


import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


snake_case			:      Tuple 							=   importlib.metadata.version('numpy')
snake_case			:      Any 							=   '.'.join([str(x) for x in sys.version_info[:3]])



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Dict:
											# lt + different version strings
											require_version_core('numpy<1000.4.5'	)
											require_version_core('numpy<1000.4'	)
											require_version_core('numpy<1000'	)

											# le
											require_version_core('numpy<=1000.4.5'	)
											require_version_core(F'''numpy<={numpy_ver}'''	)

											# eq
											require_version_core(F'''numpy=={numpy_ver}'''	)

											# ne
											require_version_core('numpy!=1000.4.5'	)

											# ge
											require_version_core('numpy>=1.0'	)
											require_version_core('numpy>=1.0.0'	)
											require_version_core(F'''numpy>={numpy_ver}'''	)

											# gt
											require_version_core('numpy>1.0.0'	)

											# mix
											require_version_core('numpy>1.0.0,<1000'	)

											# requirement w/o version
											require_version_core('numpy'	)

											# unmet requirements due to version conflict
											for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'''numpy<{numpy_ver}''']:
															try:
																			require_version_core(A_	)
															except ImportError as e:
																			self.assertIn(F'''{req} is required'''				, str(A_	)	)
																			self.assertIn('but found'				, str(A_	)	)

        # unmet requirements due to missing module
											for req in ["numpipypie>1", "numpipypie2"]:
															try:
																			require_version_core(A_	)
															except importlib.metadata.PackageNotFoundError as e:
																			self.assertIn(F'''The \'{req}\' distribution was not found and is required by this application'''				, str(A_	)	)
																			self.assertIn('Try: pip install transformers -U'				, str(A_	)	)

        # bogus requirements formats:
        # 1. whole thing
											for req in ["numpy??1.0.0", "numpy1.0.0"]:
															try:
																			require_version_core(A_	)
															except ValueError as e:
																			self.assertIn('requirement needs to be in the pip package format'				, str(A_	)	)
        # 2. only operators
											for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
															try:
																			require_version_core(A_	)
															except ValueError as e:
																			self.assertIn('need one of '				, str(A_	)	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											# matching requirement
											require_version('python>=3.6.0'	)

											# not matching requirements
											for req in ["python>9.9.9", "python<3.0.0"]:
															try:
																			require_version_core(A_	)
															except ImportError as e:
																			self.assertIn(F'''{req} is required'''				, str(A_	)	)
																			self.assertIn(F'''but found python=={python_ver}'''				, str(A_	)	)




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							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					str        =						"hf-internal-testing/tiny-stable-diffusion-pipe"
							SCREAMING_SNAKE_CASE   :					str        =						1
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						77
							SCREAMING_SNAKE_CASE   :					Dict        =						32
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						4
							SCREAMING_SNAKE_CASE   :					Optional[int]        =						64
							SCREAMING_SNAKE_CASE   :					Tuple        =						torch.manual_seed(0							)





							def 		__magic_name__			(    self				, A_=0.9999	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = UNetaDConditionModel.from_pretrained(self.model_id				, subfolder='unet'	)
											_SCREAMING_SNAKE_CASE      = unet.to(A_	)
											_SCREAMING_SNAKE_CASE      = EMAModel(unet.parameters()				, decay=A_				, model_cls=A_				, model_config=unet.config	)
											return unet, ema_unet





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = torch.randn(
											    self.batch_size				, self.num_in_channels				, self.latent_height				, self.latent_width				, generator=self.generator	).to(A_	)
											_SCREAMING_SNAKE_CASE      = torch.randint(0				, 1000				, size=(self.batch_size,)				, generator=self.generator	).to(A_	)
											_SCREAMING_SNAKE_CASE      = torch.randn(
											    self.batch_size				, self.prompt_length				, self.text_encoder_hidden_dim				, generator=self.generator	).to(A_	)
											return noisy_latents, timesteps, encoder_hidden_states





							def 		__magic_name__			(    self				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = {}
											for k, param in unet.state_dict().items():
															_SCREAMING_SNAKE_CASE      = torch.randn_like(A_	) + (param * torch.randn_like(A_	))
															updated_state_dict.update({k: updated_param}	)
											unet.load_state_dict(A_	)
											return unet





							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = 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 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = 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()	)
											_SCREAMING_SNAKE_CASE      = list(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert torch.allclose(A_				, A_	)

											# 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				, A_	):
															assert torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = 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.
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)

											# 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.
											_SCREAMING_SNAKE_CASE      = list(unet_pseudo_updated_step_one.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert ~torch.allclose(A_				, A_	)

											# 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				, A_	):
															assert ~torch.allclose(A_				, A_	)





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

											# First backprop + EMA
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_one.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											# Second backprop + EMA
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_two.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

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





							def 		__magic_name__			(    self	)->	Dict:
											# 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.
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models(decay=0.0	)
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_one.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_two.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

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





							@skip_mps
							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_dummy_inputs()

											with tempfile.TemporaryDirectory() as tmpdir:
															ema_unet.save_pretrained(A_	)
															_SCREAMING_SNAKE_CASE      = UNetaDConditionModel.from_pretrained(A_				, model_cls=A_	)
															_SCREAMING_SNAKE_CASE      = loaded_unet.to(unet.device	)

											# Since no EMA step has been performed the outputs should match.
											_SCREAMING_SNAKE_CASE      = unet(A_				, A_				, A_	).sample
											_SCREAMING_SNAKE_CASE      = loaded_unet(A_				, A_				, A_	).sample

											assert torch.allclose(A_				, A_				, atol=1e-4	)
