

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


lowercase_     =       logging.get_logger(__name__)

lowercase_     =       {
    '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 			__lowerCAmelCase					( _lowerCAmelCase							):
  _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							,       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							,       )  ->			str:
        '''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 			__lowerCAmelCase					( _lowerCAmelCase							):




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase = "default"							,       lowerCAmelCase = None							,       lowerCAmelCase = False							,       )  ->			Union[str, Any]:
        '''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 )  ->			int:
        '''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							,       )  ->			Optional[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 )  ->			List[str]:
        '''simple docstring'''






        return 13





import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
            ' Please use LayoutLMv3ImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


