



"""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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		["""vqvae"""]







						def __init__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		):
											super().__init__()
											self.register_modules(unet=lowercase__   ,		scheduler=lowercase__   ,		mel=lowercase__   ,		vqvae=lowercase__       )







						def 						__UpperCamelCase							(self       ):
											return 50 if isinstance(self.scheduler   ,		lowercase__       ) else 10_00







						@torch.no_grad()
						def __call__(self   ,		lowercase__ = 1   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = 0   ,		lowercase__ = 0   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = 0   ,		lowercase__ = 0   ,		lowercase__ = None   ,		lowercase__ = 0   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__=True   ,		):
											snake_case_					:		List[str] 					=					steps or self.get_default_steps()
											self.scheduler.set_timesteps(lowercase__       )
											snake_case_					:		Optional[int] 					=					step_generator or generator
											# For backwards compatibility
											if type(self.unet.config.sample_size       ) == int:
																snake_case_					:		List[str] 					=					(self.unet.config.sample_size, self.unet.config.sample_size)
											if noise is None:
																snake_case_					:		str 					=					randn_tensor(
																    (
																        batch_size,
																        self.unet.config.in_channels,
																        self.unet.config.sample_size[0],
																        self.unet.config.sample_size[1],
																    )   ,		generator=lowercase__   ,		device=self.device   ,		)
											snake_case_					:		Tuple 					=					noise
											snake_case_					:		str 					=					None

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

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

																if start_step > 0:
																					snake_case_					:		List[str] 					=					self.scheduler.add_noise(lowercase__   ,		lowercase__   ,		self.scheduler.timesteps[start_step - 1]       )

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

											for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]       )       ):
																if isinstance(self.unet   ,		lowercase__       ):
																					snake_case_					:		List[str] 					=					self.unet(lowercase__   ,		lowercase__   ,		lowercase__       )["""sample"""]
																else:
																					snake_case_					:		Optional[Any] 					=					self.unet(lowercase__   ,		lowercase__       )["""sample"""]

																if isinstance(self.scheduler   ,		lowercase__       ):
																					snake_case_					:		Optional[Any] 					=					self.scheduler.step(
																					    model_output=lowercase__   ,		timestep=lowercase__   ,		sample=lowercase__   ,		eta=lowercase__   ,		generator=lowercase__   ,		)["""prev_sample"""]
																else:
																					snake_case_					:		Dict 					=					self.scheduler.step(
																					    model_output=lowercase__   ,		timestep=lowercase__   ,		sample=lowercase__   ,		generator=lowercase__   ,		)["""prev_sample"""]

																if mask is not None:
																					if mask_start > 0:
																										snake_case_					:		Union[str, Any] 					=					mask[:, step, :, :mask_start]
																					if mask_end > 0:
																										snake_case_					:		List[str] 					=					mask[:, step, :, -mask_end:]

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

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

											snake_case_					:		Optional[int] 					=					[self.mel.image_to_audio(lowercase__       ) for _ in images]
											if not return_dict:
																return images, (self.mel.get_sample_rate(), audios)

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







						@torch.no_grad()
						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = 50       ):
											assert isinstance(self.scheduler   ,		lowercase__       )
											self.scheduler.set_timesteps(lowercase__       )
											snake_case_					:		Tuple 					=					np.array(
											    [np.frombuffer(image.tobytes()   ,		dtype="""uint8"""       ).reshape((1, image.height, image.width)       ) for image in images]       )
											snake_case_					:		Union[str, Any] 					=					(sample / 2_55) * 2 - 1
											snake_case_					:		List[str] 					=					torch.Tensor(lowercase__       ).to(self.device       )

