





'''simple docstring'''

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 		UpperCamelCase							(				_lowerCamelCase   :   List[Any]			):
    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(_lowerCamelCase			).astype(np.floataa			) / 2_5_5.0
    A__							=     image[None].transpose(0		,			3		,			1		,			2			)
    A__							=     torch.from_numpy(_lowerCamelCase			)
    return 2.0 * image - 1.0






class   UpperCAmelCase      (			UpperCamelCase__							):
 def __init__(							self :int		,			lowercase_ :VQModel		,			lowercase_ :UNetaDModel		,			lowercase_ :Union[
         DDIMScheduler,
         PNDMScheduler,
         LMSDiscreteScheduler,
         EulerDiscreteScheduler,
         EulerAncestralDiscreteScheduler,
         DPMSolverMultistepScheduler,
     ]		,			)->				Optional[Any]:
     super().__init__()
     self.register_modules(vqvae=lowercase_		,			unet=lowercase_		,			scheduler=lowercase_				)



 @torch.no_grad()
 def __call__(							self :Any		,			lowercase_ :Union[torch.Tensor, PIL.Image.Image] = None		,			lowercase_ :Optional[int] = 1		,			lowercase_ :Optional[int] = 1_00		,			lowercase_ :Optional[float] = 0.0		,			lowercase_ :Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			lowercase_ :Optional[str] = "pil"		,			lowercase_ :bool = True		,			)->				Union[Tuple, ImagePipelineOutput]:
     if isinstance(lowercase_		,			PIL.Image.Image				):
         A__							=     1
     elif isinstance(lowercase_		,			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(lowercase_				)}"				)

     if isinstance(lowercase_		,			PIL.Image.Image				):
         A__							=     preprocess(lowercase_				)

     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(lowercase_		,			generator=lowercase_		,			device=self.device		,			dtype=lowercase_				)

     A__							=     image.to(device=self.device		,			dtype=lowercase_				)

     # set timesteps and move to the correct device
     self.scheduler.set_timesteps(lowercase_		,			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(lowercase_				):
         # concat latents and low resolution image in the channel dimension.
         A__							=     torch.cat([latents, image]		,			dim=1				)
         A__							=     self.scheduler.scale_model_input(lowercase_		,			lowercase_				)
         # predict the noise residual
         A__							=     self.unet(lowercase_		,			lowercase_				).sample
         # compute the previous noisy sample x_t -> x_t-1
         A__							=     self.scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			**lowercase_				).prev_sample

     # decode the image latents with the VQVAE
     A__							=     self.vqvae.decode(lowercase_				).sample
     A__							=     torch.clamp(lowercase_		,			-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(lowercase_				)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=lowercase_				)










'''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
__lowerCAmelCase				:      Optional[Any]						      ="src/diffusers"
__lowerCAmelCase				:      Dict						      ="."


# This is to make sure the diffusers module imported is the one in the repo.
__lowerCAmelCase				:      int						      =importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(DIFFUSERS_PATH, "__init__.py"),
    submodule_search_locations=[DIFFUSERS_PATH],
)
__lowerCAmelCase				:      Dict						      =spec.loader.load_module()





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   Optional[Any]			):
    return line.startswith(_lowerCamelCase			) or len(_lowerCamelCase			) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"		,			_lowerCamelCase			) is not None





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     object_name.split("."			)
    A__							=     0

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

    with open(os.path.join(_lowerCamelCase		,			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(_lowerCamelCase			) 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(_lowerCamelCase			):
        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(_lowerCamelCase			) and _should_continue(lines[line_index]		,			_lowerCamelCase			):
        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(_lowerCamelCase			)


__lowerCAmelCase				:      Any						      =re.compile(R"^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)")
__lowerCAmelCase				:      List[Any]						      =re.compile(R"^\s*(\S+)->(\S+)(\s+.*|$)")
__lowerCAmelCase				:      Any						      =re.compile(R"<FILL\s+[^>]*>")





def 		UpperCamelCase							(				_lowerCamelCase   :   List[Any]			):
    A__							=     code.split("\n"			)
    A__							=     0
    while idx < len(_lowerCamelCase			) and len(lines[idx]			) == 0:
        idx += 1
    if idx < len(_lowerCamelCase			):
        return re.search(r"^(\s*)\S"		,			lines[idx]			).groups()[0]
    return ""





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple			):
    A__							=     len(get_indent(_lowerCamelCase			)			) > 0
    if has_indent:
        A__							=     F"class Bla:\n{code}"
    A__							=     black.Mode(target_versions={black.TargetVersion.PYaa}		,			line_length=1_19		,			preview=_lowerCamelCase			)
    A__							=     black.format_str(_lowerCamelCase		,			mode=_lowerCamelCase			)
    A__,      A__							=     style_docstrings_in_code(_lowerCamelCase			)
    return result[len("class Bla:\n"			) :] if has_indent else result





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple		,			_lowerCamelCase   :   List[str]=False			):
    with open(_lowerCamelCase		,			"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(_lowerCamelCase			):
        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(_lowerCamelCase			)
        A__							=     get_indent(_lowerCamelCase			)

        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(_lowerCamelCase			) and should_continue:
            line_index += 1
            if line_index >= len(_lowerCamelCase			):
                break
            A__							=     lines[line_index]
            A__							=     _should_continue(_lowerCamelCase		,			_lowerCamelCase			) and re.search(F"^{indent}# End copy"		,			_lowerCamelCase			) 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(_lowerCamelCase			)

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

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

            # 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(_lowerCamelCase			) > 0:
        # Warn the user a file has been modified.
        print(F"Detected changes, rewriting {filename}."			)
        with open(_lowerCamelCase		,			"w"		,			encoding="utf-8"		,			newline="\n"			) as f:
            f.writelines(_lowerCamelCase			)
    return diffs





def 		UpperCamelCase							(				_lowerCamelCase   :   bool = False			):
    A__							=     glob.glob(os.path.join(_lowerCamelCase		,			"**/*.py"			)		,			recursive=_lowerCamelCase			)
    A__							=     []
    for filename in all_files:
        A__							=     is_copy_consistent(_lowerCamelCase		,			_lowerCamelCase			)
        diffs += [F"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs]
    if not overwrite and len(_lowerCamelCase			) > 0:
        A__							=     "\n".join(_lowerCamelCase			)
        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__":
  __lowerCAmelCase				:      Union[str, Any]						      =argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  __lowerCAmelCase				:      int						      =parser.parse_args()

  check_copies(args.fix_and_overwrite)



