import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        FunnelBaseModel,
        FunnelForMaskedLM,
        FunnelForMultipleChoice,
        FunnelForPreTraining,
        FunnelForQuestionAnswering,
        FunnelForSequenceClassification,
        FunnelForTokenClassification,
        FunnelModel,
    )






class     __UpperCAmelCase:






 """simple docstring"""




 def __init__(    self ,       __magic_name__ ,       __magic_name__=13 ,       __magic_name__=7 ,       __magic_name__=True ,       __magic_name__=True ,       __magic_name__=True ,       __magic_name__=True ,       __magic_name__=99 ,       __magic_name__=[1, 1, 2] ,       __magic_name__=1 ,       __magic_name__=32 ,       __magic_name__=4 ,       __magic_name__=8 ,       __magic_name__=37 ,       __magic_name__="gelu_new" ,       __magic_name__=0.1 ,       __magic_name__=0.1 ,       __magic_name__=0.0 ,       __magic_name__=512 ,       __magic_name__=3 ,       __magic_name__=0.02 ,       __magic_name__=3 ,       __magic_name__=4 ,       __magic_name__=None ,       __magic_name__=False ,       ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				parent
        A_							:       Dict	      =				batch_size
        A_							:       List[str]	      =				seq_length
        A_							:       List[Any]	      =				is_training
        A_							:       Any	      =				use_input_mask
        A_							:       Union[str, Any]	      =				use_token_type_ids
        A_							:       Dict	      =				use_labels
        A_							:       Tuple	      =				vocab_size
        A_							:       int	      =				block_sizes
        A_							:       Optional[Any]	      =				num_decoder_layers
        A_							:       int	      =				d_model
        A_							:       str	      =				n_head
        A_							:       Optional[int]	      =				d_head
        A_							:       List[Any]	      =				d_inner
        A_							:       Any	      =				hidden_act
        A_							:       Tuple	      =				hidden_dropout
        A_							:       List[Any]	      =				attention_dropout
        A_							:       str	      =				activation_dropout
        A_							:       List[Any]	      =				max_position_embeddings
        A_							:       List[str]	      =				type_vocab_size
        A_							:       int	      =				2
        A_							:       Optional[Any]	      =				num_labels
        A_							:       str	      =				num_choices
        A_							:       List[str]	      =				scope
        A_							:       Any	      =				initializer_std

        # Used in the tests to check the size of the first attention layer
        A_							:       List[Any]	      =				n_head
        # Used in the tests to check the size of the first hidden state
        A_							:       Union[str, Any]	      =				self.d_model
        # Used in the tests to check the number of output hidden states/attentions
        A_							:       Dict	      =				sum(self.block_sizes      ) + (0 if base else self.num_decoder_layers)
        # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
        # the last hidden state of the first block (which is the first hidden state of the decoder).
        if not base:
               A_							:       Tuple	      =				self.num_hidden_layers + 2
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[Any]	      =				ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size      )

        A_							:       Any	      =				None
        if self.use_input_mask:
               A_							:       List[str]	      =				ids_tensor([self.batch_size, self.seq_length] ,       vocab_size=2      )

        A_							:       Tuple	      =				None
        if self.use_token_type_ids:
               A_							:       List[Any]	      =				ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size      )

        A_							:       Optional[Any]	      =				None
        A_							:       Any	      =				None
        A_							:       Tuple	      =				None
        if self.use_labels:
               A_							:       List[str]	      =				ids_tensor([self.batch_size] ,       self.type_sequence_label_size      )
               A_							:       Tuple	      =				ids_tensor([self.batch_size, self.seq_length] ,       self.num_labels      )
               A_							:       Tuple	      =				ids_tensor([self.batch_size] ,       self.num_choices      )
               A_							:       Tuple	      =				ids_tensor([self.batch_size, self.seq_length] ,       1      )

        A_							:       Dict	      =				self.get_config()

