import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case__				= '''src/diffusers'''
snake_case__				= '''.'''


# This is to make sure the diffusers module imported is the one in the repo.
snake_case__				= importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
snake_case__				= spec.loader.load_module()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[Any]:



      '''simple docstring'''

      return line.startswith(_UpperCAmelCase		) or len(_UpperCAmelCase		) <= 1 or re.search(r'''^\s*\)(\s*->.*:|:)\s*$''' ,						_UpperCAmelCase		) is not None
def       __magic_name__(  __UpperCAmelCase		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				object_name.split('''.'''		)
      _lowerCamelCase			    =				0

      # First let's find the module where our object lives.
      _lowerCamelCase			    =				parts[i]
      while i < len(_UpperCAmelCase		) and not os.path.isfile(os.path.join(_UpperCAmelCase ,						F'{module}.py'		)		):
            i += 1
            if i < len(_UpperCAmelCase		):
                  _lowerCamelCase			    =				os.path.join(_UpperCAmelCase ,						parts[i]		)
      if i >= len(_UpperCAmelCase		):
            raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'		)

      with open(os.path.join(_UpperCAmelCase ,						F'{module}.py'		) ,						'''r''' ,						encoding='''utf-8''' ,						newline='''\n'''		) as f:
            _lowerCamelCase			    =				f.readlines()

      # Now let's find the class / func in the code!
      _lowerCamelCase			    =				''''''
      _lowerCamelCase			    =				0
      for name in parts[i + 1 :]:
            while (
                line_index < len(_UpperCAmelCase		) and re.search(rF'^{indent}(class|def)\s+{name}(\(|\:)' ,						lines[line_index]		) is None
            ):
                  line_index += 1
            indent += "    "
            line_index += 1

      if line_index >= len(_UpperCAmelCase		):
            raise ValueError(F' {object_name} does not match any function or class in {module}.'		)

      # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
      _lowerCamelCase			    =				line_index
      while line_index < len(_UpperCAmelCase		) and _should_continue(lines[line_index] ,						_UpperCAmelCase		):
            line_index += 1
      # Clean up empty lines at the end (if any).
      while len(lines[line_index - 1]		) <= 1:
            line_index -= 1

      _lowerCamelCase			    =				lines[start_index:line_index]
      return "".join(_UpperCAmelCase		)


snake_case__				= re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
snake_case__				= re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
snake_case__				= re.compile(R'<FILL\s+[^>]*>')
def       __magic_name__(  __UpperCAmelCase		)						->    Tuple:



      '''simple docstring'''

      _lowerCamelCase			    =				code.split('''\n'''		)
      _lowerCamelCase			    =				0
      while idx < len(_UpperCAmelCase		) and len(lines[idx]		) == 0:
            idx += 1
      if idx < len(_UpperCAmelCase		):
            return re.search(r'''^(\s*)\S''' ,						lines[idx]		).groups()[0]
      return ""
def       __magic_name__(  __UpperCAmelCase		)						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				len(get_indent(_UpperCAmelCase		)		) > 0
      if has_indent:
            _lowerCamelCase			    =				F'class Bla:\n{code}'
      _lowerCamelCase			    =				black.Mode(target_versions={black.TargetVersion.PYaa} ,						line_length=119 ,						preview=_UpperCAmelCase		)
      _lowerCamelCase			    =				black.format_str(_UpperCAmelCase ,						mode=_UpperCAmelCase		)
      _lowerCamelCase    ,					_lowerCamelCase			    =				style_docstrings_in_code(_UpperCAmelCase		)
      return result[len('''class Bla:\n'''		) :] if has_indent else result
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=False		)						->    Dict:



      '''simple docstring'''

      with open(_UpperCAmelCase ,						'''r''' ,						encoding='''utf-8''' ,						newline='''\n'''		) as f:
            _lowerCamelCase			    =				f.readlines()
      _lowerCamelCase			    =				[]
      _lowerCamelCase			    =				0
      # Not a for loop cause `lines` is going to change (if `overwrite=True`).
      while line_index < len(_UpperCAmelCase		):
            _lowerCamelCase			    =				_re_copy_warning.search(lines[line_index]		)
            if search is None:
                  line_index += 1
                  continue

            # There is some copied code here, let's retrieve the original.
            _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				search.groups()
            _lowerCamelCase			    =				find_code_in_diffusers(_UpperCAmelCase		)
            _lowerCamelCase			    =				get_indent(_UpperCAmelCase		)

            _lowerCamelCase			    =				line_index + 1 if indent == theoretical_indent else line_index + 2
            _lowerCamelCase			    =				theoretical_indent
            _lowerCamelCase			    =				start_index

            # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
            _lowerCamelCase			    =				True
            while line_index < len(_UpperCAmelCase		) and should_continue:
                  line_index += 1
                  if line_index >= len(_UpperCAmelCase		):
                        break
                  _lowerCamelCase			    =				lines[line_index]
                  _lowerCamelCase			    =				_should_continue(_UpperCAmelCase ,						_UpperCAmelCase		) and re.search(F'^{indent}# End copy' ,						_UpperCAmelCase		) is None
            # Clean up empty lines at the end (if any).
            while len(lines[line_index - 1]		) <= 1:
                  line_index -= 1

            _lowerCamelCase			    =				lines[start_index:line_index]
            _lowerCamelCase			    =				''''''.join(_UpperCAmelCase		)

