


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

SCREAMING_SNAKE_CASE							    =							2_5_6


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      ["""melgan"""]


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       ):
        super().__init__()

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

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


     def 	snake_case_  (	self  ,       __A  ,       __A=(-1.0, 1.0)  ,       __A=False    ):
        __a	,    __a					=  output_range
        if clip:
           __a					=  torch.clip(lowerCamelCase__  ,       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  ,       __A  ,       __A=(-1.0, 1.0)  ,       __A=False    ):
        __a	,    __a					=  input_range
        __a					=  torch.clip(lowerCamelCase__  ,       lowerCamelCase__  ,       lowerCamelCase__    ) 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  ,       __A  ,       __A  ,       __A    ):
        __a					=  input_tokens > 0
        __a	,    __a					=  self.notes_encoder(
            encoder_input_tokens=lowerCamelCase__  ,       encoder_inputs_mask=lowerCamelCase__    )

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

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


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  noise_time
        if not torch.is_tensor(lowerCamelCase__    ):
           __a					=  torch.tensor([timesteps]  ,       dtype=torch.long  ,       device=input_tokens.device    )
        elif torch.is_tensor(lowerCamelCase__    ) 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=lowerCamelCase__  ,       decoder_input_tokens=lowerCamelCase__  ,       decoder_noise_time=lowerCamelCase__    )
        return logits


     @torch.no_grad()
     def __call__(	self  ,       __A  ,       __A = None  ,       __A = 100  ,       __A = True  ,       __A = "numpy"  ,       __A = None  ,       __A = 1  ,       ):
        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					=  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=lowerCamelCase__  ,       device=self.device    )

        for i, encoder_input_tokens in enumerate(lowerCamelCase__    ):
           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=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					=  ones

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

           __a					=  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					=  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					=  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					=  self.scheduler.step(lowerCamelCase__  ,       lowerCamelCase__  ,       lowerCamelCase__  ,       generator=lowerCamelCase__    ).prev_sample

           __a					=  self.scale_to_features(lowerCamelCase__  ,       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(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					=  self.melgan(input_features=full_pred_mel.astype(np.floataa    )    )
        else:
           __a					=  full_pred_mel

        if not return_dict:
           return (output,)

        return AudioPipelineOutput(audios=lowerCamelCase__    )









from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
					import torch


__A								=       logging.get_logger(__name__)







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			['''pixel_values''']


			def __init__( self     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = PILImageResampling.BILINEAR     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = 1 / 255     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> None:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)
							__lowerCamelCase									=       size if size is not None else {'shortest_edge': 256}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       crop_size if crop_size is not None else {'height': 224, 'width': 224}
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_resize
							__lowerCamelCase									=       size
							__lowerCamelCase									=       resample
							__lowerCamelCase									=       do_center_crop
							__lowerCamelCase									=       crop_size
							__lowerCamelCase									=       do_rescale
							__lowerCamelCase									=       rescale_factor
							__lowerCamelCase									=       do_normalize
							__lowerCamelCase									=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							__lowerCamelCase									=       image_std if image_std is not None else IMAGENET_STANDARD_STD


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = PILImageResampling.BICUBIC     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							if "shortest_edge" not in size:
											raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
							__lowerCamelCase									=       get_resize_output_image_size(lowerCamelCase__     ,		size=size['shortest_edge']     ,		default_to_square=lowerCamelCase__				)
							return resize(lowerCamelCase__     ,		size=lowerCamelCase__     ,		resample=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__				)
							if "height" not in size or "width" not in size:
											raise ValueError(f"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""				)
							return center_crop(lowerCamelCase__     ,		size=(size['height'], size['width'])     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__				)       -> np.ndarray:



							'''simple docstring'''
							return rescale(lowerCamelCase__     ,		scale=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		**lowerCamelCase__     ,		)       -> np.ndarray:



