




from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class 						lowercase							(   lowerCamelCase_		):
     _a							 =						["vqvae"]

     def __init__(  self		,     _a		,     _a		,     _a		,     _a		,     )						->							List[Any]:
           super().__init__()
           self.register_modules(unet=__SCREAMING_SNAKE_CASE		,     scheduler=__SCREAMING_SNAKE_CASE		,     mel=__SCREAMING_SNAKE_CASE		,     vqvae=__SCREAMING_SNAKE_CASE				)

     def 					a__	(  self				)						->							Any:
           return 50 if isinstance(self.scheduler		,     __SCREAMING_SNAKE_CASE				) else 1000

     @torch.no_grad()
     def __call__(  self		,     _a = 1		,     _a = None		,     _a = None		,     _a = 0		,     _a = 0		,     _a = None		,     _a = None		,     _a = 0		,     _a = 0		,     _a = None		,     _a = 0		,     _a = None		,     _a = None		,     _a=True		,     )						->							str:
           _A							:     str			=						steps or self.get_default_steps()
           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE				)
           _A							:     List[Any]			=						step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size				) == int:
                 _A							:     str			=						(self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
                 _A							:     Union[str, Any]			=						randn_tensor(
                     (
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size[0],
                         self.unet.config.sample_size[1],
                     )		,     generator=__SCREAMING_SNAKE_CASE		,     device=self.device		,     )
           _A							:     str			=						noise
           _A							:     Any			=						None

           if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				)
                 _A							:     int			=						self.mel.audio_slice_to_image(__SCREAMING_SNAKE_CASE				)
                 _A							:     int			=						np.frombuffer(input_image.tobytes()		,     dtype="""uint8"""				).reshape(
                     (input_image.height, input_image.width)				)
                 _A							:     Dict			=						(input_image / 255) * 2 - 1
                 _A							:     Optional[Any]			=						torch.tensor(input_image[np.newaxis, :, :]		,     dtype=torch.float				).to(self.device				)

                 if self.vqvae is not None:
                       _A							:     Optional[int]			=						self.vqvae.encode(torch.unsqueeze(__SCREAMING_SNAKE_CASE		,     0				)				).latent_dist.sample(
                           generator=__SCREAMING_SNAKE_CASE				)[0]
                       _A							:     str			=						self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                       _A							:     str			=						self.scheduler.add_noise(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     self.scheduler.timesteps[start_step - 1]				)

                 _A							:     str			=						(
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 _A							:     Union[str, Any]			=						int(mask_start_secs * pixels_per_second				)
                 _A							:     Union[str, Any]			=						int(mask_end_secs * pixels_per_second				)
                 _A							:     List[Any]			=						self.scheduler.add_noise(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     torch.tensor(self.scheduler.timesteps[start_step:]				)				)

           for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
                 if isinstance(self.unet		,     __SCREAMING_SNAKE_CASE				):
                       _A							:     Any			=						self.unet(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				)['''sample''']
                 else:
                       _A							:     int			=						self.unet(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				)['''sample''']

                 if isinstance(self.scheduler		,     __SCREAMING_SNAKE_CASE				):
                       _A							:     str			=						self.scheduler.step(
                           model_output=__SCREAMING_SNAKE_CASE		,     timestep=__SCREAMING_SNAKE_CASE		,     sample=__SCREAMING_SNAKE_CASE		,     eta=__SCREAMING_SNAKE_CASE		,     generator=__SCREAMING_SNAKE_CASE		,     )['''prev_sample''']
                 else:
                       _A							:     Tuple			=						self.scheduler.step(
                           model_output=__SCREAMING_SNAKE_CASE		,     timestep=__SCREAMING_SNAKE_CASE		,     sample=__SCREAMING_SNAKE_CASE		,     generator=__SCREAMING_SNAKE_CASE		,     )['''prev_sample''']

                 if mask is not None:
                       if mask_start > 0:
                             _A							:     Dict			=						mask[:, step, :, :mask_start]
                       if mask_end > 0:
                             _A							:     Tuple			=						mask[:, step, :, -mask_end:]

           if self.vqvae is not None:
                 # 0.18215 was scaling factor used in training to ensure unit variance
                 _A							:     Optional[Any]			=						1 / self.vqvae.config.scaling_factor * images
                 _A							:     Union[str, Any]			=						self.vqvae.decode(__SCREAMING_SNAKE_CASE				)['''sample''']

