
from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
						import torch

if is_tf_available():
						import tensorflow as tf
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[str]       	=      ['image_processor']
	a     :Tuple       	=      'SamImageProcessor'
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     int			)			->	List[str]:
					super().__init__(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.image_processor
					lowercase_	    =   -1_0
					lowercase_	    =   self.image_processor.size['''longest_edge''']
	def __call__(			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     Any=None			,				SCREAMING_SNAKE_CASE_    :     str=None			,				SCREAMING_SNAKE_CASE_    :     Tuple=None			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, TensorType]] = None			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			,				)			->	BatchEncoding:
					lowercase_	    =   self.image_processor(
					    SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

					# pop arguments that are not used in the foward but used nevertheless
					lowercase_	    =   encoding_image_processor['''original_sizes''']

					if hasattr(SCREAMING_SNAKE_CASE_			,				'''numpy'''			):  # Checks if Torch or TF tensor
									lowercase_	    =   original_sizes.numpy()

					lowercase_    ,      lowercase_    ,      lowercase_	    =   self._check_and_preprocess_points(
					    input_points=SCREAMING_SNAKE_CASE_			,				input_labels=SCREAMING_SNAKE_CASE_			,				input_boxes=SCREAMING_SNAKE_CASE_			,				)

					lowercase_	    =   self._normalize_and_convert(
					    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				input_points=SCREAMING_SNAKE_CASE_			,				input_labels=SCREAMING_SNAKE_CASE_			,				input_boxes=SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			,				)

					return encoding_image_processor
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     str=None			,				SCREAMING_SNAKE_CASE_    :     int=None			,				SCREAMING_SNAKE_CASE_    :     Tuple=None			,				SCREAMING_SNAKE_CASE_    :     List[str]="pt"			,				)			->	Dict:
					if input_points is not None:
									if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
													lowercase_	    =   [
													    self._normalize_coordinates(self.target_size			,				SCREAMING_SNAKE_CASE_			,				original_sizes[0]			) for point in input_points
													]
									else:
													lowercase_	    =   [
													    self._normalize_coordinates(self.target_size			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
													    for point, original_size in zip(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
													]
									# check that all arrays have the same shape
									if not all(point.shape == input_points[0].shape for point in input_points			):
													if input_labels is not None:
																	lowercase_    ,      lowercase_	    =   self._pad_points_and_labels(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   np.array(SCREAMING_SNAKE_CASE_			)

					if input_labels is not None:
									lowercase_	    =   np.array(SCREAMING_SNAKE_CASE_			)

					if input_boxes is not None:
									if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
													lowercase_	    =   [
													    self._normalize_coordinates(self.target_size			,				SCREAMING_SNAKE_CASE_			,				original_sizes[0]			,				is_bounding_box=SCREAMING_SNAKE_CASE_			)
													    for box in input_boxes
													]
									else:
													lowercase_	    =   [
													    self._normalize_coordinates(self.target_size			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				is_bounding_box=SCREAMING_SNAKE_CASE_			)
													    for box, original_size in zip(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
													]
									lowercase_	    =   np.array(SCREAMING_SNAKE_CASE_			)

