





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 	UpperCamelCase_							:


							def __init__(				self     :int				,					__A     :Any				,					__A     :int=13				,					__A     :Optional[Any]=7				,					__A     :Any=6				,					__A     :List[Any]=17				,					__A     :List[str]=23				,					__A     :List[str]=11				,					__A     :Optional[Any]=True				,					) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   parent
									SCREAMING_SNAKE_CASE__								=   batch_size
									SCREAMING_SNAKE_CASE__								=   seq_length
									SCREAMING_SNAKE_CASE__								=   act_dim
									SCREAMING_SNAKE_CASE__								=   state_dim
									SCREAMING_SNAKE_CASE__								=   hidden_size
									SCREAMING_SNAKE_CASE__								=   max_length
									SCREAMING_SNAKE_CASE__								=   is_training


							def   _snake_case	(				self     :int		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   floats_tensor((self.batch_size, self.seq_length, self.state_dim)		)
									SCREAMING_SNAKE_CASE__								=   floats_tensor((self.batch_size, self.seq_length, self.act_dim)		)
									SCREAMING_SNAKE_CASE__								=   floats_tensor((self.batch_size, self.seq_length, 1)		)
									SCREAMING_SNAKE_CASE__								=   floats_tensor((self.batch_size, self.seq_length, 1)		)
									SCREAMING_SNAKE_CASE__								=   ids_tensor((self.batch_size, self.seq_length)				,					vocab_size=1000		)
									SCREAMING_SNAKE_CASE__								=   random_attention_mask((self.batch_size, self.seq_length)		)

									SCREAMING_SNAKE_CASE__								=   self.get_config()

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


							def   _snake_case	(				self     :Optional[Any]		) -> Tuple:



									"""simple docstring"""



									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   _snake_case	(				self     :List[Any]				,					__A     :Union[str, Any]				,					__A     :str				,					__A     :Dict				,					__A     :Union[str, Any]				,					__A     :Tuple				,					__A     :Tuple				,					__A     :Dict				,					) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   DecisionTransformerModel(config=__A		)
									model.to(__A		)
									model.eval()
									SCREAMING_SNAKE_CASE__								=   model(__A				,					__A				,					__A				,					__A				,					__A				,					__A		)

									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   _snake_case	(				self     :Union[str, Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    (
									    SCREAMING_SNAKE_CASE__
									)						,    
									)								=   config_and_inputs
									SCREAMING_SNAKE_CASE__								=   {
									    """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 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	unittest.TestCase     ):
							lowerCamelCase_							     =       (DecisionTransformerModel,) if is_torch_available() else ()
							lowerCamelCase_							     =       ()
							lowerCamelCase_							     =       {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

							# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
							lowerCamelCase_							     =       False

							# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False
							lowerCamelCase_							     =       False


							def   _snake_case	(				self     :List[Any]		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   DecisionTransformerModelTester(self		)
									SCREAMING_SNAKE_CASE__								=   ConfigTester(self				,					config_class=__A				,					hidden_size=37		)


							def   _snake_case	(				self     :Union[str, Any]		) -> Any:



									"""simple docstring"""



									self.config_tester.run_common_tests()


							def   _snake_case	(				self     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__A		)


							@slow
							def   _snake_case	(				self     :str		) -> Union[str, Any]:



									"""simple docstring"""



									for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											SCREAMING_SNAKE_CASE__								=   DecisionTransformerModel.from_pretrained(__A		)
											self.assertIsNotNone(__A		)


							def   _snake_case	(				self     :str		) -> str:



									"""simple docstring"""



									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(__A		)
											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__								=   [
											    """states""",
											    """actions""",
											    """rewards""",
											    """returns_to_go""",
											    """timesteps""",
											    """attention_mask""",
											]

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




@require_torch
class 	UpperCamelCase_							(  unittest.TestCase     ):


							@slow
							def   _snake_case	(				self     :Union[str, Any]		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   2  # number of steps of autoregressive prediction we will perform
									SCREAMING_SNAKE_CASE__								=   10  # defined by the RL environment, may be normalized
									SCREAMING_SNAKE_CASE__								=   DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""		)
									SCREAMING_SNAKE_CASE__								=   model.to(__A		)
									SCREAMING_SNAKE_CASE__								=   model.config
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   torch.randn(1				,					1				,					config.state_dim		).to(device=__A				,					dtype=torch.floataa		)  # env.reset()

									SCREAMING_SNAKE_CASE__								=   torch.tensor(
									    [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]				,					device=__A		)

									SCREAMING_SNAKE_CASE__								=   torch.tensor(__A				,					device=__A				,					dtype=torch.floataa		).reshape(1				,					1				,					1		)
									SCREAMING_SNAKE_CASE__								=   state
									SCREAMING_SNAKE_CASE__								=   torch.zeros(1				,					0				,					config.act_dim				,					device=__A				,					dtype=torch.floataa		)
									SCREAMING_SNAKE_CASE__								=   torch.zeros(1				,					0				,					device=__A				,					dtype=torch.floataa		)
									SCREAMING_SNAKE_CASE__								=   torch.tensor(0				,					device=__A				,					dtype=torch.long		).reshape(1				,					1		)

									for step in range(__A		):
											SCREAMING_SNAKE_CASE__								=   torch.cat([actions, torch.zeros(1				,					1				,					config.act_dim				,					device=__A		)]				,					dim=1		)
											SCREAMING_SNAKE_CASE__								=   torch.cat([rewards, torch.zeros(1				,					1				,					device=__A		)]				,					dim=1		)

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

											with torch.no_grad():
													SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   model(
													    states=__A				,					actions=__A				,					rewards=__A				,					returns_to_go=__A				,					timesteps=__A				,					attention_mask=__A				,					return_dict=__A				,					)

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

											SCREAMING_SNAKE_CASE__								=   action_pred[0, -1]
											SCREAMING_SNAKE_CASE__								=   torch.cat([states, state]				,					dim=1		)
											SCREAMING_SNAKE_CASE__								=   returns_to_go[0, -1] - reward
											SCREAMING_SNAKE_CASE__								=   torch.cat([returns_to_go, pred_return.reshape(1				,					1				,					1		)]				,					dim=1		)
											SCREAMING_SNAKE_CASE__								=   torch.cat(
											    [timesteps, torch.ones((1, 1)				,					device=__A				,					dtype=torch.long		) * (step + 1)]				,					dim=1		)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'donut-swin'

 __UpperCamelCase       =      {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }

 def __init__(  self,		A_=224,		A_=4,		A_=3,		A_=96,		A_=[2, 2, 6, 2],		A_=[3, 6, 12, 24],		A_=7,		A_=4.0,		A_=True,		A_=0.0,		A_=0.0,		A_=0.1,		A_="gelu",		A_=False,		A_=0.02,		A_=1E-5,		**A_,		)	->       List[str]:
    super().__init__(**A_		)

    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=embed_dim
    UpperCAmelCase__						=depths
    UpperCAmelCase__						=len(A_		)
    UpperCAmelCase__						=num_heads
    UpperCAmelCase__						=window_size
    UpperCAmelCase__						=mlp_ratio
    UpperCAmelCase__						=qkv_bias
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=drop_path_rate
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=use_absolute_embeddings
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    UpperCAmelCase__						=int(embed_dim * 2 ** (len(A_		) - 1)		)



