

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         =						256






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					["""melgan"""]



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

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

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



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

      __magic_name__       ,  __magic_name__						       =    self.continuous_encoder(
          encoder_inputs=a__			,				encoder_inputs_mask=a__      )

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



 def 			snake_case__  (   self		:   List[Any]			,				a__		:   Dict			,				a__		:   Tuple			,				a__		:   List[Any]      ):
      __magic_name__						       =    noise_time
      if not torch.is_tensor(a__      ):
           __magic_name__						       =    torch.tensor([timesteps]			,				dtype=torch.long			,				device=input_tokens.device      )
      elif torch.is_tensor(a__      ) and len(timesteps.shape      ) == 0:
           __magic_name__						       =    timesteps[None].to(input_tokens.device      )

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

      __magic_name__						       =    self.decoder(
          encodings_and_masks=a__			,				decoder_input_tokens=a__			,				decoder_noise_time=a__      )
      return logits



 @torch.no_grad()
 def __call__(   self		:   List[Any]			,				a__		:   List[List[int]]			,				a__		:   Optional[torch.Generator] = None			,				a__		:   int = 100			,				a__		:   bool = True			,				a__		:   str = "numpy"			,				a__		:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,				a__		:   int = 1			,				):
      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(a__			,				a__      ) or callback_steps <= 0)
      ):
           raise ValueError(
               F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               F''' {type(a__      )}.'''      )

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

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

           __magic_name__						       =    self.scale_features(
               a__			,				output_range=[-1.0, 1.0]			,				clip=a__      )

           __magic_name__						       =    self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens]      ).to(device=self.device      )			,				continuous_inputs=a__			,				continuous_mask=a__			,				)

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           __magic_name__						       =    randn_tensor(
               shape=encoder_continuous_inputs.shape			,				generator=a__			,				device=self.device			,				dtype=self.decoder.dtype			,				)

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

           # Denoising diffusion loop
           for j, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
                __magic_name__						       =    self.decode(
                    encodings_and_masks=a__			,				input_tokens=a__			,				noise_time=t / self.scheduler.config.num_train_timesteps			,				)

                # Compute previous output: x_t -> x_t-1
                __magic_name__						       =    self.scheduler.step(a__			,				a__			,				a__			,				generator=a__      ).prev_sample

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

           __magic_name__						       =    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(a__			,				a__      )

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

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

      if not return_dict:
           return (output,)

      return AudioPipelineOutput(audios=a__      )









'''simple docstring'''




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Optional[Any]			,				a__		:   Any			,				a__		:   Tuple=sys.maxsize      ):
      __magic_name__						       =    '''bilinear'''
      __magic_name__						       =    max_size
      __magic_name__						       =    short_edge_length



 def __call__(   self		:   Tuple			,				a__		:   List[str]      ):
      __magic_name__						       =    []
      for img in imgs:
           __magic_name__       ,  __magic_name__						       =    img.shape[:2]
           # later: provide list and randomly choose index for resize
           __magic_name__						       =    np.random.randint(self.short_edge_length[0]			,				self.short_edge_length[1] + 1      )
           if size == 0:
                return img
           __magic_name__						       =    size * 1.0 / min(a__			,				a__      )
           if h < w:
                __magic_name__       ,  __magic_name__						       =    size, scale * w
           else:
                __magic_name__       ,  __magic_name__						       =    scale * h, size
           if max(a__			,				a__      ) > self.max_size:
                __magic_name__						       =    self.max_size * 1.0 / max(a__			,				a__      )
                __magic_name__						       =    newh * scale
                __magic_name__						       =    neww * scale
           __magic_name__						       =    int(neww + 0.5      )
           __magic_name__						       =    int(newh + 0.5      )

