




"""simple docstring"""






from typing import Any







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validation(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				# Creates data structures and fill initial step
				_lowerCamelCase	:     dict       =       {}
				_lowerCamelCase	:     dict       =       {}
				for state in states_space:
								_lowerCamelCase	:     Optional[Any]       =       observations_space[0]
								_lowerCamelCase	:     int       =       (
								    initial_probabilities[state] * emission_probabilities[state][observation]
								)
								_lowerCamelCase	:     List[Any]       =       None

				# Fills the data structure with the probabilities of
				# different transitions and pointers to previous states
				for o in range(1				,       len(lowercase__						)						):
								_lowerCamelCase	:     List[str]       =       observations_space[o]
								_lowerCamelCase	:     str       =       observations_space[o - 1]
								for state in states_space:
												# Calculates the argmax for probability function
												_lowerCamelCase	:     Dict       =       ''
												_lowerCamelCase	:     int       =       -1
												for k_state in states_space:
																_lowerCamelCase	:     Any       =       (
																    probabilities[(k_state, prior_observation)]
																    * transition_probabilities[k_state][state]
																    * emission_probabilities[state][observation]
																)
																if probability > max_probability:
																				_lowerCamelCase	:     Dict       =       probability
																				_lowerCamelCase	:     int       =       k_state

            # Update probabilities and pointers dicts
												_lowerCamelCase	:     List[str]       =       (
												    probabilities[(arg_max, prior_observation)]
												    * transition_probabilities[arg_max][state]
												    * emission_probabilities[state][observation]
												)

												_lowerCamelCase	:     Union[str, Any]       =       arg_max

    # The final observation
				_lowerCamelCase	:     Tuple       =       observations_space[len(lowercase__						) - 1]

				# argmax for given final observation
				_lowerCamelCase	:     Optional[int]       =       ''
				_lowerCamelCase	:     str       =       -1
				for k_state in states_space:
								_lowerCamelCase	:     Optional[int]       =       probabilities[(k_state, final_observation)]
								if probability > max_probability:
												_lowerCamelCase	:     int       =       probability
												_lowerCamelCase	:     Optional[int]       =       k_state
				_lowerCamelCase	:     Any       =       arg_max

				# Process pointers backwards
				_lowerCamelCase	:     Optional[Any]       =       last_state
				_lowerCamelCase	:     Union[str, Any]       =       []
				for o in range(len(lowercase__						) - 1				,       -1				,       -1						):
								result.append(lowercase__						)
								_lowerCamelCase	:     Dict       =       pointers[previous, observations_space[o]]
				result.reverse()

				return result







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_not_empty(
				    lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       )
				_validate_lists(lowercase__				,       lowercase__						)
				_validate_dicts(
				    lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       ):
				if not all(
				    [
				        observations_space,
				        states_space,
				        initial_probabilities,
				        transition_probabilities,
				        emission_probabilities,
				    ]						):
								raise ValueError('There\'s an empty parameter'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_list(lowercase__				,       'observations_space'						)
				_validate_list(lowercase__				,       'states_space'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a list'''
								raise ValueError(lowercase__						)
				else:
								for x in _object:
												if not isinstance(lowercase__				,       lowercase__						):
																_lowerCamelCase	:     int       =       f'''{var_name} must be a list of strings'''
																raise ValueError(lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       ):
				_validate_dict(lowercase__				,       'initial_probabilities'				,       lowercase__						)
				_validate_nested_dict(lowercase__				,       'transition_probabilities'						)
				_validate_nested_dict(lowercase__				,       'emission_probabilities'						)







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_validate_dict(_object				,       lowercase__				,       lowercase__						)
				for x in _object.values():
								_validate_dict(lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__ = False						):
				if not isinstance(_object				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       f'''{var_name} must be a dict'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object						):
								_lowerCamelCase	:     Optional[int]       =       f'''{var_name} all keys must be strings'''
								raise ValueError(lowercase__						)
				if not all(isinstance(lowercase__				,       lowercase__						) for x in _object.values()						):
								_lowerCamelCase	:     Optional[int]       =       'nested dictionary ' if nested else ''
								_lowerCamelCase	:     Optional[Any]       =       f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
								raise ValueError(lowercase__						)


if __name__ == "__main__":
			from doctest import testmod

			testmod()





"""simple docstring"""






import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			def    A_       (    self    ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
			@property
			def    A_       (    self    ):
							torch.manual_seed(0    )
							_lowerCamelCase	:     Union[str, Any]       =       UNetaDModel(
							    sample_size=(32, 64)    , in_channels=1    , out_channels=1    , layers_per_block=2    , block_out_channels=(128, 128)    , down_block_types=('AttnDownBlock2D', 'DownBlock2D')    , up_block_types=('UpBlock2D', 'AttnUpBlock2D')    , )
							return model
			@property
			def    A_       (    self    ):
							torch.manual_seed(0    )
							_lowerCamelCase	:     Dict       =       UNetaDConditionModel(
							    sample_size=(64, 32)    , in_channels=1    , out_channels=1    , layers_per_block=2    , block_out_channels=(128, 128)    , down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')    , up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')    , cross_attention_dim=10    , )
							return model
			@property
			def    A_       (    self    ):
							torch.manual_seed(0    )
							_lowerCamelCase	:     List[str]       =       AutoencoderKL(
							    sample_size=(128, 64)    , in_channels=1    , out_channels=1    , latent_channels=1    , layers_per_block=2    , block_out_channels=(128, 128)    , down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')    , up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')    , )
							_lowerCamelCase	:     int       =       UNetaDModel(
							    sample_size=(64, 32)    , in_channels=1    , out_channels=1    , layers_per_block=2    , block_out_channels=(128, 128)    , down_block_types=('AttnDownBlock2D', 'DownBlock2D')    , up_block_types=('UpBlock2D', 'AttnUpBlock2D')    , )
							return vqvae, unet






