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 	lowerCamelCase_     :






    '''simple docstring'''






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







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :str			  =		floats_tensor((self.batch_size, self.seq_length, self.state_dim))
        __UpperCamelCase     :Tuple			  =		floats_tensor((self.batch_size, self.seq_length, self.act_dim))
        __UpperCamelCase     :int			  =		floats_tensor((self.batch_size, self.seq_length, 1))
        __UpperCamelCase     :str			  =		floats_tensor((self.batch_size, self.seq_length, 1))
        __UpperCamelCase     :List[Any]			  =		ids_tensor((self.batch_size, self.seq_length) , vocab_size=1_000)
        __UpperCamelCase     :str			  =		random_attention_mask((self.batch_size, self.seq_length))

        __UpperCamelCase     :List[Any]			  =		self.get_config()

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







    def 			UpperCamelCase__   (	self)       ->	int:
        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 , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , )       ->	Dict:
        __UpperCamelCase     :Optional[Any]			  =		DecisionTransformerModel(config=__lowercase)
        model.to(__lowercase)
        model.eval()
        __UpperCamelCase     :Any			  =		model(__lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase)

        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)       ->	int:
        __UpperCamelCase     :Tuple			  =		self.prepare_config_and_inputs()
        (
            (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       (
            __UpperCamelCase
        )  ,       
        )     :Optional[Any]			  =		config_and_inputs
        __UpperCamelCase     :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 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						str						=      (DecisionTransformerModel,) if is_torch_available() else ()
    a__	:						List[str]						=      ()
    a__	:						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[int]						=      False
    a__	:						Any						=      False
    a__	:						str						=      False
    a__	:						List[str]						=      False
    a__	:						str						=      False
    a__	:						Any						=      False
    a__	:						Dict						=      False
    a__	:						Union[str, Any]						=      False
    a__	:						Optional[Any]						=      False







    def 			UpperCamelCase__   (	self)       ->	int:
        __UpperCamelCase     :Union[str, Any]			  =		DecisionTransformerModelTester(self)
        __UpperCamelCase     :List[Any]			  =		ConfigTester(self , config_class=__lowercase , hidden_size=37)







    def 			UpperCamelCase__   (	self)       ->	Any:
        self.config_tester.run_common_tests()







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :int			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase)







    @slow
    def 			UpperCamelCase__   (	self)       ->	Tuple:
        for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCamelCase     :List[str]			  =		DecisionTransformerModel.from_pretrained(__lowercase)
            self.assertIsNotNone(__lowercase)







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase  ,       __UpperCamelCase     :Tuple			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :Any			  =		model_class(__lowercase)
            __UpperCamelCase     :Any			  =		inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __UpperCamelCase     :Dict			  =		[*signature.parameters.keys()]

            __UpperCamelCase     :List[Any]			  =		[
                '''states''',
                '''actions''',
                '''rewards''',
                '''returns_to_go''',
                '''timesteps''',
                '''attention_mask''',
            ]

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


@require_torch
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






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

        __UpperCamelCase     :Dict			  =		torch.tensor(
            [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]] , device=__lowercase)

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

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

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

            with torch.no_grad():
                __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :Tuple			  =		model(
                    states=__lowercase , actions=__lowercase , rewards=__lowercase , returns_to_go=__lowercase , timesteps=__lowercase , attention_mask=__lowercase , return_dict=__lowercase , )

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

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










'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)

		# Initialize Result
		__SCREAMING_SNAKE_CASE       :		Tuple           =		[]

		# Traverse through all denomination
		for denomination in reversed(_lowerCamelCase					):
				# Find denominations
				while int(_lowerCamelCase					) >= int(_lowerCamelCase					):
						total_value -= int(_lowerCamelCase					)
						answer.append(_lowerCamelCase					)  # Append the "answers" array

		return answer


# Driver Code
if __name__ == "__main__":
						UpperCamelCase__       :	int    = []
						UpperCamelCase__       :	List[Any]    = '''0'''

						if (
						    input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
						    == "y"
						):
												UpperCamelCase__       :	Tuple    = int(input('''Enter the number of denominations you want to add: ''').strip())

												for i in range(0, n):
																		denominations.append(int(input(f"Denomination {i}: ").strip()))
												UpperCamelCase__       :	str    = input('''Enter the change you want to make in Indian Currency: ''').strip()
						else:
												# All denominations of Indian Currency if user does not enter
												UpperCamelCase__       :	List[Any]    = [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												UpperCamelCase__       :	str    = input('''Enter the change you want to make: ''').strip()

						if int(value) == 0 or int(value) < 0:
												print('''The total value cannot be zero or negative.''')

						else:
												print(f"Following is minimal change for {value}: ")
												UpperCamelCase__       :	int    = find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=''' ''')