


# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCamelCase        =     subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
__lowerCamelCase        =     (
    subprocess.check_output(f'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode('''utf-8''').split()
)

__lowerCamelCase        =     '''|'''.join(sys.argv[1:])
__lowerCamelCase        =     re.compile(Rf'''^({joined_dirs}).*?\.py$''')

__lowerCamelCase        =     [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')







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__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     List[str]							    =     ["""vqvae"""]

       def __init__(   self      :     str      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Mel      ,							lowerCamelCase_      :     Union[DDIMScheduler, DDPMScheduler]      ,							):
            super().__init__()
            self.register_modules(unet=lowerCamelCase_      ,							scheduler=lowerCamelCase_      ,							mel=lowerCamelCase_      ,							vqvae=lowerCamelCase_    )

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

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

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

                 if self.vqvae is not None:
                      a_    :     Dict										=    self.vqvae.encode(torch.unsqueeze(lowerCamelCase_      ,							0    )    ).latent_dist.sample(
                          generator=lowerCamelCase_    )[0]
                      a_    :     List[Any]										=    self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                      a_    :     Tuple										=    self.scheduler.add_noise(lowerCamelCase_      ,							lowerCamelCase_      ,							self.scheduler.timesteps[start_step - 1]    )

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

            for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]    )    ):
                 if isinstance(self.unet      ,							lowerCamelCase_    ):
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 else:
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]

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

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

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

            a_    :     Union[str, Any]										=    (images / 2 + 0.5).clamp(0      ,							1    )
            a_    :     Dict										=    images.cpu().permute(0      ,							2      ,							3      ,							1    ).numpy()
            a_    :     Any										=    (images * 2_5_5).round().astype("""uint8"""    )
            a_    :     int										=    list(
                (Image.fromarray(_[:, :, 0]    ) for _ in images)
                if images.shape[3] == 1
                else (Image.fromarray(lowerCamelCase_      ,							mode="""RGB"""    ).convert("""L"""    ) for _ in images)    )

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

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

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

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

            return sample

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     float    ):
            a_    :     Dict										=    acos(torch.dot(torch.flatten(lowerCamelCase_    )      ,							torch.flatten(lowerCamelCase_    )    ) / torch.norm(lowerCamelCase_    ) / torch.norm(lowerCamelCase_    )    )
            return sin((1 - alpha) * theta    ) * xa / sin(lowerCamelCase_    ) + sin(alpha * theta    ) * xa / sin(lowerCamelCase_    )



