from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.swin.modeling_tf_swin import (
         TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFSwinForImageClassification,
         TFSwinForMaskedImageModeling,
         TFSwinModel,
     )


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor







class       __snake_case :
 def __init__(	self		, snake_case__		, snake_case__=13		, snake_case__=32		, snake_case__=2		, snake_case__=3		, snake_case__=16		, snake_case__=[1, 2, 1]		, snake_case__=[2, 2, 4]		, snake_case__=2		, snake_case__=2.0		, snake_case__=True		, snake_case__=0.0		, snake_case__=0.0		, snake_case__=0.1		, snake_case__="gelu"		, snake_case__=False		, snake_case__=True		, snake_case__=0.02		, snake_case__=1e-5		, snake_case__=True		, snake_case__=None		, snake_case__=True		, snake_case__=10		, snake_case__=8		, )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=parent
       UpperCAmelCase     :			Tuple     	=batch_size
       UpperCAmelCase     :			str     	=image_size
       UpperCAmelCase     :			Dict     	=patch_size
       UpperCAmelCase     :			str     	=num_channels
       UpperCAmelCase     :			Union[str, Any]     	=embed_dim
       UpperCAmelCase     :			Dict     	=depths
       UpperCAmelCase     :			Optional[Any]     	=num_heads
       UpperCAmelCase     :			str     	=window_size
       UpperCAmelCase     :			int     	=mlp_ratio
       UpperCAmelCase     :			int     	=qkv_bias
       UpperCAmelCase     :			Tuple     	=hidden_dropout_prob
       UpperCAmelCase     :			List[Any]     	=attention_probs_dropout_prob
       UpperCAmelCase     :			Union[str, Any]     	=drop_path_rate
       UpperCAmelCase     :			Union[str, Any]     	=hidden_act
       UpperCAmelCase     :			List[str]     	=use_absolute_embeddings
       UpperCAmelCase     :			Dict     	=patch_norm
       UpperCAmelCase     :			int     	=layer_norm_eps
       UpperCAmelCase     :			Union[str, Any]     	=initializer_range
       UpperCAmelCase     :			Optional[Any]     	=is_training
       UpperCAmelCase     :			Optional[int]     	=scope
       UpperCAmelCase     :			Union[str, Any]     	=use_labels
       UpperCAmelCase     :			Dict     	=type_sequence_label_size
       UpperCAmelCase     :			Tuple     	=encoder_stride
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





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

       UpperCAmelCase     :			Dict     	=None
       if self.use_labels:
             UpperCAmelCase     :			Any     	=ids_tensor([self.batch_size]		, self.type_sequence_label_size    )

       UpperCAmelCase     :			List[str]     	=self.get_config()

       return config, pixel_values, labels
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       return SwinConfig(
           image_size=self.image_size		, patch_size=self.patch_size		, num_channels=self.num_channels		, embed_dim=self.embed_dim		, depths=self.depths		, num_heads=self.num_heads		, window_size=self.window_size		, mlp_ratio=self.mlp_ratio		, qkv_bias=self.qkv_bias		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, drop_path_rate=self.drop_path_rate		, hidden_act=self.hidden_act		, use_absolute_embeddings=self.use_absolute_embeddings		, path_norm=self.patch_norm		, layer_norm_eps=self.layer_norm_eps		, initializer_range=self.initializer_range		, encoder_stride=self.encoder_stride		, )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=TFSwinModel(config=snake_case__    )
       UpperCAmelCase     :			Optional[Any]     	=model(snake_case__    )

       UpperCAmelCase     :			List[Any]     	=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
       UpperCAmelCase     :			Dict     	=int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

       self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, expected_seq_len, expected_dim)    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=TFSwinForMaskedImageModeling(config=snake_case__    )
       UpperCAmelCase     :			str     	=model(snake_case__    )
       self.parent.assertEqual(
           result.logits.shape		, (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

       # test greyscale images
       UpperCAmelCase     :			Tuple     	=1
       UpperCAmelCase     :			Optional[Any]     	=TFSwinForMaskedImageModeling(snake_case__    )

       UpperCAmelCase     :			Union[str, Any]     	=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
       UpperCAmelCase     :			str     	=model(snake_case__    )
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, 1, self.image_size, self.image_size)    )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.type_sequence_label_size
       UpperCAmelCase     :			List[str]     	=TFSwinForImageClassification(snake_case__    )
       UpperCAmelCase     :			List[Any]     	=model(snake_case__		, labels=snake_case__    )
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size)    )

       # test greyscale images
       UpperCAmelCase     :			Dict     	=1
       UpperCAmelCase     :			Optional[int]     	=TFSwinForImageClassification(snake_case__    )
       UpperCAmelCase     :			Tuple     	=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
       UpperCAmelCase     :			str     	=model(snake_case__    )
       self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size)    )






