



def 					__lowerCamelCase					(   lowerCamelCase__						):

				"""simple docstring"""


				lowercase__     :    Tuple													=     [0 for i in range(len(lowerCamelCase__						)						)]

				# initialize interval's left pointer and right pointer
				lowercase__     :    str													=     0, 0

				for i in range(1     , len(lowerCamelCase__						)						):
								# case when current index is inside the interval
								if i <= right_pointer:
												lowercase__     :    Union[str, Any]													=     min(right_pointer - i + 1     , z_result[i - left_pointer]						)
												lowercase__     :    Tuple													=     min_edge

								while go_next(lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__						):
												z_result[i] += 1

								# if new index's result gives us more right interval,
								# we've to update left_pointer and right_pointer
								if i + z_result[i] - 1 > right_pointer:
												lowercase__     :    Union[str, Any]													=     i, i + z_result[i] - 1

				return z_result


def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__						):

				"""simple docstring"""


				return i + z_result[i] < len(lowerCamelCase__						) and s[z_result[i]] == s[i + z_result[i]]


def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__						):

				"""simple docstring"""


				lowercase__     :    List[Any]													=     0
				# concatenate 'pattern' and 'input_str' and call z_function
				# with concatenated string
				lowercase__     :    Tuple													=     z_function(pattern + input_str						)

				for val in z_result:
								# if value is greater then length of the pattern string
								# that means this index is starting position of substring
								# which is equal to pattern string
								if val >= len(lowerCamelCase__						):
												answer += 1

				return answer


if __name__ == "__main__":
				import doctest

				doctest.testmod()





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


__lowercase							 =					logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowercase							 =					256


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Tuple						=      ["""melgan"""]







    def __init__(	self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	None:
        super().__init__()

        # From MELGAN
        __UpperCamelCase     :int			  =		math.log(1E-5)  # Matches MelGAN training.
        __UpperCamelCase     :int			  =		4.0  # Largest value for most examples
        __UpperCamelCase     :str			  =		128

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Dict:
        __UpperCamelCase  ,       __UpperCamelCase     :str			  =		output_range
        if clip:
            __UpperCamelCase     :Union[str, Any]			  =		torch.clip(__lowercase , self.min_value , self.max_value)
        # Scale to [0, 1].
        __UpperCamelCase     :Union[str, Any]			  =		(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 			UpperCamelCase__   (	self , __lowercase , __lowercase=(-1.0, 1.0) , __lowercase=False)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		input_range
        __UpperCamelCase     :Optional[int]			  =		torch.clip(__lowercase , __lowercase , __lowercase) if clip else outputs
        # Scale to [0, 1].
        __UpperCamelCase     :List[str]			  =		(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 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		input_tokens > 0
        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.notes_encoder(
            encoder_input_tokens=__lowercase , encoder_inputs_mask=__lowercase)

        __UpperCamelCase  ,       __UpperCamelCase     :Union[str, Any]			  =		self.continuous_encoder(
            encoder_inputs=__lowercase , encoder_inputs_mask=__lowercase)

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	str:
        __UpperCamelCase     :Optional[int]			  =		noise_time
        if not torch.is_tensor(__lowercase):
            __UpperCamelCase     :str			  =		torch.tensor([timesteps] , dtype=torch.long , device=input_tokens.device)
        elif torch.is_tensor(__lowercase) and len(timesteps.shape) == 0:
            __UpperCamelCase     :Dict			  =		timesteps[None].to(input_tokens.device)

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

        __UpperCamelCase     :Tuple			  =		self.decoder(
            encodings_and_masks=__lowercase , decoder_input_tokens=__lowercase , decoder_noise_time=__lowercase)
        return logits







    @torch.no_grad()
    def __call__(	self , __lowercase , __lowercase = None , __lowercase = 100 , __lowercase = True , __lowercase = "numpy" , __lowercase = None , __lowercase = 1 , )       ->	Union[AudioPipelineOutput, Tuple]:
        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__lowercase , __lowercase) or callback_steps <= 0)
        ):
            raise ValueError(
                f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
                f""" {type(__lowercase)}.""")

        __UpperCamelCase     :Union[str, Any]			  =		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] , dtype=np.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		np.zeros([1, 0, self.n_dims] , np.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		torch.ones((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , device=self.device)

        for i, encoder_input_tokens in enumerate(__lowercase):
            if i == 0:
                __UpperCamelCase     :int			  =		torch.from_numpy(pred_mel[:1].copy()).to(
                    device=self.device , dtype=self.decoder.dtype)
                # The first chunk has no previous context.
                __UpperCamelCase     :int			  =		torch.zeros((1, TARGET_FEATURE_LENGTH) , dtype=__lowercase , 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.
                __UpperCamelCase     :Tuple			  =		ones

            __UpperCamelCase     :Optional[Any]			  =		self.scale_features(
                __lowercase , output_range=[-1.0, 1.0] , clip=__lowercase)

            __UpperCamelCase     :int			  =		self.encode(
                input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device) , continuous_inputs=__lowercase , continuous_mask=__lowercase , )

            # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
            __UpperCamelCase     :int			  =		randn_tensor(
                shape=encoder_continuous_inputs.shape , generator=__lowercase , device=self.device , dtype=self.decoder.dtype , )

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

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

                # Compute previous output: x_t -> x_t-1
                __UpperCamelCase     :int			  =		self.scheduler.step(__lowercase , __lowercase , __lowercase , generator=__lowercase).prev_sample

            __UpperCamelCase     :Tuple			  =		self.scale_to_features(__lowercase , input_range=[-1.0, 1.0])
            __UpperCamelCase     :List[Any]			  =		mel[:1]
            __UpperCamelCase     :Optional[Any]			  =		mel.cpu().float().numpy()

            __UpperCamelCase     :Any			  =		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(__lowercase , __lowercase)

            logger.info('''Generated segment''' , __lowercase)

        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":
            __UpperCamelCase     :Optional[Any]			  =		self.melgan(input_features=full_pred_mel.astype(np.floataa))
        else:
            __UpperCamelCase     :List[str]			  =		full_pred_mel

        if not return_dict:
            return (output,)

        return AudioPipelineOutput(audios=__lowercase)





