

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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[Any]:
       random.seed(UpperCAmelCase__			)
       np.random.seed(UpperCAmelCase__			)
       torch.manual_seed(UpperCAmelCase__			)
       torch.cuda.manual_seed_all(UpperCAmelCase__			)
       # ^^ safe to call this function even if cuda is not available







class    A__ :



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__ = 0.9999						,			UpperCamelCase__ = 0.0						,			UpperCamelCase__ = 0						,			UpperCamelCase__ = False						,			UpperCamelCase__ = 1.0						,			UpperCamelCase__ = 2 / 3						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			**UpperCamelCase__						,			)  -> Any:





           '''simple docstring'''

           if isinstance(_a						,			torch.nn.Module    ):
                  A_		   = (
                      "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"""						,			_a						,			standard_warn=_a						,			)
                  A_		   = parameters.parameters()

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

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

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

           A_		   = list(_a    )
           A_		   = [p.clone().detach() for p in parameters]

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

           A_		   = None

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

           A_		   = model_cls
           A_		   = model_config



    @classmethod
    def    snake_case_     (   cls						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = model_cls.load_config(_a						,			return_unused_kwargs=_a    )
           A_		   = model_cls.from_pretrained(_a    )

           A_		   = cls(model.parameters()						,			model_cls=_a						,			model_config=model.config    )

           ema_model.load_state_dict(_a    )
           return ema_model



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           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__."""    )

           A_		   = self.model_cls.from_config(self.model_config    )
           A_		   = self.state_dict()
           state_dict.pop("""shadow_params"""						,			_a    )

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



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = max(0						,			optimization_step - self.update_after_step - 1    )

           if step <= 0:
                  return 0.0

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

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



    @torch.no_grad()
    def    snake_case_     (   self						,			UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           if isinstance(_a						,			torch.nn.Module    ):
                  A_		   = (
                      "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"""						,			_a						,			standard_warn=_a						,			)
                  A_		   = parameters.parameters()

           A_		   = list(_a    )

           self.optimization_step += 1

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

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

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

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



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> str:





           '''simple docstring'''
           A_		   = list(_a    )
           for s_param, param in zip(self.shadow_params						,			_a    ):
                  param.data.copy_(s_param.to(param.device    ).data    )



    def    snake_case_     (   self						,			UpperCamelCase__=None						,			UpperCamelCase__=None    )  -> int:





           '''simple docstring'''
           A_		   = [
               p.to(device=_a						,			dtype=_a    ) if p.is_floating_point() else p.to(device=_a    )
               for p in self.shadow_params
           ]



    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           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__    )  -> Dict:





           '''simple docstring'''
           A_		   = [param.detach().cpu().clone() for param in parameters]



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           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						,			_a    ):
                  param.data.copy_(c_param.data    )

           # Better memory-wise.
           A_		   = None







    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = copy.deepcopy(_a    )

           A_		   = 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"""    )

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

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

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

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

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

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

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



'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



    @property
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = 1
           A_		   = 3
           A_		   = (32, 32)

           A_		   = floats_tensor((batch_size, num_channels) + sizes						,			rng=random.Random(0    )    ).to(UpperCamelCase__    )
           return image



    @property
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = UNetaDConditionModel(
               block_out_channels=(32, 64)						,			layers_per_block=2						,			sample_size=32						,			in_channels=4						,			out_channels=4						,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,			cross_attention_dim=32						,			)
           return model



    @property
    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = AutoencoderKL(
               block_out_channels=[32, 64]						,			in_channels=3						,			out_channels=3						,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,			latent_channels=4						,			)
           return model



    @property
    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           torch.manual_seed(0    )
           A_		   = RobertaSeriesConfig(
               hidden_size=32						,			project_dim=32						,			intermediate_size=37						,			layer_norm_eps=1e-0_5						,			num_attention_heads=4						,			num_hidden_layers=5						,			pad_token_id=1						,			vocab_size=5006						,			)
           return RobertaSeriesModelWithTransformation(UpperCamelCase__    )



    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           def extract(*UpperCamelCase__						,			**UpperCamelCase__    ):


                  class    A__ :



                      def __init__(   self    )  -> Dict:





                             '''simple docstring'''
                             A_		   = torch.ones([0]    )







                      def    snake_case_     (   self						,			UpperCamelCase__    )  -> str:





