
"""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    _UpperCAmelCase:



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






							'''simple docstring'''
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  seq_length
							_UpperCamelCase										=  act_dim
							_UpperCamelCase										=  state_dim
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  max_length
							_UpperCamelCase										=  is_training



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  floats_tensor((self.batch_size, self.seq_length, self.state_dim))
							_UpperCamelCase										=  floats_tensor((self.batch_size, self.seq_length, self.act_dim))
							_UpperCamelCase										=  floats_tensor((self.batch_size, self.seq_length, 1))
							_UpperCamelCase										=  floats_tensor((self.batch_size, self.seq_length, 1))
							_UpperCamelCase										=  ids_tensor((self.batch_size, self.seq_length)    ,		vocab_size=10_00)
							_UpperCamelCase										=  random_attention_mask((self.batch_size, self.seq_length))

							_UpperCamelCase										=  self.get_config()

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



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






							'''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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  DecisionTransformerModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase										=  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 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  (
							    _UpperCamelCase
							)			,  
							)										=  config_and_inputs
							_UpperCamelCase										=  {
							    '''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(   lowerCamelCase				,					lowerCamelCase				,					lowerCamelCase				,					unittest.TestCase     ):
		lowercase__      =				(DecisionTransformerModel,) if is_torch_available() else ()
		lowercase__      =				()
		lowercase__      =				{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

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



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  DecisionTransformerModelTester(self)
							_UpperCamelCase										=  ConfigTester(self    ,		config_class=__a    ,		hidden_size=37)



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






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



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






							'''simple docstring'''
							for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_UpperCamelCase										=  DecisionTransformerModel.from_pretrained(__a)
												self.assertIsNotNone(__a)




		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase			,  _UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

												_UpperCamelCase										=  [
												    '''states''',
												    '''actions''',
												    '''rewards''',
												    '''returns_to_go''',
												    '''timesteps''',
												    '''attention_mask''',
												]

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






@require_torch
class    _UpperCAmelCase(   unittest.TestCase     ):



		@slow
		def 						UpperCAmelCase							( self)	->	List[str]:






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

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

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

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

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

												with torch.no_grad():
																	_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  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))
												_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  (  # env.step(action)
												    torch.randn(1    ,		1    ,		config.state_dim).to(device=__a    ,		dtype=torch.floataa),
												    1.0,
												    False,
												    {},
												)

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








"""simple docstring"""





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


_a														=		logging.get_logger(__name__)

_a														=		{
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'levit'



		def __init__( self    ,		__a=2_24    ,		__a=3    ,		__a=3    ,		__a=2    ,		__a=1    ,		__a=16    ,		__a=[1_28, 2_56, 3_84]    ,		__a=[4, 8, 12]    ,		__a=[4, 4, 4]    ,		__a=[16, 16, 16]    ,		__a=0    ,		__a=[2, 2, 2]    ,		__a=[2, 2, 2]    ,		__a=0.02    ,		**__a    ,		)	->	List[str]:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  kernel_size
							_UpperCamelCase										=  stride
							_UpperCamelCase										=  padding
							_UpperCamelCase										=  hidden_sizes
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  depths
							_UpperCamelCase										=  key_dim
							_UpperCamelCase										=  drop_path_rate
							_UpperCamelCase										=  patch_size
							_UpperCamelCase										=  attention_ratio
							_UpperCamelCase										=  mlp_ratio
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  [
							    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				version.parse('1.11'     )



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ])




		@property
		def 						UpperCAmelCase							( self)	->	float:






							'''simple docstring'''
							return 1e-4






