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


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

SCREAMING_SNAKE_CASE			:						Optional[int]				  =		256
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""melgan"""]

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   SpectrogramNotesEncoder  ,				__SCREAMING_SNAKE_CASE			:   SpectrogramContEncoder  ,				__SCREAMING_SNAKE_CASE			:   TaFilmDecoder  ,				__SCREAMING_SNAKE_CASE			:   DDPMScheduler  ,				__SCREAMING_SNAKE_CASE			:   OnnxRuntimeModel if is_onnx_available() else Any  ,				):
   super().__init__()

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

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

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

   __a							,  __a							=	self.continuous_encoder(
       encoder_inputs=__SCREAMING_SNAKE_CASE  ,				encoder_inputs_mask=__SCREAMING_SNAKE_CASE							)

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

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   str							):
   __a							=	noise_time
   if not torch.is_tensor(__SCREAMING_SNAKE_CASE							):
    __a							=	torch.tensor([timesteps]  ,				dtype=torch.long  ,				device=input_tokens.device							)
   elif torch.is_tensor(__SCREAMING_SNAKE_CASE							) and len(timesteps.shape							) == 0:
    __a							=	timesteps[None].to(input_tokens.device							)

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

   __a							=	self.decoder(
       encodings_and_masks=__SCREAMING_SNAKE_CASE  ,				decoder_input_tokens=__SCREAMING_SNAKE_CASE  ,				decoder_noise_time=__SCREAMING_SNAKE_CASE							)
   return logits

  @torch.no_grad()
  def __call__(		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   List[List[int]]  ,				__SCREAMING_SNAKE_CASE			:   Optional[torch.Generator] = None  ,				__SCREAMING_SNAKE_CASE			:   int = 1_00  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   str = "numpy"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,				__SCREAMING_SNAKE_CASE			:   int = 1  ,				):
   if (callback_steps is None) or (
       callback_steps is not None and (not isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_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(__SCREAMING_SNAKE_CASE							)}."""							)

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

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

    __a							=	self.scale_features(
        __SCREAMING_SNAKE_CASE  ,				output_range=[-1.0, 1.0]  ,				clip=__SCREAMING_SNAKE_CASE							)

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

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

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

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

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

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

    __a							=	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(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

    logger.info("Generated segment"  ,				__SCREAMING_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":
    __a							=	self.melgan(input_features=full_pred_mel.astype(np.floataa							)							)
   else:
    __a							=	full_pred_mel

   if not return_dict:
    return (output,)

   return AudioPipelineOutput(audios=__SCREAMING_SNAKE_CASE							)



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )

 __a							=	chkpt["model"]

 # We have the base model one level deeper than the original XLM repository
 __a							=	{}
 for k, v in state_dict.items():
  if "pred_layer" in k:
   __a							=	v
  else:
   __a							=	v

 __a							=	chkpt["params"]
 __a							=	{n: v for n, v in config.items() if not isinstance(_A				,					(torch.FloatTensor, numpy.ndarray)       )}

 __a							=	chkpt["dico_word2id"]
 __a							=	{s + "</w>" if s.find("@@"       ) == -1 and i > 13 else s.replace("@@"				,					""       ): i for s, i in vocab.items()}

 # Save pytorch-model
 __a							=	pytorch_dump_folder_path + "/" + WEIGHTS_NAME
 __a							=	pytorch_dump_folder_path + "/" + CONFIG_NAME
 __a							=	pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

 print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""       )
 torch.save(_A				,					_A       )

 print(f"""Save configuration file to {pytorch_config_dump_path}"""       )
 with open(_A				,					"w"				,					encoding="utf-8"       ) as f:
  f.write(json.dumps(_A				,					indent=2       ) + "\n"       )

 print(f"""Save vocab file to {pytorch_config_dump_path}"""       )
 with open(_A				,					"w"				,					encoding="utf-8"       ) as f:
  f.write(json.dumps(_A				,					indent=2       ) + "\n"       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)


