



'''simple docstring'''





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		:Optional[int]   	=  '''src/diffusers'''
__snake_case		:List[str]   	=  '''.'''


# This is to make sure the diffusers module imported is the one in the repo.
__snake_case		:Union[str, Any]   	=  importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
__snake_case		:List[str]   	=  spec.loader.load_module()





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





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       object_name.split('''.'''       )
    __a		 =       0

    # First let's find the module where our object lives.
    __a		 =       parts[i]
    while i < len(_UpperCAmelCase       ) and not os.path.isfile(os.path.join(_UpperCAmelCase     ,				f'{module}.py'       )       ):
        i += 1
        if i < len(_UpperCAmelCase       ):
            __a		 =       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:
        __a		 =       f.readlines()

    # Now let's find the class / func in the code!
    __a		 =       ''''''
    __a		 =       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).
    __a		 =       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

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


__snake_case		:int   	=  re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
__snake_case		:List[str]   	=  re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
__snake_case		:int   	=  re.compile(r'''<FILL\s+[^>]*>''')





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       code.split('''\n'''       )
    __a		 =       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   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       len(get_indent(_UpperCAmelCase       )       ) > 0
    if has_indent:
        __a		 =       f'class Bla:\n{code}'
    __a		 =       black.Mode(target_versions={black.TargetVersion.PYaa}     ,				line_length=119     ,				preview=_UpperCAmelCase       )
    __a		 =       black.format_str(_UpperCAmelCase     ,				mode=_UpperCAmelCase       )
    __a ,     __a		 =       style_docstrings_in_code(_UpperCAmelCase       )
    return result[len('''class Bla:\n'''       ) :] if has_indent else result





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=False       ):
    with open(_UpperCAmelCase     ,				'''r'''     ,				encoding='''utf-8'''     ,				newline='''\n'''       ) as f:
        __a		 =       f.readlines()
    __a		 =       []
    __a		 =       0
    # Not a for loop cause `lines` is going to change (if `overwrite=True`).
    while line_index < len(_UpperCAmelCase       ):
        __a		 =       _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.
        __a ,     __a ,     __a		 =       search.groups()
        __a		 =       find_code_in_diffusers(_UpperCAmelCase       )
        __a		 =       get_indent(_UpperCAmelCase       )

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

        # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
        __a		 =       True
        while line_index < len(_UpperCAmelCase       ) and should_continue:
            line_index += 1
            if line_index >= len(_UpperCAmelCase       ):
                break
            __a		 =       lines[line_index]
            __a		 =       _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

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

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

        # Before comparing, use the `replace_pattern` on the original code.
        if len(_UpperCAmelCase       ) > 0:
            __a		 =       replace_pattern.replace('''with'''     ,				''''''       ).split(''','''       )
            __a		 =       [_re_replace_pattern.search(_UpperCAmelCase       ) for p in patterns]
            for pattern in patterns:
                if pattern is None:
                    continue
                __a ,     __a ,     __a		 =       pattern.groups()
                __a		 =       re.sub(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
                if option.strip() == "all-casing":
                    __a		 =       re.sub(obja.lower()     ,				obja.lower()     ,				_UpperCAmelCase       )
                    __a		 =       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
            __a		 =       blackify(lines[start_index - 1] + theoretical_code       )
            __a		 =       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:
                __a		 =       lines[:start_index] + [theoretical_code] + lines[line_index:]
                __a		 =       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   __snake_case			(    _UpperCAmelCase = False       ):
    __a		 =       glob.glob(os.path.join(_UpperCAmelCase     ,				'''**/*.py'''       )     ,				recursive=_UpperCAmelCase       )
    __a		 =       []
    for filename in all_files:
        __a		 =       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:
        __a		 =       '''\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		:Dict   	=  argparse.ArgumentParser()
 parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
 __snake_case		:str   	=  parser.parse_args()

 check_copies(args.fix_and_overwrite)










