




'''simple docstring'''




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 _lowercase	(      lowerCamelCase__						)					->       Any:





       """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 		__A       :






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_ = 0.9_9_9_9				,     UpperCamelCase_ = 0.0				,     UpperCamelCase_ = 0				,     UpperCamelCase_ = False				,     UpperCamelCase_ = 1.0				,     UpperCamelCase_ = 2 / 3				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):

              if isinstance(UpperCamelCase_				,     torch.nn.Module      ):
                     __UpperCAmelCase  :				int       				=		(
                         "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"				,     UpperCamelCase_				,     standard_warn=UpperCamelCase_				,     )
                     __UpperCAmelCase  :				Optional[int]       				=		parameters.parameters()

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

              if kwargs.get("max_value"				,     UpperCamelCase_      ) is not None:
                     __UpperCAmelCase  :				Dict       				=		"The `max_value` argument is deprecated. Please use `decay` instead."
                     deprecate("max_value"				,     "1.0.0"				,     UpperCamelCase_				,     standard_warn=UpperCamelCase_      )
                     __UpperCAmelCase  :				Optional[Any]       				=		kwargs["max_value"]

              if kwargs.get("min_value"				,     UpperCamelCase_      ) is not None:
                     __UpperCAmelCase  :				Union[str, Any]       				=		"The `min_value` argument is deprecated. Please use `min_decay` instead."
                     deprecate("min_value"				,     "1.0.0"				,     UpperCamelCase_				,     standard_warn=UpperCamelCase_      )
                     __UpperCAmelCase  :				int       				=		kwargs["min_value"]

              __UpperCAmelCase  :				Union[str, Any]       				=		list(UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		[p.clone().detach() for p in parameters]

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

              __UpperCAmelCase  :				int       				=		None

              __UpperCAmelCase  :				int       				=		decay
              __UpperCAmelCase  :				Any       				=		min_decay
              __UpperCAmelCase  :				Any       				=		update_after_step
              __UpperCAmelCase  :				int       				=		use_ema_warmup
              __UpperCAmelCase  :				Dict       				=		inv_gamma
              __UpperCAmelCase  :				Any       				=		power
              __UpperCAmelCase  :				int       				=		0
              __UpperCAmelCase  :				Dict       				=		None  # set in `step()`

              __UpperCAmelCase  :				List[str]       				=		model_cls
              __UpperCAmelCase  :				Optional[int]       				=		model_config






       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		model_cls.load_config(UpperCamelCase_				,     return_unused_kwargs=UpperCamelCase_      )
              __UpperCAmelCase  :				str       				=		model_cls.from_pretrained(UpperCamelCase_      )

              __UpperCAmelCase  :				int       				=		cls(model.parameters()				,     model_cls=UpperCamelCase_				,     model_config=model.config      )

              ema_model.load_state_dict(UpperCamelCase_      )
              return ema_model






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              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__."      )

              __UpperCAmelCase  :				int       				=		self.model_cls.from_config(self.model_config      )
              __UpperCAmelCase  :				Any       				=		self.state_dict()
              state_dict.pop("shadow_params"				,     UpperCamelCase_      )

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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		max(0				,     optimization_step - self.update_after_step - 1      )

              if step <= 0:
                     return 0.0

              if self.use_ema_warmup:
                     __UpperCAmelCase  :				Optional[int]       				=		1 - (1 + step / self.inv_gamma) ** -self.power
              else:
                     __UpperCAmelCase  :				Any       				=		(1 + step) / (10 + step)

              __UpperCAmelCase  :				Any       				=		min(UpperCamelCase_				,     self.decay      )
              # make sure decay is not smaller than min_decay
              __UpperCAmelCase  :				int       				=		max(UpperCamelCase_				,     self.min_decay      )
              return cur_decay_value






       @torch.no_grad()
       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if isinstance(UpperCamelCase_				,     torch.nn.Module      ):
                     __UpperCAmelCase  :				Optional[Any]       				=		(
                         "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"				,     UpperCamelCase_				,     standard_warn=UpperCamelCase_				,     )
                     __UpperCAmelCase  :				Tuple       				=		parameters.parameters()

              __UpperCAmelCase  :				Optional[int]       				=		list(UpperCamelCase_      )

              self.optimization_step += 1

              # Compute the decay factor for the exponential moving average.
              __UpperCAmelCase  :				Optional[Any]       				=		self.get_decay(self.optimization_step      )
              __UpperCAmelCase  :				int       				=		decay
              __UpperCAmelCase  :				int       				=		1 - decay

