
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		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Optional[Any]	 =['''vqvae''']




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




							def 			A__				(    self):
												return 5_0 if isinstance(self.scheduler       ,A__) else 1_0_0_0




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

												if audio_file is not None or raw_audio is not None:
																	self.mel.load_audio(A__       ,A__)
																	lowercase						     =						self.mel.audio_slice_to_image(A__)
																	lowercase						     =						np.frombuffer(input_image.tobytes()       ,dtype='''uint8''').reshape(
																	    (input_image.height, input_image.width))
																	lowercase						     =						(input_image / 2_5_5) * 2 - 1
																	lowercase						     =						torch.tensor(input_image[np.newaxis, :, :]       ,dtype=torch.float).to(self.device)

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

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

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

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

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

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

												lowercase						     =						(images / 2 + 0.5).clamp(0       ,1)
												lowercase						     =						images.cpu().permute(0       ,2       ,3       ,1).numpy()
												lowercase						     =						(images * 2_5_5).round().astype('''uint8''')
												lowercase						     =						list(
												    (Image.fromarray(_[:, :, 0]) for _ in images)
												    if images.shape[3] == 1
												    else (Image.fromarray(A__       ,mode='''RGB''').convert('''L''') for _ in images))

												lowercase						     =						[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 			A__				(    self       ,A__       ,A__ = 5_0):
												assert isinstance(self.scheduler       ,A__)
												self.scheduler.set_timesteps(A__)
												lowercase						     =						np.array(
												    [np.frombuffer(image.tobytes()       ,dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
												lowercase						     =						(sample / 2_5_5) * 2 - 1
												lowercase						     =						torch.Tensor(A__).to(self.device)

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

												return sample





							@staticmethod
							def 			A__				(    A__       ,A__       ,A__):
												lowercase						     =						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__)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__		:Any			  =   {
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Tuple			  =   ["DistilBertTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Union[str, Any]			  =   [
							    "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DistilBertForMaskedLM",
							    "DistilBertForMultipleChoice",
							    "DistilBertForQuestionAnswering",
							    "DistilBertForSequenceClassification",
							    "DistilBertForTokenClassification",
							    "DistilBertModel",
							    "DistilBertPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFDistilBertForMaskedLM",
							    "TFDistilBertForMultipleChoice",
							    "TFDistilBertForQuestionAnswering",
							    "TFDistilBertForSequenceClassification",
							    "TFDistilBertForTokenClassification",
							    "TFDistilBertMainLayer",
							    "TFDistilBertModel",
							    "TFDistilBertPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Union[str, Any]			  =   [
							    "FlaxDistilBertForMaskedLM",
							    "FlaxDistilBertForMultipleChoice",
							    "FlaxDistilBertForQuestionAnswering",
							    "FlaxDistilBertForSequenceClassification",
							    "FlaxDistilBertForTokenClassification",
							    "FlaxDistilBertModel",
							    "FlaxDistilBertPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_distilbert import (
							    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DistilBertConfig,
							    DistilBertOnnxConfig,
							)
							from .tokenization_distilbert import DistilBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_distilbert_fast import DistilBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_distilbert import (
														    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DistilBertForMaskedLM,
														    DistilBertForMultipleChoice,
														    DistilBertForQuestionAnswering,
														    DistilBertForSequenceClassification,
														    DistilBertForTokenClassification,
														    DistilBertModel,
														    DistilBertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_distilbert import (
														    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDistilBertForMaskedLM,
														    TFDistilBertForMultipleChoice,
														    TFDistilBertForQuestionAnswering,
														    TFDistilBertForSequenceClassification,
														    TFDistilBertForTokenClassification,
														    TFDistilBertMainLayer,
														    TFDistilBertModel,
														    TFDistilBertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_distilbert import (
														    FlaxDistilBertForMaskedLM,
														    FlaxDistilBertForMultipleChoice,
														    FlaxDistilBertForQuestionAnswering,
														    FlaxDistilBertForSequenceClassification,
														    FlaxDistilBertForTokenClassification,
														    FlaxDistilBertModel,
														    FlaxDistilBertPreTrainedModel,
														)

else:
							import sys

							lowercase__		:List[str]			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




