




import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
     import transformers



def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     random.seed(lowerCAmelCase_      )
     np.random.seed(lowerCAmelCase_      )
     torch.manual_seed(lowerCAmelCase_      )
     torch.cuda.manual_seed_all(lowerCAmelCase_      )
     # ^^ safe to call this function even if cuda is not available






class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Iterable[torch.nn.Parameter]       ,     SCREAMING_SNAKE_CASE__					:					float = 0.9_999       ,     SCREAMING_SNAKE_CASE__					:					float = 0.0       ,     SCREAMING_SNAKE_CASE__					:					int = 0       ,     SCREAMING_SNAKE_CASE__					:					bool = False       ,     SCREAMING_SNAKE_CASE__					:					Union[float, int] = 1.0       ,     SCREAMING_SNAKE_CASE__					:					Union[float, int] = 2 / 3       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any] = None       ,     SCREAMING_SNAKE_CASE__					:					Dict[str, Any] = None       ,     **SCREAMING_SNAKE_CASE__					:					Dict       ,     )				->	Any:

         if isinstance(SCREAMING_SNAKE_CASE__       ,     torch.nn.Module       ):
              lowerCAmelCase__          =			(
                  "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
                  "Please pass the parameters of the module instead."
              )
              deprecate(
                  "passing a `torch.nn.Module` to `ExponentialMovingAverage`"       ,     "1.0.0"       ,     SCREAMING_SNAKE_CASE__       ,     standard_warn=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			parameters.parameters()

              # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
              lowerCAmelCase__          =			True

         if kwargs.get("max_value"       ,     SCREAMING_SNAKE_CASE__       ) is not None:
              lowerCAmelCase__          =			"The `max_value` argument is deprecated. Please use `decay` instead."
              deprecate("max_value"       ,     "1.0.0"       ,     SCREAMING_SNAKE_CASE__       ,     standard_warn=SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			kwargs["max_value"]

         if kwargs.get("min_value"       ,     SCREAMING_SNAKE_CASE__       ) is not None:
              lowerCAmelCase__          =			"The `min_value` argument is deprecated. Please use `min_decay` instead."
              deprecate("min_value"       ,     "1.0.0"       ,     SCREAMING_SNAKE_CASE__       ,     standard_warn=SCREAMING_SNAKE_CASE__       )
              lowerCAmelCase__          =			kwargs["min_value"]

         lowerCAmelCase__          =			list(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			[p.clone().detach() for p in parameters]

         if kwargs.get("device"       ,     SCREAMING_SNAKE_CASE__       ) is not None:
              lowerCAmelCase__          =			"The `device` argument is deprecated. Please use `to` instead."
              deprecate("device"       ,     "1.0.0"       ,     SCREAMING_SNAKE_CASE__       ,     standard_warn=SCREAMING_SNAKE_CASE__       )
              self.to(device=kwargs["device"]       )

         lowerCAmelCase__          =			None

         lowerCAmelCase__          =			decay
         lowerCAmelCase__          =			min_decay
         lowerCAmelCase__          =			update_after_step
         lowerCAmelCase__          =			use_ema_warmup
         lowerCAmelCase__          =			inv_gamma
         lowerCAmelCase__          =			power
         lowerCAmelCase__          =			0
         lowerCAmelCase__          =			None  # set in `step()`

         lowerCAmelCase__          =			model_cls
         lowerCAmelCase__          =			model_config


    @classmethod
    def        a				(	cls					:					Any       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	"EMAModel":
         lowerCAmelCase__     ,		lowerCAmelCase__          =			model_cls.load_config(SCREAMING_SNAKE_CASE__       ,     return_unused_kwargs=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model_cls.from_pretrained(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			cls(model.parameters()       ,     model_cls=SCREAMING_SNAKE_CASE__       ,     model_config=model.config       )

         ema_model.load_state_dict(SCREAMING_SNAKE_CASE__       )
         return ema_model


