

"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_lowercase   :	Any												=						logging.get_logger(__name__)  # pylint: disable=invalid-name
class      _UpperCAmelCase   (							_lowerCAmelCase       ):



	def __init__(  self     :       List[Any]			,    _lowercase     :       List[str]			,    _lowercase     :       Optional[Any]						):
		super().__init__()
		self.register_modules(unet=_lowercase			,    scheduler=_lowercase						)



	@torch.no_grad()
	def __call__(  self     :       Any			,    _lowercase     :       int = 1			,    _lowercase     :       int = 1_00			,    _lowercase     :       Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    _lowercase     :       Optional[float] = None			,    _lowercase     :       bool = True			,    ):

		if audio_length_in_s is None:
			__UpperCAmelCase      		= self.unet.config.sample_size / self.unet.config.sample_rate

		__UpperCAmelCase      		= audio_length_in_s * self.unet.config.sample_rate

		__UpperCAmelCase      		= 2 ** len(self.unet.up_blocks						)
		if sample_size < 3 * down_scale_factor:
			raise ValueError(
			    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
			    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''						)

		__UpperCAmelCase      		= int(_lowercase						)
		if sample_size % down_scale_factor != 0:
			__UpperCAmelCase      		= (
			    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
			) * down_scale_factor
			logger.info(
			    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
			    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
			    ''' process.'''						)
		__UpperCAmelCase      		= int(_lowercase						)

		__UpperCAmelCase      		= next(iter(self.unet.parameters()						)						).dtype
		__UpperCAmelCase      		= (batch_size, self.unet.config.in_channels, sample_size)
		if isinstance(_lowercase			,    _lowercase						) and len(_lowercase						) != batch_size:
			raise ValueError(
			    F'''You have passed a list of generators of length {len(_lowercase						)}, but requested an effective batch'''
			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

		__UpperCAmelCase      		= randn_tensor(_lowercase			,    generator=_lowercase			,    device=self.device			,    dtype=_lowercase						)

		# set step values
		self.scheduler.set_timesteps(_lowercase			,    device=audio.device						)
		__UpperCAmelCase      		= self.scheduler.timesteps.to(_lowercase						)

		for t in self.progress_bar(self.scheduler.timesteps						):
			# 1. predict noise model_output
			__UpperCAmelCase      		= self.unet(_lowercase			,    _lowercase						).sample

			# 2. compute previous image: x_t -> t_t-1
			__UpperCAmelCase      		= self.scheduler.step(_lowercase			,    _lowercase			,    _lowercase						).prev_sample

		__UpperCAmelCase      		= audio.clamp(-1			,    1						).float().cpu().numpy()

		__UpperCAmelCase      		= audio[:, :, :original_sample_size]

		if not return_dict:
			return (audio,)

		return AudioPipelineOutput(audios=_lowercase						)







import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a__: Optional[Any]							   =						logging.get_logger(__name__)

a__: Any							   =						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      '''conditional_detr'''
 __SCREAMING_SNAKE_CASE							=      ['''past_key_values''']
 __SCREAMING_SNAKE_CASE							=      {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }



 def __init__(				self,__lowerCamelCase=True,__lowerCamelCase=None,__lowerCamelCase=3,__lowerCamelCase=300,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=256,__lowerCamelCase=0.1,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.02,__lowerCamelCase=1.0,__lowerCamelCase=False,__lowerCamelCase="sine",__lowerCamelCase="resnet50",__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=1,__lowerCamelCase=1,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=0.25,**__lowerCamelCase,):
        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.'''					)
                      A__		   =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
               elif isinstance(__lowerCamelCase,__lowerCamelCase					):
                      A__		   =		backbone_config.get('''model_type'''					)
                      A__		   =		CONFIG_MAPPING[backbone_model_type]
                      A__		   =		config_class.from_dict(__lowerCamelCase					)

        A__		   =		use_timm_backbone
        A__		   =		backbone_config
        A__		   =		num_channels
        A__		   =		num_queries
        A__		   =		d_model
        A__		   =		encoder_ffn_dim
        A__		   =		encoder_layers
        A__		   =		encoder_attention_heads
        A__		   =		decoder_ffn_dim
        A__		   =		decoder_layers
        A__		   =		decoder_attention_heads
        A__		   =		dropout
        A__		   =		attention_dropout
        A__		   =		activation_dropout
        A__		   =		activation_function
        A__		   =		init_std
        A__		   =		init_xavier_std
        A__		   =		encoder_layerdrop
        A__		   =		decoder_layerdrop
        A__		   =		encoder_layers
        A__		   =		auxiliary_loss
        A__		   =		position_embedding_type
        A__		   =		backbone
        A__		   =		use_pretrained_backbone
        A__		   =		dilation
        # Hungarian matcher
        A__		   =		class_cost
        A__		   =		bbox_cost
        A__		   =		giou_cost
        # Loss coefficients
        A__		   =		mask_loss_coefficient
        A__		   =		dice_loss_coefficient
        A__		   =		cls_loss_coefficient
        A__		   =		bbox_loss_coefficient
        A__		   =		giou_loss_coefficient
        A__		   =		focal_alpha
        super().__init__(is_encoder_decoder=__lowerCamelCase,**__lowerCamelCase					)



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



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




 def 							UpperCamelCase     (				self					):
        A__		   =		copy.deepcopy(self.__dict__					)
        if self.backbone_config is not None:
               A__		   =		self.backbone_config.to_dict()
        A__		   =		self.__class__.model_type
        return output




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      version.parse('''1.11''' )



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



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




 @property
 def 							UpperCamelCase     (				self					):
        return 12