 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=self.prepare_config_and_inputs()
       UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			str     	=config_and_inputs
       UpperCAmelCase     :			Dict     	={'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_tf
class       __snake_case (  lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   (
     (
         TFSwinModel,
         TFSwinForImageClassification,
         TFSwinForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 __lowerCamelCase     : str 	=   (
     {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
     if is_tf_available()
     else {}
 )

 __lowerCamelCase     : Tuple 	=   False
 __lowerCamelCase     : Optional[Any] 	=   False
 __lowerCamelCase     : List[Any] 	=   False
 __lowerCamelCase     : Optional[Any] 	=   False
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=TFSwinModelTester(self    )
       UpperCAmelCase     :			Dict     	=ConfigTester(self		, config_class=snake_case__		, embed_dim=37    )
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''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    )						->				Optional[Any]:
       '''simple docstring'''





       return
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case__    )
 @unittest.skip(reason='''Swin does not use inputs_embeds'''    )
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       pass
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			List[Any]     	=self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCAmelCase     :			List[Any]     	=model_class(snake_case__    )
             self.assertIsInstance(model.get_input_embeddings()		, tf.keras.layers.Layer    )
             UpperCAmelCase     :			Tuple     	=model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(snake_case__		, tf.keras.layers.Dense    )    )
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			Tuple     	=self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCAmelCase     :			Tuple     	=model_class(snake_case__    )
             UpperCAmelCase     :			Dict     	=inspect.signature(model.call    )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCAmelCase     :			List[str]     	=[*signature.parameters.keys()]

             UpperCAmelCase     :			Optional[int]     	=['''pixel_values''']
             self.assertListEqual(arg_names[:1]		, snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase     :			int     	=True

       for model_class in self.all_model_classes:
             UpperCAmelCase     :			Optional[int]     	=True
             UpperCAmelCase     :			Optional[Any]     	=False
             UpperCAmelCase     :			Optional[int]     	=True
             UpperCAmelCase     :			Optional[int]     	=model_class(snake_case__    )
             UpperCAmelCase     :			Any     	=model(**self._prepare_for_class(snake_case__		, snake_case__    )    )
             UpperCAmelCase     :			int     	=outputs.attentions
             UpperCAmelCase     :			Optional[int]     	=len(self.model_tester.depths    )
             self.assertEqual(len(snake_case__    )		, snake_case__    )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             UpperCAmelCase     :			Union[str, Any]     	=True
             UpperCAmelCase     :			List[str]     	=config.window_size**2
             UpperCAmelCase     :			Union[str, Any]     	=model_class(snake_case__    )
             UpperCAmelCase     :			Union[str, Any]     	=model(**self._prepare_for_class(snake_case__		, snake_case__    )    )
             UpperCAmelCase     :			Tuple     	=outputs.attentions
             self.assertEqual(len(snake_case__    )		, snake_case__    )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]    )		, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		, )
             UpperCAmelCase     :			Optional[Any]     	=len(snake_case__    )

             # Check attention is always last and order is fine
             UpperCAmelCase     :			int     	=True
             UpperCAmelCase     :			Optional[int]     	=True
             UpperCAmelCase     :			Union[str, Any]     	=model_class(snake_case__    )
             UpperCAmelCase     :			str     	=model(**self._prepare_for_class(snake_case__		, snake_case__    )    )

             if hasattr(self.model_tester		, '''num_hidden_states_types'''    ):
                   UpperCAmelCase     :			str     	=self.model_tester.num_hidden_states_types
             else:
                   # also another +1 for reshaped_hidden_states
                   UpperCAmelCase     :			List[Any]     	=2
             self.assertEqual(out_len + added_hidden_states		, len(snake_case__    )    )

             UpperCAmelCase     :			str     	=outputs.attentions

             self.assertEqual(len(snake_case__    )		, snake_case__    )

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]    )		, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		, )
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=model_class(snake_case__    )
       UpperCAmelCase     :			List[str]     	=model(**self._prepare_for_class(snake_case__		, snake_case__    )    )
       UpperCAmelCase     :			Optional[int]     	=outputs.hidden_states

       UpperCAmelCase     :			int     	=getattr(
           self.model_tester		, '''expected_num_hidden_layers'''		, len(self.model_tester.depths    ) + 1    )
       self.assertEqual(len(snake_case__    )		, snake_case__    )

       # Swin has a different seq_length
       UpperCAmelCase     :			Optional[Any]     	=to_atuple(config.patch_size    )

       UpperCAmelCase     :			Union[str, Any]     	=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]    )		, [num_patches, self.model_tester.embed_dim]		, )

       UpperCAmelCase     :			Optional[Any]     	=outputs.reshaped_hidden_states
       self.assertEqual(len(snake_case__    )		, snake_case__    )

       UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			str     	=reshaped_hidden_states[0].shape

       UpperCAmelCase     :			Optional[int]     	=tf.reshape(reshaped_hidden_states[0]		, (batch_size, num_channels, height * width)    )
       UpperCAmelCase     :			Dict     	=tf.transpose(snake_case__		, (0, 2, 1)    )

       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]    )		, [num_patches, self.model_tester.embed_dim]		, )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





