code: 
  '''simple docstring'''
  
  
  
  from collections import OrderedDict
  from typing import Any, List, Mapping, Optional
  
  from ... import PreTrainedTokenizer, TensorType, is_torch_available
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast, PatchingSpec
  from ...utils import logging
  
  
  __A          =    logging.get_logger(__name__)
  
  __A          =    {
      "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
      "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
      "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
      "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
      "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
  }
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							int       		=  """gpt2"""
  			lowerCamelCase							:							Optional[int]       		=  ["""past_key_values"""]
  			lowerCamelCase							:							Any       		=  {
  			    """hidden_size""": """n_embd""",
  			    """max_position_embeddings""": """n_positions""",
  			    """num_attention_heads""": """n_head""",
  			    """num_hidden_layers""": """n_layer""",
  			}
  
  
  
  			def __init__(					self       ,     lowerCamelCase__=50_257       ,     lowerCamelCase__=1_024       ,     lowerCamelCase__=768       ,     lowerCamelCase__=12       ,     lowerCamelCase__=12       ,     lowerCamelCase__=None       ,     lowerCamelCase__="gelu_new"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=1e-5       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__="cls_index"       ,     lowerCamelCase__=True       ,     lowerCamelCase__=None       ,     lowerCamelCase__=True       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=50_256       ,     lowerCamelCase__=50_256       ,     lowerCamelCase__=False       ,     lowerCamelCase__=False       ,     **lowerCamelCase__       ,     )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       vocab_size
  									lowercase__							 =       n_positions
  									lowercase__							 =       n_embd
  									lowercase__							 =       n_layer
  									lowercase__							 =       n_head
  									lowercase__							 =       n_inner
  									lowercase__							 =       activation_function
  									lowercase__							 =       resid_pdrop
  									lowercase__							 =       embd_pdrop
  									lowercase__							 =       attn_pdrop
  									lowercase__							 =       layer_norm_epsilon
  									lowercase__							 =       initializer_range
  									lowercase__							 =       summary_type
  									lowercase__							 =       summary_use_proj
  									lowercase__							 =       summary_activation
  									lowercase__							 =       summary_first_dropout
  									lowercase__							 =       summary_proj_to_labels
  									lowercase__							 =       scale_attn_weights
  									lowercase__							 =       use_cache
  									lowercase__							 =       scale_attn_by_inverse_layer_idx
  									lowercase__							 =       reorder_and_upcast_attn
  
  									lowercase__							 =       bos_token_id
  									lowercase__							 =       eos_token_id
  
  									super().__init__(bos_token_id=lowerCamelCase__       ,     eos_token_id=lowerCamelCase__       ,     **lowerCamelCase__       )
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = "default"       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = False       ,     )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__(lowerCamelCase__       ,     task=lowerCamelCase__       ,     patching_specs=lowerCamelCase__       ,     use_past=lowerCamelCase__       )
  									if not getattr(self._config       ,     """pad_token_id"""       ,     lowerCamelCase__       ):
  															# TODO: how to do that better?
  															lowercase__							 =       0
  
  
  
  			@property
  			def A__ (					self       )			->		Mapping[str, Mapping[int, str]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}       )
  									if self.use_past:
  															self.fill_with_past_key_values_(lowerCamelCase__       ,     direction="""inputs"""       )
  															lowercase__							 =       {0: """batch""", 1: """past_sequence + sequence"""}
  									else:
  															lowercase__							 =       {0: """batch""", 1: """sequence"""}
  
  									return common_inputs
  
  
  
  			@property
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self._config.n_layer
  
  
  
  			@property
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self._config.n_head
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = -1       ,     lowerCamelCase__ = False       ,     lowerCamelCase__ = None       ,     )			->		Mapping[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       super(lowerCamelCase__       ,     self       ).generate_dummy_inputs(
  									    lowerCamelCase__       ,     batch_size=lowerCamelCase__       ,     seq_length=lowerCamelCase__       ,     is_pair=lowerCamelCase__       ,     framework=lowerCamelCase__       )
  
  									# We need to order the input in the way they appears in the forward()
  									lowercase__							 =       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}       )
  
