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
  
  
  _lowerCAmelCase					:Union[str, Any]  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:List[str]  =      {
      '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 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             ='''gpt2'''
       a__             =['''past_key_values''']
       a__             ={
           '''hidden_size''': '''n_embd''',
           '''max_position_embeddings''': '''n_positions''',
           '''num_attention_heads''': '''n_head''',
           '''num_hidden_layers''': '''n_layer''',
       }
  
  
  
  
  
       def __init__(      self ,			A=5_0_2_5_7 ,			A=1_0_2_4 ,			A=7_6_8 ,			A=1_2 ,			A=1_2 ,			A=None ,			A="gelu_new" ,			A=0.1 ,			A=0.1 ,			A=0.1 ,			A=1E-5 ,			A=0.02 ,			A="cls_index" ,			A=True ,			A=None ,			A=True ,			A=0.1 ,			A=True ,			A=True ,			A=5_0_2_5_6 ,			A=5_0_2_5_6 ,			A=False ,			A=False ,			**A ,			)							->      Union[str, Any]:
           _UpperCAmelCase							:						Optional[int]       							=					vocab_size
           _UpperCAmelCase							:						Union[str, Any]       							=					n_positions
           _UpperCAmelCase							:						Optional[Any]       							=					n_embd
           _UpperCAmelCase							:						Tuple       							=					n_layer
           _UpperCAmelCase							:						Any       							=					n_head
           _UpperCAmelCase							:						Union[str, Any]       							=					n_inner
           _UpperCAmelCase							:						Optional[int]       							=					activation_function
           _UpperCAmelCase							:						Optional[int]       							=					resid_pdrop
           _UpperCAmelCase							:						Dict       							=					embd_pdrop
           _UpperCAmelCase							:						List[Any]       							=					attn_pdrop
           _UpperCAmelCase							:						Tuple       							=					layer_norm_epsilon
           _UpperCAmelCase							:						Optional[int]       							=					initializer_range
           _UpperCAmelCase							:						List[str]       							=					summary_type
           _UpperCAmelCase							:						Tuple       							=					summary_use_proj
           _UpperCAmelCase							:						List[Any]       							=					summary_activation
           _UpperCAmelCase							:						Optional[int]       							=					summary_first_dropout
           _UpperCAmelCase							:						int       							=					summary_proj_to_labels
           _UpperCAmelCase							:						Union[str, Any]       							=					scale_attn_weights
           _UpperCAmelCase							:						Any       							=					use_cache
           _UpperCAmelCase							:						Tuple       							=					scale_attn_by_inverse_layer_idx
           _UpperCAmelCase							:						Optional[Any]       							=					reorder_and_upcast_attn
  
           _UpperCAmelCase							:						Union[str, Any]       							=					bos_token_id
           _UpperCAmelCase							:						Optional[int]       							=					eos_token_id
  
           super().__init__(bos_token_id=A ,			eos_token_id=A ,			**A	)
  
  
  
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A = "default" ,			A = None ,			A = False ,			)							->      str:
           super().__init__(A ,			task=A ,			patching_specs=A ,			use_past=A	)
           if not getattr(self._config ,			'''pad_token_id''' ,			A	):
               # TODO: how to do that better?
               _UpperCAmelCase							:						Optional[int]       							=					0
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      Mapping[str, Mapping[int, str]]:
           _UpperCAmelCase							:						List[str]       							=					OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
           if self.use_past:
               self.fill_with_past_key_values_(A ,			direction='''inputs'''	)
               _UpperCAmelCase							:						Union[str, Any]       							=					{0: '''batch''', 1: '''past_sequence + sequence'''}
           else:
               _UpperCAmelCase							:						Tuple       							=					{0: '''batch''', 1: '''sequence'''}
  
           return common_inputs
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      int:
           return self._config.n_layer
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      int:
           return self._config.n_head
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A = -1 ,			A = -1 ,			A = False ,			A = None ,			)							->      Mapping[str, Any]:
           _UpperCAmelCase							:						int       							=					super(A ,			self	).generate_dummy_inputs(
               A ,			batch_size=A ,			seq_length=A ,			is_pair=A ,			framework=A	)
  
           # We need to order the input in the way they appears in the forward()
           _UpperCAmelCase							:						List[Any]       							=					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
  
