






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				,		_UpperCamelCase				,		_UpperCamelCase=1_3				,		_UpperCamelCase=7				,		_UpperCamelCase=6				,		_UpperCamelCase=1_7				,		_UpperCamelCase=2_3				,		_UpperCamelCase=1_1				,		_UpperCamelCase=True				,		)	->							str:
							UpperCAmelCase_    :      Optional[int]            =						parent
							UpperCAmelCase_    :      Dict            =						batch_size
							UpperCAmelCase_    :      List[str]            =						seq_length
							UpperCAmelCase_    :      List[Any]            =						act_dim
							UpperCAmelCase_    :      Union[str, Any]            =						state_dim
							UpperCAmelCase_    :      Union[str, Any]            =						hidden_size
							UpperCAmelCase_    :      Any            =						max_length
							UpperCAmelCase_    :      Any            =						is_training



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_    :      Tuple            =						floats_tensor((self.batch_size, self.seq_length, self.state_dim)   )
							UpperCAmelCase_    :      int            =						floats_tensor((self.batch_size, self.seq_length, self.act_dim)   )
							UpperCAmelCase_    :      int            =						floats_tensor((self.batch_size, self.seq_length, 1)   )
							UpperCAmelCase_    :      Dict            =						floats_tensor((self.batch_size, self.seq_length, 1)   )
							UpperCAmelCase_    :      List[Any]            =						ids_tensor((self.batch_size, self.seq_length)				,		vocab_size=1_0_0_0   )
							UpperCAmelCase_    :      List[Any]            =						random_attention_mask((self.batch_size, self.seq_length)   )

							UpperCAmelCase_    :      Dict            =						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				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Optional[Any]:
							UpperCAmelCase_    :      Optional[int]            =						DecisionTransformerModel(config=__lowerCAmelCase   )
							model.to(__lowerCAmelCase   )
							model.eval()
							UpperCAmelCase_    :      str            =						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 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      Optional[Any]            =						self.prepare_config_and_inputs()
							(
							    (
							    UpperCAmelCase_			
							)       ,  (
							    UpperCAmelCase_			
							)       ,  (
							    UpperCAmelCase_			
							)       ,  (
							    UpperCAmelCase_			
							)       ,  (
							    UpperCAmelCase_			
							)       ,  (
							    UpperCAmelCase_			
							)       ,  (
							    UpperCAmelCase_			
							)       ,  			
							)    :      Dict            =						config_and_inputs
							UpperCAmelCase_    :      str            =						{
							    '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      (_snake_case ,  _snake_case ,  _snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    Optional[Any]       						=  (DecisionTransformerModel,) if is_torch_available() else ()
		_snake_case    :    List[Any]       						=  ()
		_snake_case    :    Dict       						=  {'''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    :    List[Any]       						=  False

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



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      str            =						DecisionTransformerModelTester(self   )
							UpperCAmelCase_    :      str            =						ConfigTester(self				,		config_class=__lowerCAmelCase				,		hidden_size=3_7   )



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							self.config_tester.run_common_tests()



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      int            =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCAmelCase   )



		@slow
		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:






							for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_    :      Union[str, Any]            =						DecisionTransformerModel.from_pretrained(__lowerCAmelCase   )
												self.assertIsNotNone(__lowerCAmelCase   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						self.model_tester.prepare_config_and_inputs_for_common()

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

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

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





@require_torch
class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		@slow
		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      int            =						2  # number of steps of autoregressive prediction we will perform
							UpperCAmelCase_    :      Any            =						1_0  # defined by the RL environment, may be normalized
							UpperCAmelCase_    :      List[Any]            =						DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'   )
							UpperCAmelCase_    :      Optional[Any]            =						model.to(__lowerCAmelCase   )
							UpperCAmelCase_    :      Optional[Any]            =						model.config
							torch.manual_seed(0   )
							UpperCAmelCase_    :      Tuple            =						torch.randn(1				,		1				,		config.state_dim   ).to(device=__lowerCAmelCase				,		dtype=torch.floataa   )  # env.reset()

							UpperCAmelCase_    :      Any            =						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=__lowerCAmelCase   )

							UpperCAmelCase_    :      Any            =						torch.tensor(__lowerCAmelCase				,		device=__lowerCAmelCase				,		dtype=torch.floataa   ).reshape(1				,		1				,		1   )
							UpperCAmelCase_    :      List[str]            =						state
							UpperCAmelCase_    :      Dict            =						torch.zeros(1				,		0				,		config.act_dim				,		device=__lowerCAmelCase				,		dtype=torch.floataa   )
							UpperCAmelCase_    :      Optional[int]            =						torch.zeros(1				,		0				,		device=__lowerCAmelCase				,		dtype=torch.floataa   )
							UpperCAmelCase_    :      Dict            =						torch.tensor(0				,		device=__lowerCAmelCase				,		dtype=torch.long   ).reshape(1				,		1   )

							for step in range(__lowerCAmelCase   ):
												UpperCAmelCase_    :      Tuple            =						torch.cat([actions, torch.zeros(1				,		1				,		config.act_dim				,		device=__lowerCAmelCase   )]				,		dim=1   )
												UpperCAmelCase_    :      Dict            =						torch.cat([rewards, torch.zeros(1				,		1				,		device=__lowerCAmelCase   )]				,		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_    :      List[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   )   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						(  # env.step(action)
												    torch.randn(1				,		1				,		config.state_dim   ).to(device=__lowerCAmelCase				,		dtype=torch.floataa   ),
												    1.0,
												    False,
												    {},
												)

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













import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

		A__        =   {key.replace("""decoder"""    ,     """model"""    ): val for key, val in state_dict.items()}

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     max_position_embeddings=args.max_target_positions    ,     num_layers=args.decoder_layers    ,     attention_heads=args.decoder_attention_heads    ,     ffn_dim=args.decoder_ffn_embed_dim    ,     d_model=args.decoder_embed_dim    ,     layerdrop=args.decoder_layerdrop    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="""gelu"""    ,     scale_embedding=not args.no_scale_embedding    ,     tie_word_embeddings=args.share_decoder_input_output_embed    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
					parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



