




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

_lowerCamelCase      			=				2_56



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	List[Any]       		=  ['melgan']
     def __init__(     self       :      List[Any] ,   __snake_case       :      SpectrogramNotesEncoder ,   __snake_case       :      SpectrogramContEncoder ,   __snake_case       :      TaFilmDecoder ,   __snake_case       :      DDPMScheduler ,   __snake_case       :      OnnxRuntimeModel if is_onnx_available() else Any ,   ):
         super().__init__()

         # From MELGAN
         UpperCAmelCase_										=					math.log(1E-5    )  # Matches MelGAN training.
         UpperCAmelCase_										=					4.0  # Largest value for most examples
         UpperCAmelCase_										=					1_28

         self.register_modules(
             notes_encoder=__snake_case ,   continuous_encoder=__snake_case ,   decoder=__snake_case ,   scheduler=__snake_case ,   melgan=__snake_case ,   )
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Tuple ,   __snake_case       :      List[Any]=(-1.0, 1.0) ,   __snake_case       :      Tuple=False    ):
         UpperCAmelCase_			,							UpperCAmelCase_										=					output_range
         if clip:
             UpperCAmelCase_										=					torch.clip(__snake_case ,   self.min_value ,   self.max_value    )
         # Scale to [0, 1].
         UpperCAmelCase_										=					(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       :      List[Any] ,   __snake_case       :      int ,   __snake_case       :      str=(-1.0, 1.0) ,   __snake_case       :      int=False    ):
         UpperCAmelCase_			,							UpperCAmelCase_										=					input_range
         UpperCAmelCase_										=					torch.clip(__snake_case ,   __snake_case ,   __snake_case    ) if clip else outputs
         # Scale to [0, 1].
         UpperCAmelCase_										=					(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       :      Union[str, Any] ,   __snake_case       :      int ,   __snake_case       :      str    ):
         UpperCAmelCase_										=					input_tokens > 0
         UpperCAmelCase_			,							UpperCAmelCase_										=					self.notes_encoder(
             encoder_input_tokens=__snake_case ,   encoder_inputs_mask=__snake_case    )

         UpperCAmelCase_			,							UpperCAmelCase_										=					self.continuous_encoder(
             encoder_inputs=__snake_case ,   encoder_inputs_mask=__snake_case    )

         return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      List[str] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      List[Any]    ):
         UpperCAmelCase_										=					noise_time
         if not torch.is_tensor(__snake_case    ):
             UpperCAmelCase_										=					torch.tensor([timesteps] ,   dtype=torch.long ,   device=input_tokens.device    )
         elif torch.is_tensor(__snake_case    ) and len(timesteps.shape    ) == 0:
             UpperCAmelCase_										=					timesteps[None].to(input_tokens.device    )

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

         UpperCAmelCase_										=					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       :      List[Any] ,   __snake_case       :      List[List[int]] ,   __snake_case       :      Optional[torch.Generator] = None ,   __snake_case       :      int = 1_00 ,   __snake_case       :      bool = True ,   __snake_case       :      str = "numpy" ,   __snake_case       :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,   __snake_case       :      int = 1 ,   ):
         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    )}.'    )

         UpperCAmelCase_										=					np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] ,   dtype=np.floataa    )
         UpperCAmelCase_										=					np.zeros([1, 0, self.n_dims] ,   np.floataa    )
         UpperCAmelCase_										=					torch.ones((1, TARGET_FEATURE_LENGTH) ,   dtype=__snake_case ,   device=self.device    )

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

             UpperCAmelCase_										=					self.scale_features(
                 __snake_case ,   output_range=[-1.0, 1.0] ,   clip=__snake_case    )

             UpperCAmelCase_										=					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
             UpperCAmelCase_										=					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    )    ):
                 UpperCAmelCase_										=					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
                 UpperCAmelCase_										=					self.scheduler.step(__snake_case ,   __snake_case ,   __snake_case ,   generator=__snake_case    ).prev_sample

             UpperCAmelCase_										=					self.scale_to_features(__snake_case ,   input_range=[-1.0, 1.0]    )
             UpperCAmelCase_										=					mel[:1]
             UpperCAmelCase_										=					mel.cpu().float().numpy()

             UpperCAmelCase_										=					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":
             UpperCAmelCase_										=					self.melgan(input_features=full_pred_mel.astype(np.floataa    )    )
         else:
             UpperCAmelCase_										=					full_pred_mel

         if not return_dict:
             return (output,)

         return AudioPipelineOutput(audios=__snake_case    )











from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCamelCase      			=						tuple[int, int]



class       a					:

     '''simple docstring'''





     def __init__(     self       :      str ,   __snake_case       :      set[int] ,   __snake_case       :      Mapping[EdgeT, int]    ):
         UpperCAmelCase_										=					vertices
         UpperCAmelCase_										=					{
             (min(__snake_case    ), max(__snake_case    )): weight for edge, weight in edges.items()
         }
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      EdgeT ,   __snake_case       :      int    ):
         self.vertices.add(edge[0]    )
         self.vertices.add(edge[1]    )
         UpperCAmelCase_										=					weight




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					Graph({min(self.vertices    )} ,   {}    )
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42

         while len(subgraph.vertices    ) < len(self.vertices    ):
             UpperCAmelCase_										=					max(self.edges.values()    ) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         UpperCAmelCase_										=					edge
                         UpperCAmelCase_										=					weight

             subgraph.add_edge(__snake_case ,   __snake_case    )

         return subgraph




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str = "p107_network.txt"				)  ->							int:
    UpperCAmelCase_										=					os.path.abspath(os.path.dirname(__UpperCamelCase				)				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					{}
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42

    with open(__UpperCamelCase				) as f:
        UpperCAmelCase_										=					f.read().strip().split('''\n'''				)

    UpperCAmelCase_										=					[line.split(''','''				) for line in data]

    for edgea in range(1	,					len(__UpperCamelCase				)				):
        for edgea in range(__UpperCamelCase				):
            if adjaceny_matrix[edgea][edgea] != "-":
                UpperCAmelCase_										=					int(adjaceny_matrix[edgea][edgea]				)

    UpperCAmelCase_										=					Graph(set(range(len(__UpperCamelCase				)				)				)	,					__UpperCamelCase				)

    UpperCAmelCase_										=					graph.prims_algorithm()

    UpperCAmelCase_										=					sum(graph.edges.values()				)
    UpperCAmelCase_										=					sum(subgraph.edges.values()				)

    return initial_total - optimal_total


if __name__ == "__main__":
 print(F"{solution() = }")