import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def   __snake_case			(    _UpperCAmelCase       ):
    __a ,     __a		 =       image.size
    __a ,     __a		 =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    __a		 =       image.resize((w, h)     ,				resample=PIL_INTERPOLATION['''lanczos''']       )
    __a		 =       np.array(_UpperCAmelCase       ).astype(np.floataa       ) / 2_55.0
    __a		 =       image[None].transpose(0     ,				3     ,				1     ,				2       )
    __a		 =       torch.from_numpy(_UpperCAmelCase       )
    return 2.0 * image - 1.0

class 					_A (			__UpperCAmelCase					):



       def __init__(	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  VQModel  ,	__SCREAMING_SNAKE_CASE							:  UNetaDModel  ,	__SCREAMING_SNAKE_CASE							:  Union[
               DDIMScheduler,
               PNDMScheduler,
               LMSDiscreteScheduler,
               EulerDiscreteScheduler,
               EulerAncestralDiscreteScheduler,
               DPMSolverMultistepScheduler,
           ]  ,	):


           '''simple docstring'''

           super().__init__()
           self.register_modules(vqvae=__SCREAMING_SNAKE_CASE  ,	unet=__SCREAMING_SNAKE_CASE  ,	scheduler=__SCREAMING_SNAKE_CASE)





       @torch.no_grad()
       def __call__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[torch.Tensor, PIL.Image.Image] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 1  ,	__SCREAMING_SNAKE_CASE							:  Optional[int] = 100  ,	__SCREAMING_SNAKE_CASE							:  Optional[float] = 0.0  ,	__SCREAMING_SNAKE_CASE							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,	__SCREAMING_SNAKE_CASE							:  Optional[str] = "pil"  ,	__SCREAMING_SNAKE_CASE							:  bool = True  ,	):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
               __a		 =       1
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor):
               __a		 =       image.shape[0]
           else:
               raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__SCREAMING_SNAKE_CASE)}')

           if isinstance(__SCREAMING_SNAKE_CASE  ,	PIL.Image.Image):
               __a		 =       preprocess(__SCREAMING_SNAKE_CASE)

           __a ,     __a		 =       image.shape[-2:]

           # in_channels should be 6: 3 for latents, 3 for low resolution image
           __a		 =       (batch_size, self.unet.config.in_channels // 2, height, width)
           __a		 =       next(self.unet.parameters()).dtype

           __a		 =       randn_tensor(__SCREAMING_SNAKE_CASE  ,	generator=__SCREAMING_SNAKE_CASE  ,	device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

           __a		 =       image.to(device=self.device  ,	dtype=__SCREAMING_SNAKE_CASE)

           # set timesteps and move to the correct device
           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  ,	device=self.device)
           __a		 =       self.scheduler.timesteps

           # scale the initial noise by the standard deviation required by the scheduler
           __a		 =       latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           __a		 =       '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
           __a		 =       {}
           if accepts_eta:
               __a		 =       eta

           for t in self.progress_bar(__SCREAMING_SNAKE_CASE):
               # concat latents and low resolution image in the channel dimension.
               __a		 =       torch.cat([latents, image]  ,	dim=1)
               __a		 =       self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
               # predict the noise residual
               __a		 =       self.unet(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE).sample
               # compute the previous noisy sample x_t -> x_t-1
               __a		 =       self.scheduler.step(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	**__SCREAMING_SNAKE_CASE).prev_sample

           # decode the image latents with the VQVAE
           __a		 =       self.vqvae.decode(__SCREAMING_SNAKE_CASE).sample
           __a		 =       torch.clamp(__SCREAMING_SNAKE_CASE  ,	-1.0  ,	1.0)
           __a		 =       image / 2 + 0.5
           __a		 =       image.cpu().permute(0  ,	2  ,	3  ,	1).numpy()

           if output_type == "pil":
               __a		 =       self.numpy_to_pil(__SCREAMING_SNAKE_CASE)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE)





