



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							(			UpperCAmelCase__				):
		__lowerCAmelCase	:    Optional[Any]											=					["""vqvae"""]



		def __init__(     self     :Optional[int]   ,__lowercase     :AutoencoderKL   ,__lowercase     :UNetaDConditionModel   ,__lowercase     :Mel   ,__lowercase     :Union[DDIMScheduler, DDPMScheduler]   ,):
						super().__init__()
						self.register_modules(unet=UpperCAmelCase__   ,scheduler=UpperCAmelCase__   ,mel=UpperCAmelCase__   ,vqvae=UpperCAmelCase__  )



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						return 5_0 if isinstance(self.scheduler   ,UpperCAmelCase__  ) else 1_0_0_0



		@torch.no_grad()
		def __call__(     self     :Tuple   ,__lowercase     :int = 1   ,__lowercase     :str = None   ,__lowercase     :np.ndarray = None   ,__lowercase     :int = 0   ,__lowercase     :int = 0   ,__lowercase     :int = None   ,__lowercase     :torch.Generator = None   ,__lowercase     :float = 0   ,__lowercase     :float = 0   ,__lowercase     :torch.Generator = None   ,__lowercase     :float = 0   ,__lowercase     :torch.Tensor = None   ,__lowercase     :torch.Tensor = None   ,__lowercase     :Union[str, Any]=True   ,):
						snake_case__	:      Union[str, Any]     		=	steps or self.get_default_steps()
						self.scheduler.set_timesteps(UpperCAmelCase__  )
						snake_case__	:      List[str]     		=	step_generator or generator
						# For backwards compatibility
						if type(self.unet.config.sample_size  ) == int:
										snake_case__	:      str     		=	(self.unet.config.sample_size, self.unet.config.sample_size)
						if noise is None:
										snake_case__	:      Optional[Any]     		=	randn_tensor(
										    (
										        batch_size,
										        self.unet.config.in_channels,
										        self.unet.config.sample_size[0],
										        self.unet.config.sample_size[1],
										    )   ,generator=UpperCAmelCase__   ,device=self.device   ,)
						snake_case__	:      List[Any]     		=	noise
						snake_case__	:      int     		=	None

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

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

										if start_step > 0:
														snake_case__	:      int     		=	self.scheduler.add_noise(UpperCAmelCase__   ,UpperCAmelCase__   ,self.scheduler.timesteps[start_step - 1]  )

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

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

										if isinstance(self.scheduler   ,UpperCAmelCase__  ):
														snake_case__	:      Dict     		=	self.scheduler.step(
														    model_output=UpperCAmelCase__   ,timestep=UpperCAmelCase__   ,sample=UpperCAmelCase__   ,eta=UpperCAmelCase__   ,generator=UpperCAmelCase__   ,)['''prev_sample''']
										else:
														snake_case__	:      List[str]     		=	self.scheduler.step(
														    model_output=UpperCAmelCase__   ,timestep=UpperCAmelCase__   ,sample=UpperCAmelCase__   ,generator=UpperCAmelCase__   ,)['''prev_sample''']

										if mask is not None:
														if mask_start > 0:
																		snake_case__	:      List[Any]     		=	mask[:, step, :, :mask_start]
														if mask_end > 0:
																		snake_case__	:      Optional[int]     		=	mask[:, step, :, -mask_end:]

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

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

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

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



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

						for t in self.progress_bar(torch.flip(self.scheduler.timesteps   ,(0,)  )  ):
										snake_case__	:      Union[str, Any]     		=	t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
										snake_case__	:      Tuple     		=	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__	:      Dict     		=	1 - alpha_prod_t
										snake_case__	:      List[Any]     		=	self.unet(UpperCAmelCase__   ,UpperCAmelCase__  )['''sample''']
										snake_case__	:      Optional[int]     		=	(1 - alpha_prod_t_prev) ** 0.5 * model_output
										snake_case__	:      Tuple     		=	(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
										snake_case__	:      Optional[Any]     		=	sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

						return sample






		@staticmethod
		def 	__lowerCamelCase			(     __lowercase     :torch.Tensor   ,__lowercase     :torch.Tensor   ,__lowercase     :float  ):
						snake_case__	:      Optional[int]     		=	acos(torch.dot(torch.flatten(UpperCAmelCase__  )   ,torch.flatten(UpperCAmelCase__  )  ) / torch.norm(UpperCAmelCase__  ) / torch.norm(UpperCAmelCase__  )  )
						return sin((1 - alpha) * theta  ) * xa / sin(UpperCAmelCase__  ) + sin(alpha * theta  ) * xa / sin(UpperCAmelCase__  )







import os

import pytest

from transformers.dynamic_module_utils import get_imports


_lowerCamelCase	:		Any							       =   """
import os
"""

_lowerCamelCase	:		Optional[int]							       =   """
def foo():
    import os
    return False
"""

_lowerCamelCase	:		List[Any]							       =   """
def foo():
    def bar():
        if True:
            import os
        return False
    return bar()
"""

_lowerCamelCase	:		List[Any]							       =   """
import os

try:
    import bar
except ImportError:
    raise ValueError()
"""

_lowerCamelCase	:		Union[str, Any]							       =   """
import os

def foo():
    try:
        import bar
    except ImportError:
        raise ValueError()
"""

_lowerCamelCase	:		List[Any]							       =   """
import os

try:
    import bar
except (ImportError, AttributeError):
    raise ValueError()
"""

_lowerCamelCase	:		List[Any]							       =   """
import os

try:
    import bar
except ImportError as e:
    raise ValueError()
"""

_lowerCamelCase	:		str							       =   """
import os

try:
    import bar
except:
    raise ValueError()
"""

_lowerCamelCase	:		Optional[Any]							       =   """
import os

try:
    import bar
    import baz
except ImportError:
    raise ValueError()
"""

_lowerCamelCase	:		Any							       =   """
import os

try:
    import bar
    import baz
except ImportError:
    x = 1
    raise ValueError()
"""

_lowerCamelCase	:		Dict							       =   [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]







@pytest.mark.parametrize('''case''' ,				lowercase_						)
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       int:



       """simple docstring"""

       A__       =    os.path.join(lowercase_ ,				'''test_file.py'''						)
       with open(lowercase_ ,				'''w'''						) as _tmp_file:
              _tmp_file.write(lowercase_						)

       A__       =    get_imports(lowercase_						)
       assert parsed_imports == ["os"]