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

       UpperCAmelCase     :			List[Any]     	=to_atuple(self.model_tester.image_size    )

       for model_class in self.all_model_classes:
             UpperCAmelCase     :			List[str]     	=True
             self.check_hidden_states_output(snake_case__		, snake_case__		, snake_case__		, snake_case__    )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCAmelCase     :			Optional[Any]     	=True

             self.check_hidden_states_output(snake_case__		, snake_case__		, snake_case__		, snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			Any     	=self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase     :			Union[str, Any]     	=3

       UpperCAmelCase     :			List[Any]     	=to_atuple(self.model_tester.image_size    )
       UpperCAmelCase     :			Tuple     	=to_atuple(config.patch_size    )

       UpperCAmelCase     :			Tuple     	=image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       UpperCAmelCase     :			List[str]     	=image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
             UpperCAmelCase     :			Union[str, Any]     	=True
             self.check_hidden_states_output(snake_case__		, snake_case__		, snake_case__		, (padded_height, padded_width)    )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCAmelCase     :			Tuple     	=True
             self.check_hidden_states_output(snake_case__		, snake_case__		, snake_case__		, (padded_height, padded_width)    )






 @slow
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase     :			Tuple     	=TFSwinModel.from_pretrained(snake_case__    )
             self.assertIsNotNone(snake_case__    )







@require_vision
@require_tf
class       __snake_case (  unittest.TestCase ):
 @cached_property
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       return (
           AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''    )
           if is_vision_available()
           else None
       )






 @slow
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''    )
       UpperCAmelCase     :			Dict     	=self.default_image_processor

       UpperCAmelCase     :			List[Any]     	=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
       UpperCAmelCase     :			str     	=image_processor(images=snake_case__		, return_tensors='''tf'''    )

       # forward pass
       UpperCAmelCase     :			int     	=model(snake_case__    )

       # verify the logits
       UpperCAmelCase     :			Tuple     	=tf.TensorShape((1, 1000)    )
       self.assertEqual(outputs.logits.shape		, snake_case__    )
       UpperCAmelCase     :			List[str]     	=tf.constant([-0.0948, -0.6454, -0.0921]    )
       self.assertTrue(np.allclose(outputs.logits[0, :3]		, snake_case__		, atol=1e-4    )    )

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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Any     	=states.reshape(1		,       -1		,       model.config.state_dim    )
      UpperCAmelCase     :			Optional[int]     	=actions.reshape(1		,       -1		,       model.config.act_dim    )
      UpperCAmelCase     :			Optional[Any]     	=returns_to_go.reshape(1		,       -1		,       1    )
      UpperCAmelCase     :			Optional[int]     	=timesteps.reshape(1		,       -1    )

      if model.config.max_length is not None:
            UpperCAmelCase     :			str     	=states[:, -model.config.max_length :]
            UpperCAmelCase     :			Any     	=actions[:, -model.config.max_length :]
            UpperCAmelCase     :			List[Any]     	=returns_to_go[:, -model.config.max_length :]
            UpperCAmelCase     :			List[str]     	=timesteps[:, -model.config.max_length :]

            # pad all tokens to sequence length
            UpperCAmelCase     :			Optional[int]     	=torch.cat(
                [torch.zeros(model.config.max_length - states.shape[1]    ), torch.ones(states.shape[1]    )]    )
            UpperCAmelCase     :			int     	=attention_mask.to(dtype=torch.long		,       device=states.device    ).reshape(1		,       -1    )
            UpperCAmelCase     :			Optional[int]     	=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    )
            UpperCAmelCase     :			Tuple     	=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    )
            UpperCAmelCase     :			Tuple     	=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    )
            UpperCAmelCase     :			Union[str, Any]     	=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:
            UpperCAmelCase     :			Tuple     	=None

      UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			int     	=model(
          states=__lowerCAmelCase		,       actions=__lowerCAmelCase		,       rewards=__lowerCAmelCase		,       returns_to_go=__lowerCAmelCase		,       timesteps=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       return_dict=__lowerCAmelCase		,       )

      return action_preds[0, -1]


# build the environment

__snake_case         =     gym.make('''Hopper-v3''')
__snake_case         =     env.observation_space.shape[0]
__snake_case         =     env.action_space.shape[0]
__snake_case         =     10_00
__snake_case         =     '''cuda'''
__snake_case         =     1000.0  # normalization for rewards/returns
__snake_case         =     36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case         =     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,
    ]
)
__snake_case         =     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,
    ]
)
__snake_case         =     torch.from_numpy(state_mean).to(device=device)
__snake_case         =     torch.from_numpy(state_std).to(device=device)

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

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

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

          __snake_case         =     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),
          )
          __snake_case         =     action
          __snake_case         =     action.detach().cpu().numpy()

          __snake_case   ,	__snake_case   ,	__snake_case   ,	__snake_case         =     env.step(action)

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

          __snake_case         =     target_return[0, -1] - (reward / scale)
          __snake_case         =     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
          __snake_case         =     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
