

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 				lowercase       :




				def __init__(      self   ,     snake_case   ,     snake_case=13   ,     snake_case=7   ,     snake_case=6   ,     snake_case=17   ,     snake_case=23   ,     snake_case=11   ,     snake_case=True   ,     ):
											snake_case_           =					parent
											snake_case_           =					batch_size
											snake_case_           =					seq_length
											snake_case_           =					act_dim
											snake_case_           =					state_dim
											snake_case_           =					hidden_size
											snake_case_           =					max_length
											snake_case_           =					is_training




				def 					a							(      self ):
											snake_case_           =					floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
											snake_case_           =					floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
											snake_case_           =					floats_tensor((self.batch_size, self.seq_length, 1) )
											snake_case_           =					floats_tensor((self.batch_size, self.seq_length, 1) )
											snake_case_           =					ids_tensor((self.batch_size, self.seq_length)   ,     vocab_size=1000 )
											snake_case_           =					random_attention_mask((self.batch_size, self.seq_length) )

											snake_case_           =					self.get_config()

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




				def 					a							(      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 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     ):
											snake_case_           =					DecisionTransformerModel(config=snake_case )
											model.to(snake_case )
											model.eval()
											snake_case_           =					model(snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case )

											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 					a							(      self ):
											snake_case_           =					self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					(
											    snake_case_
											)       ,					
											)           =					config_and_inputs
											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 				lowercase       ( lowercase_			,						lowercase_			,						lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    (DecisionTransformerModel,) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE      :				List[str]     =    ()
				__SCREAMING_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
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    False

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




				def 					a							(      self ):
											snake_case_           =					DecisionTransformerModelTester(self )
											snake_case_           =					ConfigTester(self   ,     config_class=snake_case   ,     hidden_size=37 )




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




				def 					a							(      self ):
											snake_case_           =					self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case )




				@slow
				def 					a							(      self ):
											for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_           =					DecisionTransformerModel.from_pretrained(snake_case )
																		self.assertIsNotNone(snake_case )




				def 					a							(      self ):
											snake_case_       ,					snake_case_           =					self.model_tester.prepare_config_and_inputs_for_common()

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

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

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







@require_torch
class 				lowercase       ( unittest.TestCase    ):




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

											snake_case_           =					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=snake_case )

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

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

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

																		with torch.no_grad():
																									snake_case_       ,					snake_case_       ,					snake_case_           =					model(
																									    states=snake_case   ,     actions=snake_case   ,     rewards=snake_case   ,     returns_to_go=snake_case   ,     timesteps=snake_case   ,     attention_mask=snake_case   ,     return_dict=snake_case   ,     )

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

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







import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

_UpperCAmelCase	:     Optional[int]		       = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

_UpperCAmelCase	:     Dict		       = [file for file in filepaths if file != file.lower()]
if upper_files:
					print(F'''{len(upper_files)} files contain uppercase characters:''')
					print("""\n""".join(upper_files) + """\n""")

_UpperCAmelCase	:     Tuple		       = [file for file in filepaths if """ """ in file]
if space_files:
					print(F'''{len(space_files)} files contain space characters:''')
					print("""\n""".join(space_files) + """\n""")

_UpperCAmelCase	:     Union[str, Any]		       = [file for file in filepaths if """-""" in file]
if hyphen_files:
					print(F'''{len(hyphen_files)} files contain hyphen characters:''')
					print("""\n""".join(hyphen_files) + """\n""")

_UpperCAmelCase	:     List[Any]		       = [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(F'''{len(nodir_files)} files are not in a directory:''')
					print("""\n""".join(nodir_files) + """\n""")

_UpperCAmelCase	:     List[str]		       = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)




