

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  (		_SCREAMING_SNAKE_CASE							,						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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				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=A_ ,				)
				snake_case_      		= 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							)
				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							)
				snake_case_      		= 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 ,				)
				snake_case_      		= ClapTextModelWithProjection(A_							)
				snake_case_      		= RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta' ,				model_max_length=77							)

				snake_case_      		= 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_ ,				)

				snake_case_      		= SpeechTaHifiGan(A_							)

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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=0							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'cpu'  # ensure determinism for the device-dependent torch.Generator

				snake_case_      		= self.get_dummy_components()
				snake_case_      		= AudioLDMPipeline(**A_							)
				snake_case_      		= audioldm_pipe.to(A_							)
				audioldm_pipe.set_progress_bar_config(disable=A_							)

				snake_case_      		= self.get_dummy_inputs(A_							)
				snake_case_      		= audioldm_pipe(**A_							)
				snake_case_      		= output.audios[0]

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

				snake_case_      		= audio[:10]
				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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_dummy_components()
				snake_case_      		= AudioLDMPipeline(**A_							)
				snake_case_      		= audioldm_pipe.to(A_							)
				snake_case_      		= audioldm_pipe.to(A_							)
				audioldm_pipe.set_progress_bar_config(disable=A_							)

				snake_case_      		= self.get_dummy_inputs(A_							)
				snake_case_      		= 3 * [inputs['prompt']]

				# forward
				snake_case_      		= audioldm_pipe(**A_							)
				snake_case_      		= output.audios[0]

				snake_case_      		= self.get_dummy_inputs(A_							)
				snake_case_      		= 3 * [inputs.pop('prompt'							)]

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

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

				snake_case_      		= prompt_embeds

				# forward
				snake_case_      		= audioldm_pipe(**A_							)
				snake_case_      		= output.audios[0]

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_dummy_components()
				snake_case_      		= AudioLDMPipeline(**A_							)
				snake_case_      		= audioldm_pipe.to(A_							)
				snake_case_      		= audioldm_pipe.to(A_							)
				audioldm_pipe.set_progress_bar_config(disable=A_							)

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

				# forward
				snake_case_      		= audioldm_pipe(**A_							)
				snake_case_      		= output.audios[0]

				snake_case_      		= self.get_dummy_inputs(A_							)
				snake_case_      		= 3 * [inputs.pop('prompt'							)]

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

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

					embeds.append(A_							)

				snake_case_		,       snake_case_      		= embeds

				# forward
				snake_case_      		= audioldm_pipe(**A_							)
				snake_case_      		= output.audios[0]

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

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

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

				snake_case_      		= audio[:10]
				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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

				snake_case_      		= 'A hammer hitting a wooden surface'

				# test num_waveforms_per_prompt=1 (default)
				snake_case_      		= 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
				snake_case_      		= 2
				snake_case_      		= 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
				snake_case_      		= 2
				snake_case_      		= 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
				snake_case_      		= 2
				snake_case_      		= 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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

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

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

				snake_case_      		= audioldm_pipe(audio_length_in_s=0.032 ,				**A_							)
				snake_case_      		= output.audios[0]

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.get_dummy_components()
				snake_case_      		= AudioLDMPipeline(**A_							)
				snake_case_      		= audioldm_pipe.to(A_							)
				audioldm_pipe.set_progress_bar_config(disable=A_							)

				snake_case_      		= ['hey']

				snake_case_      		= audioldm_pipe(A_ ,				num_inference_steps=1							)
				snake_case_      		= output.audios.shape
				assert audio_shape == (1, 2_56)

				snake_case_      		= audioldm_pipe.vocoder.config
				config.model_in_dim *= 2
				snake_case_      		= SpeechTaHifiGan(A_							).to(A_							)
				snake_case_      		= audioldm_pipe(A_ ,				num_inference_steps=1							)
				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, 2_56)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A_							)


			def 			__lowerCAmelCase (    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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_							)







@slow
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase="cpu" ,				__UpperCamelCase=torch.floataa ,				__UpperCamelCase=0							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= AudioLDMPipeline.from_pretrained('cvssp/audioldm'							)
				snake_case_      		= audioldm_pipe.to(A_							)
				audioldm_pipe.set_progress_bar_config(disable=A_							)

				snake_case_      		= self.get_inputs(A_							)
				snake_case_      		= 25
				snake_case_      		= audioldm_pipe(**A_							).audios[0]