                   _UpperCAmelCase							:						Optional[Any]       							=					common_inputs['''input_ids'''].shape
                   # Not using the same length for past_key_values
                   _UpperCAmelCase							:						Optional[Any]       							=					seqlen + 2
                   _UpperCAmelCase							:						Union[str, Any]       							=					(
                       batch,
                       self.num_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // self.num_attention_heads,
                   )
                   _UpperCAmelCase							:						List[Any]       							=					[
                       (torch.zeros(A	), torch.zeros(A	)) for _ in range(self.num_layers	)
                   ]
  
           _UpperCAmelCase							:						int       							=					common_inputs['''attention_mask''']
           if self.use_past:
               _UpperCAmelCase							:						int       							=					ordered_inputs['''attention_mask'''].dtype
               _UpperCAmelCase							:						Dict       							=					torch.cat(
                   [ordered_inputs['''attention_mask'''], torch.ones(A ,			A ,			dtype=A	)] ,			dim=1	)
  
           return ordered_inputs
  
  
  
  
  
       @property
       def 				__lowerCAmelCase			(      self	)							->      int:
           return 1_3
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  # Lint as: python3
  import sys
  from collections.abc import Mapping
  from typing import TYPE_CHECKING
  
  import numpy as np
  import pyarrow as pa
  
  from .. import config
  from ..utils.py_utils import map_nested
  from .formatting import TensorFormatter
  
  
  if TYPE_CHECKING:
    import tensorflow as tf
  class 							_UpperCAmelCase							(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A=None ,			**A	)							->      List[str]:
           super().__init__(features=A	)
           _UpperCAmelCase							:						Any       							=					tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           import tensorflow as tf
  
           if isinstance(A ,			A	) and column:
               if all(
                   isinstance(A ,			tf.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                   return tf.stack(A	)
               elif all(
                   isinstance(A ,			(tf.Tensor, tf.RaggedTensor)	) and x.ndim == 1 and x.dtype == column[0].dtype
                   for x in column	):
                   # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                   return tf.ragged.stack(A	)
  
           return column
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      int:
           import tensorflow as tf
  
           if value is None:
               return value
  
           _UpperCAmelCase							:						Any       							=					{}
  
           if isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.integer	):
               _UpperCAmelCase							:						Union[str, Any]       							=					{'''dtype''': tf.intaa}
           elif isinstance(A ,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype ,			np.floating	):
               _UpperCAmelCase							:						Dict       							=					{'''dtype''': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
               import PIL.Image
  
               if isinstance(A ,			PIL.Image.Image	):
                   _UpperCAmelCase							:						Optional[int]       							=					np.asarray(A	)
  
           return tf.convert_to_tensor(A ,			**{**default_dtype, **self.tf_tensor_kwargs}	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
           import tensorflow as tf
  
           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
               import torch
  
               if isinstance(A ,			torch.Tensor	):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
           if hasattr(A ,			'''__array__'''	) and not isinstance(A ,			tf.Tensor	):
               _UpperCAmelCase							:						List[str]       							=					data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A ,			np.ndarray	):
               if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
           elif isinstance(A ,			(list, tuple)	):
               return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
           return self._tensorize(A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
           return map_nested(self._recursive_tensorize ,			A ,			map_list=A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
           _UpperCAmelCase							:						str       							=					self.numpy_arrow_extractor().extract_row(A	)
           _UpperCAmelCase							:						List[Any]       							=					self.python_features_decoder.decode_row(A	)
           return self.recursive_tensorize(A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      "tf.Tensor":
           _UpperCAmelCase							:						Union[str, Any]       							=					self.numpy_arrow_extractor().extract_column(A	)
           _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_column(A ,			pa_table.column_names[0]	)
           _UpperCAmelCase							:						str       							=					self.recursive_tensorize(A	)
           _UpperCAmelCase							:						Optional[Any]       							=					self._consolidate(A	)
           return column
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Mapping:
           _UpperCAmelCase							:						Dict       							=					self.numpy_arrow_extractor().extract_batch(A	)
           _UpperCAmelCase							:						Tuple       							=					self.python_features_decoder.decode_batch(A	)
           _UpperCAmelCase							:						Union[str, Any]       							=					self.recursive_tensorize(A	)
           for column_name in batch:
               _UpperCAmelCase							:						str       							=					self._consolidate(batch[column_name]	)
           return batch
  
  
  