           _A							:     str			=						(images / 2 + 0.5).clamp(0		,     1				)
           _A							:     Union[str, Any]			=						images.cpu().permute(0		,     2		,     3		,     1				).numpy()
           _A							:     Any			=						(images * 255).round().astype("""uint8"""				)
           _A							:     Union[str, Any]			=						list(
               (Image.fromarray(_[:, :, 0]				) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(__SCREAMING_SNAKE_CASE		,     mode="""RGB"""				).convert("""L"""				) for _ in images)				)

           _A							:     Any			=						[self.mel.image_to_audio(__SCREAMING_SNAKE_CASE				) for _ in images]
           if not return_dict:
                 return images, (self.mel.get_sample_rate(), audios)

           return BaseOutput(**AudioPipelineOutput(np.array(__SCREAMING_SNAKE_CASE				)[:, np.newaxis, :]				)		,     **ImagePipelineOutput(__SCREAMING_SNAKE_CASE				)				)

     @torch.no_grad()
     def 					a__	(  self		,     _a		,     _a = 50				)						->							int:
           assert isinstance(self.scheduler		,     __SCREAMING_SNAKE_CASE				)
           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE				)
           _A							:     List[str]			=						np.array(
               [np.frombuffer(image.tobytes()		,     dtype="""uint8"""				).reshape((1, image.height, image.width)				) for image in images]				)
           _A							:     Tuple			=						(sample / 255) * 2 - 1
           _A							:     List[Any]			=						torch.Tensor(__SCREAMING_SNAKE_CASE				).to(self.device				)

           for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,     (0,)				)				):
                 _A							:     List[str]			=						t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                 _A							:     Union[str, Any]			=						self.scheduler.alphas_cumprod[t]
                 _A							:     List[str]			=						(
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 _A							:     Optional[int]			=						1 - alpha_prod_t
                 _A							:     List[Any]			=						self.unet(__SCREAMING_SNAKE_CASE		,     __SCREAMING_SNAKE_CASE				)['''sample''']
                 _A							:     int			=						(1 - alpha_prod_t_prev) ** 0.5 * model_output
                 _A							:     str			=						(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 _A							:     List[Any]			=						sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

           return sample



     @staticmethod
     def 					a__	(  _a		,     _a		,     _a				)						->							List[str]:
           _A							:     Union[str, Any]			=						acos(torch.dot(torch.flatten(__SCREAMING_SNAKE_CASE				)		,     torch.flatten(__SCREAMING_SNAKE_CASE				)				) / torch.norm(__SCREAMING_SNAKE_CASE				) / torch.norm(__SCREAMING_SNAKE_CASE				)				)
           return sin((1 - alpha) * theta				) * xa / sin(__SCREAMING_SNAKE_CASE				) + sin(alpha * theta				) * xa / sin(__SCREAMING_SNAKE_CASE				)



'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowercase :  int    							=       logging.get_logger(__name__)

_lowercase :  List[Any]    							=       {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     '''nat'''

					lowerCAmelCase_        =     {
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=4				,							__SCREAMING_SNAKE_CASE=3				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=[3, 4, 6, 5]				,							__SCREAMING_SNAKE_CASE=[2, 4, 8, 16]				,							__SCREAMING_SNAKE_CASE=7				,							__SCREAMING_SNAKE_CASE=3.0				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE="gelu"				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=1E-5				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       patch_size
											lowercase_	:     List[Any]													=       num_channels
											lowercase_	:     str													=       embed_dim
											lowercase_	:     List[str]													=       depths
											lowercase_	:     str													=       len(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       num_heads
											lowercase_	:     int													=       kernel_size
											lowercase_	:     Union[str, Any]													=       mlp_ratio
											lowercase_	:     Optional[int]													=       qkv_bias
											lowercase_	:     List[Any]													=       hidden_dropout_prob
											lowercase_	:     Optional[int]													=       attention_probs_dropout_prob
											lowercase_	:     List[Any]													=       drop_path_rate
											lowercase_	:     List[Any]													=       hidden_act
											lowercase_	:     int													=       layer_norm_eps
											lowercase_	:     int													=       initializer_range
											# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
											# this indicates the channel dimension after the last stage of the model
											lowercase_	:     Dict													=       int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE		) - 1)		)
											lowercase_	:     Tuple													=       layer_scale_init_value
											lowercase_	:     Union[str, Any]													=       ['''stem'''] + [F'''stage{idx}''' for idx in range(1				,							len(__SCREAMING_SNAKE_CASE		) + 1		)]
											lowercase_						, lowercase_	:     int													=       get_aligned_output_features_output_indices(
											    out_features=__SCREAMING_SNAKE_CASE				,							out_indices=__SCREAMING_SNAKE_CASE				,							stage_names=self.stage_names		)



