

"""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 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			List[Any],					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			int=1_3,					UpperCAmelCase__							:			int=7,					UpperCAmelCase__							:			Optional[Any]=6,					UpperCAmelCase__							:			List[str]=1_7,					UpperCAmelCase__							:			List[str]=2_3,					UpperCAmelCase__							:			Any=1_1,					UpperCAmelCase__							:			Dict=True,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						act_dim
    __lowercase								=						state_dim
    __lowercase								=						hidden_size
    __lowercase								=						max_length
    __lowercase								=						is_training


 def 					_lowercase (			self							:			Any	):
    __lowercase								=						floats_tensor((self.batch_size, self.seq_length, self.state_dim)	)
    __lowercase								=						floats_tensor((self.batch_size, self.seq_length, self.act_dim)	)
    __lowercase								=						floats_tensor((self.batch_size, self.seq_length, 1)	)
    __lowercase								=						floats_tensor((self.batch_size, self.seq_length, 1)	)
    __lowercase								=						ids_tensor((self.batch_size, self.seq_length),					vocab_size=1_0_0_0	)
    __lowercase								=						random_attention_mask((self.batch_size, self.seq_length)	)

    __lowercase								=						self.get_config()

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


 def 					_lowercase (			self							:			Dict	):
    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 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[Any],					):
    __lowercase								=						DecisionTransformerModel(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)

    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 					_lowercase (			self							:			int	):
    __lowercase								=						self.prepare_config_and_inputs()
    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs
    __lowercase								=						{
        "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  (	lowercase     ,lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	(DecisionTransformerModel,) if is_torch_available() else ()
 __UpperCAmelCase	:					int   		=	()
 __UpperCAmelCase	:					Tuple   		=	{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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


 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						DecisionTransformerModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					hidden_size=3_7	)


 def 					_lowercase (			self							:			str	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__	)


 @slow
 def 					_lowercase (			self							:			int	):
    for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase								=						DecisionTransformerModel.from_pretrained(UpperCAmelCase__	)
       self.assertIsNotNone(UpperCAmelCase__	)



 def 					_lowercase (			self							:			Any	):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowercase								=						[
           "states",
           "actions",
           "rewards",
           "returns_to_go",
           "timesteps",
           "attention_mask",
       ]

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







@require_torch
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







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

    __lowercase								=						torch.tensor(
        [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]],					device=UpperCAmelCase__	)

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

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

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

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

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

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







"""simple docstring"""





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class 					_lowerCAmelCase  (	pl.LightningModule   ):







 """simple docstring"""







 def __init__(			self							:			Optional[Any],					UpperCAmelCase__							:			str	):
    super().__init__()
    __lowercase								=						model
    __lowercase								=						2
    __lowercase								=						nn.Linear(self.model.config.hidden_size,					self.num_labels	)



 def 					_lowercase (			self							:			Optional[int]	):
    pass







def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						str:
   '''simple docstring'''


   __lowercase								=						LongformerModel.from_pretrained(UpperCamelCase_)
   __lowercase								=						LightningModel(UpperCamelCase_)

   __lowercase								=						torch.load(UpperCamelCase_,			map_location=torch.device("cpu"))
   lightning_model.load_state_dict(ckpt["state_dict"])

   # init longformer question answering model
   __lowercase								=						LongformerForQuestionAnswering.from_pretrained(UpperCamelCase_)

   # transfer weights
   longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
   longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
   longformer_for_qa.eval()

   # save model
   longformer_for_qa.save_pretrained(UpperCamelCase_)

   print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""")


if __name__ == "__main__":
   _a       =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--longformer_model',
       default=None,
       type=str,
       required=True,
       help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
   )
   parser.add_argument(
       '--longformer_question_answering_ckpt_path',
       default=None,
       type=str,
       required=True,
       help='Path the official PyTorch Lightning Checkpoint.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _a       =							parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )




