

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


							def     _snake_case				(      self     ,      _snake_case=0.9_999)				->					Optional[int]:
								UpperCAmelCase_		:   Optional[int]													=       UNetaDConditionModel.from_pretrained(self.model_id     ,      subfolder='unet')
								UpperCAmelCase_		:   Dict													=       unet.to(_snake_case)
								UpperCAmelCase_		:   List[str]													=       EMAModel(unet.parameters()     ,      decay=_snake_case     ,      model_cls=_snake_case     ,      model_config=unet.config)
								return unet, ema_unet


							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   Tuple													=       torch.randn(
								    self.batch_size     ,      self.num_in_channels     ,      self.latent_height     ,      self.latent_width     ,      generator=self.generator).to(_snake_case)
								UpperCAmelCase_		:   Tuple													=       torch.randint(0     ,      1000     ,      size=(self.batch_size,)     ,      generator=self.generator).to(_snake_case)
								UpperCAmelCase_		:   Any													=       torch.randn(
								    self.batch_size     ,      self.prompt_length     ,      self.text_encoder_hidden_dim     ,      generator=self.generator).to(_snake_case)
								return noisy_latents, timesteps, encoder_hidden_states


							def     _snake_case				(      self     ,      _snake_case)				->					List[str]:
								UpperCAmelCase_		:   Tuple													=       {}
								for k, param in unet.state_dict().items():
									UpperCAmelCase_		:   Optional[int]													=       torch.randn_like(_snake_case) + (param * torch.randn_like(_snake_case))
									updated_state_dict.update({k: updated_param})
								unet.load_state_dict(_snake_case)
								return unet


							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Tuple													=       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)				->					List[Any]:
								UpperCAmelCase_						,     UpperCAmelCase_		:   List[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())
								UpperCAmelCase_		:   Optional[int]													=       list(unet.parameters())
								for s_param, param in zip(ema_unet.shadow_params     ,      _snake_case):
									assert torch.allclose(_snake_case     ,      _snake_case)

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


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Dict													=       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.
								UpperCAmelCase_		:   Any													=       self.simulate_backprop(_snake_case)

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

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


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

								# First backprop + EMA
								UpperCAmelCase_		:   Union[str, Any]													=       self.simulate_backprop(_snake_case)
								ema_unet.step(unet_step_one.parameters())
								UpperCAmelCase_		:   Optional[Any]													=       ema_unet.shadow_params

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

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


							def     _snake_case				(      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.
								UpperCAmelCase_						,     UpperCAmelCase_		:   str													=       self.get_models(decay=0.0)
								UpperCAmelCase_		:   Optional[int]													=       self.simulate_backprop(_snake_case)
								ema_unet.step(unet_step_one.parameters())
								UpperCAmelCase_		:   Optional[Any]													=       ema_unet.shadow_params

								UpperCAmelCase_		:   Any													=       self.simulate_backprop(_snake_case)
								ema_unet.step(unet_step_two.parameters())
								UpperCAmelCase_		:   List[str]													=       ema_unet.shadow_params

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





							@skip_mps
							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Tuple													=       self.get_models()
								UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   Tuple													=       self.get_dummy_inputs()

								with tempfile.TemporaryDirectory() as tmpdir:
									ema_unet.save_pretrained(_snake_case)
									UpperCAmelCase_		:   str													=       UNetaDConditionModel.from_pretrained(_snake_case     ,      model_cls=_snake_case)
									UpperCAmelCase_		:   int													=       loaded_unet.to(unet.device)

								# Since no EMA step has been performed the outputs should match.
								UpperCAmelCase_		:   Optional[int]													=       unet(_snake_case     ,      _snake_case     ,      _snake_case).sample
								UpperCAmelCase_		:   Tuple													=       loaded_unet(_snake_case     ,      _snake_case     ,      _snake_case).sample

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









'''simple docstring'''


import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class        lowercase    (	unittest.TestCase			):


							def __init__(      self     ,      _snake_case     ,      _snake_case=3     ,      _snake_case=32     ,      _snake_case=3     ,      _snake_case=10     ,      _snake_case=[10, 20, 30, 40]     ,      _snake_case=[1, 1, 2, 1]     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case="relu"     ,      _snake_case=3     ,      _snake_case=None     ,      )				->					Optional[Any]:
								UpperCAmelCase_		:   List[Any]													=       parent
								UpperCAmelCase_		:   Union[str, Any]													=       batch_size
								UpperCAmelCase_		:   List[Any]													=       image_size
								UpperCAmelCase_		:   List[Any]													=       num_channels
								UpperCAmelCase_		:   Dict													=       embeddings_size
								UpperCAmelCase_		:   str													=       hidden_sizes
								UpperCAmelCase_		:   List[Any]													=       depths
								UpperCAmelCase_		:   Optional[Any]													=       is_training
								UpperCAmelCase_		:   str													=       use_labels
								UpperCAmelCase_		:   List[str]													=       hidden_act
								UpperCAmelCase_		:   Dict													=       num_labels
								UpperCAmelCase_		:   List[Any]													=       scope
								UpperCAmelCase_		:   Tuple													=       len(_snake_case)


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   int													=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								UpperCAmelCase_		:   Optional[int]													=       self.get_config()

								return config, pixel_values


							def     _snake_case				(      self)				->					Union[str, Any]:
								return ResNetConfig(
								    num_channels=self.num_channels     ,      embeddings_size=self.embeddings_size     ,      hidden_sizes=self.hidden_sizes     ,      depths=self.depths     ,      hidden_act=self.hidden_act     ,      num_labels=self.num_labels     ,      image_size=self.image_size     ,      )


