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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											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=A_						,				)
											UpperCamelCase   			=     DDIMScheduler(
											    beta_start=0.00_085						,				beta_end=0.012						,				beta_schedule='scaled_linear'						,				clip_sample=A_						,				set_alpha_to_one=A_						,				)
											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-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				projection_dim=32						,				)
											UpperCamelCase   			=     ClapTextModelWithProjection(A_     )
											UpperCamelCase   			=     RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'						,				model_max_length=77     )

											UpperCamelCase   			=     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=A_						,				)

											UpperCamelCase   			=     SpeechTaHifiGan(A_     )

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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=0     )->		Optional[int]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






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

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

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

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

											UpperCamelCase   			=     audio[:10]
											UpperCamelCase   			=     np.array(
											    [-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033]     )

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







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







											'''simple docstring'''






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

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

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

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

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

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

											UpperCamelCase   			=     prompt_embeds

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

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







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







											'''simple docstring'''






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

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

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

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

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

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

																		embeds.append(A_     )

											UpperCamelCase			,	UpperCamelCase   			=     embeds

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

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







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






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

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

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

											UpperCamelCase   			=     audio[:10]
											UpperCamelCase   			=     np.array(
											    [-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032]     )

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







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






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

											UpperCamelCase   			=     'A hammer hitting a wooden surface'

											# test num_waveforms_per_prompt=1 (default)
											UpperCamelCase   			=     audioldm_pipe(A_						,				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(A_						,				num_inference_steps=2						,				num_waveforms_per_prompt=A_     ).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=A_     ).audios

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







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






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

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

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

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

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







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







											'''simple docstring'''






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

											UpperCamelCase   			=     ['hey']

											UpperCamelCase   			=     audioldm_pipe(A_						,				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(A_     ).to(A_     )
											UpperCamelCase   			=     audioldm_pipe(A_						,				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     )->		List[Any]:







											'''simple docstring'''






											self._test_attention_slicing_forward_pass(test_mean_pixel_difference=A_     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''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 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_     )


@slow
class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







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







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self						,				A_						,				A_="cpu"						,				A_=torch.floataa						,				A_=0     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     torch.Generator(device=A_     ).manual_seed(A_     )
											UpperCamelCase   			=     np.random.RandomState(A_     ).standard_normal((1, 8, 128, 16)     )
											UpperCamelCase   			=     torch.from_numpy(A_     ).to(device=A_						,				dtype=A_     )
											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     )->		Optional[int]:







											'''simple docstring'''






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

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

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

											UpperCamelCase   			=     audio[77230:77240]
											UpperCamelCase   			=     np.array(
											    [-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315]     )
											UpperCamelCase   			=     np.abs(expected_slice - audio_slice     ).max()
											assert max_diff < 1e-2







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







											'''simple docstring'''






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

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

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

											UpperCamelCase   			=     audio[27780:27790]
											UpperCamelCase   			=     np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212]     )
											UpperCamelCase   			=     np.abs(expected_slice - audio_slice     ).max()
											assert max_diff < 3e-2








from __future__ import annotations






class      UpperCamelCase:


					def __init__(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						str:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		text, pattern
											__snake_case		,       __snake_case				=		len(SCREAMING_SNAKE_CASE    ), len(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						int:




											'''simple docstring'''

											for i in range(self.patLen - 1  ,     -1  ,     -1    ):
																	if char == self.pattern[i]:
																							return i
											return -1


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		int    )      ->						int:




											'''simple docstring'''

											for i in range(self.patLen - 1  ,     -1  ,     -1    ):
																	if self.pattern[i] != self.text[current_pos + i]:
																							return current_pos + i
											return -1






					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						list[int]:




											'''simple docstring'''

											__snake_case				=		[]
											for i in range(self.textLen - self.patLen + 1    ):
																	__snake_case				=		self.mismatch_in_text(SCREAMING_SNAKE_CASE    )
																	if mismatch_index == -1:
																							positions.append(SCREAMING_SNAKE_CASE    )
																	else:
																							__snake_case				=		self.match_in_pattern(self.text[mismatch_index]    )
																							__snake_case				=		(
																							    mismatch_index - match_index
																							)  # shifting index lgtm [py/multiple-definition]
											return positions




A				:       str      =  'ABAABA'
A				:       Optional[Any]      =  'AB'
A				:       Any      =  BoyerMooreSearch(text, pattern)
A				:       Tuple      =  bms.bad_character_heuristic()

if len(positions) == 0:
	print('No match found')
else:
	print('Pattern found in following positions: ')
	print(positions)





