

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 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase :    Tuple		     =							['melgan']


    def __init__(				self			:	int,lowercase_			:	SpectrogramNotesEncoder,lowercase_			:	SpectrogramContEncoder,lowercase_			:	TaFilmDecoder,lowercase_			:	DDPMScheduler,lowercase_			:	OnnxRuntimeModel if is_onnx_available() else Any,)->   None:





        '''simple docstring'''







        super().__init__()

        # From MELGAN
        A__   				=       math.log(1E-5		)  # Matches MelGAN training.
        A__   				=       4.0  # Largest value for most examples
        A__   				=       1_2_8

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


    def 						snake_case__					(				self			:	int,lowercase_			:	List[Any],lowercase_			:	str=(-1.0, 1.0),lowercase_			:	Optional[Any]=False		)->   Tuple:





        '''simple docstring'''







        A__				,						A__   				=       output_range
        if clip:
            A__   				=       torch.clip(lowercase_,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 						snake_case__					(				self			:	Optional[Any],lowercase_			:	List[str],lowercase_			:	List[Any]=(-1.0, 1.0),lowercase_			:	Tuple=False		)->   Dict:





        '''simple docstring'''







        A__				,						A__   				=       input_range
        A__   				=       torch.clip(lowercase_,lowercase_,lowercase_		) 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 						snake_case__					(				self			:	Dict,lowercase_			:	Dict,lowercase_			:	int,lowercase_			:	Optional[int]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       input_tokens > 0
        A__				,						A__   				=       self.notes_encoder(
            encoder_input_tokens=lowercase_,encoder_inputs_mask=lowercase_		)

        A__				,						A__   				=       self.continuous_encoder(
            encoder_inputs=lowercase_,encoder_inputs_mask=lowercase_		)

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


    def 						snake_case__					(				self			:	str,lowercase_			:	Union[str, Any],lowercase_			:	Union[str, Any],lowercase_			:	Tuple		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       noise_time
        if not torch.is_tensor(lowercase_		):
            A__   				=       torch.tensor([timesteps],dtype=torch.long,device=input_tokens.device		)
        elif torch.is_tensor(lowercase_		) 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=lowercase_,decoder_input_tokens=lowercase_,decoder_noise_time=lowercase_		)
        return logits


    @torch.no_grad()
    def __call__(				self			:	Any,lowercase_			:	List[List[int]],lowercase_			:	Optional[torch.Generator] = None,lowercase_			:	int = 1_0_0,lowercase_			:	bool = True,lowercase_			:	str = "numpy",lowercase_			:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None,lowercase_			:	int = 1,)->   Union[AudioPipelineOutput, Tuple]:





        '''simple docstring'''







        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_		)}.'		)

        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=lowercase_,device=self.device		)

        for i, encoder_input_tokens in enumerate(lowercase_		):
            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=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.
                A__   				=       ones

            A__   				=       self.scale_features(
                lowercase_,output_range=[-1.0, 1.0],clip=lowercase_		)

            A__   				=       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
            A__   				=       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		)		):
                A__   				=       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
                A__   				=       self.scheduler.step(lowercase_,lowercase_,lowercase_,generator=lowercase_		).prev_sample

            A__   				=       self.scale_to_features(lowercase_,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(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":
            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=lowercase_		)




import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)


class 			A						(							nn.Module			):







    """simple docstring"""





    def __init__(				self			:	Union[str, Any]		)->   List[str]:





        '''simple docstring'''







        super().__init__()
        A__   				=       nn.Linear(3,4		)
        A__   				=       nn.BatchNormad(4		)
        A__   				=       nn.Linear(4,5		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







        return self.lineara(self.batchnorm(self.lineara(lowercase_		)		)		)




class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[str]		)->   Any:





        '''simple docstring'''







        A__   				=       ModelForTest()
        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,model.state_dict()		)
            A__   				=       os.path.join(lowercase_,'index.json'		)
            self.assertTrue(os.path.isfile(lowercase_		)		)
            # TODO: add tests on what is inside the index

            for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                A__   				=       os.path.join(lowercase_,F'{key}.dat'		)
                self.assertTrue(os.path.isfile(lowercase_		)		)
                # TODO: add tests on the fact weights are properly loaded


    def 						snake_case__					(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       [torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
            A__   				=       torch.randn(2,3,dtype=lowercase_		)
            with TemporaryDirectory() as tmp_dir:
                A__   				=       offload_weight(lowercase_,'weight',lowercase_,{}		)
                A__   				=       os.path.join(lowercase_,'weight.dat'		)
                self.assertTrue(os.path.isfile(lowercase_		)		)
                self.assertDictEqual(lowercase_,{'weight': {'shape': [2, 3], 'dtype': str(lowercase_		).split('.'		)[1]}}		)

                A__   				=       load_offloaded_weight(lowercase_,index['weight']		)
                self.assertTrue(torch.equal(lowercase_,lowercase_		)		)


    def 						snake_case__					(				self			:	Optional[int]		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       ModelForTest()
        A__   				=       model.state_dict()
        A__   				=       {k: v for k, v in state_dict.items() if 'linear2' not in k}
        A__   				=       {k: v for k, v in state_dict.items() if 'linear2' in k}

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)

        A__   				=       {k: v for k, v in state_dict.items() if 'weight' in k}
        A__   				=       {k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            # Duplicates are removed
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)


    def 						snake_case__					(				self			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       {'a.1': 0, 'a.10': 1, 'a.2': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1': 0, 'a.2': 2}		)

        A__   				=       {'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1.a': 0, 'a.2.a': 2}		)

