




'''simple docstring'''





import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
  import torch

  from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
  import pyanvml.pyanvml as nvml


a		:	int       =      logging.get_logger(__name__)




class      UpperCamelCase       (	snake_case__       ):
  a     :PyTorchBenchmarkArguments
  a     :PretrainedConfig
  a     :str          =					"PyTorch"



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return torch.__version__



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self._prepare_inference_func(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
      return self._measure_speed(_inference )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[str]							       =      self._prepare_inference_func(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
      return self._measure_memory(_inference )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[Any]							       =      self._prepare_train_func(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
      return self._measure_speed(_train )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Any							       =      self._prepare_train_func(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
      return self._measure_memory(_train )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.config_dict[model_name]

      if self.args.torchscript:
          _a				:							Any							       =      True

      _a				:							Any							       =      (
          hasattr(__lowerCAmelCase		,     """architectures""" )
          and isinstance(config.architectures		,     __lowerCAmelCase )
          and len(config.architectures ) > 0
      )
      if not self.args.only_pretrain_model and has_model_class_in_config:
          try:
              _a				:							Optional[int]							       =      config.architectures[0]
              _a				:							str							       =      __import__("""transformers"""		,     fromlist=[model_class] )
              _a				:							str							       =      getattr(__lowerCAmelCase		,     __lowerCAmelCase )
              _a				:							List[Any]							       =      model_cls(__lowerCAmelCase )
          except ImportError:
              raise ImportError(
                  f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
                  """ set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
      else:
          _a				:							List[str]							       =      MODEL_MAPPING[config.__class__](__lowerCAmelCase )

      model.eval()
      model.to(self.args.device )

      # encoder-decoder has vocab size saved differently
      _a				:							str							       =      config.vocab_size if hasattr(__lowerCAmelCase		,     """vocab_size""" ) else config.encoder.vocab_size
      _a				:							int							       =      torch.randint(__lowerCAmelCase		,     (batch_size, sequence_length)		,     dtype=torch.long		,     device=self.args.device )

      if self.args.fpaa:
          logger.info("""Running training in Mixed Precision...""" )
          if not self.args.is_gpu:
              raise ValueError("""Mixed precision is possible only for GPU.""" )
          # amp seems to have memory leaks so that memory usage
          # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
          model.half()

      if self.args.torchscript:
          with torch.no_grad():
              _a				:							str							       =      torch.jit.trace(__lowerCAmelCase		,     __lowerCAmelCase )
      else:
          _a				:							Tuple							       =      model

      def encoder_decoder_forward():
          with torch.no_grad():
              _a				:							Tuple							       =      inference_model(__lowerCAmelCase		,     decoder_input_ids=__lowerCAmelCase )
          return outputs

      def encoder_forward():
          with torch.no_grad():
              _a				:							Union[str, Any]							       =      inference_model(__lowerCAmelCase )
          return outputs

      _a				:							Union[str, Any]							       =      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
      return _forward



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							int							       =      self.config_dict[model_name]

      _a				:							List[str]							       =      (
          hasattr(__lowerCAmelCase		,     """architectures""" )
          and isinstance(config.architectures		,     __lowerCAmelCase )
          and len(config.architectures ) > 0
      )
      if not self.args.only_pretrain_model and has_model_class_in_config:
          try:
              _a				:							Dict							       =      config.architectures[0]
              _a				:							Optional[Any]							       =      __import__("""transformers"""		,     fromlist=[model_class] )
              _a				:							int							       =      getattr(__lowerCAmelCase		,     __lowerCAmelCase )
              _a				:							List[str]							       =      model_cls(__lowerCAmelCase )
          except ImportError:
              raise ImportError(
                  f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
                  """ set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
      else:
          _a				:							Any							       =      MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__lowerCAmelCase )

      if self.args.torchscript:
          raise NotImplementedError("""Training for torchscript is currently not implemented""" )
      else:
          _a				:							Tuple							       =      model

      model.train()
      model.to(self.args.device )

      # encoder-decoder has vocab size saved differently
      _a				:							Dict							       =      config.vocab_size if hasattr(__lowerCAmelCase		,     """vocab_size""" ) else config.encoder.vocab_size
      _a				:							Tuple							       =      torch.randint(__lowerCAmelCase		,     (batch_size, sequence_length)		,     dtype=torch.long		,     device=self.args.device )

      if self.args.fpaa:
          logger.info("""Running training in Mixed Precision...""" )
          if not self.args.is_gpu:
              raise ValueError("""Mixed precision is possible only for GPU.""" )