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            fake_token_labels,
        )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return FunnelConfig(
            vocab_size=self.vocab_size ,       block_sizes=self.block_sizes ,       num_decoder_layers=self.num_decoder_layers ,       d_model=self.d_model ,       n_head=self.n_head ,       d_head=self.d_head ,       d_inner=self.d_inner ,       hidden_act=self.hidden_act ,       hidden_dropout=self.hidden_dropout ,       attention_dropout=self.attention_dropout ,       activation_dropout=self.activation_dropout ,       max_position_embeddings=self.max_position_embeddings ,       type_vocab_size=self.type_vocab_size ,       initializer_std=self.initializer_std ,       )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				FunnelModel(config=__magic_name__      )
        model.to(__magic_name__      )
        model.eval()
        A_							:       List[Any]	      =				model(__magic_name__ ,       attention_mask=__magic_name__ ,       token_type_ids=__magic_name__      )
        A_							:       Tuple	      =				model(__magic_name__ ,       token_type_ids=__magic_name__      )
        A_							:       Union[str, Any]	      =				model(__magic_name__      )
        self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.d_model)      )

        A_							:       Optional[int]	      =				False
        A_							:       str	      =				model(__magic_name__      )
        self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.d_model)      )

        A_							:       str	      =				False
        A_							:       str	      =				model(__magic_name__      )
        self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.d_model)      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       Tuple	      =				FunnelBaseModel(config=__magic_name__      )
        model.to(__magic_name__      )
        model.eval()
        A_							:       Dict	      =				model(__magic_name__ ,       attention_mask=__magic_name__ ,       token_type_ids=__magic_name__      )
        A_							:       Union[str, Any]	      =				model(__magic_name__ ,       token_type_ids=__magic_name__      )
        A_							:       Dict	      =				model(__magic_name__      )
        self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, 2, self.d_model)      )

        A_							:       Optional[int]	      =				False
        A_							:       Optional[int]	      =				model(__magic_name__      )
        self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, 3, self.d_model)      )

        A_							:       Dict	      =				False
        A_							:       Any	      =				model(__magic_name__      )
        self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, 2, self.d_model)      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       int	      =				self.num_labels
        A_							:       List[Any]	      =				FunnelForPreTraining(config=__magic_name__      )
        model.to(__magic_name__      )
        model.eval()
        A_							:       str	      =				model(__magic_name__ ,       attention_mask=__magic_name__ ,       token_type_ids=__magic_name__ ,       labels=__magic_name__      )
        self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length)      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       List[str]	      =				FunnelForMaskedLM(config=__magic_name__      )
        model.to(__magic_name__      )
        model.eval()
        A_							:       Union[str, Any]	      =				model(__magic_name__ ,       attention_mask=__magic_name__ ,       token_type_ids=__magic_name__ ,       labels=__magic_name__      )
        self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size)      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       List[str]	      =				self.num_labels
        A_							:       Optional[int]	      =				FunnelForSequenceClassification(__magic_name__      )
        model.to(__magic_name__      )
        model.eval()
        A_							:       Union[str, Any]	      =				model(__magic_name__ ,       attention_mask=__magic_name__ ,       token_type_ids=__magic_name__ ,       labels=__magic_name__      )
        self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels)      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				self.num_choices
        A_							:       Optional[int]	      =				FunnelForMultipleChoice(config=__magic_name__      )
        model.to(__magic_name__      )
        model.eval()
        A_							:       Union[str, Any]	      =				input_ids.unsqueeze(1      ).expand(-1 ,       self.num_choices ,       -1      ).contiguous()
        A_							:       Optional[Any]	      =				token_type_ids.unsqueeze(1      ).expand(-1 ,       self.num_choices ,       -1      ).contiguous()
        A_							:       Optional[int]	      =				input_mask.unsqueeze(1      ).expand(-1 ,       self.num_choices ,       -1      ).contiguous()
        A_							:       int	      =				model(
            __magic_name__ ,       attention_mask=__magic_name__ ,       token_type_ids=__magic_name__ ,       labels=__magic_name__ ,       )
        self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_choices)      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				self.num_labels
        A_							:       Optional[int]	      =				FunnelForTokenClassification(config=__magic_name__      )
        model.to(__magic_name__      )
        model.eval()
        A_							:       Optional[int]	      =				model(__magic_name__ ,       attention_mask=__magic_name__ ,       token_type_ids=__magic_name__ ,       labels=__magic_name__      )
        self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.num_labels)      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       List[Any]	      =				FunnelForQuestionAnswering(config=__magic_name__      )
        model.to(__magic_name__      )
        model.eval()
        A_							:       Any	      =				model(
            __magic_name__ ,       attention_mask=__magic_name__ ,       token_type_ids=__magic_name__ ,       start_positions=__magic_name__ ,       end_positions=__magic_name__ ,       )
        self.parent.assertEqual(result.start_logits.shape ,       (self.batch_size, self.seq_length)      )
        self.parent.assertEqual(result.end_logits.shape ,       (self.batch_size, self.seq_length)      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				self.prepare_config_and_inputs()
        (
            (
            A_
        )      ,							(
            A_
        )      ,							(
            A_
        )      ,							(
            A_
        )      ,							(
            A_
        )      ,							(
            A_
        )      ,							(
            A_
        )      ,							(
            A_
        )      ,							
        )							:       Optional[Any]	      =				config_and_inputs
        A_							:       Any	      =				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict






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






 """simple docstring"""




 __magic_name__          =   False
 __magic_name__          =   False
 __magic_name__          =   (
     (
         FunnelModel,
         FunnelForMaskedLM,
         FunnelForPreTraining,
         FunnelForQuestionAnswering,
         FunnelForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __magic_name__          =   (
     {
         """feature-extraction""": (FunnelBaseModel, FunnelModel),
         """fill-mask""": FunnelForMaskedLM,
         """question-answering""": FunnelForQuestionAnswering,
         """text-classification""": FunnelForSequenceClassification,
         """token-classification""": FunnelForTokenClassification,
         """zero-shot""": FunnelForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__=False      ):

        """simple docstring"""

        A_							:       List[Any]	      =				super()._prepare_for_class(__magic_name__ ,       __magic_name__ ,       return_labels=__magic_name__      )

        if return_labels:
               if model_class in get_values(__magic_name__      ):
                      A_							:       Optional[Any]	      =				torch.zeros(
                          (self.model_tester.batch_size, self.model_tester.seq_length) ,       dtype=torch.long ,       device=__magic_name__      )
        return inputs_dict
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[Any]	      =				FunnelModelTester(self      )
        A_							:       Optional[int]	      =				ConfigTester(self ,       config_class=__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        self.config_tester.run_common_tests()
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[Any]	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Tuple	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        if hasattr(__magic_name__ ,       '''weight'''      ) and module.weight is not None:
               module.weight.data.fill_(3      )
        if hasattr(__magic_name__ ,       '''bias'''      ) and module.bias is not None:
               module.bias.data.fill_(3      )

        for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
               if hasattr(__magic_name__ ,       __magic_name__      ) and getattr(__magic_name__ ,       __magic_name__      ) is not None:
                      A_							:       List[str]	      =				getattr(__magic_name__ ,       __magic_name__      )
                      weight.data.fill_(3      )






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






 """simple docstring"""




 __magic_name__          =   False
 __magic_name__          =   False
 __magic_name__          =   (
     (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
 )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				FunnelModelTester(self ,       base=__magic_name__      )
        A_							:       Optional[int]	      =				ConfigTester(self ,       config_class=__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        self.config_tester.run_common_tests()
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_base_model(*__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_      ,							A_							:       Optional[Any]	      =				self.model_tester.prepare_config_and_inputs_for_common()
        A_							:       Dict	      =				True

        for model_class in self.all_model_classes:
               if model_class.__name__ == "FunnelBaseModel":
                      continue
               A_							:       Tuple	      =				model_class(__magic_name__      )
               model.to(__magic_name__      )
               model.train()
               A_							:       Optional[int]	      =				self._prepare_for_class(__magic_name__ ,       __magic_name__ ,       return_labels=__magic_name__      )
               A_							:       Optional[int]	      =				model(**__magic_name__      ).loss
               loss.backward()
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        if hasattr(__magic_name__ ,       '''weight'''      ) and module.weight is not None:
               module.weight.data.fill_(3      )
        if hasattr(__magic_name__ ,       '''bias'''      ) and module.bias is not None:
               module.bias.data.fill_(3      )

        for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
               if hasattr(__magic_name__ ,       __magic_name__      ) and getattr(__magic_name__ ,       __magic_name__      ) is not None:
                      A_							:       Optional[int]	      =				getattr(__magic_name__ ,       __magic_name__      )
                      weight.data.fill_(3      )