  									# Need to add the past_keys
  									if self.use_past:
  															if not is_torch_available():
  																					raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
  															else:
  																					import torch
  
  																					lowercase__ ,						lowercase__							 =       common_inputs["""input_ids"""].shape
  																					# Not using the same length for past_key_values
  																					lowercase__							 =       seqlen + 2
  																					lowercase__							 =       (
  																					    batch,
  																					    self.num_attention_heads,
  																					    past_key_values_length,
  																					    self._config.hidden_size // self.num_attention_heads,
  																					)
  																					lowercase__							 =       [
  																					    (torch.zeros(lowerCamelCase__       ), torch.zeros(lowerCamelCase__       )) for _ in range(self.num_layers       )
  																					]
  
  									lowercase__							 =       common_inputs["""attention_mask"""]
  									if self.use_past:
  															lowercase__							 =       ordered_inputs["""attention_mask"""].dtype
  															lowercase__							 =       torch.cat(
  															    [ordered_inputs["""attention_mask"""], torch.ones(lowerCamelCase__       ,     lowerCamelCase__       ,     dtype=lowerCamelCase__       )]       ,     dim=1       )
  
  									return ordered_inputs
  
  
  
  			@property
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return 13
  
  
  
  
  
  
  
style_context: 
  '''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          =    logging.get_logger(__name__)
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							PyTorchBenchmarkArguments
  			lowerCamelCase							:							PretrainedConfig
  			lowerCamelCase							:							str       		=  "PyTorch"
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return torch.__version__
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_dict[model_name]
  
  									if self.args.torchscript:
  															lowercase__							 =       True
  
  									lowercase__							 =       (
  									    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:
  																					lowercase__							 =       config.architectures[0]
  																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       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:
  															lowercase__							 =       MODEL_MAPPING[config.__class__](lowerCamelCase__       )
  
  									model.eval()
  									model.to(self.args.device       )
  
  									# encoder-decoder has vocab size saved differently
  									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
  									lowercase__							 =       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():
  																					lowercase__							 =       torch.jit.trace(lowerCamelCase__       ,     lowerCamelCase__       )
  									else:
  															lowercase__							 =       model
  
  									def encoder_decoder_forward():
  															with torch.no_grad():
  																					lowercase__							 =       inference_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       )
  															return outputs
  
  									def encoder_forward():
  															with torch.no_grad():
  																					lowercase__							 =       inference_model(lowerCamelCase__       )
  															return outputs
  
  									lowercase__							 =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
  									return _forward
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_dict[model_name]
  
  									lowercase__							 =       (
  									    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:
  																					lowercase__							 =       config.architectures[0]
  																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       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:
  															lowercase__							 =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase__       )
  
  									if self.args.torchscript:
  															raise NotImplementedError("""Training for torchscript is currently not implemented"""       )
  									else:
  															lowercase__							 =       model
  
  									model.train()
  									model.to(self.args.device       )
  
  									# encoder-decoder has vocab size saved differently
  									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
  									lowercase__							 =       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():
  															lowercase__							 =       train_model(lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
  															loss.backward()
  															return loss
  
  									def compute_loss_and_backprob_encoder_decoder():
  															lowercase__							 =       train_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
  															loss.backward()
  															return loss
  
  									lowercase__							 =       (
  									    compute_loss_and_backprob_encoder_decoder
  									    if config.is_encoder_decoder
  									    else compute_loss_and_backprob_encoder
  									)
  									return _train
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''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
  															lowercase__							 =       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__       )			->		[Memory, MemorySummary]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									try:
  															if self.args.trace_memory_line_by_line:
  																					lowercase__							 =       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."""       )
  																											lowercase__							 =       """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()
  																											lowercase__							 =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
  																											lowercase__							 =       nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase__       )
  																											lowercase__							 =       meminfo.used
  																											lowercase__							 =       Memory(lowerCamelCase__       )
  																											# shutdown nvml
  																											nvml.nvmlShutdown()
  															else:
  																					# cpu
  																					lowercase__							 =       measure_peak_memory_cpu(lowerCamelCase__       )
  																					lowercase__							 =       Memory(lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else memory_bytes
  
  															if self.args.trace_memory_line_by_line:
  																					lowercase__							 =       stop_memory_tracing(lowerCamelCase__       )
  															else:
  																					lowercase__							 =       None
  
  															return memory, summary
  									except RuntimeError as e:
  															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
  															return "N/A", None
  
  
  
  
  
  
  