          # amp seems to have memory leaks so that memory usage
          # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
          model.half()

      def compute_loss_and_backprob_encoder():
          _a				:							int							       =      train_model(__lowerCAmelCase		,     labels=__lowerCAmelCase )[0]
          loss.backward()
          return loss

      def compute_loss_and_backprob_encoder_decoder():
          _a				:							str							       =      train_model(__lowerCAmelCase		,     decoder_input_ids=__lowerCAmelCase		,     labels=__lowerCAmelCase )[0]
          loss.backward()
          return loss

      _a				:							Any							       =      (
          compute_loss_and_backprob_encoder_decoder
          if config.is_encoder_decoder
          else compute_loss_and_backprob_encoder
      )
      return _train



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      try:
          if self.args.is_tpu or self.args.torchscript:
              # run additional 10 times to stabilize compilation for tpu and torchscript
              logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation""" )
              timeit.repeat(
                  __lowerCAmelCase		,     repeat=1		,     number=5		,     )

          # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
          _a				:							List[str]							       =      timeit.repeat(
              __lowerCAmelCase		,     repeat=self.args.repeat		,     number=10		,     )

          if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
              import torch_xla.debug.metrics as met

              self.print_fn(met.metrics_report() )

          return min(__lowerCAmelCase ) / 10.0
      except RuntimeError as e:
          self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
          return "N/A"



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      try:
          if self.args.trace_memory_line_by_line:
              _a				:							Any							       =      start_memory_tracing("""transformers""" )

          if self.args.is_tpu:
              # tpu
              raise NotImplementedError(
                  """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
                  """ `--no-memory` or `args.memory=False`""" )
          elif self.args.is_gpu:
              if not is_pyanvml_available():
                  logger.warning(
                      """py3nvml not installed, we won't log GPU memory usage. """
                      """Install py3nvml (pip install py3nvml) to log information about GPU.""" )
                  _a				:							Tuple							       =      """N/A"""
              else:
                  logger.info(
                      """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
                      """ on the same GPU.""" )
                  # init nvml
                  nvml.nvmlInit()
                  func()
                  _a				:							Optional[Any]							       =      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
                  _a				:							str							       =      nvml.nvmlDeviceGetMemoryInfo(__lowerCAmelCase )
                  _a				:							Any							       =      meminfo.used
                  _a				:							str							       =      Memory(__lowerCAmelCase )
                  # shutdown nvml
                  nvml.nvmlShutdown()
          else:
              # cpu
              _a				:							Tuple							       =      measure_peak_memory_cpu(__lowerCAmelCase )
              _a				:							str							       =      Memory(__lowerCAmelCase ) if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else memory_bytes

          if self.args.trace_memory_line_by_line:
              _a				:							str							       =      stop_memory_tracing(__lowerCAmelCase )
          else:
              _a				:							List[str]							       =      None

          return memory, summary
      except RuntimeError as e:
          self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
          return "N/A", None






'''simple docstring'''


import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin






class 						lowerCAmelCase__      (      UpperCAmelCase__     ,							UpperCAmelCase__     ,							unittest.TestCase						):
			lowerCAmelCase			:						Any							 =  UNetaDModel
			lowerCAmelCase			:						List[Any]							 =  "sample"




			@property
			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   4
					_UpperCAmelCase		:							Any             =   14
					_UpperCAmelCase		:							Any             =   16

					_UpperCAmelCase		:							Optional[int]             =   floats_tensor((batch_size, num_features, seq_len)			).to(lowerCamelCase__			)
					_UpperCAmelCase		:							Dict             =   torch.tensor([10] * batch_size			).to(lowerCamelCase__			)

					return {"sample": noise, "timestep": time_step}




			@property
			def 				lowerCAmelCase__     (		self	:  Tuple			) ->List[Any]:



					'''simple docstring'''







					return (4, 14, 16)




			@property
			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Tuple:



					'''simple docstring'''







					return (4, 14, 16)




			def 				lowerCAmelCase__     (		self	:  int			) ->Tuple:



					'''simple docstring'''







					pass




			def 				lowerCAmelCase__     (		self	:  Dict			) ->Tuple:



					'''simple docstring'''







					pass




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Any			) ->List[str]:



					'''simple docstring'''







					super().test_determinism()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					super().test_outputs_equivalence()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->List[str]:



					'''simple docstring'''







					super().test_from_save_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					super().test_from_save_pretrained_variant()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  str			) ->int:



					'''simple docstring'''







					super().test_model_from_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Union[str, Any]:



					'''simple docstring'''







					super().test_output()




			def 				lowerCAmelCase__     (		self	:  int			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   {
					    "block_out_channels": (32, 64, 1_28, 2_56),
					    "in_channels": 14,
					    "out_channels": 14,
					    "time_embedding_type": "positional",
					    "use_timestep_embedding": True,
					    "flip_sin_to_cos": False,
					    "freq_shift": 1.0,
					    "out_block_type": "OutConv1DBlock",
					    "mid_block_type": "MidResTemporalBlock1D",
					    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
					    "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
					    "act_fn": "mish",
					}
					_UpperCAmelCase		:							List[Any]             =   self.dummy_input
					return init_dict, inputs_dict




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							int             =   UNetaDModel.from_pretrained(
					    "bglick13/hopper-medium-v2-value-function-hor32"						,	output_loading_info=lowerCamelCase__						,	subfolder="unet"			)
					self.assertIsNotNone(lowerCamelCase__			)
					self.assertEqual(len(loading_info["missing_keys"]			)						,	0			)

					model.to(lowerCamelCase__			)
					_UpperCAmelCase		:							Dict             =   model(**self.dummy_input			)

					assert image is not None, "Make sure output is not None"




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							str             =   UNetaDModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32"						,	subfolder="unet"			)
					torch.manual_seed(0			)
					if torch.cuda.is_available():
							torch.cuda.manual_seed_all(0			)

					_UpperCAmelCase		:							str             =   model.config.in_channels
					_UpperCAmelCase		:							Optional[int]             =   16
					_UpperCAmelCase		:							List[str]             =   torch.randn((1, seq_len, num_features)			).permute(
					    0						,	2						,	1			)  # match original, we can update values and remove
					_UpperCAmelCase		:							Tuple             =   torch.full((num_features,)						,	0			)

					with torch.no_grad():
							_UpperCAmelCase		:							List[Any]             =   model(lowerCamelCase__						,	lowerCamelCase__			).sample.permute(0						,	2						,	1			)

					_UpperCAmelCase		:							Optional[int]             =   output[0, -3:, -3:].flatten()
					# fmt: off
					_UpperCAmelCase		:							Dict             =   torch.tensor([-2.1_3_7_1_7_2, 1.1_4_2_6_0_1_6, 0.3_6_8_8_6_8_7, -0.7_6_6_9_2_2, 0.7_3_0_3_1_4_6, 0.1_1_0_3_8_8_6_4, -0.4_7_6_0_6_3_3, 0.1_3_2_7_0_1_7_2, 0.0_2_5_9_1_3_4_8]			)
					# fmt: on
					self.assertTrue(torch.allclose(lowerCamelCase__						,	lowerCamelCase__						,	rtol=1E-3			)			)




			def 				lowerCAmelCase__     (		self	:  Any			) ->List[str]:



					'''simple docstring'''







					pass






			@slow
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   "harmonai/maestro-150k"
					_UpperCAmelCase		:							Tuple             =   UNetaDModel.from_pretrained(lowerCamelCase__						,	subfolder="unet"			)
					model.to(lowerCamelCase__			)

					_UpperCAmelCase		:							List[str]             =   6_55_36
					_UpperCAmelCase		:							Any             =   torch.sin(torch.arange(lowerCamelCase__			)[None, None, :].repeat(1						,	2						,	1			)			).to(lowerCamelCase__			)
					_UpperCAmelCase		:							List[Any]             =   torch.tensor([1]			).to(lowerCamelCase__			)

					with torch.no_grad():
							_UpperCAmelCase		:							List[str]             =   model(lowerCamelCase__						,	lowerCamelCase__			).sample

					_UpperCAmelCase		:							Union[str, Any]             =   output.abs().sum()
					_UpperCAmelCase		:							Any             =   output.abs().max()

					assert (output_sum - 2_2_4.0_8_9_6).abs() < 0.5
					assert (output_max - 0.0_6_0_7).abs() < 4E-4






class 						lowerCAmelCase__      (      UpperCAmelCase__     ,							UpperCAmelCase__     ,							unittest.TestCase						):
			lowerCAmelCase			:						Union[str, Any]							 =  UNetaDModel
			lowerCAmelCase			:						Union[str, Any]							 =  "sample"




			@property
			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->str:



					'''simple docstring'''







					_UpperCAmelCase		:							Tuple             =   4
					_UpperCAmelCase		:							Optional[int]             =   14
					_UpperCAmelCase		:							Optional[int]             =   16