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

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







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= AudioLDMPipeline.from_pretrained('cvssp/audioldm'							)
				snake_case_      		= LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config							)
				snake_case_      		= audioldm_pipe.to(A_							)
				audioldm_pipe.set_progress_bar_config(disable=A_							)

				snake_case_      		= self.get_inputs(A_							)
				snake_case_      		= audioldm_pipe(**A_							).audios[0]

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

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



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A											=     logging.get_logger(__name__)

A											=     {
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  """unispeech-sat"""


			def __init__(    self ,				__UpperCamelCase=32 ,				__UpperCamelCase=7_68 ,				__UpperCamelCase=12 ,				__UpperCamelCase=12 ,				__UpperCamelCase=30_72 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=1E-5 ,				__UpperCamelCase="group" ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,				__UpperCamelCase=(5, 2, 2, 2, 2, 2, 2) ,				__UpperCamelCase=(10, 3, 3, 3, 3, 2, 2) ,				__UpperCamelCase=False ,				__UpperCamelCase=1_28 ,				__UpperCamelCase=16 ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase=0.05 ,				__UpperCamelCase=10 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=10 ,				__UpperCamelCase=0 ,				__UpperCamelCase=3_20 ,				__UpperCamelCase=2 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=1_00 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase="mean" ,				__UpperCamelCase=False ,				__UpperCamelCase=False ,				__UpperCamelCase=2_56 ,				__UpperCamelCase=(5_12, 5_12, 5_12, 5_12, 15_00) ,				__UpperCamelCase=(5, 3, 3, 1, 1) ,				__UpperCamelCase=(1, 2, 3, 1, 1) ,				__UpperCamelCase=5_12 ,				__UpperCamelCase=0 ,				__UpperCamelCase=1 ,				__UpperCamelCase=2 ,				__UpperCamelCase=5_04 ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase ,				pad_token_id=__UpperCamelCase ,				bos_token_id=__UpperCamelCase ,				eos_token_id=__UpperCamelCase							)
				snake_case_      		= hidden_size
				snake_case_      		= feat_extract_norm
				snake_case_      		= feat_extract_activation
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= conv_bias
				snake_case_      		= num_conv_pos_embeddings
				snake_case_      		= num_conv_pos_embedding_groups
				snake_case_      		= len(self.conv_dim							)
				snake_case_      		= num_hidden_layers
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= num_attention_heads
				snake_case_      		= hidden_dropout
				snake_case_      		= attention_dropout
				snake_case_      		= activation_dropout
				snake_case_      		= feat_proj_dropout
				snake_case_      		= final_dropout
				snake_case_      		= layerdrop
				snake_case_      		= layer_norm_eps
				snake_case_      		= initializer_range
				snake_case_      		= vocab_size
				snake_case_      		= num_clusters
				snake_case_      		= do_stable_layer_norm
				snake_case_      		= use_weighted_layer_sum

				if (
				    (len(self.conv_stride							) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
				    or (len(self.conv_dim							) != self.num_feat_extract_layers)
				):
					raise ValueError(
					    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
					    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
					    f""" {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,"""
					    f""" `len(config.conv_kernel) = {len(self.conv_kernel							)}`."""							)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				snake_case_      		= apply_spec_augment
				snake_case_      		= mask_time_prob
				snake_case_      		= mask_time_length
				snake_case_      		= mask_time_min_masks
				snake_case_      		= mask_feature_prob
				snake_case_      		= mask_feature_length
				snake_case_      		= mask_feature_min_masks

				# parameters for pretraining with codevector quantized representations
				snake_case_      		= num_codevectors_per_group
				snake_case_      		= num_codevector_groups
				snake_case_      		= contrastive_logits_temperature
				snake_case_      		= feat_quantizer_dropout
				snake_case_      		= num_negatives
				snake_case_      		= codevector_dim
				snake_case_      		= proj_codevector_dim
				snake_case_      		= diversity_loss_weight

				# ctc loss
				snake_case_      		= ctc_loss_reduction
				snake_case_      		= ctc_zero_infinity

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				snake_case_      		= classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= list(__UpperCamelCase							)
				snake_case_      		= xvector_output_dim







			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return functools.reduce(operator.mul ,				self.conv_stride ,				1							)
