
"""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   :					int     ,A_   :					List[Any]     ,A_   :					str=13     ,A_   :					List[Any]=7     ,A_   :					List[str]=6     ,A_   :					Any=17     ,A_   :					int=23     ,A_   :					Optional[int]=11     ,A_   :					Union[str, Any]=True     ,)   ->      int:
							A        =      parent
							A        =      batch_size
							A        =      seq_length
							A        =      act_dim
							A        =      state_dim
							A        =      hidden_size
							A        =      max_length
							A        =      is_training


						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Union[str, Any]:
							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=1000					)
							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   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Any:
							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   :					List[Any]     ,A_   :					str     ,A_   :					int     ,A_   :					Union[str, Any]     ,A_   :					int     ,A_   :					str     ,A_   :					int     ,A_   :					Tuple     ,)   ->      List[str]:
							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   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Optional[Any]:
							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  lowerCAmelCase_ (					snake_case_						,    snake_case_						,    snake_case_						,    unittest.TestCase			):



						'''simple docstring'''


						_lowerCamelCase:			Tuple					=			(DecisionTransformerModel,) if is_torch_available() else ()
						_lowerCamelCase:			str					=			()
						_lowerCamelCase:			int					=			{'''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:			Any					=			False
						_lowerCamelCase:			List[Any]					=			False
						_lowerCamelCase:			List[Any]					=			False
						_lowerCamelCase:			Any					=			False
						_lowerCamelCase:			List[str]					=			False
						_lowerCamelCase:			List[str]					=			False
						_lowerCamelCase:			Dict					=			False
						_lowerCamelCase:			List[Any]					=			False
						_lowerCamelCase:			Optional[Any]					=			False


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      Optional[int]:
							A        =      DecisionTransformerModelTester(self					)
							A        =      ConfigTester(self     ,config_class=A_     ,hidden_size=37					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Any:
							self.config_tester.run_common_tests()


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Optional[int]:
							A        =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_					)


						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Optional[Any]:
							for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A        =      DecisionTransformerModel.from_pretrained(A_					)
								self.assertIsNotNone(A_					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Any:
							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  lowerCAmelCase_ (					unittest.TestCase			):



						'''simple docstring'''


						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      List[Any]:
							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(
							    [[0.24_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=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					)

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__A				=1_6
__A				=3_2





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__ = 1_6    ):
				lowerCamelCase_						=			AutoTokenizer.from_pretrained("bert-base-cased"    )
				lowerCamelCase_						=			load_dataset("glue"	, "mrpc"    )

				def tokenize_function(lowerCamelCase__    ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCamelCase_						=			tokenizer(examples["sentence1"]	, examples["sentence2"]	, truncation=lowerCamelCase__	, max_length=lowerCamelCase__    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								lowerCamelCase_						=			datasets.map(
								    lowerCamelCase__	, batched=lowerCamelCase__	, remove_columns=["idx", "sentence1", "sentence2"]	, )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCamelCase_						=			tokenized_datasets.rename_column("label"	, "labels"    )

				def collate_fn(lowerCamelCase__    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCamelCase_						=			1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCamelCase_						=			1_6
								elif accelerator.mixed_precision != "no":
												lowerCamelCase_						=			8
								else:
												lowerCamelCase_						=			None

								return tokenizer.pad(
								    lowerCamelCase__	, padding="longest"	, max_length=lowerCamelCase__	, pad_to_multiple_of=lowerCamelCase__	, return_tensors="pt"	, )

				# Instantiate dataloaders.
				lowerCamelCase_						=			DataLoader(
				    tokenized_datasets["train"]	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__    )
				lowerCamelCase_						=			DataLoader(
				    tokenized_datasets["validation"]	, shuffle=lowerCamelCase__	, collate_fn=lowerCamelCase__	, batch_size=lowerCamelCase__    )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
			from accelerate.test_utils.training import mocked_dataloaders

