





"""simple docstring"""



import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a_ =			"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
a_ =			importlib.util.spec_from_file_location(
    """transformers""",
    os.path.join(PATH_TO_TRANSFORMERS, """__init__.py"""),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
a_ =			spec.loader.load_module()

a_ =			transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
a_ =			re.compile("""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


a_ =			{
    """CLIPConfigMixin""",
    """DecisionTransformerConfigMixin""",
    """EncoderDecoderConfigMixin""",
    """RagConfigMixin""",
    """SpeechEncoderDecoderConfigMixin""",
    """VisionEncoderDecoderConfigMixin""",
    """VisionTextDualEncoderConfigMixin""",
}







def 		__lowercase     (   )   ->Any:



    '''simple docstring'''







    __A    :						str						 =     []

    for config_class in list(CONFIG_MAPPING.values()						):
        __A    :						Any						 =     False

        # source code of `config_class`
        __A    :						Optional[Any]						 =     inspect.getsource(snake_case_						)
        __A    :						List[Any]						 =     _re_checkpoint.findall(snake_case_						)

        for checkpoint in checkpoints:
            # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
            # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
            __A    :						List[Any]						 =     checkpoint

            # verify the checkpoint name corresponds to the checkpoint link
            __A    :						str						 =     F"""https://huggingface.co/{ckpt_name}"""
            if ckpt_link == ckpt_link_from_name:
                __A    :						List[Any]						 =     True
                break

        __A    :						str						 =     config_class.__name__
        if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
            configs_without_checkpoint.append(snake_case_						)

    if len(snake_case_						) > 0:
        __A    :						Dict						 =     '\n'.join(sorted(snake_case_						)						)
        raise ValueError(F"""The following configurations don\'t contain any valid checkpoint:\n{message}"""						)


if __name__ == "__main__":
       check_config_docstrings_have_checkpoints()








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      ):
 __lowercase :							Any        = ['vqvae']
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, )					->				Union[str, Any]:




        '''simple docstring'''
        super().__init__()
        self.register_modules(unet=lowerCAmelCase__				, scheduler=lowerCAmelCase__				, mel=lowerCAmelCase__				, vqvae=lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        return 50 if isinstance(self.scheduler				, lowerCAmelCase__ ) else 1_000
 @torch.no_grad()
 def __call__(   self				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 0				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__=True				, )					->				Union[
     Union[AudioPipelineOutput, ImagePipelineOutput],
     Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
 ]:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   steps or self.get_default_steps()
        self.scheduler.set_timesteps(lowerCAmelCase__ )
        lowercase__:   Optional[int]  			=   step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size ) == int:
               lowercase__:   Optional[int]  			=   (self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
               lowercase__:   List[str]  			=   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				, )
        lowercase__:   List[Any]  			=   noise
        lowercase__:   int  			=   None

        if audio_file is not None or raw_audio is not None:
               self.mel.load_audio(lowerCAmelCase__				, lowerCAmelCase__ )
               lowercase__:   int  			=   self.mel.audio_slice_to_image(lowerCAmelCase__ )
               lowercase__:   int  			=   np.frombuffer(input_image.tobytes()				, dtype='uint8' ).reshape(
                   (input_image.height, input_image.width) )
               lowercase__:   str  			=   (input_image / 255) * 2 - 1
               lowercase__:   Union[str, Any]  			=   torch.tensor(input_image[np.newaxis, :, :]				, dtype=torch.float ).to(self.device )

               if self.vqvae is not None:
                      lowercase__:   Optional[int]  			=   self.vqvae.encode(torch.unsqueeze(lowerCAmelCase__				, 0 ) ).latent_dist.sample(
                          generator=lowerCAmelCase__ )[0]
                      lowercase__:   Dict  			=   self.vqvae.config.scaling_factor * input_images

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

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

               if isinstance(self.scheduler				, lowerCAmelCase__ ):
                      lowercase__:   List[str]  			=   self.scheduler.step(
                          model_output=lowerCAmelCase__				, timestep=lowerCAmelCase__				, sample=lowerCAmelCase__				, eta=lowerCAmelCase__				, generator=lowerCAmelCase__				, )['prev_sample']
               else:
                      lowercase__:   int  			=   self.scheduler.step(
                          model_output=lowerCAmelCase__				, timestep=lowerCAmelCase__				, sample=lowerCAmelCase__				, generator=lowerCAmelCase__				, )['prev_sample']

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

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

        lowercase__:   Dict  			=   (images / 2 + 0.5).clamp(0				, 1 )
        lowercase__:   List[Any]  			=   images.cpu().permute(0				, 2				, 3				, 1 ).numpy()
        lowercase__:   int  			=   (images * 255).round().astype('uint8' )
        lowercase__:   Optional[Any]  			=   list(
            (Image.fromarray(_[:, :, 0] ) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(lowerCAmelCase__				, mode='RGB' ).convert('L' ) for _ in images) )

        lowercase__:   Dict  			=   [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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = 50 )					->				np.ndarray:




        '''simple docstring'''
        assert isinstance(self.scheduler				, lowerCAmelCase__ )
        self.scheduler.set_timesteps(lowerCAmelCase__ )
        lowercase__:   List[str]  			=   np.array(
            [np.frombuffer(image.tobytes()				, dtype='uint8' ).reshape((1, image.height, image.width) ) for image in images] )
        lowercase__:   str  			=   (sample / 255) * 2 - 1
        lowercase__:   str  			=   torch.Tensor(lowerCAmelCase__ ).to(self.device )

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

        return sample
 @staticmethod
 def 		SCREAMING_SNAKE_CASE__   (   lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				torch.Tensor:




        '''simple docstring'''
        lowercase__:   Any  			=   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__ )


