



import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
			from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


_lowerCamelCase    :		Optional[Any]		      =    logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase    :		List[Any]		      =    256


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Optional[Any]		=		['melgan']







						def __init__(  self    :				Optional[Any]       ,	snake_case    :				SpectrogramNotesEncoder       ,	snake_case    :				SpectrogramContEncoder       ,	snake_case    :				TaFilmDecoder       ,	snake_case    :				DDPMScheduler       ,	snake_case    :				OnnxRuntimeModel if is_onnx_available() else Any       ,	):



											'''simple docstring'''


											super().__init__()

											# From MELGAN
											SCREAMING_SNAKE_CASE							:     List[Any]						=				math.log(1E-5	)  # Matches MelGAN training.
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				4.0  # Largest value for most examples
											SCREAMING_SNAKE_CASE							:     List[Any]						=				128

											self.register_modules(
											    notes_encoder=snake_case       ,	continuous_encoder=snake_case       ,	decoder=snake_case       ,	scheduler=snake_case       ,	melgan=snake_case       ,	)







						def       lowerCamelCase_				(  self    :				int       ,	snake_case    :				int       ,	snake_case    :				Union[str, Any]=(-1.0, 1.0)       ,	snake_case    :				Optional[Any]=False	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				output_range
											if clip:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.clip(snake_case       ,	self.min_value       ,	self.max_value	)
											# Scale to [0, 1].
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				(features - self.min_value) / (self.max_value - self.min_value)
											# Scale to [min_out, max_out].
											return zero_one * (max_out - min_out) + min_out







						def       lowerCamelCase_				(  self    :				int       ,	snake_case    :				str       ,	snake_case    :				Optional[Any]=(-1.0, 1.0)       ,	snake_case    :				Tuple=False	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Tuple						=				input_range
											SCREAMING_SNAKE_CASE							:     Any						=				torch.clip(snake_case       ,	snake_case       ,	snake_case	) if clip else outputs
											# Scale to [0, 1].
											SCREAMING_SNAKE_CASE							:     Any						=				(outputs - min_out) / (max_out - min_out)
											# Scale to [self.min_value, self.max_value].
											return zero_one * (self.max_value - self.min_value) + self.min_value







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Dict       ,	snake_case    :				Optional[int]       ,	snake_case    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				input_tokens > 0
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     List[Any]						=				self.notes_encoder(
											    encoder_input_tokens=snake_case       ,	encoder_inputs_mask=snake_case	)

											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				self.continuous_encoder(
											    encoder_inputs=snake_case       ,	encoder_inputs_mask=snake_case	)

											return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]







						def       lowerCamelCase_				(  self    :				Dict       ,	snake_case    :				Dict       ,	snake_case    :				Tuple       ,	snake_case    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				noise_time
											if not torch.is_tensor(snake_case	):
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.tensor([timesteps]       ,	dtype=torch.long       ,	device=input_tokens.device	)
											elif torch.is_tensor(snake_case	) and len(timesteps.shape	) == 0:
																SCREAMING_SNAKE_CASE							:     Tuple						=				timesteps[None].to(input_tokens.device	)

											# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
											SCREAMING_SNAKE_CASE							:     Any						=				timesteps * torch.ones(input_tokens.shape[0]       ,	dtype=timesteps.dtype       ,	device=timesteps.device	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.decoder(
											    encodings_and_masks=snake_case       ,	decoder_input_tokens=snake_case       ,	decoder_noise_time=snake_case	)
											return logits







						@torch.no_grad()
						def __call__(  self    :				Dict       ,	snake_case    :				List[List[int]]       ,	snake_case    :				Optional[torch.Generator] = None       ,	snake_case    :				int = 100       ,	snake_case    :				bool = True       ,	snake_case    :				str = "numpy"       ,	snake_case    :				Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,	snake_case    :				int = 1       ,	):



											'''simple docstring'''


											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(snake_case       ,	snake_case	) or callback_steps <= 0)
											):
																raise ValueError(
																    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																    f''' {type(snake_case	)}.'''	)