					if input_boxes is not None:
									if return_tensors == "pt":
													lowercase_	    =   torch.from_numpy(SCREAMING_SNAKE_CASE_			)
													# boxes batch size of 1 by default
													lowercase_	    =   input_boxes.unsqueeze(1			) if len(input_boxes.shape			) != 3 else input_boxes
									elif return_tensors == "tf":
													lowercase_	    =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE_			)
													# boxes batch size of 1 by default
													lowercase_	    =   tf.expand_dims(SCREAMING_SNAKE_CASE_			,				1			) if len(input_boxes.shape			) != 3 else input_boxes
									encoding_image_processor.update({'''input_boxes''': input_boxes}			)
					if input_points is not None:
									if return_tensors == "pt":
													lowercase_	    =   torch.from_numpy(SCREAMING_SNAKE_CASE_			)
													# point batch size of 1 by default
													lowercase_	    =   input_points.unsqueeze(1			) if len(input_points.shape			) != 4 else input_points
									elif return_tensors == "tf":
													lowercase_	    =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE_			)
													# point batch size of 1 by default
													lowercase_	    =   tf.expand_dims(SCREAMING_SNAKE_CASE_			,				1			) if len(input_points.shape			) != 4 else input_points
									encoding_image_processor.update({'''input_points''': input_points}			)
					if input_labels is not None:
									if return_tensors == "pt":
													lowercase_	    =   torch.from_numpy(SCREAMING_SNAKE_CASE_			)
													# point batch size of 1 by default
													lowercase_	    =   input_labels.unsqueeze(1			) if len(input_labels.shape			) != 3 else input_labels
									elif return_tensors == "tf":
													lowercase_	    =   tf.convert_to_tensor(SCREAMING_SNAKE_CASE_			)
													# point batch size of 1 by default
													lowercase_	    =   tf.expand_dims(SCREAMING_SNAKE_CASE_			,				1			) if len(input_labels.shape			) != 3 else input_labels
									encoding_image_processor.update({'''input_labels''': input_labels}			)

					return encoding_image_processor
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Dict			)			->	Union[str, Any]:
					lowercase_	    =   max([point.shape[0] for point in input_points]			)
					lowercase_	    =   []
					for i, point in enumerate(SCREAMING_SNAKE_CASE_			):
									if point.shape[0] != expected_nb_points:
													lowercase_	    =   np.concatenate(
													    [point, np.zeros((expected_nb_points - point.shape[0], 2)			) + self.point_pad_value]			,				axis=0			)
													lowercase_	    =   np.append(input_labels[i]			,				[self.point_pad_value]			)
									processed_input_points.append(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   processed_input_points
					return input_points, input_labels
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Dict=False			)			->	np.ndarray:
					lowercase_    ,      lowercase_	    =   original_size
					lowercase_    ,      lowercase_	    =   self.image_processor._get_preprocess_shape(SCREAMING_SNAKE_CASE_			,				longest_edge=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   deepcopy(SCREAMING_SNAKE_CASE_			).astype(SCREAMING_SNAKE_CASE_			)

					if is_bounding_box:
									lowercase_	    =   coords.reshape(-1			,				2			,				2			)

					lowercase_	    =   coords[..., 0] * (new_w / old_w)
					lowercase_	    =   coords[..., 1] * (new_h / old_h)

					if is_bounding_box:
									lowercase_	    =   coords.reshape(-1			,				4			)

					return coords
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				SCREAMING_SNAKE_CASE_    :     Tuple=None			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=None			,				)			->	List[str]:
					if input_points is not None:
									if hasattr(SCREAMING_SNAKE_CASE_			,				'''numpy'''			):  # Checks for TF or Torch tensor
													lowercase_	    =   input_points.numpy().tolist()

									if not isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) or not isinstance(input_points[0]			,				SCREAMING_SNAKE_CASE_			):
													raise ValueError('''Input points must be a list of list of floating points.'''			)
									lowercase_	    =   [np.array(SCREAMING_SNAKE_CASE_			) for input_point in input_points]
					else:
									lowercase_	    =   None

					if input_labels is not None:
									if hasattr(SCREAMING_SNAKE_CASE_			,				'''numpy'''			):
													lowercase_	    =   input_labels.numpy().tolist()

									if not isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) or not isinstance(input_labels[0]			,				SCREAMING_SNAKE_CASE_			):
													raise ValueError('''Input labels must be a list of list integers.'''			)
									lowercase_	    =   [np.array(SCREAMING_SNAKE_CASE_			) for label in input_labels]
					else:
									lowercase_	    =   None

					if input_boxes is not None:
									if hasattr(SCREAMING_SNAKE_CASE_			,				'''numpy'''			):
													lowercase_	    =   input_boxes.numpy().tolist()

