



"""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__       :



						def __init__(self    ,  _lowercase    ,  _lowercase=13    ,  _lowercase=7    ,  _lowercase=6    ,  _lowercase=17    ,  _lowercase=23    ,  _lowercase=11    ,  _lowercase=True    ,  ):





													'''simple docstring'''

													__a		:		int						=							parent
													__a		:		Dict						=							batch_size
													__a		:		Dict						=							seq_length
													__a		:		Optional[int]						=							act_dim
													__a		:		Dict						=							state_dim
													__a		:		Tuple						=							hidden_size
													__a		:		List[Any]						=							max_length
													__a		:		int						=							is_training



						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		Any						=							floats_tensor((self.batch_size, self.seq_length, self.state_dim)				)
													__a		:		Any						=							floats_tensor((self.batch_size, self.seq_length, self.act_dim)				)
													__a		:		Tuple						=							floats_tensor((self.batch_size, self.seq_length, 1)				)
													__a		:		Union[str, Any]						=							floats_tensor((self.batch_size, self.seq_length, 1)				)
													__a		:		Union[str, Any]						=							ids_tensor((self.batch_size, self.seq_length)    ,  vocab_size=1000				)
													__a		:		int						=							random_attention_mask((self.batch_size, self.seq_length)				)

													__a		:		Any						=							self.get_config()

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



						def      lowerCAmelCase__(self				):





													'''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      lowerCAmelCase__(self    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  _lowercase    ,  ):





													'''simple docstring'''

													__a		:		Any						=							DecisionTransformerModel(config=__UpperCAmelCase				)
													model.to(__UpperCAmelCase				)
													model.eval()
													__a		:		Any						=							model(__UpperCAmelCase    ,  __UpperCAmelCase    ,  __UpperCAmelCase    ,  __UpperCAmelCase    ,  __UpperCAmelCase    ,  __UpperCAmelCase				)

													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      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		Dict						=							self.prepare_config_and_inputs()
													(
													    __a   
													)		:		Optional[int]						=							config_and_inputs
													__a		:		List[Any]						=							{
													    "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__       (		__snake_case  ,       __snake_case  ,       __snake_case  ,       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      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		int						=							DecisionTransformerModelTester(self				)
													__a		:		Union[str, Any]						=							ConfigTester(self    ,  config_class=__UpperCAmelCase    ,  hidden_size=37				)



						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													self.config_tester.run_common_tests()



						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		Optional[int]						=							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase				)



						@slow
						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__a		:		List[str]						=							DecisionTransformerModel.from_pretrained(__UpperCAmelCase				)
																				self.assertIsNotNone(__UpperCAmelCase				)





						def      lowerCAmelCase__(self				):





													'''simple docstring'''

													__a		:		Any						=							self.model_tester.prepare_config_and_inputs_for_common()

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

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

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







@require_torch
class SCREAMING_SNAKE_CASE__       (		unittest.TestCase   ):



						@slow
						def      lowerCAmelCase__(self				):





													'''simple docstring'''

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

													__a		:		Tuple						=							torch.tensor(
													    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]    ,  device=__UpperCAmelCase				)

													__a		:		List[str]						=							torch.tensor(__UpperCAmelCase    ,  device=__UpperCAmelCase    ,  dtype=torch.floataa				).reshape(1    ,  1    ,  1				)
													__a		:		str						=							state
													__a		:		Tuple						=							torch.zeros(1    ,  0    ,  config.act_dim    ,  device=__UpperCAmelCase    ,  dtype=torch.floataa				)
													__a		:		int						=							torch.zeros(1    ,  0    ,  device=__UpperCAmelCase    ,  dtype=torch.floataa				)
													__a		:		List[str]						=							torch.tensor(0    ,  device=__UpperCAmelCase    ,  dtype=torch.long				).reshape(1    ,  1				)

													for step in range(__UpperCAmelCase				):
																				__a		:		List[Any]						=							torch.cat([actions, torch.zeros(1    ,  1    ,  config.act_dim    ,  device=__UpperCAmelCase				)]    ,  dim=1				)
																				__a		:		int						=							torch.cat([rewards, torch.zeros(1    ,  1    ,  device=__UpperCAmelCase				)]    ,  dim=1				)

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

																				with torch.no_grad():
																											__a		:		int						=							model(
																											    states=__UpperCAmelCase    ,  actions=__UpperCAmelCase    ,  rewards=__UpperCAmelCase    ,  returns_to_go=__UpperCAmelCase    ,  timesteps=__UpperCAmelCase    ,  attention_mask=__UpperCAmelCase    ,  return_dict=__UpperCAmelCase    ,  )

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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase		       =      {
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      ["""BlipImageProcessor"""]


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """BlipModel""",
						    """BlipPreTrainedModel""",
						    """BlipForConditionalGeneration""",
						    """BlipForQuestionAnswering""",
						    """BlipVisionModel""",
						    """BlipTextModel""",
						    """BlipForImageTextRetrieval""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFBlipModel""",
						    """TFBlipPreTrainedModel""",
						    """TFBlipForConditionalGeneration""",
						    """TFBlipForQuestionAnswering""",
						    """TFBlipVisionModel""",
						    """TFBlipTextModel""",
						    """TFBlipForImageTextRetrieval""",
						]

if TYPE_CHECKING:
						from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
						from .processing_blip import BlipProcessor

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_blip import BlipImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blip import (
												    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlipForConditionalGeneration,
												    BlipForImageTextRetrieval,
												    BlipForQuestionAnswering,
												    BlipModel,
												    BlipPreTrainedModel,
												    BlipTextModel,
												    BlipVisionModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blip import (
												    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFBlipForConditionalGeneration,
												    TFBlipForImageTextRetrieval,
												    TFBlipForQuestionAnswering,
												    TFBlipModel,
												    TFBlipPreTrainedModel,
												    TFBlipTextModel,
												    TFBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