           if img.dtype == np.uinta:
                __magic_name__						       =    Image.fromarray(a__      )
                __magic_name__						       =    pil_image.resize((neww, newh)			,				PILImageResampling.BILINEAR      )
                __magic_name__						       =    np.asarray(a__      )
           else:
                __magic_name__						       =    img.permute(2			,				0			,				1      ).unsqueeze(0      )  # 3, 0, 1)  # hw(c) -> nchw
                __magic_name__						       =    nn.functional.interpolate(
                    a__			,				(newh, neww)			,				mode=self.interp_method			,				align_corners=a__      ).squeeze(0      )
           img_augs.append(a__      )

      return img_augs






class   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   List[Any]			,				a__		:   Tuple      ):
      __magic_name__						       =    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,				cfg.INPUT.MAX_SIZE_TEST      )
      __magic_name__						       =    cfg.INPUT.FORMAT
      __magic_name__						       =    cfg.SIZE_DIVISIBILITY
      __magic_name__						       =    cfg.PAD_VALUE
      __magic_name__						       =    cfg.INPUT.MAX_SIZE_TEST
      __magic_name__						       =    cfg.MODEL.DEVICE
      __magic_name__						       =    torch.tensor(cfg.MODEL.PIXEL_STD      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,				1			,				1      )
      __magic_name__						       =    torch.tensor(cfg.MODEL.PIXEL_MEAN      ).to(self.device      ).view(len(cfg.MODEL.PIXEL_STD      )			,				1			,				1      )
      __magic_name__						       =    lambda a__      : (x - self.pixel_mean) / self.pixel_std



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   Dict      ):
      __magic_name__						       =    tuple(max(a__      ) for s in zip(*[img.shape for img in images]      )      )
      __magic_name__						       =    [im.shape[-2:] for im in images]
      __magic_name__						       =    [
          nn.functional.pad(
              a__			,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,				value=self.pad_value			,				)
          for size, im in zip(a__			,				a__      )
      ]

      return torch.stack(a__      ), torch.tensor(a__      )



 def __call__(   self		:   Dict			,				a__		:   Dict			,				a__		:   List[str]=False      ):
      with torch.no_grad():
           if not isinstance(a__			,				a__      ):
                __magic_name__						       =    [images]
           if single_image:
                assert len(a__      ) == 1
           for i in range(len(a__      )      ):
                if isinstance(images[i]			,				torch.Tensor      ):
                     images.insert(a__			,				images.pop(a__      ).to(self.device      ).float()      )
                elif not isinstance(images[i]			,				torch.Tensor      ):
                     images.insert(
                         a__			,				torch.as_tensor(img_tensorize(images.pop(a__      )			,				input_format=self.input_format      )      )
                         .to(self.device      )
                         .float()			,				)
            # resize smallest edge
           __magic_name__						       =    torch.tensor([im.shape[:2] for im in images]      )
           __magic_name__						       =    self.aug(a__      )
           # transpose images and convert to torch tensors
           # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
           # now normalize before pad to avoid useless arithmetic
           __magic_name__						       =    [self.normalizer(a__      ) for x in images]
           # now pad them to do the following operations
           __magic_name__       ,  __magic_name__						       =    self.pad(a__      )
           # Normalize

           if self.size_divisibility > 0:
                raise NotImplementedError()
           # pad
           __magic_name__						       =    torch.true_divide(a__			,				a__      )
           if single_image:
                return images[0], sizes[0], scales_yx[0]
           else:
                return images, sizes, scales_yx




def 						UpperCamelCase	(			a		,						a			)				->     List[Any]:






     '''simple docstring'''
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes




def 						UpperCamelCase	(			a		,						a			)				->     Any:






     '''simple docstring'''
     assert torch.isfinite(a			).all(), "Box tensor contains infinite or NaN!"
     __magic_name__       ,  __magic_name__						       =    box_size
     tensor[:, 0].clamp_(min=0		,						max=a			)
     tensor[:, 1].clamp_(min=0		,						max=a			)
     tensor[:, 2].clamp_(min=0		,						max=a			)
     tensor[:, 3].clamp_(min=0		,						max=a			)