									if (
									    not isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									    or not isinstance(input_boxes[0]			,				SCREAMING_SNAKE_CASE_			)
									    or not isinstance(input_boxes[0][0]			,				SCREAMING_SNAKE_CASE_			)
									):
													raise ValueError('''Input boxes must be a list of list of list of floating points.'''			)
									lowercase_	    =   [np.array(SCREAMING_SNAKE_CASE_			).astype(np.floataa			) for box in input_boxes]
					else:
									lowercase_	    =   None

					return input_points, input_labels, input_boxes
	@property
	def 		_lowercase    (			self    :     int			)			->	List[Any]:
					lowercase_	    =   self.image_processor.model_input_names
					return list(dict.fromkeys(SCREAMING_SNAKE_CASE_			)			)




	def 		_lowercase    (			self    :     Optional[int]			,				*SCREAMING_SNAKE_CASE_    :     str			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Tuple:
					return self.image_processor.post_process_masks(*SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)






"""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        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['vqvae']



		def __init__(   self							,						__a							,						__a							,						__a							,						__a							,						)			->			List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(unet=__a							,						scheduler=__a							,						mel=__a							,						vqvae=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return 50 if isinstance(self.scheduler							,						__a) else 10_00



		@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							,						)			->			Union[
		    Union[AudioPipelineOutput, ImagePipelineOutput],
		    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
		]:






							'''simple docstring'''
							_UpperCamelCase									=    steps or self.get_default_steps()
							self.scheduler.set_timesteps(__a)
							_UpperCamelCase									=    step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size) == int:
												_UpperCamelCase									=    (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
												_UpperCamelCase									=    randn_tensor(
												    (
												        batch_size,
												        self.unet.config.in_channels,
												        self.unet.config.sample_size[0],
												        self.unet.config.sample_size[1],
												    )							,						generator=__a							,						device=self.device							,						)
							_UpperCamelCase									=    noise
							_UpperCamelCase									=    None

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

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

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

												_UpperCamelCase									=    (
												    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
												)
												_UpperCamelCase									=    int(mask_start_secs * pixels_per_second)
												_UpperCamelCase									=    int(mask_end_secs * pixels_per_second)
												_UpperCamelCase									=    self.scheduler.add_noise(__a							,						__a							,						torch.tensor(self.scheduler.timesteps[start_step:]))

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

												if isinstance(self.scheduler							,						__a):
																	_UpperCamelCase									=    self.scheduler.step(
																	    model_output=__a							,						timestep=__a							,						sample=__a							,						eta=__a							,						generator=__a							,						)['''prev_sample''']
												else:
																	_UpperCamelCase									=    self.scheduler.step(
																	    model_output=__a							,						timestep=__a							,						sample=__a							,						generator=__a							,						)['''prev_sample''']

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

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

							_UpperCamelCase									=    (images / 2 + 0.5).clamp(0							,						1)
							_UpperCamelCase									=    images.cpu().permute(0							,						2							,						3							,						1).numpy()
							_UpperCamelCase									=    (images * 2_55).round().astype('''uint8''')
							_UpperCamelCase									=    list(
							    (Image.fromarray(_[:, :, 0]) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__a							,						mode='''RGB''').convert('''L''') for _ in images))

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

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



		@torch.no_grad()
		def 		UpperCAmelCase				(   self							,						__a							,						__a = 50)			->			np.ndarray:






							'''simple docstring'''
							assert isinstance(self.scheduler							,						__a)
							self.scheduler.set_timesteps(__a)
							_UpperCamelCase									=    np.array(
							    [np.frombuffer(image.tobytes()							,						dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
							_UpperCamelCase									=    (sample / 2_55) * 2 - 1
							_UpperCamelCase									=    torch.Tensor(__a).to(self.device)

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

							return sample




		@staticmethod
		def 		UpperCAmelCase				(   __a							,						__a							,						__a)			->			torch.Tensor:






							'''simple docstring'''
							_UpperCamelCase									=    acos(torch.dot(torch.flatten(__a)							,						torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a))
							return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)






