



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,		):
        A			:	List[Any]										=       parent
        A			:	List[str]										=       batch_size
        A			:	Union[str, Any]										=       seq_length
        A			:	List[Any]										=       act_dim
        A			:	List[str]										=       state_dim
        A			:	str										=       hidden_size
        A			:	Tuple										=       max_length
        A			:	int										=       is_training






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       floats_tensor((self.batch_size, self.seq_length, self.state_dim)		)
        A			:	List[str]										=       floats_tensor((self.batch_size, self.seq_length, self.act_dim)		)
        A			:	str										=       floats_tensor((self.batch_size, self.seq_length, 1)		)
        A			:	List[Any]										=       floats_tensor((self.batch_size, self.seq_length, 1)		)
        A			:	Optional[int]										=       ids_tensor((self.batch_size, self.seq_length),		vocab_size=1000		)
        A			:	List[Any]										=       random_attention_mask((self.batch_size, self.seq_length)		)

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

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






    def       _lowerCAmelCase (	self		):
        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,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		):
        A			:	int										=       DecisionTransformerModel(config=lowerCamelCase__		)
        model.to(lowerCamelCase__		)
        model.eval()
        A			:	Optional[Any]										=       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       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       self.prepare_config_and_inputs()
        (
            A							
        )			:	Optional[Any]										=       config_and_inputs
        A			:	Dict										=       {
            """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__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Union[str, Any] =				(DecisionTransformerModel,) if is_torch_available() else ()
    __lowerCamelCase      :			Optional[Any] =				()
    __lowerCamelCase      :			Tuple =				{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       DecisionTransformerModelTester(self		)
        A			:	Tuple										=       ConfigTester(self,		config_class=lowerCamelCase__,		hidden_size=37		)






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






    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__		)






    @slow
    def       _lowerCAmelCase (	self		):
        for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A			:	str										=       DecisionTransformerModel.from_pretrained(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	int										=       self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

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

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

            A			:	Optional[int]										=       torch.ones(1,		states.shape[1]		).to(dtype=torch.long,		device=states.device		)

            with torch.no_grad():
                A			:	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		)		)
            A			:	Optional[int]										=       (  # env.step(action)
                torch.randn(1,		1,		config.state_dim		).to(device=lowerCamelCase__,		dtype=torch.floataa		),
                1.0,
                False,
                {},
            )

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







import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE_:Optional[Any]					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Union[str, Any]					=						{
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Dict =				"conditional_detr"
    __lowerCamelCase      :			str =				["past_key_values"]
    __lowerCamelCase      :			str =				{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(	self,		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=3,		lowerCamelCase__=300,		lowerCamelCase__=6,		lowerCamelCase__=2048,		lowerCamelCase__=8,		lowerCamelCase__=6,		lowerCamelCase__=2048,		lowerCamelCase__=8,		lowerCamelCase__=0.0,		lowerCamelCase__=0.0,		lowerCamelCase__=True,		lowerCamelCase__="relu",		lowerCamelCase__=256,		lowerCamelCase__=0.1,		lowerCamelCase__=0.0,		lowerCamelCase__=0.0,		lowerCamelCase__=0.02,		lowerCamelCase__=1.0,		lowerCamelCase__=False,		lowerCamelCase__="sine",		lowerCamelCase__="resnet50",		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=2,		lowerCamelCase__=5,		lowerCamelCase__=2,		lowerCamelCase__=1,		lowerCamelCase__=1,		lowerCamelCase__=2,		lowerCamelCase__=5,		lowerCamelCase__=2,		lowerCamelCase__=0.25,		**lowerCamelCase__,		):
        if backbone_config is not None and use_timm_backbone:
            raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""		)

        if not use_timm_backbone:
            if backbone_config is None:
                logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
                A			:	List[Any]										=       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]		)
            elif isinstance(lowerCamelCase__,		lowerCamelCase__		):
                A			:	Any										=       backbone_config.get("""model_type"""		)
                A			:	Optional[Any]										=       CONFIG_MAPPING[backbone_model_type]
                A			:	Tuple										=       config_class.from_dict(lowerCamelCase__		)

        A			:	Dict										=       use_timm_backbone
        A			:	int										=       backbone_config
        A			:	Union[str, Any]										=       num_channels
        A			:	Optional[Any]										=       num_queries
        A			:	Union[str, Any]										=       d_model
        A			:	str										=       encoder_ffn_dim
        A			:	List[Any]										=       encoder_layers
        A			:	Tuple										=       encoder_attention_heads
        A			:	Union[str, Any]										=       decoder_ffn_dim
        A			:	Tuple										=       decoder_layers
        A			:	int										=       decoder_attention_heads
        A			:	Union[str, Any]										=       dropout
        A			:	List[str]										=       attention_dropout
        A			:	Optional[int]										=       activation_dropout
        A			:	Optional[Any]										=       activation_function
        A			:	Any										=       init_std
        A			:	List[Any]										=       init_xavier_std
        A			:	Any										=       encoder_layerdrop
        A			:	List[str]										=       decoder_layerdrop
        A			:	int										=       encoder_layers
        A			:	Union[str, Any]										=       auxiliary_loss
        A			:	Union[str, Any]										=       position_embedding_type
        A			:	Tuple										=       backbone
        A			:	Dict										=       use_pretrained_backbone
        A			:	int										=       dilation
        # Hungarian matcher
        A			:	List[Any]										=       class_cost
        A			:	List[Any]										=       bbox_cost
        A			:	int										=       giou_cost
        # Loss coefficients
        A			:	List[Any]										=       mask_loss_coefficient
        A			:	Any										=       dice_loss_coefficient
        A			:	int										=       cls_loss_coefficient
        A			:	Tuple										=       bbox_loss_coefficient
        A			:	List[Any]										=       giou_loss_coefficient
        A			:	int										=       focal_alpha
        super().__init__(is_encoder_decoder=lowerCamelCase__,		**lowerCamelCase__		)






    @property
    def       _lowerCAmelCase (	self		):
        return self.encoder_attention_heads






    @property
    def       _lowerCAmelCase (	self		):
        return self.d_model






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       copy.deepcopy(self.__dict__		)
        if self.backbone_config is not None:
            A			:	List[Any]										=       self.backbone_config.to_dict()
        A			:	List[str]										=       self.__class__.model_type
        return output






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Tuple =				version.parse("1.11"   )






    @property
    def       _lowerCAmelCase (	self		):
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ("""pixel_mask""", {0: """batch"""}),
            ]		)






    @property
    def       _lowerCAmelCase (	self		):
        return 1e-5






    @property
    def       _lowerCAmelCase (	self		):
        return 12