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       )				->	Union[str, Any]:
         if self.model_cls is None:
              raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."       )

         if self.model_config is None:
              raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."       )

         lowerCAmelCase__          =			self.model_cls.from_config(self.model_config       )
         lowerCAmelCase__          =			self.state_dict()
         state_dict.pop("shadow_params"       ,     SCREAMING_SNAKE_CASE__       )

         model.register_to_config(**SCREAMING_SNAKE_CASE__       )
         self.copy_to(model.parameters()       )
         model.save_pretrained(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	float:
         lowerCAmelCase__          =			max(0       ,     optimization_step - self.update_after_step - 1       )

         if step <= 0:
              return 0.0

         if self.use_ema_warmup:
              lowerCAmelCase__          =			1 - (1 + step / self.inv_gamma) ** -self.power
         else:
              lowerCAmelCase__          =			(1 + step) / (10 + step)

         lowerCAmelCase__          =			min(SCREAMING_SNAKE_CASE__       ,     self.decay       )
         # make sure decay is not smaller than min_decay
         lowerCAmelCase__          =			max(SCREAMING_SNAKE_CASE__       ,     self.min_decay       )
         return cur_decay_value


    @torch.no_grad()
    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Iterable[torch.nn.Parameter]       )				->	List[str]:
         if isinstance(SCREAMING_SNAKE_CASE__       ,     torch.nn.Module       ):
              lowerCAmelCase__          =			(
                  "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
                  "Please pass the parameters of the module instead."
              )
              deprecate(
                  "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"       ,     "1.0.0"       ,     SCREAMING_SNAKE_CASE__       ,     standard_warn=SCREAMING_SNAKE_CASE__       ,     )
              lowerCAmelCase__          =			parameters.parameters()

         lowerCAmelCase__          =			list(SCREAMING_SNAKE_CASE__       )

         self.optimization_step += 1

         # Compute the decay factor for the exponential moving average.
         lowerCAmelCase__          =			self.get_decay(self.optimization_step       )
         lowerCAmelCase__          =			decay
         lowerCAmelCase__          =			1 - decay

         lowerCAmelCase__          =			contextlib.nullcontext
         if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
              import deepspeed

         for s_param, param in zip(self.shadow_params       ,     SCREAMING_SNAKE_CASE__       ):
              if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   lowerCAmelCase__          =			deepspeed.zero.GatheredParameters(SCREAMING_SNAKE_CASE__       ,     modifier_rank=SCREAMING_SNAKE_CASE__       )

              with context_manager():
                   if param.requires_grad:
                        s_param.sub_(one_minus_decay * (s_param - param)       )
                   else:
                        s_param.copy_(SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Iterable[torch.nn.Parameter]       )				->	None:
         lowerCAmelCase__          =			list(SCREAMING_SNAKE_CASE__       )
         for s_param, param in zip(self.shadow_params       ,     SCREAMING_SNAKE_CASE__       ):
              param.data.copy_(s_param.to(param.device       ).data       )


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple=None       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       )				->	None:
         lowerCAmelCase__          =			[
             p.to(device=SCREAMING_SNAKE_CASE__       ,     dtype=SCREAMING_SNAKE_CASE__       ) if p.is_floating_point() else p.to(device=SCREAMING_SNAKE_CASE__       )
             for p in self.shadow_params
         ]


    def        a				(	self					:					Union[str, Any]       )				->	dict:
         return {
             "decay": self.decay,
             "min_decay": self.min_decay,
             "optimization_step": self.optimization_step,
             "update_after_step": self.update_after_step,
             "use_ema_warmup": self.use_ema_warmup,
             "inv_gamma": self.inv_gamma,
             "power": self.power,
             "shadow_params": self.shadow_params,
         }


    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Iterable[torch.nn.Parameter]       )				->	None:
         lowerCAmelCase__          =			[param.detach().cpu().clone() for param in parameters]


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Iterable[torch.nn.Parameter]       )				->	None:
         if self.temp_stored_params is None:
              raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"       )
         for c_param, param in zip(self.temp_stored_params       ,     SCREAMING_SNAKE_CASE__       ):
              param.data.copy_(c_param.data       )

