
'''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_     ):
					lowerCAmelCase_        =     ['''vqvae''']






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return 50 if isinstance(self.scheduler				,							__SCREAMING_SNAKE_CASE		) else 10_00






					@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				,							):


											"""simple docstring"""





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

											if audio_file is not None or raw_audio is not None:
																	self.mel.load_audio(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     int													=       self.mel.audio_slice_to_image(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     int													=       np.frombuffer(input_image.tobytes()				,							dtype='''uint8'''		).reshape(
																	    (input_image.height, input_image.width)		)
																	lowercase_	:     Dict													=       (input_image / 2_55) * 2 - 1
																	lowercase_	:     Optional[Any]													=       torch.tensor(input_image[np.newaxis, :, :]				,							dtype=torch.float		).to(self.device		)

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

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

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

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

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

											lowercase_	:     str													=       (images / 2 + 0.5).clamp(0				,							1		)
											lowercase_	:     Union[str, Any]													=       images.cpu().permute(0				,							2				,							3				,							1		).numpy()
											lowercase_	:     Any													=       (images * 2_55).round().astype('''uint8'''		)
											lowercase_	:     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)		)

											lowercase_	:     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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = 50		):


											"""simple docstring"""





											assert isinstance(self.scheduler				,							__SCREAMING_SNAKE_CASE		)
											self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[str]													=       np.array(
											    [np.frombuffer(image.tobytes()				,							dtype='''uint8'''		).reshape((1, image.height, image.width)		) for image in images]		)
											lowercase_	:     Tuple													=       (sample / 2_55) * 2 - 1
											lowercase_	:     List[Any]													=       torch.Tensor(__SCREAMING_SNAKE_CASE		).to(self.device		)

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

											return sample






					@staticmethod
					def 	_snake_case    (   __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     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'''







import torch

from transformers import AutoModel






class  lowerCAmelCase__   (  torch.nn.Module     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"		):


											"""simple docstring"""





											super(__SCREAMING_SNAKE_CASE				,							self		).__init__()

											lowercase_	:     Tuple													=       AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE				,							return_dict=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       torch.nn.CosineSimilarity(3				,							1E-0_8		)
											lowercase_	:     Optional[Any]													=       torch.nn.Softmax(dim=1		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.bert(**__SCREAMING_SNAKE_CASE		).last_hidden_state






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return token_embeddings.sum(2				,							keepdim=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=1		):


											"""simple docstring"""





											return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       W_supports['''sizes'''].tolist()
											lowercase_	:     Dict													=       W_supports['''start_token_id'''].item()
											lowercase_	:     List[Any]													=       W_supports['''end_token_id'''].item()

											del W_supports["sizes"]
											del W_supports["start_token_id"]
											del W_supports["end_token_id"]

											lowercase_	:     List[str]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       None
											lowercase_	:     Dict													=       None

											lowercase_	:     Tuple													=       W_supports['''input_ids'''] == start_token_id
											lowercase_	:     Any													=       W_supports['''input_ids'''] == end_token_id

											for i, size in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == 0:
																							lowercase_	:     List[str]													=       0
																	else:
																							lowercase_	:     List[Any]													=       support_sizes[i - 1]

																	lowercase_	:     str													=       S[s : s + size][start_token_masks[s : s + size]]
																	lowercase_	:     Optional[int]													=       S[s : s + size][end_token_masks[s : s + size]]

																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_start.T		).sum(1		).softmax(0		)
																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_end.T		).sum(1		).softmax(0		)

																	if p_starts is not None:
																							lowercase_	:     Tuple													=       torch.vstack((p_starts, p_start)		)
																							lowercase_	:     Optional[Any]													=       torch.vstack((p_ends, p_end)		)
																	else:
																							lowercase_	:     str													=       p_start
																							lowercase_	:     int													=       p_end

											return p_starts, p_ends



