





"""simple docstring"""
import inspect
import unittest

from transformers import RegNetConfig, 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.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor






class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=10		,SCREAMING_SNAKE_CASE__=[10, 20, 30, 40]		,SCREAMING_SNAKE_CASE__=[1, 1, 2, 1]		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__="relu"		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=None		,)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  parent
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  batch_size
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  image_size
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  num_channels
             __SCREAMING_SNAKE_CASE       :Dict		    =  embeddings_size
             __SCREAMING_SNAKE_CASE       :Any		    =  hidden_sizes
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  depths
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  is_training
             __SCREAMING_SNAKE_CASE       :int		    =  use_labels
             __SCREAMING_SNAKE_CASE       :List[str]		    =  hidden_act
             __SCREAMING_SNAKE_CASE       :str		    =  num_labels
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  scope
             __SCREAMING_SNAKE_CASE       :Tuple		    =  len(_SCREAMING_SNAKE_CASE							)




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             __SCREAMING_SNAKE_CASE       :str		    =  self.get_config()

             return config, pixel_values




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





             """simple docstring"""






             return RegNetConfig(
                 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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  FlaxRegNetModel(config=_SCREAMING_SNAKE_CASE							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  model(_SCREAMING_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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.num_labels
             __SCREAMING_SNAKE_CASE       :Dict		    =  FlaxRegNetForImageClassification(config=_SCREAMING_SNAKE_CASE							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  model(_SCREAMING_SNAKE_CASE							)
             self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.prepare_config_and_inputs()
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  config_and_inputs
             __SCREAMING_SNAKE_CASE       :Any		    =  {"pixel_values": pixel_values}
             return config, inputs_dict






@require_flax
class  _SCREAMING_SNAKE_CASE(       SCREAMING_SNAKE_CASE__   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       SCREAMING_SNAKE_CASE_       :				Optional[Any]     						=  False
       SCREAMING_SNAKE_CASE_       :				Dict     						=  False
       SCREAMING_SNAKE_CASE_       :				List[Any]     						=  False




       def    _UpperCamelCase					(						self							)      ->				None:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  FlaxRegNetModelTester(self							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  ConfigTester(self		,config_class=_SCREAMING_SNAKE_CASE		,has_text_modality=_SCREAMING_SNAKE_CASE							)




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





             """simple docstring"""






             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    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             return




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE							)




       @unittest.skip(reason='''RegNet does not use inputs_embeds'''							)
       def    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             pass




       @unittest.skip(reason='''RegNet does not support input and output embeddings'''							)
       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             pass




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  model_class(_SCREAMING_SNAKE_CASE							)
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  inspect.signature(model.__call__							)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  [*signature.parameters.keys()]

                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  ["pixel_values"]
                   self.assertListEqual(arg_names[:1]		,_SCREAMING_SNAKE_CASE							)




       def    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             def check_hidden_states_output(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :Any		    =  model_class(_SCREAMING_SNAKE_CASE							)

                   __SCREAMING_SNAKE_CASE       :Dict		    =  model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE							)							)

                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                   __SCREAMING_SNAKE_CASE       :List[str]		    =  self.model_tester.num_stages
                   self.assertEqual(len(_SCREAMING_SNAKE_CASE							)		,expected_num_stages + 1							)

             __SCREAMING_SNAKE_CASE       :int		    =  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE       :str		    =  True
                   check_hidden_states_output(_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE							)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  True

                   check_hidden_states_output(_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__							):
                         __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self._prepare_for_class(_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE							)
                         __SCREAMING_SNAKE_CASE       :str		    =  model_class(_SCREAMING_SNAKE_CASE							)

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

                         with self.subTest('''JIT Enabled'''							):
                               __SCREAMING_SNAKE_CASE       :List[str]		    =  model_jitted(**_SCREAMING_SNAKE_CASE							).to_tuple()

                         with self.subTest('''JIT Disabled'''							):
                               with jax.disable_jit():
                                     __SCREAMING_SNAKE_CASE       :List[Any]		    =  model_jitted(**_SCREAMING_SNAKE_CASE							).to_tuple()

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




def 					__lowerCamelCase       (							)      ->				Tuple:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image







@require_flax
class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       @cached_property
       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040'''							) if is_vision_available() else None




       @slow
       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''							)

             __SCREAMING_SNAKE_CASE       :str		    =  self.default_image_processor
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  prepare_img()
             __SCREAMING_SNAKE_CASE       :List[str]		    =  image_processor(images=_SCREAMING_SNAKE_CASE		,return_tensors='''np'''							)

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  model(**_SCREAMING_SNAKE_CASE							)

             # verify the logits
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  (1, 10_00)
             self.assertEqual(outputs.logits.shape		,_SCREAMING_SNAKE_CASE							)

             __SCREAMING_SNAKE_CASE       :List[str]		    =  jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]							)

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






"""simple docstring"""
import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler






class  _SCREAMING_SNAKE_CASE(       A  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,)      ->				Optional[int]:





             """simple docstring"""






             super().__init__()
             __SCREAMING_SNAKE_CASE       :Any		    =  value_function
             __SCREAMING_SNAKE_CASE       :List[str]		    =  unet
             __SCREAMING_SNAKE_CASE       :int		    =  scheduler
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  env
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  env.get_dataset()
             __SCREAMING_SNAKE_CASE       :str		    =  {}
             for key in self.data.keys():
                   try:
                         __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.data[key].mean()
                   except:  # noqa: E722
                         pass
             __SCREAMING_SNAKE_CASE       :Dict		    =  {}
             for key in self.data.keys():
                   try:
                         __SCREAMING_SNAKE_CASE       :str		    =  self.data[key].std()
                   except:  # noqa: E722
                         pass
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  env.observation_space.shape[0]
             __SCREAMING_SNAKE_CASE       :int		    =  env.action_space.shape[0]




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






             return (x_in - self.means[key]) / self.stds[key]




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






             return x_in * self.stds[key] + self.means[key]




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             if type(SCREAMING_SNAKE_CASE__							) is dict:
                   return {k: self.to_torch(SCREAMING_SNAKE_CASE__							) for k, v in x_in.items()}
             elif torch.is_tensor(SCREAMING_SNAKE_CASE__							):
                   return x_in.to(self.unet.device							)
             return torch.tensor(SCREAMING_SNAKE_CASE__		,device=self.unet.device							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             for key, val in cond.items():
                   __SCREAMING_SNAKE_CASE       :Dict		    =  val.clone()
             return x_in




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  x.shape[0]
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  None
             for i in tqdm.tqdm(self.scheduler.timesteps							):
                   # create batch of timesteps to pass into model
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  torch.full((batch_size,)		,SCREAMING_SNAKE_CASE__		,device=self.unet.device		,dtype=torch.long							)
                   for _ in range(SCREAMING_SNAKE_CASE__							):
                         with torch.enable_grad():
                               x.requires_grad_()

                               # permute to match dimension for pre-trained models
                               __SCREAMING_SNAKE_CASE       :str		    =  self.value_function(x.permute(0		,2		,1							)		,SCREAMING_SNAKE_CASE__							).sample
                               __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.autograd.grad([y.sum()]		,[x]							)[0]

                               __SCREAMING_SNAKE_CASE       :int		    =  self.scheduler._get_variance(SCREAMING_SNAKE_CASE__							)
                               __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  torch.exp(0.5 * posterior_variance							)
                               __SCREAMING_SNAKE_CASE       :List[str]		    =  model_std * grad

                         __SCREAMING_SNAKE_CASE       :Dict		    =  0
                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  x.detach()
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  x + scale * grad
                         __SCREAMING_SNAKE_CASE       :Any		    =  self.reset_xa(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.action_dim							)

                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.unet(x.permute(0		,2		,1							)		,SCREAMING_SNAKE_CASE__							).sample.permute(0		,2		,1							)

                   # TODO: verify deprecation of this kwarg
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.scheduler.step(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,predict_epsilon=SCREAMING_SNAKE_CASE__							)['''prev_sample''']

                   # apply conditions to the trajectory (set the initial state)
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  self.reset_xa(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.action_dim							)
                   __SCREAMING_SNAKE_CASE       :Dict		    =  self.to_torch(SCREAMING_SNAKE_CASE__							)
             return x, y




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=64		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.1							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.normalize(SCREAMING_SNAKE_CASE__		,'''observations'''							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  obs[None].repeat(SCREAMING_SNAKE_CASE__		,axis=0							)

             __SCREAMING_SNAKE_CASE       :str		    =  {0: self.to_torch(SCREAMING_SNAKE_CASE__							)}
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  (batch_size, planning_horizon, self.state_dim + self.action_dim)

             # generate initial noise and apply our conditions (to make the trajectories start at current state)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  randn_tensor(SCREAMING_SNAKE_CASE__		,device=self.unet.device							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.reset_xa(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.action_dim							)
             __SCREAMING_SNAKE_CASE       :Any		    =  self.to_torch(SCREAMING_SNAKE_CASE__							)

             # run the diffusion process
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Tuple		    =  self.run_diffusion(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             # sort output trajectories by value
             __SCREAMING_SNAKE_CASE       :Any		    =  y.argsort(0		,descending=SCREAMING_SNAKE_CASE__							).squeeze()
             __SCREAMING_SNAKE_CASE       :Any		    =  x[sorted_idx]
             __SCREAMING_SNAKE_CASE       :str		    =  sorted_values[:, :, : self.action_dim]
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  actions.detach().cpu().numpy()
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.de_normalize(SCREAMING_SNAKE_CASE__		,key='''actions'''							)

             # select the action with the highest value
             if y is not None:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  0
             else:
                   # if we didn't run value guiding, select a random action
                   __SCREAMING_SNAKE_CASE       :Any		    =  np.random.randint(0		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :int		    =  denorm_actions[selected_index, 0]
             return denorm_actions