			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       'cpu'  # ensure determinism for the device-dependent torch.Generator
							_lowerCamelCase	:     Optional[int]       =       Mel(
							    x_res=self.dummy_unet.config.sample_size[1]    , y_res=self.dummy_unet.config.sample_size[0]    , )

							_lowerCamelCase	:     List[str]       =       DDPMScheduler()
							_lowerCamelCase	:     List[Any]       =       AudioDiffusionPipeline(vqvae=lowercase    , unet=self.dummy_unet    , mel=lowercase    , scheduler=lowercase    )
							_lowerCamelCase	:     List[Any]       =       pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )

							_lowerCamelCase	:     Union[str, Any]       =       torch.Generator(device=lowercase    ).manual_seed(42    )
							_lowerCamelCase	:     Union[str, Any]       =       pipe(generator=lowercase    , steps=4    )
							_lowerCamelCase	:     Optional[Any]       =       output.audios[0]
							_lowerCamelCase	:     int       =       output.images[0]

							_lowerCamelCase	:     Dict       =       torch.Generator(device=lowercase    ).manual_seed(42    )
							_lowerCamelCase	:     Dict       =       pipe(generator=lowercase    , steps=4    , return_dict=lowercase    )
							_lowerCamelCase	:     List[str]       =       output[0][0]

							assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
							assert (
							    image.height == self.dummy_unet.config.sample_size[0]
							    and image.width == self.dummy_unet.config.sample_size[1]
							)
							_lowerCamelCase	:     Optional[Any]       =       np.frombuffer(image.tobytes()    , dtype='uint8'    )[:10]
							_lowerCamelCase	:     Dict       =       np.frombuffer(image_from_tuple.tobytes()    , dtype='uint8'    )[:10]
							_lowerCamelCase	:     List[str]       =       np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() == 0

							_lowerCamelCase	:     List[Any]       =       Mel(
							    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]    , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]    , )

							_lowerCamelCase	:     Dict       =       DDIMScheduler()
							_lowerCamelCase	:     Tuple       =       self.dummy_vqvae_and_unet
							_lowerCamelCase	:     List[str]       =       AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]    , unet=dummy_vqvae_and_unet[1]    , mel=lowercase    , scheduler=lowercase    )
							_lowerCamelCase	:     Tuple       =       pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )

							np.random.seed(0    )
							_lowerCamelCase	:     Any       =       np.random.uniform(-1    , 1    , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)    )
							_lowerCamelCase	:     Optional[int]       =       torch.Generator(device=lowercase    ).manual_seed(42    )
							_lowerCamelCase	:     Dict       =       pipe(raw_audio=lowercase    , generator=lowercase    , start_step=5    , steps=10    )
							_lowerCamelCase	:     str       =       output.images[0]

							assert (
							    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
							    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
							)
							_lowerCamelCase	:     Dict       =       np.frombuffer(image.tobytes()    , dtype='uint8'    )[:10]
							_lowerCamelCase	:     Union[str, Any]       =       np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0

							_lowerCamelCase	:     Dict       =       self.dummy_unet_condition
							_lowerCamelCase	:     Optional[int]       =       AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]    , unet=lowercase    , mel=lowercase    , scheduler=lowercase    )
							_lowerCamelCase	:     Dict       =       pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )

							np.random.seed(0    )
							_lowerCamelCase	:     Optional[int]       =       torch.rand((1, 1, 10)    )
							_lowerCamelCase	:     Optional[Any]       =       pipe(generator=lowercase    , encoding=lowercase    )
							_lowerCamelCase	:     Dict       =       output.images[0]
							_lowerCamelCase	:     Tuple       =       np.frombuffer(image.tobytes()    , dtype='uint8'    )[:10]
							_lowerCamelCase	:     List[str]       =       np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0






@slow
@require_torch_gpu
class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			def    A_       (    self    ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       torch_device

							_lowerCamelCase	:     Dict       =       DiffusionPipeline.from_pretrained('teticio/audio-diffusion-ddim-256'    )
							_lowerCamelCase	:     List[str]       =       pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )

							_lowerCamelCase	:     int       =       torch.Generator(device=lowercase    ).manual_seed(42    )
							_lowerCamelCase	:     Tuple       =       pipe(generator=lowercase    )
							_lowerCamelCase	:     Dict       =       output.audios[0]
							_lowerCamelCase	:     Dict       =       output.images[0]

							assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
							assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
							_lowerCamelCase	:     Optional[int]       =       np.frombuffer(image.tobytes()    , dtype='uint8'    )[:10]
							_lowerCamelCase	:     int       =       np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() == 0