            # Remove any nested `Copied from` comments to avoid circular copies
            _lowerCamelCase			    =				[line for line in theoretical_code.split('''\n'''		) if _re_copy_warning.search(_UpperCAmelCase		) is None]
            _lowerCamelCase			    =				'''\n'''.join(_UpperCAmelCase		)

            # Before comparing, use the `replace_pattern` on the original code.
            if len(_UpperCAmelCase		) > 0:
                  _lowerCamelCase			    =				replace_pattern.replace('''with''' ,						''''''		).split(''','''		)
                  _lowerCamelCase			    =				[_re_replace_pattern.search(_UpperCAmelCase		) for p in patterns]
                  for pattern in patterns:
                        if pattern is None:
                              continue
                        _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				pattern.groups()
                        _lowerCamelCase			    =				re.sub(_UpperCAmelCase ,						_UpperCAmelCase ,						_UpperCAmelCase		)
                        if option.strip() == "all-casing":
                              _lowerCamelCase			    =				re.sub(obja.lower() ,						obja.lower() ,						_UpperCAmelCase		)
                              _lowerCamelCase			    =				re.sub(obja.upper() ,						obja.upper() ,						_UpperCAmelCase		)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
                  _lowerCamelCase			    =				blackify(lines[start_index - 1] + theoretical_code		)
                  _lowerCamelCase			    =				theoretical_code[len(lines[start_index - 1]		) :]

            # Test for a diff and act accordingly.
            if observed_code != theoretical_code:
                  diffs.append([object_name, start_index]		)
                  if overwrite:
                        _lowerCamelCase			    =				lines[:start_index] + [theoretical_code] + lines[line_index:]
                        _lowerCamelCase			    =				start_index + 1

      if overwrite and len(_UpperCAmelCase		) > 0:
            # Warn the user a file has been modified.
            print(F'Detected changes, rewriting {filename}.'		)
            with open(_UpperCAmelCase ,						'''w''' ,						encoding='''utf-8''' ,						newline='''\n'''		) as f:
                  f.writelines(_UpperCAmelCase		)
      return diffs
def       __magic_name__(  __UpperCAmelCase = False		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				glob.glob(os.path.join(_UpperCAmelCase ,						'''**/*.py'''		) ,						recursive=_UpperCAmelCase		)
      _lowerCamelCase			    =				[]
      for filename in all_files:
            _lowerCamelCase			    =				is_copy_consistent(_UpperCAmelCase ,						_UpperCAmelCase		)
            diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
      if not overwrite and len(_UpperCAmelCase		) > 0:
            _lowerCamelCase			    =				'''\n'''.join(_UpperCAmelCase		)
            raise Exception(
                '''Found the following copy inconsistencies:\n'''
                + diff
                + '''\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'''		)


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     snake_case__				= parser.parse_args()

     check_copies(args.fix_and_overwrite)
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  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  ['vqvae']



     def __init__(					self				,				A_				,				A_				,				A_				,				A_				,				) ->				Tuple:






           """simple docstring"""
           super().__init__()
           self.register_modules(unet=A_				,				scheduler=A_				,				mel=A_				,				vqvae=A_						)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return 50 if isinstance(self.scheduler				,				A_						) else 10_00



     @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				,				) ->				Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:






           """simple docstring"""
           _lowerCamelCase			    =				steps or self.get_default_steps()
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				step_generator or generator
           # For backwards compatibility
           if type(self.unet.config.sample_size						) == int:
                 _lowerCamelCase			    =				(self.unet.config.sample_size, self.unet.config.sample_size)
           if noise is None:
                 _lowerCamelCase			    =				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				,				)
           _lowerCamelCase			    =				noise
           _lowerCamelCase			    =				None

           if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(A_				,				A_						)
                 _lowerCamelCase			    =				self.mel.audio_slice_to_image(A_						)
                 _lowerCamelCase			    =				np.frombuffer(input_image.tobytes()				,				dtype='''uint8'''						).reshape(
                     (input_image.height, input_image.width)						)
                 _lowerCamelCase			    =				(input_image / 2_55) * 2 - 1
                 _lowerCamelCase			    =				torch.tensor(input_image[np.newaxis, :, :]				,				dtype=torch.float						).to(self.device						)

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

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

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

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

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

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

           _lowerCamelCase			    =				(images / 2 + 0.5).clamp(0				,				1						)
           _lowerCamelCase			    =				images.cpu().permute(0				,				2				,				3				,				1						).numpy()
           _lowerCamelCase			    =				(images * 2_55).round().astype('''uint8'''						)
           _lowerCamelCase			    =				list(
               (Image.fromarray(_[:, :, 0]						) for _ in images)
               if images.shape[3] == 1
               else (Image.fromarray(A_				,				mode='''RGB'''						).convert('''L'''						) for _ in images)						)

           _lowerCamelCase			    =				[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 		UpperCamelCase_						(					self				,				A_				,				A_ = 50						) ->				np.ndarray:






           """simple docstring"""
           assert isinstance(self.scheduler				,				A_						)
           self.scheduler.set_timesteps(A_						)
           _lowerCamelCase			    =				np.array(
               [np.frombuffer(image.tobytes()				,				dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
           _lowerCamelCase			    =				(sample / 2_55) * 2 - 1
           _lowerCamelCase			    =				torch.Tensor(A_						).to(self.device						)

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

           return sample





     @staticmethod
     def 		UpperCamelCase_						(					A_				,				A_				,				A_						) ->				torch.Tensor:






           """simple docstring"""
           _lowerCamelCase			    =				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_						)