




'''simple docstring'''



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      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   str										=      ['''vqvae''']


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)   ->	Union[str, Any]:



            '''simple docstring'''




            super().__init__()
            self.register_modules(unet=__SCREAMING_SNAKE_CASE     ,	scheduler=__SCREAMING_SNAKE_CASE     ,	mel=__SCREAMING_SNAKE_CASE     ,	vqvae=__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            return 50 if isinstance(self.scheduler     ,	__SCREAMING_SNAKE_CASE	) else 1000


       @torch.no_grad()
       def __call__( self     ,	__SCREAMING_SNAKE_CASE = 1     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 0     ,	__SCREAMING_SNAKE_CASE = 0     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 0     ,	__SCREAMING_SNAKE_CASE = 0     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = 0     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE=True     ,	)   ->	Union[
           Union[AudioPipelineOutput, ImagePipelineOutput],
           Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
       ]:



            '''simple docstring'''




            __snake_case            =	steps or self.get_default_steps()
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	step_generator or generator
            # For backwards compatibility
            if type(self.unet.config.sample_size	) == int:
                 __snake_case            =	(self.unet.config.sample_size, self.unet.config.sample_size)
            if noise is None:
                 __snake_case            =	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     ,	)
            __snake_case            =	noise
            __snake_case            =	None

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

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

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

                 __snake_case            =	(
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 __snake_case            =	int(mask_start_secs * pixels_per_second	)
                 __snake_case            =	int(mask_end_secs * pixels_per_second	)
                 __snake_case            =	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	):
                      __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)['''sample''']
                 else:
                      __snake_case            =	self.unet(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)['''sample''']

                 if isinstance(self.scheduler     ,	__SCREAMING_SNAKE_CASE	):
                      __snake_case            =	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:
                      __snake_case            =	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:
                           __snake_case            =	mask[:, step, :, :mask_start]
                      if mask_end > 0:
                           __snake_case            =	mask[:, step, :, -mask_end:]

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

            __snake_case            =	(images / 2 + 0.5).clamp(0     ,	1	)
            __snake_case            =	images.cpu().permute(0     ,	2     ,	3     ,	1	).numpy()
            __snake_case            =	(images * 255).round().astype('''uint8'''	)
            __snake_case            =	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)	)

            __snake_case            =	[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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 50	)   ->	np.ndarray:



            '''simple docstring'''




            assert isinstance(self.scheduler     ,	__SCREAMING_SNAKE_CASE	)
            self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	np.array(
                [np.frombuffer(image.tobytes()     ,	dtype='''uint8'''	).reshape((1, image.height, image.width)	) for image in images]	)
            __snake_case            =	(sample / 255) * 2 - 1
            __snake_case            =	torch.Tensor(__SCREAMING_SNAKE_CASE	).to(self.device	)

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

            return sample




       @staticmethod
       def    lowerCAmelCase   ( __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	torch.Tensor:



            '''simple docstring'''




            __snake_case            =	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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_							:	List[str]	     =		{
    '''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	int	     =		[
         '''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTBigCodeForSequenceClassification''',
         '''GPTBigCodeForTokenClassification''',
         '''GPTBigCodeForCausalLM''',
         '''GPTBigCodeModel''',
         '''GPTBigCodePreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_bigcode import (
              GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTBigCodeForCausalLM,
              GPTBigCodeForSequenceClassification,
              GPTBigCodeForTokenClassification,
              GPTBigCodeModel,
              GPTBigCodePreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase_							:	Any	     =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






