
'''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  A				(							__snake_case						):
     __magic_name__						=		['''vqvae''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			) ->				str:






         """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"""






         A       :   Optional[Any]					=						steps or self.get_default_steps()
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE	)
         A       :   Optional[Any]					=						step_generator or generator
         # For backwards compatibility
         if type(self.unet.config.sample_size	) == int:
             A       :   Union[str, Any]					=						(self.unet.config.sample_size, self.unet.config.sample_size)
         if noise is None:
             A       :   int					=						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       :   Union[str, Any]					=						noise
         A       :   int					=						None

         if audio_file is not None or raw_audio is not None:
             self.mel.load_audio(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
             A       :   Tuple					=						self.mel.audio_slice_to_image(SCREAMING_SNAKE_CASE	)
             A       :   Optional[int]					=						np.frombuffer(input_image.tobytes()						,			dtype='''uint8'''	).reshape(
                 (input_image.height, input_image.width)	)
             A       :   Tuple					=						(input_image / 255) * 2 - 1
             A       :   Union[str, 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       :   Optional[Any]					=						self.vqvae.config.scaling_factor * input_images

             if start_step > 0:
                 A       :   Union[str, Any]					=						self.scheduler.add_noise(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			self.scheduler.timesteps[start_step - 1]	)

             A       :   Optional[Any]					=						(
                 self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
             )
             A       :   str					=						int(mask_start_secs * pixels_per_second	)
             A       :   int					=						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       :   List[str]					=						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       :   Any					=						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       :   Optional[int]					=						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       :   str					=						mask[:, step, :, :mask_start]
                 if mask_end > 0:
                     A       :   List[str]					=						mask[:, step, :, -mask_end:]

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

         A       :   Tuple					=						(images / 2 + 0.5).clamp(0						,			1	)
         A       :   Union[str, Any]					=						images.cpu().permute(0						,			2						,			3						,			1	).numpy()
         A       :   Optional[Any]					=						(images * 255).round().astype('''uint8'''	)
         A       :   Optional[int]					=						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 				__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	)
         A       :   Union[str, Any]					=						np.array(
             [np.frombuffer(image.tobytes()						,			dtype='''uint8'''	).reshape((1, image.height, image.width)	) for image in images]	)
         A       :   int					=						(sample / 255) * 2 - 1
         A       :   Union[str, Any]					=						torch.Tensor(SCREAMING_SNAKE_CASE	).to(self.device	)

         for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,			(0,)	)	):
             A       :   Union[str, Any]					=						t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
             A       :   Union[str, Any]					=						self.scheduler.alphas_cumprod[t]
             A       :   Tuple					=						(
                 self.scheduler.alphas_cumprod[prev_timestep]
                 if prev_timestep >= 0
                 else self.scheduler.final_alpha_cumprod
             )
             A       :   List[Any]					=						1 - alpha_prod_t
             A       :   Dict					=						self.unet(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)['''sample''']
             A       :   List[str]					=						(1 - alpha_prod_t_prev) ** 0.5 * model_output
             A       :   List[Any]					=						(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
             A       :   Union[str, Any]					=						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"""






         A       :   List[str]					=						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


lowercase__		:				Tuple          =	logging.get_logger(__name__)

lowercase__		:				Any          =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """dpr"""



      def __init__(      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple=3_0_5_2_2    ,			SCREAMING_SNAKE_CASE_				:      Any=7_6_8    ,			SCREAMING_SNAKE_CASE_				:      Dict=1_2    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1_2    ,			SCREAMING_SNAKE_CASE_				:      int=3_0_7_2    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="gelu"    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=5_1_2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=0.02    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1E-12    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="absolute"    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Optional[int]								= vocab_size
        lowerCAmelCase_   :				Tuple								= hidden_size
        lowerCAmelCase_   :				int								= num_hidden_layers
        lowerCAmelCase_   :				str								= num_attention_heads
        lowerCAmelCase_   :				Any								= hidden_act
        lowerCAmelCase_   :				List[str]								= intermediate_size
        lowerCAmelCase_   :				int								= hidden_dropout_prob
        lowerCAmelCase_   :				Union[str, Any]								= attention_probs_dropout_prob
        lowerCAmelCase_   :				Tuple								= max_position_embeddings
        lowerCAmelCase_   :				List[str]								= type_vocab_size
        lowerCAmelCase_   :				str								= initializer_range
        lowerCAmelCase_   :				str								= layer_norm_eps
        lowerCAmelCase_   :				List[str]								= projection_dim
        lowerCAmelCase_   :				List[str]								= position_embedding_type





