
'''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__ :		Any					=logging.get_logger(__name__)

A__ :		List[Any]					={
    '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					(			_SCREAMING_SNAKE_CASE      ):
 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      :     int					, lowerCamelCase      :     int=5_02_57					, lowerCamelCase      :     Tuple=10_24					, lowerCamelCase      :     Any=7_68					, lowerCamelCase      :     List[Any]=12					, lowerCamelCase      :     int=12					, lowerCamelCase      :     List[Any]=None					, lowerCamelCase      :     Union[str, Any]="gelu_new"					, lowerCamelCase      :     str=0.1					, lowerCamelCase      :     Union[str, Any]=0.1					, lowerCamelCase      :     Any=0.1					, lowerCamelCase      :     List[Any]=1e-5					, lowerCamelCase      :     Any=0.02					, lowerCamelCase      :     Optional[int]="cls_index"					, lowerCamelCase      :     List[str]=True					, lowerCamelCase      :     Optional[int]=None					, lowerCamelCase      :     List[Any]=True					, lowerCamelCase      :     Union[str, Any]=0.1					, lowerCamelCase      :     Optional[int]=True					, lowerCamelCase      :     Optional[int]=True					, lowerCamelCase      :     Any=5_02_56					, lowerCamelCase      :     int=5_02_56					, lowerCamelCase      :     Optional[Any]=False					, lowerCamelCase      :     List[str]=False					, **lowerCamelCase      :     int					, ):



   """simple docstring"""





   __A		:  List[str]       				=		vocab_size
   __A		:  Optional[Any]       				=		n_positions
   __A		:  Union[str, Any]       				=		n_embd
   __A		:  Union[str, Any]       				=		n_layer
   __A		:  Union[str, Any]       				=		n_head
   __A		:  Dict       				=		n_inner
   __A		:  int       				=		activation_function
   __A		:  str       				=		resid_pdrop
   __A		:  Optional[int]       				=		embd_pdrop
   __A		:  List[str]       				=		attn_pdrop
   __A		:  Dict       				=		layer_norm_epsilon
   __A		:  str       				=		initializer_range
   __A		:  Union[str, Any]       				=		summary_type
   __A		:  List[str]       				=		summary_use_proj
   __A		:  Optional[Any]       				=		summary_activation
   __A		:  List[str]       				=		summary_first_dropout
   __A		:  Optional[Any]       				=		summary_proj_to_labels
   __A		:  int       				=		scale_attn_weights
   __A		:  Tuple       				=		use_cache
   __A		:  Union[str, Any]       				=		scale_attn_by_inverse_layer_idx
   __A		:  int       				=		reorder_and_upcast_attn

   __A		:  int       				=		bos_token_id
   __A		:  List[str]       				=		eos_token_id

   super().__init__(bos_token_id=lowerCamelCase					, eos_token_id=lowerCamelCase					, **lowerCamelCase							)







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     str					, lowerCamelCase      :     PretrainedConfig					, lowerCamelCase      :     str = "default"					, lowerCamelCase      :     List[PatchingSpec] = None					, lowerCamelCase      :     bool = 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?
     __A		:  Optional[Any]       				=		0
 @property
 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  Optional[int]       				=		OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
   if self.use_past:
     self.fill_with_past_key_values_(lowerCamelCase					, direction="""inputs"""							)
     __A		:  Optional[Any]       				=		{0: """batch""", 1: """past_sequence + sequence"""}
   else:
     __A		:  int       				=		{0: """batch""", 1: """sequence"""}

   return common_inputs
 @property
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   return self._config.n_layer
 @property
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   return self._config.n_head
 def 		lowercase_( self      :     Union[str, Any]					, lowerCamelCase      :     PreTrainedTokenizer					, lowerCamelCase      :     int = -1					, lowerCamelCase      :     int = -1					, lowerCamelCase      :     bool = False					, lowerCamelCase      :     Optional[TensorType] = None					, ):



   """simple docstring"""





   __A		:  Tuple       				=		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()
   __A		:  List[str]       				=		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

       __A				,       __A		:  Any       				=		common_inputs["""input_ids"""].shape
       # Not using the same length for past_key_values
       __A		:  int       				=		seqlen + 2
       __A		:  str       				=		(
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       __A		:  Union[str, Any]       				=		[
           (torch.zeros(lowerCamelCase							), torch.zeros(lowerCamelCase							)) for _ in range(self.num_layers							)
       ]

   __A		:  Tuple       				=		common_inputs["""attention_mask"""]
   if self.use_past:
     __A		:  str       				=		ordered_inputs["""attention_mask"""].dtype
     __A		:  Dict       				=		torch.cat(
         [ordered_inputs["""attention_mask"""], torch.ones(lowerCamelCase					, lowerCamelCase					, dtype=lowerCamelCase							)]					, dim=1							)

   return ordered_inputs






 @property
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   return 13





'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


A__ :		Optional[int]					=logging.get_logger(__name__)







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Union[str, Any]					, *lowerCamelCase      :     Dict					, **lowerCamelCase      :     Optional[Any]							):



   """simple docstring"""





   warnings.warn(
       """The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
       """ use ViTImageProcessor instead."""					, lowerCamelCase					, )
   super().__init__(*lowerCamelCase					, **lowerCamelCase							)



