





import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str    ,      UpperCamelCase__:							Optional[int]    ,      UpperCamelCase__:							Optional[Any]    ,      UpperCamelCase__:							List[str]    ,      UpperCamelCase__:							int    ,      UpperCamelCase__:							int			):
		SCREAMING_SNAKE_CASE__								=					states.reshape(1    ,      -1    ,      model.config.state_dim			)
		SCREAMING_SNAKE_CASE__								=					actions.reshape(1    ,      -1    ,      model.config.act_dim			)
		SCREAMING_SNAKE_CASE__								=					returns_to_go.reshape(1    ,      -1    ,      1			)
		SCREAMING_SNAKE_CASE__								=					timesteps.reshape(1    ,      -1			)

		if model.config.max_length is not None:
				SCREAMING_SNAKE_CASE__								=					states[:, -model.config.max_length :]
				SCREAMING_SNAKE_CASE__								=					actions[:, -model.config.max_length :]
				SCREAMING_SNAKE_CASE__								=					returns_to_go[:, -model.config.max_length :]
				SCREAMING_SNAKE_CASE__								=					timesteps[:, -model.config.max_length :]

				# pad all tokens to sequence length
				SCREAMING_SNAKE_CASE__								=					torch.cat(
				    [torch.zeros(model.config.max_length - states.shape[1]			), torch.ones(states.shape[1]			)]			)
				SCREAMING_SNAKE_CASE__								=					attention_mask.to(dtype=torch.long    ,      device=states.device			).reshape(1    ,      -1			)
				SCREAMING_SNAKE_CASE__								=					torch.cat(
				    [
				        torch.zeros(
				            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)    ,      device=states.device    ,      ),
				        states,
				    ]    ,      dim=1    ,      ).to(dtype=torch.floataa			)
				SCREAMING_SNAKE_CASE__								=					torch.cat(
				    [
				        torch.zeros(
				            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)    ,      device=actions.device    ,      ),
				        actions,
				    ]    ,      dim=1    ,      ).to(dtype=torch.floataa			)
				SCREAMING_SNAKE_CASE__								=					torch.cat(
				    [
				        torch.zeros(
				            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)    ,      device=returns_to_go.device    ,      ),
				        returns_to_go,
				    ]    ,      dim=1    ,      ).to(dtype=torch.floataa			)
				SCREAMING_SNAKE_CASE__								=					torch.cat(
				    [
				        torch.zeros(
				            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])    ,      device=timesteps.device			),
				        timesteps,
				    ]    ,      dim=1    ,      ).to(dtype=torch.long			)
		else:
				SCREAMING_SNAKE_CASE__								=					None

		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					model(
		    states=UpperCamelCase__    ,      actions=UpperCamelCase__    ,      rewards=UpperCamelCase__    ,      returns_to_go=UpperCamelCase__    ,      timesteps=UpperCamelCase__    ,      attention_mask=UpperCamelCase__    ,      return_dict=UpperCamelCase__    ,      )

		return action_preds[0, -1]


# build the environment

_lowerCamelCase       =	gym.make('Hopper-v3')
_lowerCamelCase       =	env.observation_space.shape[0]
_lowerCamelCase       =	env.action_space.shape[0]
_lowerCamelCase       =	1000
_lowerCamelCase       =	"""cuda"""
_lowerCamelCase       =	1000.0  # normalization for rewards/returns
_lowerCamelCase       =	3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCamelCase       =	np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
_lowerCamelCase       =	np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
_lowerCamelCase       =	torch.from_numpy(state_mean).to(device=device)
_lowerCamelCase       =	torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_lowerCamelCase       =	DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
_lowerCamelCase       =	model.to(device)
model.eval()

for ep in range(10):
	_lowerCamelCase       =	0, 0
	_lowerCamelCase       =	env.reset()
	_lowerCamelCase       =	torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
	_lowerCamelCase       =	torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
	_lowerCamelCase       =	torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
	_lowerCamelCase       =	torch.zeros(0, device=device, dtype=torch.floataa)

	_lowerCamelCase       =	torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
	for t in range(max_ep_len):
		env.render()
		# add padding
		_lowerCamelCase       =	torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
		_lowerCamelCase       =	torch.cat([rewards, torch.zeros(1, device=device)])

		_lowerCamelCase       =	get_action(
		    model,
		    (states.to(dtype=torch.floataa) - state_mean) / state_std,
		    actions.to(dtype=torch.floataa),
		    rewards.to(dtype=torch.floataa),
		    target_return.to(dtype=torch.floataa),
		    timesteps.to(dtype=torch.long),
		)
		_lowerCamelCase       =	action
		_lowerCamelCase       =	action.detach().cpu().numpy()

		_lowerCamelCase       =	env.step(action)

		_lowerCamelCase       =	torch.from_numpy(state).to(device=device).reshape(1, state_dim)
		_lowerCamelCase       =	torch.cat([states, cur_state], dim=0)
		_lowerCamelCase       =	reward

		_lowerCamelCase       =	target_return[0, -1] - (reward / scale)
		_lowerCamelCase       =	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		_lowerCamelCase       =	torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

		episode_return += reward
		episode_length += 1

		if done:
			break





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
      from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      Optional[Any]	) ->				List[Any]:
           lowerCamelCase__					     = self.config_class(**self.inputs_dict	)
           self.parent.assertTrue(hasattr(A__		,     'hidden_sizes'	)	)
           self.parent.assertTrue(hasattr(A__		,     'num_encoder_blocks'	)	)






