


"""simple docstring"""


import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import DecisionTransformerModel
   from transformers.models.decision_transformer.modeling_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
   )

class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=13 ,     lowerCamelCase__=7 ,     lowerCamelCase__=6 ,     lowerCamelCase__=17 ,     lowerCamelCase__=23 ,     lowerCamelCase__=11 ,     lowerCamelCase__=True ,     )      ->							List[Any]:
    lowercase__       :       Optional[Any]				=						parent
    lowercase__       :       Optional[int]				=						batch_size
    lowercase__       :       Any				=						seq_length
    lowercase__       :       Optional[Any]				=						act_dim
    lowercase__       :       str				=						state_dim
    lowercase__       :       Union[str, Any]				=						hidden_size
    lowercase__       :       Any				=						max_length
    lowercase__       :       Optional[Any]				=						is_training


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       Any				=						floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
    lowercase__       :       str				=						floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
    lowercase__       :       List[str]				=						floats_tensor((self.batch_size, self.seq_length, 1)						)
    lowercase__       :       Optional[Any]				=						floats_tensor((self.batch_size, self.seq_length, 1)						)
    lowercase__       :       List[Any]				=						ids_tensor((self.batch_size, self.seq_length) ,     vocab_size=1000						)
    lowercase__       :       Optional[Any]				=						random_attention_mask((self.batch_size, self.seq_length)						)

    lowercase__       :       str				=						self.get_config()

    return (
        config,
        states,
        actions,
        rewards,
        returns_to_go,
        timesteps,
        attention_mask,
    )


   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return DecisionTransformerConfig(
        batch_size=self.batch_size ,     seq_length=self.seq_length ,     act_dim=self.act_dim ,     state_dim=self.state_dim ,     hidden_size=self.hidden_size ,     max_length=self.max_length ,     )


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     )      ->							Tuple:
    lowercase__       :       Optional[Any]				=						DecisionTransformerModel(config=lowerCamelCase__						)
    model.to(lowerCamelCase__						)
    model.eval()
    lowercase__       :       str				=						model(lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)

    self.parent.assertEqual(result.state_preds.shape ,     states.shape						)
    self.parent.assertEqual(result.action_preds.shape ,     actions.shape						)
    self.parent.assertEqual(result.return_preds.shape ,     returns_to_go.shape						)
    self.parent.assertEqual(
        result.last_hidden_state.shape ,     (self.batch_size, self.seq_length * 3, self.hidden_size)						)  # seq length *3 as there are 3 modelities: states, returns and actions







   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       List[str]				=						self.prepare_config_and_inputs()
    (
        (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       (
        lowercase__
    )	,       
    )       :       Tuple				=						config_and_inputs
    lowercase__       :       int				=						{
        """states""": states,
        """actions""": actions,
        """rewards""": rewards,
        """returns_to_go""": returns_to_go,
        """timesteps""": timesteps,
        """attention_mask""": attention_mask,
    }
    return config, inputs_dict

@require_torch
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Dict									=    (DecisionTransformerModel,) if is_torch_available() else ()
   _a   :       List[Any]									=    ()
   _a   :       List[str]									=    {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

   # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
   _a   :       Union[str, Any]									=    False

   # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
   _a   :       Optional[Any]									=    False
   _a   :       List[str]									=    False
   _a   :       Dict									=    False
   _a   :       List[str]									=    False
   _a   :       List[Any]									=    False
   _a   :       Any									=    False
   _a   :       int									=    False
   _a   :       Any									=    False
   _a   :       Optional[int]									=    False


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Union[str, Any]				=						DecisionTransformerModelTester(self						)
    lowercase__       :       Optional[int]				=						ConfigTester(self ,     config_class=lowerCamelCase__ ,     hidden_size=37						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    self.config_tester.run_common_tests()


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       List[str]				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase__						)


   @slow
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowercase__       :       Dict				=						DecisionTransformerModel.from_pretrained(lowerCamelCase__						)
     self.assertIsNotNone(lowerCamelCase__						)







   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__	,       lowercase__       :       Any				=						self.model_tester.prepare_config_and_inputs_for_common()

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

     lowercase__       :       Optional[Any]				=						[
         """states""",
         """actions""",
         """rewards""",
         """returns_to_go""",
         """timesteps""",
         """attention_mask""",
     ]

     self.assertListEqual(arg_names[: len(lowerCamelCase__						)] ,     lowerCamelCase__						)

