





'''simple docstring'''






import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()


class 	_lowercase    (				unittest.TestCase					):
   _UpperCamelCase									= """hf-internal-testing/tiny-stable-diffusion-pipe"""
   _UpperCamelCase									= 1
   _UpperCamelCase									= 77
   _UpperCamelCase									= 32
   _UpperCamelCase									= 4
   _UpperCamelCase									= _UpperCamelCase									= 64
   _UpperCamelCase									= torch.manual_seed(0					)


   def        snake_case					(			self     , _UpperCAmelCase=0.9999       ):
       A							:							Any				=    UNetaDConditionModel.from_pretrained(self.model_id     , subfolder='''unet'''       )
       A							:							Tuple				=    unet.to(_UpperCAmelCase       )
       A							:							Optional[int]				=    EMAModel(unet.parameters()     , decay=_UpperCAmelCase     , model_cls=_UpperCAmelCase     , model_config=unet.config       )
       return unet, ema_unet


   def        snake_case					(			self       ):
       A							:							Optional[int]				=    torch.randn(
           self.batch_size     , self.num_in_channels     , self.latent_height     , self.latent_width     , generator=self.generator       ).to(_UpperCAmelCase       )
       A							:							Dict				=    torch.randint(0     , 1_000     , size=(self.batch_size,)     , generator=self.generator       ).to(_UpperCAmelCase       )
       A							:							str				=    torch.randn(
           self.batch_size     , self.prompt_length     , self.text_encoder_hidden_dim     , generator=self.generator       ).to(_UpperCAmelCase       )
       return noisy_latents, timesteps, encoder_hidden_states


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							Union[str, Any]				=    {}
       for k, param in unet.state_dict().items():
           A							:							Dict				=    torch.randn_like(_UpperCAmelCase       ) + (param * torch.randn_like(_UpperCAmelCase       ))
           updated_state_dict.update({k: updated_param}       )
       unet.load_state_dict(_UpperCAmelCase       )
       return unet


   def        snake_case					(			self       ):
       A,						A							:							Union[str, Any]				=    self.get_models()
       # Take the first (hypothetical) EMA step.
       ema_unet.step(unet.parameters()       )
       assert ema_unet.optimization_step == 1

       # Take two more.
       for _ in range(2       ):
           ema_unet.step(unet.parameters()       )
       assert ema_unet.optimization_step == 3


   def        snake_case					(			self       ):
       A,						A							:							Any				=    self.get_models()
       # Since the `unet` is not being updated (i.e., backprop'd)
       # there won't be any difference between the `params` of `unet`
       # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
       ema_unet.step(unet.parameters()       )
       A							:							int				=    list(unet.parameters()       )
       for s_param, param in zip(ema_unet.shadow_params     , _UpperCAmelCase       ):
           assert torch.allclose(_UpperCAmelCase     , _UpperCAmelCase       )

       # The above holds true even if we call `ema.step()` multiple times since
       # `unet` params are still not being updated.
       for _ in range(4       ):
           ema_unet.step(unet.parameters()       )
       for s_param, param in zip(ema_unet.shadow_params     , _UpperCAmelCase       ):
           assert torch.allclose(_UpperCAmelCase     , _UpperCAmelCase       )


   def        snake_case					(			self       ):
       A,						A							:							Any				=    self.get_models()
       # Here we simulate the parameter updates for `unet`. Since there might
       # be some parameters which are initialized to zero we take extra care to
       # initialize their values to something non-zero before the multiplication.
       A							:							Dict				=    self.simulate_backprop(_UpperCAmelCase       )

       # Take the EMA step.
       ema_unet.step(unet_pseudo_updated_step_one.parameters()       )

       # Now the EMA'd parameters won't be equal to the original model parameters.
       A							:							List[str]				=    list(unet_pseudo_updated_step_one.parameters()       )
       for s_param, param in zip(ema_unet.shadow_params     , _UpperCAmelCase       ):
           assert ~torch.allclose(_UpperCAmelCase     , _UpperCAmelCase       )