							'''simple docstring'''
							return normalize(lowerCamelCase__     ,		mean=lowerCamelCase__     ,		std=lowerCamelCase__     ,		data_format=lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = ChannelDimension.FIRST     ,		**lowerCamelCase__     ,		)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       do_resize if do_resize is not None else self.do_resize
							__lowerCamelCase									=       size if size is not None else self.size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		default_to_square=lowerCamelCase__				)
							__lowerCamelCase									=       resample if resample is not None else self.resample
							__lowerCamelCase									=       do_center_crop if do_center_crop is not None else self.do_center_crop
							__lowerCamelCase									=       crop_size if crop_size is not None else self.crop_size
							__lowerCamelCase									=       get_size_dict(lowerCamelCase__     ,		param_name='crop_size'				)
							__lowerCamelCase									=       do_rescale if do_rescale is not None else self.do_rescale
							__lowerCamelCase									=       rescale_factor if rescale_factor is not None else self.rescale_factor
							__lowerCamelCase									=       do_normalize if do_normalize is not None else self.do_normalize
							__lowerCamelCase									=       image_mean if image_mean is not None else self.image_mean
							__lowerCamelCase									=       image_std if image_std is not None else self.image_std

							__lowerCamelCase									=       make_list_of_images(lowerCamelCase__				)

							if not valid_images(lowerCamelCase__				):
											raise ValueError(
											    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
											    'torch.Tensor, tf.Tensor or jax.ndarray.'				)

							if do_resize and size is None:
											raise ValueError('Size must be specified if do_resize is True.'				)

							if do_center_crop and crop_size is None:
											raise ValueError('Crop size must be specified if do_center_crop is True.'				)

							if do_rescale and rescale_factor is None:
											raise ValueError('Rescale factor must be specified if do_rescale is True.'				)

							if do_normalize and (image_mean is None or image_std is None):
											raise ValueError('Image mean and std must be specified if do_normalize is True.'				)

							# All transformations expect numpy arrays.
							__lowerCamelCase									=       [to_numpy_array(lowerCamelCase__				) for image in images]

							if do_resize:
											__lowerCamelCase									=       [self.resize(image=lowerCamelCase__     ,		size=lowerCamelCase__     ,		resample=lowerCamelCase__				) for image in images]

							if do_center_crop:
											__lowerCamelCase									=       [self.center_crop(image=lowerCamelCase__     ,		size=lowerCamelCase__				) for image in images]

							if do_rescale:
											__lowerCamelCase									=       [self.rescale(image=lowerCamelCase__     ,		scale=lowerCamelCase__				) for image in images]

							if do_normalize:
											__lowerCamelCase									=       [self.normalize(image=lowerCamelCase__     ,		mean=lowerCamelCase__     ,		std=lowerCamelCase__				) for image in images]

							__lowerCamelCase									=       [to_channel_dimension_format(lowerCamelCase__     ,		lowerCamelCase__				) for image in images]

							__lowerCamelCase									=       {'pixel_values': images}
							return BatchFeature(data=lowerCamelCase__     ,		tensor_type=lowerCamelCase__				)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = None				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       outputs.logits

							# Resize logits and compute semantic segmentation maps
							if target_sizes is not None:
											if len(lowerCamelCase__				) != len(lowerCamelCase__				):
															raise ValueError(
															    'Make sure that you pass in as many target sizes as the batch dimension of the logits'				)

											if is_torch_tensor(lowerCamelCase__				):
															__lowerCamelCase									=       target_sizes.numpy()

											__lowerCamelCase									=       []

											for idx in range(len(lowerCamelCase__				)				):
															__lowerCamelCase									=       torch.nn.functional.interpolate(
															    logits[idx].unsqueeze(dim=0				)     ,		size=target_sizes[idx]     ,		mode='bilinear'     ,		align_corners=lowerCamelCase__				)
															__lowerCamelCase									=       resized_logits[0].argmax(dim=0				)
															semantic_segmentation.append(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       logits.argmax(dim=1				)
											__lowerCamelCase									=       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

							return semantic_segmentation


