code: 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_:      str        =			logging.get_logger(__name__)
  
  A_:      Optional[int]        =			{
      '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 _lowercase					(       __UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					"""gpt2"""
  							lowerCAmelCase__       =					["""past_key_values"""]
  							lowerCAmelCase__       =					{
  							    """hidden_size""": """n_embd""",
  							    """max_position_embeddings""": """n_positions""",
  							    """num_attention_heads""": """n_head""",
  							    """num_hidden_layers""": """n_layer""",
  							}
  
  
  
  							def __init__(    self     ,   UpperCAmelCase=50257     ,   UpperCAmelCase=1024     ,   UpperCAmelCase=768     ,   UpperCAmelCase=12     ,   UpperCAmelCase=12     ,   UpperCAmelCase=None     ,   UpperCAmelCase="gelu_new"     ,   UpperCAmelCase=0.1     ,   UpperCAmelCase=0.1     ,   UpperCAmelCase=0.1     ,   UpperCAmelCase=1e-5     ,   UpperCAmelCase=0.02     ,   UpperCAmelCase="cls_index"     ,   UpperCAmelCase=True     ,   UpperCAmelCase=None     ,   UpperCAmelCase=True     ,   UpperCAmelCase=0.1     ,   UpperCAmelCase=True     ,   UpperCAmelCase=True     ,   UpperCAmelCase=50256     ,   UpperCAmelCase=50256     ,   UpperCAmelCase=False     ,   UpperCAmelCase=False     ,   **UpperCAmelCase     ,   ):
  
  
  
  
  
  
  
  														'''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 _lowercase					(       __UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase = "default"     ,   UpperCAmelCase = None     ,   UpperCAmelCase = False     ,   ):
  
  
  
  
  
  
  
  														'''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     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''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     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return self._config.n_layer
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return self._config.n_head
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase = -1     ,   UpperCAmelCase = -1     ,   UpperCAmelCase = False     ,   UpperCAmelCase = None     ,   ):
  
  
  
  
  
  
  
  														'''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     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return 13
  
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_:      int        =			{
      'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
      'tokenization_bart': ['BartTokenizer'],
  }
  
  try:
  				if not is_tokenizers_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      List[str]        =			['BartTokenizerFast']
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      int        =			[
  				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'BartForCausalLM',
  				    'BartForConditionalGeneration',
  				    'BartForQuestionAnswering',
  				    'BartForSequenceClassification',
  				    'BartModel',
  				    'BartPretrainedModel',
  				    'PretrainedBartModel',
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      Any        =			[
  				    'TFBartForConditionalGeneration',
  				    'TFBartForSequenceClassification',
  				    'TFBartModel',
  				    'TFBartPretrainedModel',
  				]
  
  try:
  				if not is_flax_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      str        =			[
  				    'FlaxBartDecoderPreTrainedModel',
  				    'FlaxBartForCausalLM',
  				    'FlaxBartForConditionalGeneration',
  				    'FlaxBartForQuestionAnswering',
  				    'FlaxBartForSequenceClassification',
  				    'FlaxBartModel',
  				    'FlaxBartPreTrainedModel',
  				]
  
  if TYPE_CHECKING:
  				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  				from .tokenization_bart import BartTokenizer
  
  				try:
  								if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .tokenization_bart_fast import BartTokenizerFast
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_bart import (
  								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    BartForCausalLM,
  								    BartForConditionalGeneration,
  								    BartForQuestionAnswering,
  								    BartForSequenceClassification,
  								    BartModel,
  								    BartPretrainedModel,
  								    PretrainedBartModel,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_bart import (
  								    TFBartForConditionalGeneration,
  								    TFBartForSequenceClassification,
  								    TFBartModel,
  								    TFBartPretrainedModel,
  								)
  
  				try:
  								if not is_flax_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_flax_bart import (
  								    FlaxBartDecoderPreTrainedModel,
  								    FlaxBartForCausalLM,
  								    FlaxBartForConditionalGeneration,
  								    FlaxBartForQuestionAnswering,
  								    FlaxBartForSequenceClassification,
  								    FlaxBartModel,
  								    FlaxBartPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  