

import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_SCREAMING_SNAKE_CASE  :						int         =    {
    '''n_samples''': 64,
    '''horizon''': 32,
    '''num_inference_steps''': 20,
    '''n_guide_steps''': 2,  # can set to 0 for faster sampling, does not use value network
    '''scale_grad_by_std''': True,
    '''scale''': 0.1,
    '''eta''': 0.0,
    '''t_grad_cutoff''': 2,
    '''device''': '''cpu''',
}


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						int         =    '''hopper-medium-v2'''
       _SCREAMING_SNAKE_CASE  :						Any         =    gym.make(env_name)

       _SCREAMING_SNAKE_CASE  :						int         =    ValueGuidedRLPipeline.from_pretrained(
           '''bglick13/hopper-medium-v2-value-function-hor32''',
           env=env,
       )

       env.seed(0)
       _SCREAMING_SNAKE_CASE  :						List[str]         =    env.reset()
       _SCREAMING_SNAKE_CASE  :						str         =    0
       _SCREAMING_SNAKE_CASE  :						Any         =    0
       _SCREAMING_SNAKE_CASE  :						List[str]         =    1000
       _SCREAMING_SNAKE_CASE  :						Tuple         =    [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     _SCREAMING_SNAKE_CASE  :						Dict         =    pipeline(obs, planning_horizon=32)

                     # execute action in environment
                     _SCREAMING_SNAKE_CASE    ,							_SCREAMING_SNAKE_CASE    ,							_SCREAMING_SNAKE_CASE    ,							_SCREAMING_SNAKE_CASE  :						int         =    env.step(denorm_actions)
                     _SCREAMING_SNAKE_CASE  :						Dict         =    env.get_normalized_score(total_reward)

                     # update return
                     total_reward += reward
                     total_score += score
                     print(
                         F"Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"
                         F" {total_score}"
                     )

                     # save observations for rendering
                     rollout.append(next_observation.copy())

                     _SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    next_observation
       except KeyboardInterrupt:
              pass

       print(F"Total reward: {total_reward}")





import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
       from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Optional[Any]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]=3			,     __lowerCamelCase		:	int=32			,     __lowerCamelCase		:	Tuple=3			,     __lowerCamelCase		:	Optional[int]=10			,     __lowerCamelCase		:	List[Any]=[8, 16, 32, 64]			,     __lowerCamelCase		:	Dict=[1, 1, 2, 1]			,     __lowerCamelCase		:	int=True			,     __lowerCamelCase		:	Dict=True			,     __lowerCamelCase		:	Optional[int]="relu"			,     __lowerCamelCase		:	Optional[Any]=3			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Optional[int]=["stage2", "stage3", "stage4"]			,     __lowerCamelCase		:	Tuple=[2, 3, 4]			,     __lowerCamelCase		:	List[Any]=1			,     )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					parent
       SCREAMING_SNAKE_CASE__					=					batch_size
       SCREAMING_SNAKE_CASE__					=					image_size
       SCREAMING_SNAKE_CASE__					=					num_channels
       SCREAMING_SNAKE_CASE__					=					embeddings_size
       SCREAMING_SNAKE_CASE__					=					hidden_sizes
       SCREAMING_SNAKE_CASE__					=					depths
       SCREAMING_SNAKE_CASE__					=					is_training
       SCREAMING_SNAKE_CASE__					=					use_labels
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					num_labels
       SCREAMING_SNAKE_CASE__					=					scope
       SCREAMING_SNAKE_CASE__					=					len(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					out_features
       SCREAMING_SNAKE_CASE__					=					out_indices
       SCREAMING_SNAKE_CASE__					=					num_groups






  def    lowercase_	(							self		:	Optional[Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       SCREAMING_SNAKE_CASE__					=					None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__					=					ids_tensor([self.batch_size]			,     self.num_labels      )

       SCREAMING_SNAKE_CASE__					=					self.get_config()

       return config, pixel_values, labels






  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       return BitConfig(
           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			,     out_features=self.out_features			,     out_indices=self.out_indices			,     num_groups=self.num_groups			,     )






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Union[str, Any]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					BitModel(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(
           result.last_hidden_state.shape			,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,     )






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[int]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.num_labels
       SCREAMING_SNAKE_CASE__					=					BitForImageClassification(__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     labels=__lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Union[str, Any]      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					BitBackbone(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps      )			,     len(config.out_features      )      )
       self.parent.assertListEqual(list(result.feature_maps[0].shape      )			,     [self.batch_size, self.hidden_sizes[1], 4, 4]      )