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int=1_3		,     A__     :      Optional[Any]=6_4		,     A__     :      List[Any]=3		,     A__     :      Dict=4		,     A__     :      int=[2, 2, 2, 2]		,     A__     :      int=[8, 4, 2, 1]		,     A__     :      Any=[1_6, 3_2, 6_4, 1_2_8]		,     A__     :      List[str]=[1, 4, 8, 1_6]		,     A__     :      List[str]=False		,     A__     :      Optional[int]=True		,     A__     :      Any="gelu"		,     A__     :      List[str]=0.1		,     A__     :      Union[str, Any]=0.02		,     A__     :      Any=3		,     A__     :      Dict=None		,     ) ->				List[str]:
           lowerCamelCase__					     = parent
           lowerCamelCase__					     = batch_size
           lowerCamelCase__					     = image_size
           lowerCamelCase__					     = num_channels
           lowerCamelCase__					     = num_encoder_blocks
           lowerCamelCase__					     = sr_ratios
           lowerCamelCase__					     = depths
           lowerCamelCase__					     = hidden_sizes
           lowerCamelCase__					     = downsampling_rates
           lowerCamelCase__					     = is_training
           lowerCamelCase__					     = use_labels
           lowerCamelCase__					     = hidden_act
           lowerCamelCase__					     = hidden_dropout_prob
           lowerCamelCase__					     = initializer_range
           lowerCamelCase__					     = num_labels
           lowerCamelCase__					     = scope




     def        __snake_case      (			self     :      Any	) ->				str:
           lowerCamelCase__					     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

           lowerCamelCase__					     = PoolFormerConfig(
               image_size=self.image_size		,     num_channels=self.num_channels		,     num_encoder_blocks=self.num_encoder_blocks		,     depths=self.depths		,     hidden_sizes=self.hidden_sizes		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     initializer_range=self.initializer_range		,     )

           return config, pixel_values, labels




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      Optional[int]		,     A__     :      List[Any]		,     A__     :      Optional[Any]	) ->				str:
           lowerCamelCase__					     = PoolFormerModel(config=A__	)
           model.to(A__	)
           model.eval()
           lowerCamelCase__					     = model(A__	)
           lowerCamelCase__					     = lowerCamelCase__					     = self.image_size // 32.0
           self.parent.assertEqual(
               result.last_hidden_state.shape		,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)




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






@require_torch
class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Dict   =(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     _snake_case:		Dict   =(
         {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _snake_case:		str   =False
     _snake_case:		Tuple   =False
     _snake_case:		Optional[int]   =False
     _snake_case:		Dict   =False
     _snake_case:		Tuple   =False




     def        __snake_case      (			self     :      List[Any]	) ->				Optional[Any]:
           lowerCamelCase__					     = PoolFormerModelTester(self	)
           lowerCamelCase__					     = PoolFormerConfigTester(self		,     config_class=A__	)




     def        __snake_case      (			self     :      str	) ->				List[str]:
           self.config_tester.run_common_tests()




     def        __snake_case      (			self     :      int	) ->				List[str]:
           lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A__	)




     @unittest.skip('PoolFormer does not use inputs_embeds'	)
     def        __snake_case      (			self     :      Dict	) ->				Tuple:
           pass




     @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'	)
     def        __snake_case      (			self     :      List[str]	) ->				str:
           pass




     def        __snake_case      (			self     :      Dict	) ->				Optional[int]:
           def check_hidden_states_output(A__     :      str		,     A__     :      List[Any]		,     A__     :      List[Any]	):
                 lowerCamelCase__					     = model_class(A__	)
                 model.to(A__	)
                 model.eval()

                 with torch.no_grad():
                       lowerCamelCase__					     = model(**self._prepare_for_class(A__		,     A__	)	)

                 lowerCamelCase__					     = outputs.hidden_states

                 lowerCamelCase__					     = self.model_tester.num_encoder_blocks
                 self.assertEqual(len(A__	)		,     A__	)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:]	)		,     [
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]		,     )

           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCamelCase__					     = True
                 check_hidden_states_output(A__		,     A__		,     A__	)

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

                 check_hidden_states_output(A__		,     A__		,     A__	)




     def        __snake_case      (			self     :      Any	) ->				Tuple:
           if not self.model_tester.is_training:
                 return

           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase__					     = True

           for model_class in self.all_model_classes:
                 if model_class in get_values(A__	):
                       continue
                 lowerCamelCase__					     = model_class(A__	)
                 model.to(A__	)
                 model.train()
                 lowerCamelCase__					     = self._prepare_for_class(A__		,     A__		,     return_labels=A__	)
                 lowerCamelCase__					     = model(**A__	).loss
                 loss.backward()




     def        __snake_case      (			self     :      Tuple	) ->				Any:
           lowerCamelCase__     ,						lowerCamelCase__					     = self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowerCamelCase__					     = ['pixel_values']
                 self.assertListEqual(arg_names[:1]		,     A__	)




     @slow
     def        __snake_case      (			self     :      Dict	) ->				int:
           for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase__					     = PoolFormerModel.from_pretrained(A__	)
                 self.assertIsNotNone(A__	)

def 	lowerCAmelCase_	(     )    ->	Dict:


      '''simple docstring'''




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






@require_torch
class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @slow
     def        __snake_case      (			self     :      Any	) ->				Dict:
           lowerCamelCase__					     = PoolFormerImageProcessor()
           lowerCamelCase__					     = PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'	).to(A__	)

           lowerCamelCase__					     = image_processor(images=prepare_img()		,     return_tensors='pt'	).to(A__	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase__					     = model(**A__	)

           # verify the logits
           lowerCamelCase__					     = torch.Size((1, 1_0_0_0)	)
           self.assertEqual(outputs.logits.shape		,     A__	)

           lowerCamelCase__					     = torch.tensor([-0.6113, 0.1685, -0.0492]	).to(A__	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,     A__		,     atol=1E-4	)	)