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



   @slow
   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Optional[Any]				=						2  # number of steps of autoregressive prediction we will perform
    lowercase__       :       Optional[int]				=						10  # defined by the RL environment, may be normalized
    lowercase__       :       List[str]				=						DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""						)
    lowercase__       :       Union[str, Any]				=						model.to(lowerCamelCase__						)
    lowercase__       :       Optional[int]				=						model.config
    torch.manual_seed(0						)
    lowercase__       :       Optional[int]				=						torch.randn(1 ,     1 ,     config.state_dim						).to(device=lowerCamelCase__ ,     dtype=torch.floataa						)  # env.reset()

    lowercase__       :       Optional[Any]				=						torch.tensor(
        [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]] ,     device=lowerCamelCase__						)

    lowercase__       :       int				=						torch.tensor(lowerCamelCase__ ,     device=lowerCamelCase__ ,     dtype=torch.floataa						).reshape(1 ,     1 ,     1						)
    lowercase__       :       Optional[Any]				=						state
    lowercase__       :       Optional[Any]				=						torch.zeros(1 ,     0 ,     config.act_dim ,     device=lowerCamelCase__ ,     dtype=torch.floataa						)
    lowercase__       :       List[Any]				=						torch.zeros(1 ,     0 ,     device=lowerCamelCase__ ,     dtype=torch.floataa						)
    lowercase__       :       Optional[Any]				=						torch.tensor(0 ,     device=lowerCamelCase__ ,     dtype=torch.long						).reshape(1 ,     1						)

    for step in range(lowerCamelCase__						):
     lowercase__       :       Optional[int]				=						torch.cat([actions, torch.zeros(1 ,     1 ,     config.act_dim ,     device=lowerCamelCase__						)] ,     dim=1						)
     lowercase__       :       str				=						torch.cat([rewards, torch.zeros(1 ,     1 ,     device=lowerCamelCase__						)] ,     dim=1						)

     lowercase__       :       str				=						torch.ones(1 ,     states.shape[1]						).to(dtype=torch.long ,     device=states.device						)

     with torch.no_grad():
      lowercase__	,       lowercase__	,       lowercase__       :       Optional[Any]				=						model(
          states=lowerCamelCase__ ,     actions=lowerCamelCase__ ,     rewards=lowerCamelCase__ ,     returns_to_go=lowerCamelCase__ ,     timesteps=lowerCamelCase__ ,     attention_mask=lowerCamelCase__ ,     return_dict=lowerCamelCase__ ,     )

     self.assertEqual(action_pred.shape ,     actions.shape						)
     self.assertTrue(torch.allclose(action_pred[0, -1] ,     expected_outputs[step] ,     atol=1E-4						)						)
     lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       str				=						(  # env.step(action)
         torch.randn(1 ,     1 ,     config.state_dim						).to(device=lowerCamelCase__ ,     dtype=torch.floataa						),
         1.0,
         False,
         {},
     )

     lowercase__       :       Any				=						action_pred[0, -1]
     lowercase__       :       int				=						torch.cat([states, state] ,     dim=1						)
     lowercase__       :       str				=						returns_to_go[0, -1] - reward
     lowercase__       :       int				=						torch.cat([returns_to_go, pred_return.reshape(1 ,     1 ,     1						)] ,     dim=1						)
     lowercase__       :       int				=						torch.cat(
         [timesteps, torch.ones((1, 1) ,     device=lowerCamelCase__ ,     dtype=torch.long						) * (step + 1)] ,     dim=1						)



"""simple docstring"""


import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Optional[int]									=    LayoutLMTokenizer
   _a   :       Dict									=    LayoutLMTokenizerFast
   _a   :       Dict									=    True
   _a   :       Any									=    True


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    super().setUp()

    lowercase__       :       int				=						[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    lowercase__       :       Dict				=						os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""						) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							Optional[int]:
    return LayoutLMTokenizer.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       List[str]				=						"""UNwant\u00E9d,running"""
    lowercase__       :       Optional[int]				=						"""unwanted, running"""
    return input_text, output_text


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						self.tokenizer_class(self.vocab_file						)

    lowercase__       :       Optional[Any]				=						tokenizer.tokenize("""UNwant\u00E9d,running"""						)
    self.assertListEqual(lowerCamelCase__ ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]						)
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__						) ,     [7, 4, 5, 10, 8, 9]						)







   def 					UpperCAmelCase__(						self						)      ->							Any:
    pass