       # verify channels
       self.parent.assertEqual(len(model.channels      )			,     len(config.out_features      )      )
       self.parent.assertListEqual(model.channels			,     config.hidden_sizes[1:]      )

       # verify backbone works with out_features=None
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					BitBackbone(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps      )			,     1      )
       self.parent.assertListEqual(list(result.feature_maps[0].shape      )			,     [self.batch_size, self.hidden_sizes[-1], 1, 1]      )

       # verify channels
       self.parent.assertEqual(len(model.channels      )			,     1      )
       self.parent.assertListEqual(model.channels			,     [config.hidden_sizes[-1]]      )







  def    lowercase_	(							self		:	Tuple      )	->							int:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					config_and_inputs
       SCREAMING_SNAKE_CASE__					=					{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class  UpperCAmelCase__	( A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  a     =      (
      {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
      if is_torch_available()
      else {}
  )

  a     =      False
  a     =      False
  a     =      False
  a     =      False
  a     =      False






  def    lowercase_	(							self		:	Optional[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					BitModelTester(self      )
       SCREAMING_SNAKE_CASE__					=					ConfigTester(self			,     config_class=__lowerCamelCase			,     has_text_modality=__lowerCamelCase      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							str:
       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    lowercase_	(							self		:	str      )	->							Dict:
       return






  @unittest.skip(reason='''Bit does not output attentions'''      )
  def    lowercase_	(							self		:	Any      )	->							Dict:
       pass






  @unittest.skip(reason='''Bit does not use inputs_embeds'''      )
  def    lowercase_	(							self		:	Tuple      )	->							Optional[int]:
       pass






  @unittest.skip(reason='''Bit does not support input and output embeddings'''      )
  def    lowercase_	(							self		:	Optional[int]      )	->							str:
       pass






  def    lowercase_	(							self		:	str      )	->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					=					model_class(__lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					inspect.signature(model.forward      )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            SCREAMING_SNAKE_CASE__					=					[*signature.parameters.keys()]

            SCREAMING_SNAKE_CASE__					=					['''pixel_values''']
            self.assertListEqual(arg_names[:1]			,     __lowerCamelCase      )






  def    lowercase_	(							self		:	Dict      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					=					model_class(config=__lowerCamelCase      )
            for name, module in model.named_modules():
                 if isinstance(__lowerCamelCase			,     (nn.BatchNormad, nn.GroupNorm)      ):
                      self.assertTrue(
                          torch.all(module.weight == 1      )			,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,     )
                      self.assertTrue(
                          torch.all(module.bias == 0      )			,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,     )






  def    lowercase_	(							self		:	str      )	->							Optional[Any]:
       def check_hidden_states_output(__lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Optional[Any]      ):
            SCREAMING_SNAKE_CASE__					=					model_class(__lowerCamelCase      )
            model.to(__lowerCamelCase      )
            model.eval()

            with torch.no_grad():
                 SCREAMING_SNAKE_CASE__					=					model(**self._prepare_for_class(__lowerCamelCase			,     __lowerCamelCase      )      )

            SCREAMING_SNAKE_CASE__					=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            SCREAMING_SNAKE_CASE__					=					self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase      )			,     expected_num_stages + 1      )

            # Bit's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]      )			,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,     )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__					=					['''preactivation''', '''bottleneck''']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 SCREAMING_SNAKE_CASE__					=					layer_type
                 SCREAMING_SNAKE_CASE__					=					True
                 check_hidden_states_output(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 SCREAMING_SNAKE_CASE__					=					True

                 check_hidden_states_output(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )






  @unittest.skip(reason='''Bit does not use feedforward chunking'''      )
  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       pass






  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase      )







  @slow
  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__					=					BitModel.from_pretrained(__lowerCamelCase      )
            self.assertIsNotNone(__lowerCamelCase      )



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
     return image






@require_torch
@require_vision
class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  @cached_property
  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ) if is_vision_available() else None
       )







  @slow
  def    lowercase_	(							self		:	str      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.default_image_processor
       SCREAMING_SNAKE_CASE__					=					prepare_img()
       SCREAMING_SNAKE_CASE__					=					image_processor(images=__lowerCamelCase			,     return_tensors='''pt'''      ).to(__lowerCamelCase      )

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					=					model(**__lowerCamelCase      )

       # verify the logits
       SCREAMING_SNAKE_CASE__					=					torch.Size((1, 1000)      )
       self.assertEqual(outputs.logits.shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.tensor([[-0.6526, -0.5263, -1.4398]]      ).to(__lowerCamelCase      )

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






@require_torch
class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (BitBackbone,) if is_torch_available() else ()
  a     =      BitConfig

  a     =      False






  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					BitModelTester(self      )