         # Better memory-wise.
         lowerCAmelCase__          =			None


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					dict       )				->	None:
         lowerCAmelCase__          =			copy.deepcopy(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			state_dict.get("decay"       ,     self.decay       )
         if self.decay < 0.0 or self.decay > 1.0:
              raise ValueError("Decay must be between 0 and 1"       )

         lowerCAmelCase__          =			state_dict.get("min_decay"       ,     self.min_decay       )
         if not isinstance(self.min_decay       ,     SCREAMING_SNAKE_CASE__       ):
              raise ValueError("Invalid min_decay"       )

         lowerCAmelCase__          =			state_dict.get("optimization_step"       ,     self.optimization_step       )
         if not isinstance(self.optimization_step       ,     SCREAMING_SNAKE_CASE__       ):
              raise ValueError("Invalid optimization_step"       )

         lowerCAmelCase__          =			state_dict.get("update_after_step"       ,     self.update_after_step       )
         if not isinstance(self.update_after_step       ,     SCREAMING_SNAKE_CASE__       ):
              raise ValueError("Invalid update_after_step"       )

         lowerCAmelCase__          =			state_dict.get("use_ema_warmup"       ,     self.use_ema_warmup       )
         if not isinstance(self.use_ema_warmup       ,     SCREAMING_SNAKE_CASE__       ):
              raise ValueError("Invalid use_ema_warmup"       )

         lowerCAmelCase__          =			state_dict.get("inv_gamma"       ,     self.inv_gamma       )
         if not isinstance(self.inv_gamma       ,     (float, int)       ):
              raise ValueError("Invalid inv_gamma"       )

         lowerCAmelCase__          =			state_dict.get("power"       ,     self.power       )
         if not isinstance(self.power       ,     (float, int)       ):
              raise ValueError("Invalid power"       )

         lowerCAmelCase__          =			state_dict.get("shadow_params"       ,     SCREAMING_SNAKE_CASE__       )
         if shadow_params is not None:
              lowerCAmelCase__          =			shadow_params
              if not isinstance(self.shadow_params       ,     SCREAMING_SNAKE_CASE__       ):
                   raise ValueError("shadow_params must be a list"       )
              if not all(isinstance(SCREAMING_SNAKE_CASE__       ,     torch.Tensor       ) for p in self.shadow_params       ):
                   raise ValueError("shadow_params must all be Tensors"       )






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
UpperCamelCase								=					'src/diffusers'
UpperCamelCase								=					'.'


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



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     return line.startswith(lowerCAmelCase_      ) or len(lowerCAmelCase_      ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"       ,    lowerCAmelCase_      ) is not None



def 							_A       (						lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__          =			object_name.split("."      )
     lowerCAmelCase__          =			0

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

     lowerCAmelCase__          =			lines[start_index:line_index]
     return "".join(lowerCAmelCase_      )


UpperCamelCase								=					re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
UpperCamelCase								=					re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
UpperCamelCase								=					re.compile(R'<FILL\s+[^>]*>')



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			code.split("\n"      )
     lowerCAmelCase__          =			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 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			len(get_indent(lowerCAmelCase_      )      ) > 0
     if has_indent:
          lowerCAmelCase__          =			F'class Bla:\n{code}'
     lowerCAmelCase__          =			black.Mode(target_versions={black.TargetVersion.PYaa}       ,    line_length=119       ,    preview=lowerCAmelCase_      )
     lowerCAmelCase__          =			black.format_str(lowerCAmelCase_       ,    mode=lowerCAmelCase_      )
     lowerCAmelCase__     ,		lowerCAmelCase__          =			style_docstrings_in_code(lowerCAmelCase_      )
     return result[len("class Bla:\n"      ) :] if has_indent else result



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: List[Any]=False      ):
     """simple docstring"""

     with open(lowerCAmelCase_       ,    "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(lowerCAmelCase_      ):
          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(lowerCAmelCase_      )
          lowerCAmelCase__          =			get_indent(lowerCAmelCase_      )

          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(lowerCAmelCase_      ) and should_continue:
               line_index += 1
               if line_index >= len(lowerCAmelCase_      ):
                    break
               lowerCAmelCase__          =			lines[line_index]
               lowerCAmelCase__          =			_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

          lowerCAmelCase__          =			lines[start_index:line_index]
          lowerCAmelCase__          =			"".join(lowerCAmelCase_      )

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

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

     lowerCAmelCase__          =			glob.glob(os.path.join(lowerCAmelCase_       ,    "**/*.py"      )       ,    recursive=lowerCAmelCase_      )
     lowerCAmelCase__          =			[]
     for filename in all_files:
          lowerCAmelCase__          =			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:
          lowerCAmelCase__          =			"\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__":
     UpperCamelCase								=					argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     UpperCamelCase								=					parser.parse_args()

     check_copies(args.fix_and_overwrite)