       # Ensure this is the case when we take multiple EMA steps.
       for _ in range(4       ):
           ema_unet.step(unet.parameters()       )
       for s_param, param in zip(ema_unet.shadow_params     , _UpperCAmelCase       ):
           assert ~torch.allclose(_UpperCAmelCase     , _UpperCAmelCase       )


   def        snake_case					(			self       ):
       # If we call EMA step after a backpropagation consecutively for two times,
       # the shadow params from those two steps should be different.
       A,						A							:							Optional[Any]				=    self.get_models()

       # First backprop + EMA
       A							:							int				=    self.simulate_backprop(_UpperCAmelCase       )
       ema_unet.step(unet_step_one.parameters()       )
       A							:							str				=    ema_unet.shadow_params

       # Second backprop + EMA
       A							:							List[Any]				=    self.simulate_backprop(_UpperCAmelCase       )
       ema_unet.step(unet_step_two.parameters()       )
       A							:							Optional[Any]				=    ema_unet.shadow_params

       for step_one, step_two in zip(_UpperCAmelCase     , _UpperCAmelCase       ):
           assert ~torch.allclose(_UpperCAmelCase     , _UpperCAmelCase       )


   def        snake_case					(			self       ):
       # If there's no decay even if there are backprops, EMA steps
       # won't take any effect i.e., the shadow params would remain the
       # same.
       A,						A							:							Dict				=    self.get_models(decay=0.0       )
       A							:							Dict				=    self.simulate_backprop(_UpperCAmelCase       )
       ema_unet.step(unet_step_one.parameters()       )
       A							:							Optional[int]				=    ema_unet.shadow_params

       A							:							Optional[int]				=    self.simulate_backprop(_UpperCAmelCase       )
       ema_unet.step(unet_step_two.parameters()       )
       A							:							Tuple				=    ema_unet.shadow_params

       for step_one, step_two in zip(_UpperCAmelCase     , _UpperCAmelCase       ):
           assert torch.allclose(_UpperCAmelCase     , _UpperCAmelCase       )


   @skip_mps
   def        snake_case					(			self       ):
       A,						A							:							List[str]				=    self.get_models()
       A,						A,						A							:							Union[str, Any]				=    self.get_dummy_inputs()

       with tempfile.TemporaryDirectory() as tmpdir:
           ema_unet.save_pretrained(_UpperCAmelCase       )
           A							:							int				=    UNetaDConditionModel.from_pretrained(_UpperCAmelCase     , model_cls=_UpperCAmelCase       )
           A							:							List[str]				=    loaded_unet.to(unet.device       )

       # Since no EMA step has been performed the outputs should match.
       A							:							Union[str, Any]				=    unet(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ).sample
       A							:							Union[str, Any]				=    loaded_unet(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase       ).sample

       assert torch.allclose(_UpperCAmelCase     , _UpperCAmelCase     , atol=1E-4       )







'''simple docstring'''






import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def 				_lowerCamelCase(	UpperCamelCase__     :			Any		)		->	List[str]:
    return EnvironmentCommand()





class 	_lowercase    (				a					):


   @staticmethod
   def        snake_case					(			_UpperCAmelCase       ):
       A							:							List[str]				=    parser.add_parser('''env'''     , help='''Print relevant system environment info.'''       )
       download_parser.set_defaults(func=_UpperCAmelCase       )


   def        snake_case					(			self       ):
       A							:							Optional[int]				=    {
           '''`datasets` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''PyArrow version''': pyarrow.__version__,
           '''Pandas version''': pandas.__version__,
       }

       print('''\nCopy-and-paste the text below in your GitHub issue.\n'''       )
       print(self.format_dict(_UpperCAmelCase       )       )

       return info


   @staticmethod
   def        snake_case					(			_UpperCAmelCase       ):
       return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]       ) + "\n"