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					str:
								UpperCAmelCase_		:   Any													=       FlaxResNetModel(config=_snake_case)
								UpperCAmelCase_		:   Optional[Any]													=       model(_snake_case)

								# Output shape (b, c, h, w)
								self.parent.assertEqual(
								    result.last_hidden_state.shape     ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,      )


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					List[str]:
								UpperCAmelCase_		:   Union[str, Any]													=       self.num_labels
								UpperCAmelCase_		:   Optional[Any]													=       FlaxResNetForImageClassification(config=_snake_case)
								UpperCAmelCase_		:   Optional[Any]													=       model(_snake_case)
								self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels))





							def     _snake_case				(      self)				->					str:
								UpperCAmelCase_		:   List[Any]													=       self.prepare_config_and_inputs()
								UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       config_and_inputs
								UpperCAmelCase_		:   str													=       {'pixel_values': pixel_values}
								return config, inputs_dict



@require_flax
class        lowercase    (	a_,       unittest.TestCase			):
							_lowerCamelCase		:   Dict= (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

							_lowerCamelCase		:   Any= False
							_lowerCamelCase		:   List[Any]= False
							_lowerCamelCase		:   str= False


							def     _snake_case				(      self)				->					None:
								UpperCAmelCase_		:   Any													=       FlaxResNetModelTester(self)
								UpperCAmelCase_		:   Optional[int]													=       ConfigTester(self     ,      config_class=_snake_case     ,      has_text_modality=_snake_case)


							def     _snake_case				(      self)				->					Tuple:
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()


							def     _snake_case				(      self)				->					int:
								return


							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   Optional[Any]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_		:   Union[str, Any]													=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case)


							@unittest.skip(reason='ResNet does not use inputs_embeds')
							def     _snake_case				(      self)				->					Optional[Any]:
								pass


							@unittest.skip(reason='ResNet does not support input and output embeddings')
							def     _snake_case				(      self)				->					Union[str, Any]:
								pass


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_						,     UpperCAmelCase_		:   List[Any]													=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									UpperCAmelCase_		:   Any													=       model_class(_snake_case)
									UpperCAmelCase_		:   str													=       inspect.signature(model.__call__)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCAmelCase_		:   Tuple													=       [*signature.parameters.keys()]

									UpperCAmelCase_		:   List[str]													=       ['pixel_values']
									self.assertListEqual(arg_names[:1]     ,      _snake_case)


							def     _snake_case				(      self)				->					List[Any]:
								def check_hidden_states_output(_snake_case     ,      _snake_case     ,      _snake_case):
									UpperCAmelCase_		:   Union[str, Any]													=       model_class(_snake_case)

									UpperCAmelCase_		:   Any													=       model(**self._prepare_for_class(_snake_case     ,      _snake_case))

									UpperCAmelCase_		:   List[Any]													=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									UpperCAmelCase_		:   int													=       self.model_tester.num_stages
									self.assertEqual(len(_snake_case)     ,      expected_num_stages + 1)


							@unittest.skip(reason='ResNet does not use feedforward chunking')
							def     _snake_case				(      self)				->					List[Any]:
								pass





							def     _snake_case				(      self)				->					Tuple:
								UpperCAmelCase_						,     UpperCAmelCase_		:   Any													=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										UpperCAmelCase_		:   str													=       self._prepare_for_class(_snake_case     ,      _snake_case)
										UpperCAmelCase_		:   str													=       model_class(_snake_case)

										@jax.jit
										def model_jitted(_snake_case     ,      **_snake_case):
											return model(pixel_values=_snake_case     ,      **_snake_case)

										with self.subTest('JIT Enabled'):
											UpperCAmelCase_		:   Optional[Any]													=       model_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:   Union[str, Any]													=       model_jitted(**_snake_case).to_tuple()

										self.assertEqual(len(_snake_case)     ,      len(_snake_case))
										for jitted_output, output in zip(_snake_case     ,      _snake_case):
											self.assertEqual(jitted_output.shape     ,      output.shape)







def     SCREAMING_SNAKE_CASE(	)     ->     Dict:
	UpperCAmelCase_		:   Tuple													=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image







@require_flax
class        lowercase    (	unittest.TestCase			):


							@cached_property
							def     _snake_case				(      self)				->					List[str]:
								return AutoImageProcessor.from_pretrained('microsoft/resnet-50') if is_vision_available() else None





							@slow
							def     _snake_case				(      self)				->					Union[str, Any]:
								UpperCAmelCase_		:   Union[str, Any]													=       FlaxResNetForImageClassification.from_pretrained('microsoft/resnet-50')

								UpperCAmelCase_		:   List[str]													=       self.default_image_processor
								UpperCAmelCase_		:   Union[str, Any]													=       prepare_img()
								UpperCAmelCase_		:   int													=       image_processor(images=_snake_case     ,      return_tensors='np')

								UpperCAmelCase_		:   Any													=       model(**_snake_case)

								# verify the logits
								UpperCAmelCase_		:   Optional[int]													=       (1, 1000)
								self.assertEqual(outputs.logits.shape     ,      _snake_case)

								UpperCAmelCase_		:   Tuple													=       jnp.array([-11.1_069, -9.7_877, -8.3_777])

								self.assertTrue(jnp.allclose(outputs.logits[0, :3]     ,      _snake_case     ,      atol=1e-4))






