
"""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


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

lowerCamelCase__           =	256




class 				A__ (			_lowerCamelCase):
		A_		: Dict            =			['melgan']

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

			# From MELGAN
			__lowerCAmelCase    :		int		    =   math.log(1E-5				)  # Matches MelGAN training.
			__lowerCAmelCase    :		Any		    =   4.0  # Largest value for most examples
			__lowerCAmelCase    :		Union[str, Any]		    =   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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=(-1.0, 1.0) ,	_SCREAMING_SNAKE_CASE=False				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   output_range
			if clip:
				__lowerCAmelCase    :		Tuple		    =   torch.clip(_SCREAMING_SNAKE_CASE ,	self.min_value ,	self.max_value				)
			# Scale to [0, 1].
			__lowerCAmelCase    :		List[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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=(-1.0, 1.0) ,	_SCREAMING_SNAKE_CASE=False				):
			__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   input_range
			__lowerCAmelCase    :		Any		    =   torch.clip(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) if clip else outputs
			# Scale to [0, 1].
			__lowerCAmelCase    :		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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Any		    =   input_tokens > 0
			__lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   self.notes_encoder(
			    encoder_input_tokens=_SCREAMING_SNAKE_CASE ,	encoder_inputs_mask=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[int]		    =   self.continuous_encoder(
			    encoder_inputs=_SCREAMING_SNAKE_CASE ,	encoder_inputs_mask=_SCREAMING_SNAKE_CASE				)

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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Tuple		    =   noise_time
			if not torch.is_tensor(_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		int		    =   torch.tensor([timesteps] ,	dtype=torch.long ,	device=input_tokens.device				)
			elif torch.is_tensor(_SCREAMING_SNAKE_CASE				) and len(timesteps.shape				) == 0:
				__lowerCAmelCase    :		Tuple		    =   timesteps[None].to(input_tokens.device				)

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

			__lowerCAmelCase    :		List[Any]		    =   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 ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 1_00 ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = "numpy" ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 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				)}."				)

			__lowerCAmelCase    :		int		    =   np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] ,	dtype=np.floataa				)
			__lowerCAmelCase    :		List[str]		    =   np.zeros([1, 0, self.n_dims] ,	np.floataa				)
			__lowerCAmelCase    :		Dict		    =   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:
					__lowerCAmelCase    :		str		    =   torch.from_numpy(pred_mel[:1].copy()				).to(
					    device=self.device ,	dtype=self.decoder.dtype				)
					# The first chunk has no previous context.
					__lowerCAmelCase    :		Union[str, Any]		    =   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.
					__lowerCAmelCase    :		List[str]		    =   ones

				__lowerCAmelCase    :		List[Any]		    =   self.scale_features(
				    _SCREAMING_SNAKE_CASE ,	output_range=[-1.0, 1.0] ,	clip=_SCREAMING_SNAKE_CASE				)

				__lowerCAmelCase    :		Tuple		    =   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
				__lowerCAmelCase    :		int		    =   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				)				):
					__lowerCAmelCase    :		Tuple		    =   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
					__lowerCAmelCase    :		Optional[int]		    =   self.scheduler.step(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	generator=_SCREAMING_SNAKE_CASE				).prev_sample

				__lowerCAmelCase    :		Tuple		    =   self.scale_to_features(_SCREAMING_SNAKE_CASE ,	input_range=[-1.0, 1.0]				)
				__lowerCAmelCase    :		Union[str, Any]		    =   mel[:1]
				__lowerCAmelCase    :		int		    =   mel.cpu().float().numpy()

				__lowerCAmelCase    :		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(_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":
				__lowerCAmelCase    :		Any		    =   self.melgan(input_features=full_pred_mel.astype(np.floataa				)				)
			else:
				__lowerCAmelCase    :		Dict		    =   full_pred_mel

			if not return_dict:
				return (output,)

			return AudioPipelineOutput(audios=_SCREAMING_SNAKE_CASE				)

