





"""simple docstring"""




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


a		:					Union[str, Any]	      =     logging.get_logger(__name__)  # pylint: disable=invalid-name

a		:					List[str]	      =     256


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Tuple    =["""melgan"""]






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

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

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






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

								a,			a :						Union[str, Any]      =							self.continuous_encoder(
								    encoder_inputs=lowerCAmelCase__ ,   encoder_inputs_mask=lowerCAmelCase__ )

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






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

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

								a :						List[Any]      =							self.decoder(
								    encodings_and_masks=lowerCAmelCase__ ,   decoder_input_tokens=lowerCAmelCase__ ,   decoder_noise_time=lowerCAmelCase__ )
								return logits






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

								a :						List[Any]      =							np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] ,   dtype=np.floataa )
								a :						int      =							np.zeros([1, 0, self.n_dims] ,   np.floataa )
								a :						Dict      =							torch.ones((1, TARGET_FEATURE_LENGTH) ,   dtype=lowerCAmelCase__ ,   device=self.device )

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

									a :						Optional[int]      =							self.scale_features(
									    lowerCAmelCase__ ,   output_range=[-1.0, 1.0] ,   clip=lowerCAmelCase__ )

									a :						List[Any]      =							self.encode(
									    input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) ,   continuous_inputs=lowerCAmelCase__ ,   continuous_mask=lowerCAmelCase__ ,   )

									# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
									a :						List[str]      =							randn_tensor(
									    shape=encoder_continuous_inputs.shape ,   generator=lowerCAmelCase__ ,   device=self.device ,   dtype=self.decoder.dtype ,   )

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

									# Denoising diffusion loop
									for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
										a :						List[str]      =							self.decode(
										    encodings_and_masks=lowerCAmelCase__ ,   input_tokens=lowerCAmelCase__ ,   noise_time=t / self.scheduler.config.num_train_timesteps ,   )

										# Compute previous output: x_t -> x_t-1
										a :						Optional[Any]      =							self.scheduler.step(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   generator=lowerCAmelCase__ ).prev_sample

									a :						Optional[Any]      =							self.scale_to_features(lowerCAmelCase__ ,   input_range=[-1.0, 1.0] )
									a :						List[Any]      =							mel[:1]
									a :						Optional[int]      =							mel.cpu().float().numpy()

									a :						List[str]      =							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(lowerCAmelCase__ ,   lowerCAmelCase__ )

									logger.info("Generated segment" ,   lowerCAmelCase__ )

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

								if not return_dict:
									return (output,)

								return AudioPipelineOutput(audios=lowerCAmelCase__ )









"""simple docstring"""




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a		:					Optional[int]	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("roberta."   ):
			a :						List[Any]      =							"roberta_prelayernorm." + tensor_key[len("roberta."   ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(".self.LayerNorm.weight"   ) or tensor_key.endswith(".self.LayerNorm.bias"   ):
			continue

		a :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					Any	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint-repo''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		a		:					Tuple	      =     parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


