




'''simple docstring'''



import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


UpperCamelCase_    	=				trt.Logger(trt.Logger.WARNING)
UpperCamelCase_    	=				absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

UpperCamelCase_    	=				logging.getLogger(__name__)

UpperCamelCase_    	=				argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    "--onnx_model_path",
    default=None,
    type=str,
    required=True,
    help="Path to ONNX model: ",
)

parser.add_argument(
    "--output_dir",
    default=None,
    type=str,
    required=True,
    help="The output directory where the model checkpoints and predictions will be written.",
)

# Other parameters

parser.add_argument(
    "--tokenizer_name",
    default="",
    type=str,
    required=True,
    help="Pretrained tokenizer name or path if not the same as model_name",
)

parser.add_argument(
    "--version_2_with_negative",
    action="store_true",
    help="If true, the SQuAD examples contain some that do not have an answer.",
)
parser.add_argument(
    "--null_score_diff_threshold",
    type=float,
    default=0.0,
    help="If null_score - best_non_null is greater than the threshold predict null.",
)

parser.add_argument(
    "--max_seq_length",
    default=3_8_4,
    type=int,
    help=(
        "The maximum total input sequence length after WordPiece tokenization. Sequences "
        "longer than this will be truncated, and sequences shorter than this will be padded."
    ),
)
parser.add_argument(
    "--doc_stride",
    default=1_2_8,
    type=int,
    help="When splitting up a long document into chunks, how much stride to take between chunks.",
)

parser.add_argument("--per_device_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.")

parser.add_argument(
    "--n_best_size",
    default=2_0,
    type=int,
    help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
)
parser.add_argument(
    "--max_answer_length",
    default=3_0,
    type=int,
    help=(
        "The maximum length of an answer that can be generated. This is needed because the start "
        "and end predictions are not conditioned on one another."
    ),
)

parser.add_argument("--seed", type=int, default=4_2, help="random seed for initialization")

parser.add_argument(
    "--dataset_name",
    type=str,
    default=None,
    required=True,
    help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--dataset_config_name",
    type=str,
    default=None,
    help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
)
parser.add_argument("--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument(
    "--fp16",
    action="store_true",
    help="Whether to use 16-bit (mixed) precision instead of 32-bit",
)
parser.add_argument(
    "--int8",
    action="store_true",
    help="Whether to use INT8",
)

UpperCamelCase_    	=				parser.parse_args()

if args.tokenizer_name:
      UpperCamelCase_    	=				AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
      raise ValueError(
          "You are instantiating a new tokenizer from scratch. This is not supported by this script."
          "You can do it from another script, save it, and load it from here, using --tokenizer_name."
      )

logger.info("Training/evaluation parameters %s", args)

UpperCamelCase_    	=				args.per_device_eval_batch_size

UpperCamelCase_    	=				(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
UpperCamelCase_    	=				True

UpperCamelCase_    	=				"temp_engine/bert-fp32.engine"
if args.fpaa:
      UpperCamelCase_    	=				"temp_engine/bert-fp16.engine"
if args.inta:
      UpperCamelCase_    	=				"temp_engine/bert-int8.engine"

# import ONNX file
if not os.path.exists("temp_engine"):
      os.makedirs("temp_engine")

UpperCamelCase_    	=				1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
      with open(args.onnx_model_path, "rb") as model:
            if not parser.parse(model.read()):
                  for error in range(parser.num_errors):
                        print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
      UpperCamelCase_    	=				[network.get_input(i) for i in range(network.num_inputs)]
      UpperCamelCase_    	=				[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

      with builder.create_builder_config() as config:
            UpperCamelCase_    	=				1 << 5_0
            if STRICT_TYPES:
                  config.set_flag(trt.BuilderFlag.STRICT_TYPES)
            if args.fpaa:
                  config.set_flag(trt.BuilderFlag.FPaa)
            if args.inta:
                  config.set_flag(trt.BuilderFlag.INTa)
            UpperCamelCase_    	=				builder.create_optimization_profile()
            config.add_optimization_profile(profile)
            for i in range(len(input_names)):
                  profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
            UpperCamelCase_    	=				builder.build_engine(network, config)

            # serialize_engine and store in file (can be directly loaded and deserialized):
            with open(engine_name, "wb") as f:
                  f.write(engine.serialize())




def   lowercase__(			__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					Optional[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Tuple    					=						np.asarray(inputs['input_ids']      ,dtype=np.intaa    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						np.asarray(inputs['attention_mask']      ,dtype=np.intaa    )
      SCREAMING_SNAKE_CASE   :      Dict    					=						np.asarray(inputs['token_type_ids']      ,dtype=np.intaa    )

      # Copy inputs
      cuda.memcpy_htod_async(d_inputs[0]      ,input_ids.ravel()      ,__UpperCamelCase    )
      cuda.memcpy_htod_async(d_inputs[1]      ,attention_mask.ravel()      ,__UpperCamelCase    )
      cuda.memcpy_htod_async(d_inputs[2]      ,token_type_ids.ravel()      ,__UpperCamelCase    )
      # start time
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						time.time()
      # Run inference
      context.execute_async(
          bindings=[int(__UpperCamelCase    ) for d_inp in d_inputs] + [int(__UpperCamelCase    ), int(__UpperCamelCase    )]      ,stream_handle=stream.handle    )
      # Transfer predictions back from GPU
      cuda.memcpy_dtoh_async(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      cuda.memcpy_dtoh_async(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      # Synchronize the stream and take time
      stream.synchronize()
      # end time
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						time.time()
      SCREAMING_SNAKE_CASE   :      Tuple    					=						end_time - start_time
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						(h_outputa, h_outputa)
      # print(outputs)
      return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
UpperCamelCase_    	=				Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_info()
else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
      set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
      # Downloading and loading a dataset from the hub.
      UpperCamelCase_    	=				load_dataset(args.dataset_name, args.dataset_config_name)
else:
      raise ValueError("Evaluation requires a dataset name")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

UpperCamelCase_    	=				raw_datasets["validation"].column_names

UpperCamelCase_    	=				"question" if "question" in column_names else column_names[0]
UpperCamelCase_    	=				"context" if "context" in column_names else column_names[1]
UpperCamelCase_    	=				"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
UpperCamelCase_    	=				tokenizer.padding_side == "right"

if args.max_seq_length > tokenizer.model_max_length:
      logger.warning(
          F"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
          F"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
      )

UpperCamelCase_    	=				min(args.max_seq_length, tokenizer.model_max_length)




def   lowercase__(			__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[q.lstrip() for q in examples[question_column_name]]

      # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
      # in one example possible giving several features when a context is long, each of those features having a
      # context that overlaps a bit the context of the previous feature.
      SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer(
          examples[question_column_name if pad_on_right else context_column_name]      ,examples[context_column_name if pad_on_right else question_column_name]      ,truncation='only_second' if pad_on_right else 'only_first'      ,max_length=__UpperCamelCase      ,stride=args.doc_stride      ,return_overflowing_tokens=__UpperCamelCase      ,return_offsets_mapping=__UpperCamelCase      ,padding='max_length'      ,)

      # Since one example might give us several features if it has a long context, we need a map from a feature to
      # its corresponding example. This key gives us just that.
      SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenized_examples.pop('overflow_to_sample_mapping'    )

      # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
      # corresponding example_id and we will store the offset mappings.
      SCREAMING_SNAKE_CASE   :      List[str]    					=						[]

      for i in range(len(tokenized_examples['input_ids']    )    ):
            # Grab the sequence corresponding to that example (to know what is the context and what is the question).
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						tokenized_examples.sequence_ids(__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						1 if pad_on_right else 0

            # One example can give several spans, this is the index of the example containing this span of text.
            SCREAMING_SNAKE_CASE   :      List[str]    					=						sample_mapping[i]
            tokenized_examples["example_id"].append(examples['id'][sample_index]    )

            # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
            # position is part of the context or not.
            SCREAMING_SNAKE_CASE   :      List[str]    					=						[
                (o if sequence_ids[k] == context_index else None)
                for k, o in enumerate(tokenized_examples['offset_mapping'][i]    )
            ]

      return tokenized_examples


UpperCamelCase_    	=				raw_datasets["validation"]
# Validation Feature Creation
UpperCamelCase_    	=				eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="Running tokenizer on validation dataset",
)

UpperCamelCase_    	=				default_data_collator

UpperCamelCase_    	=				eval_dataset.remove_columns(["example_id", "offset_mapping"])
UpperCamelCase_    	=				DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)




def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Dict      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Dict="eval"    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						postprocess_qa_predictions(
          examples=__UpperCamelCase      ,features=__UpperCamelCase      ,predictions=__UpperCamelCase      ,version_2_with_negative=args.version_2_with_negative      ,n_best_size=args.n_best_size      ,max_answer_length=args.max_answer_length      ,null_score_diff_threshold=args.null_score_diff_threshold      ,output_dir=args.output_dir      ,prefix=__UpperCamelCase      ,)
      # Format the result to the format the metric expects.
      if args.version_2_with_negative:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						[
                {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
            ]
      else:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

      SCREAMING_SNAKE_CASE   :      Any    					=						[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
      return EvalPrediction(predictions=__UpperCamelCase      ,label_ids=__UpperCamelCase    )


UpperCamelCase_    	=				load_metric("squad_v2" if args.version_2_with_negative else "squad")

# Evaluation!
logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
      # setup for TRT inferrence
      for i in range(len(input_names)):
            context.set_binding_shape(i, INPUT_SHAPE)
      assert context.all_binding_shapes_specified




      def   lowercase__(			__UpperCamelCase:					Any    ):

            """simple docstring"""






            return trt.volume(engine.get_binding_shape(__UpperCamelCase    )    ) * engine.get_binding_dtype(__UpperCamelCase    ).itemsize

      # Allocate device memory for inputs and outputs.
      UpperCamelCase_    	=				[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

      # Allocate output buffer
      UpperCamelCase_    	=				cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
      UpperCamelCase_    	=				cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
      UpperCamelCase_    	=				cuda.mem_alloc(h_outputa.nbytes)
      UpperCamelCase_    	=				cuda.mem_alloc(h_outputa.nbytes)

      # Create a stream in which to copy inputs/outputs and run inference.
      UpperCamelCase_    	=				cuda.Stream()

      # Evaluation
      logger.info("***** Running Evaluation *****")
      logger.info(F"""  Num examples = {len(eval_dataset)}""")
      logger.info(F"""  Batch size = {args.per_device_eval_batch_size}""")

      UpperCamelCase_    	=				0.0
      UpperCamelCase_    	=				0
      UpperCamelCase_    	=				timeit.default_timer()

      UpperCamelCase_    	=				None
      for step, batch in enumerate(eval_dataloader):
            UpperCamelCase_	,    UpperCamelCase_    	=				model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
            total_time += infer_time
            niter += 1

            UpperCamelCase_	,    UpperCamelCase_    	=				outputs
            UpperCamelCase_    	=				torch.tensor(start_logits)
            UpperCamelCase_    	=				torch.tensor(end_logits)

            # necessary to pad predictions and labels for being gathered
            UpperCamelCase_    	=				accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_0_0)
            UpperCamelCase_    	=				accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_0_0)

            UpperCamelCase_    	=				(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
            UpperCamelCase_    	=				logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_0_0)

      if all_preds is not None:
            UpperCamelCase_    	=				nested_truncate(all_preds, len(eval_dataset))

      UpperCamelCase_    	=				timeit.default_timer() - start_time
      logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
      # Inference time from TRT
      logger.info("Average Inference Time = {:.3f} ms".format(total_time * 1_0_0_0 / niter))
      logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 1_0_0_0))





      logger.info("Total Number of Inference =  %d", niter)

UpperCamelCase_    	=				post_processing_function(eval_examples, eval_dataset, all_preds)
UpperCamelCase_    	=				metric.compute(predictions=prediction.predictions, references=prediction.label_ids)





logger.info(F"""Evaluation metrics: {eval_metric}""")







'''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 __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=6					,  A=17					,  A=23					,  A=11					,  A=True					,  )       ->	Tuple:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    act_dim
      _a           =    state_dim
      _a           =    hidden_size
      _a           =    max_length
      _a           =    is_training



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


      """simple docstring"""



      _a           =    floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    ids_tensor((self.batch_size, self.seq_length)					,  vocab_size=1_000			)
      _a           =    random_attention_mask((self.batch_size, self.seq_length)			)

      _a           =    self.get_config()

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



 def    a__    (self			)       ->	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    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	List[Any]:


      """simple docstring"""



      _a           =    DecisionTransformerModel(config=A			)
      model.to(A			)
      model.eval()
      _a           =    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    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {
          '''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 __A     (    A  ,  A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[Any]		=			(DecisionTransformerModel,) if is_torch_available() else ()
 __lowerCamelCase       :			List[str]		=			()
 __lowerCamelCase       :			Tuple		=			{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			str		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			List[str]		=			False



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


      """simple docstring"""



      _a           =    DecisionTransformerModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



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


      """simple docstring"""



      self.config_tester.run_common_tests()



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



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


      """simple docstring"""



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



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


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

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

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

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






@require_torch
class __A     (    unittest.TestCase		):



 '''simple docstring'''







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


      """simple docstring"""



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

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

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

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

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

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

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