			__A				=mocked_dataloaders  # noqa: F811





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				# For testing only
				if os.environ.get("TESTING_MOCKED_DATALOADERS"	, lowerCamelCase__    ) == "1":
								lowerCamelCase_						=			2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								lowerCamelCase_						=			Accelerator(
								    cpu=args.cpu	, mixed_precision=args.mixed_precision	, log_with="all"	, project_dir=args.project_dir    )
				else:
								lowerCamelCase_						=			Accelerator(cpu=args.cpu	, mixed_precision=args.mixed_precision    )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCamelCase_						=			config["lr"]
				lowerCamelCase_						=			int(config["num_epochs"]    )
				lowerCamelCase_						=			int(config["seed"]    )
				lowerCamelCase_						=			int(config["batch_size"]    )
				set_seed(lowerCamelCase__    )

				lowerCamelCase_	,    lowerCamelCase_						=			get_dataloaders(lowerCamelCase__	, lowerCamelCase__    )
				lowerCamelCase_						=			evaluate.load("glue"	, "mrpc"    )

				# If the batch size is too big we use gradient accumulation
				lowerCamelCase_						=			1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCamelCase_						=			batch_size // MAX_GPU_BATCH_SIZE
								lowerCamelCase_						=			MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCamelCase_						=			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"	, return_dict=lowerCamelCase__    )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				lowerCamelCase_						=			model.to(accelerator.device    )

				# Instantiate optimizer
				lowerCamelCase_						=			AdamW(params=model.parameters()	, lr=lowerCamelCase__    )

				# Instantiate scheduler
				lowerCamelCase_						=			get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase__	, num_warmup_steps=1_0_0	, num_training_steps=(len(lowerCamelCase__    ) * num_epochs) // gradient_accumulation_steps	, )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			accelerator.prepare(
				    lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								lowerCamelCase_						=			os.path.split(lowerCamelCase__    )[-1].split("."    )[0]
								accelerator.init_trackers(lowerCamelCase__	, lowerCamelCase__    )

				# Now we train the model
				for epoch in range(lowerCamelCase__    ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												lowerCamelCase_						=			0
								for step, batch in enumerate(lowerCamelCase__    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												lowerCamelCase_						=			model(**lowerCamelCase__    )
												lowerCamelCase_						=			outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCamelCase_						=			loss / gradient_accumulation_steps
												accelerator.backward(lowerCamelCase__    )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(lowerCamelCase__    ):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device    )
												with torch.no_grad():
																lowerCamelCase_						=			model(**lowerCamelCase__    )
												lowerCamelCase_						=			outputs.logits.argmax(dim=-1    )
												lowerCamelCase_	,    lowerCamelCase_						=			accelerator.gather_for_metrics((predictions, batch["labels"])    )
												metric.add_batch(
												    predictions=lowerCamelCase__	, references=lowerCamelCase__	, )

								lowerCamelCase_						=			metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'epoch {epoch}:'	, lowerCamelCase__    )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(lowerCamelCase__    ),
												        "epoch": epoch,
												    }	, step=lowerCamelCase__	, )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			argparse.ArgumentParser(description="Simple example of training script."    )
				parser.add_argument(
				    "--mixed_precision"	, type=lowerCamelCase__	, default=lowerCamelCase__	, choices=["no", "fp16", "bf16", "fp8"]	, help="Whether to use mixed precision. Choose"
				    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
				    "and an Nvidia Ampere GPU."	, )
				parser.add_argument("--cpu"	, action="store_true"	, help="If passed, will train on the CPU."    )
				parser.add_argument(
				    "--with_tracking"	, action="store_true"	, help="Whether to load in all available experiment trackers from the environment and use them for logging."	, )
				parser.add_argument(
				    "--project_dir"	, type=lowerCamelCase__	, default="logs"	, help="Location on where to store experiment tracking logs` and relevent project information"	, )
				lowerCamelCase_						=			parser.parse_args()
				lowerCamelCase_						=			{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(lowerCamelCase__	, lowerCamelCase__    )


if __name__ == "__main__":
			main()