@require_torch
@require_sentencepiece
@require_tokenizers
class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[Any]	      =				13
        A_							:       Tuple	      =				7
        A_							:       Any	      =				torch.arange(0 ,       batch_size * sequence_length      ).long().reshape(__magic_name__ ,       __magic_name__      )
        A_							:       Dict	      =				[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
        A_							:       Optional[int]	      =				torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]      )

        A_							:       Any	      =				FunnelModel.from_pretrained('''sgugger/funnel-random-tiny'''      )
        A_							:       Union[str, Any]	      =				model(__magic_name__ ,       token_type_ids=__magic_name__      )[0].abs()

        A_							:       Tuple	      =				torch.tensor(23_44.83_52      )
        A_							:       int	      =				torch.tensor(0.80_52      )
        self.assertTrue(torch.allclose(output.sum() ,       __magic_name__ ,       atol=1e-4      )      )
        self.assertTrue(torch.allclose(output.mean() ,       __magic_name__ ,       atol=1e-4      )      )

        A_							:       Optional[Any]	      =				torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]      )
        A_							:       str	      =				model(__magic_name__ ,       attention_mask=__magic_name__ ,       token_type_ids=__magic_name__      )[0].abs()

        A_							:       Dict	      =				torch.tensor(23_43.84_25      )
        A_							:       Tuple	      =				torch.tensor(0.80_49      )
        self.assertTrue(torch.allclose(output.sum() ,       __magic_name__ ,       atol=1e-4      )      )
        self.assertTrue(torch.allclose(output.mean() ,       __magic_name__ ,       atol=1e-4      )      )
 @slow
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				FunnelTokenizer.from_pretrained('''huggingface/funnel-small'''      )
        A_							:       List[str]	      =				FunnelModel.from_pretrained('''huggingface/funnel-small'''      )
        A_							:       Tuple	      =				tokenizer('''Hello! I am the Funnel Transformer model.''' ,       return_tensors='''pt'''      )
        A_							:       int	      =				model(**__magic_name__      )[0]

        A_							:       str	      =				torch.tensor(2_35.72_46      )
        A_							:       Dict	      =				torch.tensor(0.02_56      )
        self.assertTrue(torch.allclose(output.sum() ,       __magic_name__ ,       atol=1e-4      )      )
        self.assertTrue(torch.allclose(output.mean() ,       __magic_name__ ,       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       a__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a							)		->	int:
       # we don't care about the past rewards in this model

       A_							:       Dict	      =				states.reshape(1  ,   -1  ,   model.config.state_dim							)
       A_							:       Dict	      =				actions.reshape(1  ,   -1  ,   model.config.act_dim							)
       A_							:       Dict	      =				returns_to_go.reshape(1  ,   -1  ,   1							)
       A_							:       Tuple	      =				timesteps.reshape(1  ,   -1							)

       if model.config.max_length is not None:
              A_							:       int	      =				states[:, -model.config.max_length :]
              A_							:       Optional[int]	      =				actions[:, -model.config.max_length :]
              A_							:       Any	      =				returns_to_go[:, -model.config.max_length :]
              A_							:       Tuple	      =				timesteps[:, -model.config.max_length :]

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

       A_      ,							A_      ,							A_							:       List[str]	      =				model(
           states=a  ,   actions=a  ,   rewards=a  ,   returns_to_go=a  ,   timesteps=a  ,   attention_mask=a  ,   return_dict=a  ,   )

       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        =       1_0_0_0
_lowerCAmelCase        =       'cuda'
_lowerCAmelCase        =       1000.0  # normalization for rewards/returns
_lowerCAmelCase        =       3_6_0_0 / 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(1_0):
    _lowerCAmelCase  ,			_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  ,			_lowerCAmelCase  ,			_lowerCAmelCase  ,			_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