"""simple docstring"""







import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'detr'
		A_		: Union[str, Any]            =			['past_key_values']
		A_		: Any            =			{
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=1_00 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=20_48 ,	_SCREAMING_SNAKE_CASE=8 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE="relu" ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1.0 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE="sine" ,	_SCREAMING_SNAKE_CASE="resnet50" ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.1 ,	**_SCREAMING_SNAKE_CASE ,	):
			if backbone_config is not None and use_timm_backbone:
				raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'				)

			if not use_timm_backbone:
				if backbone_config is None:
					logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'				)
					__lowerCAmelCase    :		str		    =   CONFIG_MAPPING['resnet'](out_features=['stage4']				)
				elif isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		str		    =   backbone_config.get('model_type'				)
					__lowerCAmelCase    :		str		    =   CONFIG_MAPPING[backbone_model_type]
					__lowerCAmelCase    :		str		    =   config_class.from_dict(_SCREAMING_SNAKE_CASE				)
				# set timm attributes to None
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   None, None, None

			__lowerCAmelCase    :		int		    =   use_timm_backbone
			__lowerCAmelCase    :		str		    =   backbone_config
			__lowerCAmelCase    :		Tuple		    =   num_channels
			__lowerCAmelCase    :		Union[str, Any]		    =   num_queries
			__lowerCAmelCase    :		Tuple		    =   d_model
			__lowerCAmelCase    :		Dict		    =   encoder_ffn_dim
			__lowerCAmelCase    :		Dict		    =   encoder_layers
			__lowerCAmelCase    :		Optional[int]		    =   encoder_attention_heads
			__lowerCAmelCase    :		Union[str, Any]		    =   decoder_ffn_dim
			__lowerCAmelCase    :		Union[str, Any]		    =   decoder_layers
			__lowerCAmelCase    :		Any		    =   decoder_attention_heads
			__lowerCAmelCase    :		Tuple		    =   dropout
			__lowerCAmelCase    :		Union[str, Any]		    =   attention_dropout
			__lowerCAmelCase    :		Any		    =   activation_dropout
			__lowerCAmelCase    :		Tuple		    =   activation_function
			__lowerCAmelCase    :		Any		    =   init_std
			__lowerCAmelCase    :		Any		    =   init_xavier_std
			__lowerCAmelCase    :		str		    =   encoder_layerdrop
			__lowerCAmelCase    :		Tuple		    =   decoder_layerdrop
			__lowerCAmelCase    :		Any		    =   encoder_layers
			__lowerCAmelCase    :		List[Any]		    =   auxiliary_loss
			__lowerCAmelCase    :		Tuple		    =   position_embedding_type
			__lowerCAmelCase    :		Dict		    =   backbone
			__lowerCAmelCase    :		Dict		    =   use_pretrained_backbone
			__lowerCAmelCase    :		Optional[Any]		    =   dilation
			# Hungarian matcher
			__lowerCAmelCase    :		Dict		    =   class_cost
			__lowerCAmelCase    :		str		    =   bbox_cost
			__lowerCAmelCase    :		Optional[Any]		    =   giou_cost
			# Loss coefficients
			__lowerCAmelCase    :		Tuple		    =   mask_loss_coefficient
			__lowerCAmelCase    :		List[Any]		    =   dice_loss_coefficient
			__lowerCAmelCase    :		List[str]		    =   bbox_loss_coefficient
			__lowerCAmelCase    :		Optional[int]		    =   giou_loss_coefficient
			__lowerCAmelCase    :		Union[str, Any]		    =   eos_coefficient
			super().__init__(is_encoder_decoder=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		@property
		def 							__lowerCamelCase							(   self				):
			return self.encoder_attention_heads

		@property
		def 							__lowerCamelCase							(   self				):
			return self.d_model

		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return cls(backbone_config=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   copy.deepcopy(self.__dict__				)
			if output["backbone_config"] is not None:
				__lowerCAmelCase    :		Dict		    =   self.backbone_config.to_dict()
			__lowerCAmelCase    :		List[str]		    =   self.__class__.model_type
			return output




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			version.parse('1.11')

		@property
		def 							__lowerCamelCase							(   self				):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			        ('pixel_mask', {0: 'batch'}),
			    ]				)

		@property
		def 							__lowerCamelCase							(   self				):
			return 1E-5







		@property
		def 							__lowerCamelCase							(   self				):
			return 12