

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		     =							['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 unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import BridgeTowerImageProcessor


class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	bool = True,lowercase_			:	Dict[str, int] = None,lowercase_			:	int = 3_2,lowercase_			:	bool = True,lowercase_			:	Union[int, float] = 1 / 2_5_5,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073],lowercase_			:	Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711],lowercase_			:	bool = True,lowercase_			:	Tuple=7,lowercase_			:	str=3_0,lowercase_			:	Union[str, Any]=4_0_0,lowercase_			:	Dict=3,)->   List[Any]:





        '''simple docstring'''







        A__   				=       parent
        A__   				=       do_resize
        A__   				=       size if size is not None else {'shortest_edge': 2_8_8}
        A__   				=       size_divisor
        A__   				=       do_rescale
        A__   				=       rescale_factor
        A__   				=       do_normalize
        A__   				=       do_center_crop
        A__   				=       image_mean
        A__   				=       image_std
        A__   				=       do_pad
        A__   				=       batch_size
        A__   				=       num_channels
        A__   				=       min_resolution
        A__   				=       max_resolution


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





        '''simple docstring'''







        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }


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





        '''simple docstring'''







        if not batched:
            A__   				=       self.size['shortest_edge']
            A__   				=       image_inputs[0]
            if isinstance(lowercase_,Image.Image		):
                A__				,						A__   				=       image.size
            else:
                A__				,						A__   				=       image.shape[1], image.shape[2]
            A__   				=       size / min(lowercase_,lowercase_		)
            if h < w:
                A__				,						A__   				=       size, scale * w
            else:
                A__				,						A__   				=       scale * h, size

            A__   				=       int((1_3_3_3 / 8_0_0) * size		)
            if max(lowercase_,lowercase_		) > max_size:
                A__   				=       max_size / max(lowercase_,lowercase_		)
                A__   				=       newh * scale
                A__   				=       neww * scale

            A__				,						A__   				=       int(newh + 0.5		), int(neww + 0.5		)
            A__				,						A__   				=       (
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            A__   				=       []
            for image in image_inputs:
                A__				,						A__   				=       self.get_expected_values([image]		)
                expected_values.append((expected_height, expected_width)		)
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[0]		)[0]
            A__   				=       max(lowercase_,key=lambda lowercase_		: item[1]		)[1]

        return expected_height, expected_width




@require_torch
@require_vision
class 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							BridgeTowerImageProcessor if is_vision_available() else None


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





        '''simple docstring'''







        A__   				=       BridgeTowerImageProcessingTester(self		)


    @property
    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.image_processor_tester.prepare_image_processor_dict()


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





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase_,'image_mean'		)		)
        self.assertTrue(hasattr(lowercase_,'image_std'		)		)
        self.assertTrue(hasattr(lowercase_,'do_normalize'		)		)
        self.assertTrue(hasattr(lowercase_,'do_resize'		)		)
        self.assertTrue(hasattr(lowercase_,'size'		)		)
        self.assertTrue(hasattr(lowercase_,'size_divisor'		)		)


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





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int		)->   Any:





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,Image.Image		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


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





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,numpify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,np.ndarray		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)


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





        '''simple docstring'''







        A__   				=       self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        A__   				=       prepare_image_inputs(self.image_processor_tester,equal_resolution=lowercase_,torchify=lowercase_		)
        for image in image_inputs:
            self.assertIsInstance(lowercase_,torch.Tensor		)

        # Test not batched input
        A__   				=       image_processing(image_inputs[0],return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_		)
        self.assertEqual(
            encoded_images.shape,(1, self.image_processor_tester.num_channels, expected_height, expected_width),)

        # Test batched
        A__   				=       image_processing(lowercase_,return_tensors='pt'		).pixel_values

        A__				,						A__   				=       self.image_processor_tester.get_expected_values(lowercase_,batched=lowercase_		)
        self.assertEqual(
            encoded_images.shape,(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            ),)