											for t in self.progress_bar(torch.flip(self.scheduler.timesteps   ,		(0,)       )       ):
																snake_case_					:		List[str] 					=					t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
																snake_case_					:		Optional[int] 					=					self.scheduler.alphas_cumprod[t]
																snake_case_					:		Any 					=					(
																    self.scheduler.alphas_cumprod[prev_timestep]
																    if prev_timestep >= 0
																    else self.scheduler.final_alpha_cumprod
																)
																snake_case_					:		str 					=					1 - alpha_prod_t
																snake_case_					:		List[Any] 					=					self.unet(lowercase__   ,		lowercase__       )["""sample"""]
																snake_case_					:		Tuple 					=					(1 - alpha_prod_t_prev) ** 0.5 * model_output
																snake_case_					:		Any 					=					(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
																snake_case_					:		List[Any] 					=					sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

											return sample







						@staticmethod
						def 						__UpperCamelCase							(lowercase__   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		List[str] 					=					acos(torch.dot(torch.flatten(lowercase__       )   ,		torch.flatten(lowercase__       )       ) / torch.norm(lowercase__       ) / torch.norm(lowercase__       )       )
											return sin((1 - alpha) * theta       ) * xa / sin(lowercase__       ) + sin(alpha * theta       ) * xa / sin(lowercase__       )









"""simple docstring"""

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
			from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Dict							       =		"""deberta-v2"""







						def __init__(self   ,		lowercase__=12_81_00   ,		lowercase__=15_36   ,		lowercase__=24   ,		lowercase__=24   ,		lowercase__=61_44   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=0   ,		lowercase__=0.02   ,		lowercase__=1e-7   ,		lowercase__=False   ,		lowercase__=-1   ,		lowercase__=0   ,		lowercase__=True   ,		lowercase__=None   ,		lowercase__=0   ,		lowercase__="gelu"   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )

											snake_case_					:		Union[str, Any] 					=					hidden_size
											snake_case_					:		str 					=					num_hidden_layers
											snake_case_					:		Tuple 					=					num_attention_heads
											snake_case_					:		Dict 					=					intermediate_size
											snake_case_					:		Optional[int] 					=					hidden_act
											snake_case_					:		Union[str, Any] 					=					hidden_dropout_prob
											snake_case_					:		Any 					=					attention_probs_dropout_prob
											snake_case_					:		List[Any] 					=					max_position_embeddings
											snake_case_					:		Union[str, Any] 					=					type_vocab_size
											snake_case_					:		Union[str, Any] 					=					initializer_range
											snake_case_					:		List[Any] 					=					relative_attention
											snake_case_					:		Dict 					=					max_relative_positions
											snake_case_					:		Optional[int] 					=					pad_token_id
											snake_case_					:		List[str] 					=					position_biased_input

											# Backwards compatibility
											if type(lowercase__       ) == str:
																snake_case_					:		Union[str, Any] 					=					[x.strip() for x in pos_att_type.lower().split("""|"""       )]

											snake_case_					:		Optional[int] 					=					pos_att_type
											snake_case_					:		List[str] 					=					vocab_size
											snake_case_					:		Tuple 					=					layer_norm_eps

											snake_case_					:		List[Any] 					=					kwargs.get("""pooler_hidden_size"""   ,		lowercase__       )
											snake_case_					:		List[str] 					=					pooler_dropout
											snake_case_					:		int 					=					pooler_hidden_act







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						@property
						def 						__UpperCamelCase							(self       ):
											if self.task == "multiple-choice":
																snake_case_					:		List[Any] 					=					{0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
																snake_case_					:		int 					=					{0: """batch""", 1: """sequence"""}
											if self._config.type_vocab_size > 0:
																return OrderedDict(
																    [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]       )
											else:
																return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]       )







						@property
						def 						__UpperCamelCase							(self       ):
											return 12







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = -1   ,		lowercase__ = -1   ,		lowercase__ = -1   ,		lowercase__ = False   ,		lowercase__ = None   ,		lowercase__ = 3   ,		lowercase__ = 40   ,		lowercase__ = 40   ,		lowercase__ = None   ,		):
											snake_case_					:		str 					=					super().generate_dummy_inputs(preprocessor=lowercase__   ,		framework=lowercase__       )
											if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																del dummy_inputs["token_type_ids"]
											return dummy_inputs