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

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

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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		list(UpperCamelCase_      )
              for s_param, param in zip(self.shadow_params				,     UpperCamelCase_      ):
                     param.data.copy_(s_param.to(param.device      ).data      )






       def 			_snake_case						(      self				,     UpperCamelCase_=None				,     UpperCamelCase_=None      ):
              __UpperCAmelCase  :				str       				=		[
                  p.to(device=UpperCamelCase_				,     dtype=UpperCamelCase_      ) if p.is_floating_point() else p.to(device=UpperCamelCase_      )
                  for p in self.shadow_params
              ]






       def 			_snake_case						(      self      ):
              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 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				str       				=		[param.detach().cpu().clone() for param in parameters]






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              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				,     UpperCamelCase_      ):
                     param.data.copy_(c_param.data      )

              # Better memory-wise.
              __UpperCAmelCase  :				str       				=		None







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Tuple       				=		copy.deepcopy(UpperCamelCase_      )

              __UpperCAmelCase  :				Tuple       				=		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"      )

              __UpperCAmelCase  :				Dict       				=		state_dict.get("min_decay"				,     self.min_decay      )
              if not isinstance(self.min_decay				,     UpperCamelCase_      ):
                     raise ValueError("Invalid min_decay"      )

              __UpperCAmelCase  :				Optional[Any]       				=		state_dict.get("optimization_step"				,     self.optimization_step      )
              if not isinstance(self.optimization_step				,     UpperCamelCase_      ):
                     raise ValueError("Invalid optimization_step"      )

              __UpperCAmelCase  :				List[str]       				=		state_dict.get("update_after_step"				,     self.update_after_step      )
              if not isinstance(self.update_after_step				,     UpperCamelCase_      ):
                     raise ValueError("Invalid update_after_step"      )

              __UpperCAmelCase  :				Dict       				=		state_dict.get("use_ema_warmup"				,     self.use_ema_warmup      )
              if not isinstance(self.use_ema_warmup				,     UpperCamelCase_      ):
                     raise ValueError("Invalid use_ema_warmup"      )

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

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

              __UpperCAmelCase  :				str       				=		state_dict.get("shadow_params"				,     UpperCamelCase_      )
              if shadow_params is not None:
                     __UpperCAmelCase  :				Union[str, Any]       				=		shadow_params
                     if not isinstance(self.shadow_params				,     UpperCamelCase_      ):
                            raise ValueError("shadow_params must be a list"      )
                     if not all(isinstance(UpperCamelCase_				,     torch.Tensor      ) for p in self.shadow_params      ):
                            raise ValueError("shadow_params must all be Tensors"      )







'''simple docstring'''




import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_a   :	Union[str, Any] 							=   HfApi()

_a   :	int 							=   {}
# fmt: off
_a   :	Optional[int] 							=   torch.tensor([
    -0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
    1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
    -1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
    0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
_a   :	Optional[Any] 							=   torch.tensor([
    -2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
    1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
    -2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
    2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
_a   :	int 							=   torch.tensor([
    -0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
    -0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
    -0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
    0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
_a   :	str 							=   torch.tensor([
    0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
    -0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
    0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
    -0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
_a   :	Union[str, Any] 							=   torch.tensor([
    0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
    -0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
    0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
    -0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
_a   :	Any 							=   torch.tensor([
    0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
    -0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
    0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
    -0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
_a   :	List[Any] 							=   torch.tensor([
    0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
    -0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
    0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
    -0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
_a   :	Optional[int] 							=   torch.tensor([
    0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
    -0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
    0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
    -0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
_a   :	Tuple 							=   torch.tensor([
    -1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
    1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
    -2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
    1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
_a   :	List[Any] 							=   torch.tensor([
    -1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
    0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
    -2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
    1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
_a   :	Optional[Any] 							=   torch.tensor([
    -1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
    0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
    -2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
    1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
_a   :	Union[str, Any] 							=   torch.tensor([
    -2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
    1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
    -3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
    3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
_a   :	Optional[int] 							=   torch.tensor([
    -2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
    1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
    -2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
    2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
_a   :	Union[str, Any] 							=   torch.tensor([
    -2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
    1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
    -3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
    3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
_a   :	str 							=   torch.tensor([
    -1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
    1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
    -2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
    1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on

_a   :	Optional[Any] 							=   api.list_models(filter="diffusers")
for mod in models:
   if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
      _a   :	List[str] 							=   "/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

      print(f"""Started running {mod.modelId}!!!""")

      if mod.modelId.startswith("CompVis"):
         _a   :	int 							=   UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
      else:
         _a   :	Optional[int] 							=   UNetaDModel.from_pretrained(local_checkpoint)

      torch.manual_seed(0)
      random.seed(0)

      _a   :	str 							=   torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
      _a   :	str 							=   torch.tensor([10] * noise.shape[0])
      with torch.no_grad():
         _a   :	str 							=   model(noise, time_step).sample

      assert torch.allclose(
          logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1e-3
      )
      print(f"""{mod.modelId} has passed successfully!!!""")






