





import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_pytesseract_available():
      from PIL import Image

      from transformers import LayoutLMvaImageProcessor

class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     def __init__(							self		:					Union[str, Any]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Any=7		,lowerCamelCase__		:					List[Any]=3		,lowerCamelCase__		:					Any=18		,lowerCamelCase__		:					Any=30		,lowerCamelCase__		:					Optional[int]=400		,lowerCamelCase__		:					Tuple=True		,lowerCamelCase__		:					Optional[int]=None		,lowerCamelCase__		:					Optional[int]=True		,)  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		size if size is not None else {"""height""": 18, """width""": 18}
           SCREAMING_SNAKE_CASE	=		parent
           SCREAMING_SNAKE_CASE	=		batch_size
           SCREAMING_SNAKE_CASE	=		num_channels
           SCREAMING_SNAKE_CASE	=		image_size
           SCREAMING_SNAKE_CASE	=		min_resolution
           SCREAMING_SNAKE_CASE	=		max_resolution
           SCREAMING_SNAKE_CASE	=		do_resize
           SCREAMING_SNAKE_CASE	=		size
           SCREAMING_SNAKE_CASE	=		apply_ocr






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	str:




           '''simple docstring'''


           return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}

@require_torch
@require_pytesseract
class 	UpperCamelCase__				(   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Tuple				=    LayoutLMvaImageProcessor if is_pytesseract_available() else None






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		LayoutLMvaImageProcessingTester(self )






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           return self.image_processor_tester.prepare_image_processor_dict()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_resize""" ) )
           self.assertTrue(hasattr(lowerCamelCase__		,"""size""" ) )
           self.assertTrue(hasattr(lowerCamelCase__		,"""apply_ocr""" ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.image_processing_class.from_dict(self.image_processor_dict )
           self.assertEqual(image_processor.size		,{"""height""": 18, """width""": 18} )

           SCREAMING_SNAKE_CASE	=		self.image_processing_class.from_dict(self.image_processor_dict		,size=42 )
           self.assertEqual(image_processor.size		,{"""height""": 42, """width""": 42} )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	int:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           SCREAMING_SNAKE_CASE	=		prepare_image_inputs(self.image_processor_tester		,equal_resolution=lowerCamelCase__ )
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__		,Image.Image )

           # Test not batched input
           SCREAMING_SNAKE_CASE	=		image_processing(image_inputs[0]		,return_tensors="""pt""" )
           self.assertEqual(
               encoding.pixel_values.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,)

           self.assertIsInstance(encoding.words		,lowerCamelCase__ )
           self.assertIsInstance(encoding.boxes		,lowerCamelCase__ )

           # Test batched
           SCREAMING_SNAKE_CASE	=		image_processing(lowerCamelCase__		,return_tensors="""pt""" ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           SCREAMING_SNAKE_CASE	=		prepare_image_inputs(self.image_processor_tester		,equal_resolution=lowerCamelCase__		,numpify=lowerCamelCase__ )
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__		,np.ndarray )

           # Test not batched input
           SCREAMING_SNAKE_CASE	=		image_processing(image_inputs[0]		,return_tensors="""pt""" ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,)

           # Test batched
           SCREAMING_SNAKE_CASE	=		image_processing(lowerCamelCase__		,return_tensors="""pt""" ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE	=		prepare_image_inputs(self.image_processor_tester		,equal_resolution=lowerCamelCase__		,torchify=lowerCamelCase__ )
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__		,torch.Tensor )

           # Test not batched input
           SCREAMING_SNAKE_CASE	=		image_processing(image_inputs[0]		,return_tensors="""pt""" ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,)

           # Test batched
           SCREAMING_SNAKE_CASE	=		image_processing(lowerCamelCase__		,return_tensors="""pt""" ).pixel_values
           self.assertEqual(
               encoded_images.shape		,(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )		,)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		LayoutLMvaImageProcessor()

           from datasets import load_dataset

           SCREAMING_SNAKE_CASE	=		load_dataset("""hf-internal-testing/fixtures_docvqa"""		,split="""test""" )

           SCREAMING_SNAKE_CASE	=		Image.open(ds[0]["""file"""] ).convert("""RGB""" )

           SCREAMING_SNAKE_CASE	=		image_processing(lowerCamelCase__		,return_tensors="""pt""" )

           self.assertEqual(encoding.pixel_values.shape		,(1, 3, 224, 224) )
           self.assertEqual(len(encoding.words )		,len(encoding.boxes ) )

           # fmt: off
           # the words and boxes were obtained with Tesseract 4.1.1
           SCREAMING_SNAKE_CASE	=		[["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
           SCREAMING_SNAKE_CASE	=		[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
           # fmt: on

           self.assertListEqual(encoding.words		,lowerCamelCase__ )
           self.assertListEqual(encoding.boxes		,lowerCamelCase__ )

           # with apply_OCR = False
           SCREAMING_SNAKE_CASE	=		LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		image_processing(lowerCamelCase__		,return_tensors="""pt""" )

           self.assertEqual(encoding.pixel_values.shape		,(1, 3, 224, 224) )









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__				:



     '''simple docstring'''

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




           '''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        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					Union[str, Any]		,)  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		DecisionTransformerModel(config=lowerCamelCase__ )
           model.to(lowerCamelCase__ )
           model.eval()
           SCREAMING_SNAKE_CASE	=		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        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[int]:




           '''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__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Tuple				=    (DecisionTransformerModel,) if is_torch_available() else ()
     __snake_case					:			Dict				=    ()
     __snake_case					:			List[Any]				=    {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		DecisionTransformerModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,hidden_size=37 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Dict:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__ )






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




           '''simple docstring'''


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






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




           '''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(lowerCamelCase__ )
                 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(lowerCamelCase__ )]		,lowerCamelCase__ )

@require_torch
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Optional[int]:




           '''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(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model.config
           torch.manual_seed(0 )
           SCREAMING_SNAKE_CASE	=		torch.randn(1		,1		,config.state_dim ).to(device=lowerCamelCase__		,dtype=torch.floataa )  # env.reset()

           SCREAMING_SNAKE_CASE	=		torch.tensor(
               [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]		,device=lowerCamelCase__ )

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

           for step in range(lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		torch.cat([actions, torch.zeros(1		,1		,config.act_dim		,device=lowerCamelCase__ )]		,dim=1 )
                 SCREAMING_SNAKE_CASE	=		torch.cat([rewards, torch.zeros(1		,1		,device=lowerCamelCase__ )]		,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=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 ) )
                 SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		(  # env.step(action)
                     torch.randn(1		,1		,config.state_dim ).to(device=lowerCamelCase__		,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=lowerCamelCase__		,dtype=torch.long ) * (step + 1)]		,dim=1 )