                             '''simple docstring'''
                             self.pixel_values.to(UpperCamelCase__    )
                             return self

                  return Out()

           return extract



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = """cpu"""  # ensure determinism for the device-dependent torch.Generator
           A_		   = self.dummy_cond_unet
           A_		   = PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
           A_		   = self.dummy_vae
           A_		   = self.dummy_text_encoder
           A_		   = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
           A_		   = 77

           A_		   = self.dummy_image.to(UpperCamelCase__    )
           A_		   = init_image / 2 + 0.5

           # make sure here that pndm scheduler skips prk
           A_		   = AltDiffusionImgaImgPipeline(
               unet=UpperCamelCase__						,			scheduler=UpperCamelCase__						,			vae=UpperCamelCase__						,			text_encoder=UpperCamelCase__						,			tokenizer=UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			feature_extractor=self.dummy_extractor						,			)
           A_		   = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,			do_normalize=UpperCamelCase__    )
           A_		   = alt_pipe.to(UpperCamelCase__    )
           alt_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """A painting of a squirrel eating a burger"""
           A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			)

           A_		   = output.images

           A_		   = torch.Generator(device=UpperCamelCase__    ).manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			guidance_scale=6.0						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			return_dict=UpperCamelCase__						,			)[0]

           A_		   = image[0, -3:, -3:, -1]
           A_		   = image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_		   = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-3
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 5e-3



    @unittest.skipIf(torch_device != """cuda"""						,			"""This test requires a GPU"""    )
    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = self.dummy_cond_unet
           A_		   = PNDMScheduler(skip_prk_steps=UpperCamelCase__    )
           A_		   = self.dummy_vae
           A_		   = self.dummy_text_encoder
           A_		   = XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""    )
           A_		   = 77

           A_		   = self.dummy_image.to(UpperCamelCase__    )

           # put models in fp16
           A_		   = unet.half()
           A_		   = vae.half()
           A_		   = bert.half()

           # make sure here that pndm scheduler skips prk
           A_		   = AltDiffusionImgaImgPipeline(
               unet=UpperCamelCase__						,			scheduler=UpperCamelCase__						,			vae=UpperCamelCase__						,			text_encoder=UpperCamelCase__						,			tokenizer=UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			feature_extractor=self.dummy_extractor						,			)
           A_		   = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor						,			do_normalize=UpperCamelCase__    )
           A_		   = alt_pipe.to(UpperCamelCase__    )
           alt_pipe.set_progress_bar_config(disable=UpperCamelCase__    )

           A_		   = """A painting of a squirrel eating a burger"""
           A_		   = torch.manual_seed(0    )
           A_		   = alt_pipe(
               [prompt]						,			generator=UpperCamelCase__						,			num_inference_steps=2						,			output_type="""np"""						,			image=UpperCamelCase__						,			).images

           assert image.shape == (1, 32, 32, 3)







    @unittest.skipIf(torch_device != """cuda"""						,			"""This test requires a GPU"""    )
    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""    )
           # resize to resolution that is divisible by 8 but not 16 or 32
           A_		   = init_image.resize((760, 504)    )

           A_		   = """BAAI/AltDiffusion"""
           A_		   = AltDiffusionImgaImgPipeline.from_pretrained(
               UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			)
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )
           pipe.enable_attention_slicing()

           A_		   = """A fantasy landscape, trending on artstation"""

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			strength=0.75						,			guidance_scale=7.5						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)
           A_		   = output.images[0]

           A_		   = image[255:258, 383:386, -1]

           assert image.shape == (504, 760, 3)
           A_		   = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]    )

           assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2


@slow
@require_torch_gpu
class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()







    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           A_		   = load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/img2img/sketch-mountains-input.jpg"""    )
           A_		   = init_image.resize((768, 512)    )
           A_		   = load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""    )

           A_		   = """BAAI/AltDiffusion"""
           A_		   = AltDiffusionImgaImgPipeline.from_pretrained(
               UpperCamelCase__						,			safety_checker=UpperCamelCase__						,			)
           pipe.to(UpperCamelCase__    )
           pipe.set_progress_bar_config(disable=UpperCamelCase__    )
           pipe.enable_attention_slicing()

           A_		   = """A fantasy landscape, trending on artstation"""

           A_		   = torch.manual_seed(0    )
           A_		   = pipe(
               prompt=UpperCamelCase__						,			image=UpperCamelCase__						,			strength=0.75						,			guidance_scale=7.5						,			generator=UpperCamelCase__						,			output_type="""np"""						,			)
           A_		   = output.images[0]

           assert image.shape == (512, 768, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image    ).max() < 1e-2