					_UpperCAmelCase		:							Optional[int]             =   floats_tensor((batch_size, num_features, seq_len)			).to(lowerCamelCase__			)
					_UpperCAmelCase		:							Union[str, Any]             =   torch.tensor([10] * batch_size			).to(lowerCamelCase__			)

					return {"sample": noise, "timestep": time_step}




			@property
			def 				lowerCAmelCase__     (		self	:  Dict			) ->Optional[int]:



					'''simple docstring'''







					return (4, 14, 16)




			@property
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					return (4, 14, 1)




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Dict:



					'''simple docstring'''







					super().test_determinism()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Optional[Any]:



					'''simple docstring'''







					super().test_outputs_equivalence()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  str			) ->Optional[int]:



					'''simple docstring'''







					super().test_from_save_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Dict:



					'''simple docstring'''







					super().test_from_save_pretrained_variant()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  int			) ->Dict:



					'''simple docstring'''







					super().test_model_from_pretrained()




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Dict:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							str             =   self.prepare_init_args_and_inputs_for_common()
					_UpperCAmelCase		:							Optional[int]             =   self.model_class(**lowerCamelCase__			)
					model.to(lowerCamelCase__			)
					model.eval()

					with torch.no_grad():
							_UpperCAmelCase		:							int             =   model(**lowerCamelCase__			)

							if isinstance(lowerCamelCase__						,	lowerCamelCase__			):
									_UpperCAmelCase		:							List[Any]             =   output.sample

					self.assertIsNotNone(lowerCamelCase__			)
					_UpperCAmelCase		:							Any             =   torch.Size((inputs_dict["sample"].shape[0], 1)			)
					self.assertEqual(output.shape						,	lowerCamelCase__						,	"Input and output shapes do not match"			)




			def 				lowerCAmelCase__     (		self	:  Tuple			) ->Dict:



					'''simple docstring'''







					pass




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->List[str]:



					'''simple docstring'''







					pass




			def 				lowerCAmelCase__     (		self	:  Dict			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							List[str]             =   {
					    "in_channels": 14,
					    "out_channels": 14,
					    "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
					    "up_block_types": [],
					    "out_block_type": "ValueFunction",
					    "mid_block_type": "ValueFunctionMidBlock1D",
					    "block_out_channels": [32, 64, 1_28, 2_56],
					    "layers_per_block": 1,
					    "downsample_each_block": True,
					    "use_timestep_embedding": True,
					    "freq_shift": 1.0,
					    "flip_sin_to_cos": False,
					    "time_embedding_type": "positional",
					    "act_fn": "mish",
					}
					_UpperCAmelCase		:							List[Any]             =   self.dummy_input
					return init_dict, inputs_dict




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							Any             =   UNetaDModel.from_pretrained(
					    "bglick13/hopper-medium-v2-value-function-hor32"						,	output_loading_info=lowerCamelCase__						,	subfolder="value_function"			)
					self.assertIsNotNone(lowerCamelCase__			)
					self.assertEqual(len(vf_loading_info["missing_keys"]			)						,	0			)

					value_function.to(lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   value_function(**self.dummy_input			)

					assert image is not None, "Make sure output is not None"




			@unittest.skipIf(torch_device == "mps"						,	"mish op not supported in MPS"			)
			def 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							List[str]             =   UNetaDModel.from_pretrained(
					    "bglick13/hopper-medium-v2-value-function-hor32"						,	output_loading_info=lowerCamelCase__						,	subfolder="value_function"			)
					torch.manual_seed(0			)
					if torch.cuda.is_available():
							torch.cuda.manual_seed_all(0			)

					_UpperCAmelCase		:							Tuple             =   value_function.config.in_channels
					_UpperCAmelCase		:							Tuple             =   14
					_UpperCAmelCase		:							int             =   torch.randn((1, seq_len, num_features)			).permute(
					    0						,	2						,	1			)  # match original, we can update values and remove
					_UpperCAmelCase		:							Tuple             =   torch.full((num_features,)						,	0			)

					with torch.no_grad():
							_UpperCAmelCase		:							int             =   value_function(lowerCamelCase__						,	lowerCamelCase__			).sample

					# fmt: off
					_UpperCAmelCase		:							Union[str, Any]             =   torch.tensor([1_6_5.2_5] * seq_len			)
					# fmt: on
					self.assertTrue(torch.allclose(lowerCamelCase__						,	lowerCamelCase__						,	rtol=1E-3			)			)






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



					'''simple docstring'''







					pass