											SCREAMING_SNAKE_CASE							:     Dict						=				np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]       ,	dtype=np.floataa	)
											SCREAMING_SNAKE_CASE							:     str						=				np.zeros([1, 0, self.n_dims]       ,	np.floataa	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				torch.ones((1, TARGET_FEATURE_LENGTH)       ,	dtype=snake_case       ,	device=self.device	)

											for i, encoder_input_tokens in enumerate(snake_case	):
																if i == 0:
																					SCREAMING_SNAKE_CASE							:     Tuple						=				torch.from_numpy(pred_mel[:1].copy()	).to(
																					    device=self.device       ,	dtype=self.decoder.dtype	)
																					# The first chunk has no previous context.
																					SCREAMING_SNAKE_CASE							:     Tuple						=				torch.zeros((1, TARGET_FEATURE_LENGTH)       ,	dtype=snake_case       ,	device=self.device	)
																else:
																					# The full song pipeline does not feed in a context feature, so the mask
																					# will be all 0s after the feature converter. Because we know we're
																					# feeding in a full context chunk from the previous prediction, set it
																					# to all 1s.
																					SCREAMING_SNAKE_CASE							:     Dict						=				ones

																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.scale_features(
																    snake_case       ,	output_range=[-1.0, 1.0]       ,	clip=snake_case	)

																SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.encode(
																    input_tokens=torch.IntTensor([encoder_input_tokens]	).to(device=self.device	)       ,	continuous_inputs=snake_case       ,	continuous_mask=snake_case       ,	)

																# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
																SCREAMING_SNAKE_CASE							:     Any						=				randn_tensor(
																    shape=encoder_continuous_inputs.shape       ,	generator=snake_case       ,	device=self.device       ,	dtype=self.decoder.dtype       ,	)

																# set step values
																self.scheduler.set_timesteps(snake_case	)

																# Denoising diffusion loop
																for j, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
																					SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.decode(
																					    encodings_and_masks=snake_case       ,	input_tokens=snake_case       ,	noise_time=t / self.scheduler.config.num_train_timesteps       ,	)

																					# Compute previous output: x_t -> x_t-1
																					SCREAMING_SNAKE_CASE							:     Any						=				self.scheduler.step(snake_case       ,	snake_case       ,	snake_case       ,	generator=snake_case	).prev_sample

																SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.scale_to_features(snake_case       ,	input_range=[-1.0, 1.0]	)
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				mel[:1]
																SCREAMING_SNAKE_CASE							:     str						=				mel.cpu().float().numpy()

																SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.concatenate([full_pred_mel, pred_mel[:1]]       ,	axis=1	)

																# call the callback, if provided
																if callback is not None and i % callback_steps == 0:
																					callback(snake_case       ,	snake_case	)

																logger.info('Generated segment'       ,	snake_case	)

											if output_type == "numpy" and not is_onnx_available():
																raise ValueError(
																    'Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.'	)
											elif output_type == "numpy" and self.melgan is None:
																raise ValueError(
																    'Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.'	)

											if output_type == "numpy":
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.melgan(input_features=full_pred_mel.astype(np.floataa	)	)
											else:
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				full_pred_mel

											if not return_dict:
																return (output,)

											return AudioPipelineOutput(audios=snake_case	)




from __future__ import annotations

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase = None   )     ->     list[list[str]]:
					SCREAMING_SNAKE_CASE							:     Dict						=				word_bank or []
					# create a table
					SCREAMING_SNAKE_CASE							:     int						=				len(__lowerCAmelCase   ) + 1

					SCREAMING_SNAKE_CASE							:     list[list[list[str]]]						=				[]
					for _ in range(__lowerCAmelCase   ):
										table.append([]   )
					# seed value
					SCREAMING_SNAKE_CASE							:     Tuple						=				[[]]  # because empty string has empty combination

					# iterate through the indices
					for i in range(__lowerCAmelCase   ):
										# condition
										if table[i] != []:
															for word in word_bank:
																				# slice condition
																				if target[i : i + len(__lowerCAmelCase   )] == word:
																									SCREAMING_SNAKE_CASE							:     list[list[str]]						=				[
																									    [word, *way] for way in table[i]
																									]
																									# adds the word to every combination the current position holds
																									# now,push that combination to the table[i+len(word)]
																									table[i + len(__lowerCAmelCase   )] += new_combinations

    # combinations are in reverse order so reverse for better output
					for combination in table[len(__lowerCAmelCase   )]:
										combination.reverse()

					return table[len(__lowerCAmelCase   )]


if __name__ == "__main__":
			print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
			print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
			print(
			    all_construct(
			        """hexagonosaurus""",
			        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
			    )
			)