
						assert max_diff < 3E-2







"""simple docstring"""





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase  :      Any							      =		logging.getLogger(__name__)

if __name__ == "__main__":
							__UpperCamelCase  :      Union[str, Any]							      =		argparse.ArgumentParser(
							    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
							)
							parser.add_argument(
							    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
							)
							parser.add_argument(
							    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
							)
							parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
							__UpperCamelCase  :      Any							      =		parser.parse_args()

							logger.info(F'''Loading data from {args.data_file}''')
							with open(args.data_file, '''rb''') as fp:
														__UpperCamelCase  :      List[Any]							      =		pickle.load(fp)

							logger.info('''Counting occurrences for MLM.''')
							__UpperCamelCase  :      Dict							      =		Counter()
							for tk_ids in data:
														counter.update(tk_ids)
							__UpperCamelCase  :      Optional[Any]							      =		[0] * args.vocab_size
							for k, v in counter.items():
														__UpperCamelCase  :      Dict							      =		v

							logger.info(F'''Dump to {args.token_counts_dump}''')
							with open(args.token_counts_dump, '''wb''') as handle:
														pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)




