

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_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Optional[Any]										=				['''vqvae''']
							def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      AutoencoderKL       ,SCREAMING_SNAKE_CASE__		:      UNetaDConditionModel       ,SCREAMING_SNAKE_CASE__		:      Mel       ,SCREAMING_SNAKE_CASE__		:      Union[DDIMScheduler, DDPMScheduler]       ,):
								super().__init__()
								self.register_modules(unet=SCREAMING_SNAKE_CASE__       ,scheduler=SCREAMING_SNAKE_CASE__       ,mel=SCREAMING_SNAKE_CASE__       ,vqvae=SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Tuple):
								return 5_0 if isinstance(self.scheduler       ,SCREAMING_SNAKE_CASE__) else 1_0_0_0
							@torch.no_grad()
							def __call__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      int = 1       ,SCREAMING_SNAKE_CASE__		:      str = None       ,SCREAMING_SNAKE_CASE__		:      np.ndarray = None       ,SCREAMING_SNAKE_CASE__		:      int = 0       ,SCREAMING_SNAKE_CASE__		:      int = 0       ,SCREAMING_SNAKE_CASE__		:      int = None       ,SCREAMING_SNAKE_CASE__		:      torch.Generator = None       ,SCREAMING_SNAKE_CASE__		:      float = 0       ,SCREAMING_SNAKE_CASE__		:      float = 0       ,SCREAMING_SNAKE_CASE__		:      torch.Generator = None       ,SCREAMING_SNAKE_CASE__		:      float = 0       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor = None       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor = None       ,SCREAMING_SNAKE_CASE__		:      List[Any]=True       ,):
								__lowerCamelCase    :    Union[str, Any] 	=			steps or self.get_default_steps()
								self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			step_generator or generator
								# For backwards compatibility
								if type(self.unet.config.sample_size) == int:
									__lowerCamelCase    :    int 	=			(self.unet.config.sample_size, self.unet.config.sample_size)
								if noise is None:
									__lowerCamelCase    :    Tuple 	=			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       ,)
								__lowerCamelCase    :    Any 	=			noise
								__lowerCamelCase    :    Dict 	=			None

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

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

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

									__lowerCamelCase    :    Union[str, Any] 	=			(
									    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
									)
									__lowerCamelCase    :    List[str] 	=			int(mask_start_secs * pixels_per_second)
									__lowerCamelCase    :    List[str] 	=			int(mask_end_secs * pixels_per_second)
									__lowerCamelCase    :    Tuple 	=			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__):
										__lowerCamelCase    :    Any 	=			self.unet(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)['sample']
									else:
										__lowerCamelCase    :    Dict 	=			self.unet(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)['sample']

									if isinstance(self.scheduler       ,SCREAMING_SNAKE_CASE__):
										__lowerCamelCase    :    Dict 	=			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:
										__lowerCamelCase    :    Any 	=			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:
											__lowerCamelCase    :    Dict 	=			mask[:, step, :, :mask_start]
										if mask_end > 0:
											__lowerCamelCase    :    Optional[int] 	=			mask[:, step, :, -mask_end:]

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

								__lowerCamelCase    :    Union[str, Any] 	=			(images / 2 + 0.5).clamp(0       ,1)
								__lowerCamelCase    :    str 	=			images.cpu().permute(0       ,2       ,3       ,1).numpy()
								__lowerCamelCase    :    List[Any] 	=			(images * 2_5_5).round().astype('uint8')
								__lowerCamelCase    :    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))

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

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

								return sample


							@staticmethod
							def    lowerCAmelCase					(		SCREAMING_SNAKE_CASE__		:      torch.Tensor       ,SCREAMING_SNAKE_CASE__		:      torch.Tensor       ,SCREAMING_SNAKE_CASE__		:      float):
								__lowerCamelCase    :    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__)







from typing import TYPE_CHECKING

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


a		={
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RoFormerTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RoFormerForCausalLM""",
					    """RoFormerForMaskedLM""",
					    """RoFormerForMultipleChoice""",
					    """RoFormerForQuestionAnswering""",
					    """RoFormerForSequenceClassification""",
					    """RoFormerForTokenClassification""",
					    """RoFormerLayer""",
					    """RoFormerModel""",
					    """RoFormerPreTrainedModel""",
					    """load_tf_weights_in_roformer""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRoFormerForCausalLM""",
					    """TFRoFormerForMaskedLM""",
					    """TFRoFormerForMultipleChoice""",
					    """TFRoFormerForQuestionAnswering""",
					    """TFRoFormerForSequenceClassification""",
					    """TFRoFormerForTokenClassification""",
					    """TFRoFormerLayer""",
					    """TFRoFormerModel""",
					    """TFRoFormerPreTrainedModel""",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaxRoFormerForMaskedLM""",
					    """FlaxRoFormerForMultipleChoice""",
					    """FlaxRoFormerForQuestionAnswering""",
					    """FlaxRoFormerForSequenceClassification""",
					    """FlaxRoFormerForTokenClassification""",
					    """FlaxRoFormerModel""",
					    """FlaxRoFormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
					from .tokenization_roformer import RoFormerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roformer_fast import RoFormerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roformer import (
										    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RoFormerForCausalLM,
										    RoFormerForMaskedLM,
										    RoFormerForMultipleChoice,
										    RoFormerForQuestionAnswering,
										    RoFormerForSequenceClassification,
										    RoFormerForTokenClassification,
										    RoFormerLayer,
										    RoFormerModel,
										    RoFormerPreTrainedModel,
										    load_tf_weights_in_roformer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roformer import (
										    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRoFormerForCausalLM,
										    TFRoFormerForMaskedLM,
										    TFRoFormerForMultipleChoice,
										    TFRoFormerForQuestionAnswering,
										    TFRoFormerForSequenceClassification,
										    TFRoFormerForTokenClassification,
										    TFRoFormerLayer,
										    TFRoFormerModel,
										    TFRoFormerPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roformer import (
										    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxRoFormerForMaskedLM,
										    FlaxRoFormerForMultipleChoice,
										    FlaxRoFormerForQuestionAnswering,
										    FlaxRoFormerForSequenceClassification,
										    FlaxRoFormerForTokenClassification,
										    FlaxRoFormerModel,
										    FlaxRoFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




