






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class 		_snake_case	(	A__ ,     A__ ,     A__       ):






			@register_to_config
			def __init__(							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a = False		,       )   ->							int:
						super().__init__()

						SCREAMING_SNAKE_CASE							=			nn.Embedding(a		,       a)

						SCREAMING_SNAKE_CASE							=			nn.Embedding(a		,       a)
						SCREAMING_SNAKE_CASE							=			False

						SCREAMING_SNAKE_CASE							=			nn.Dropout(p=a)

						SCREAMING_SNAKE_CASE							=			TaConfig(
						    vocab_size=a		,       d_model=a		,       num_heads=a		,       d_kv=a		,       d_ff=a		,       dropout_rate=a		,       feed_forward_proj=a		,       is_decoder=a		,       is_encoder_decoder=a		,       )

						SCREAMING_SNAKE_CASE							=			nn.ModuleList()
						for lyr_num in range(a):
									SCREAMING_SNAKE_CASE							=			TaBlock(a)
									self.encoders.append(a)

						SCREAMING_SNAKE_CASE							=			TaLayerNorm(a)
						SCREAMING_SNAKE_CASE							=			nn.Dropout(p=a)







			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a)   ->							Optional[int]:
						SCREAMING_SNAKE_CASE							=			self.token_embedder(a)

						SCREAMING_SNAKE_CASE							=			encoder_input_tokens.shape[1]
						SCREAMING_SNAKE_CASE							=			torch.arange(a		,       device=encoder_input_tokens.device)
						x += self.position_encoding(a)

						SCREAMING_SNAKE_CASE							=			self.dropout_pre(a)

						# inverted the attention mask
						SCREAMING_SNAKE_CASE							=			encoder_input_tokens.size()
						SCREAMING_SNAKE_CASE							=			self.get_extended_attention_mask(a		,       a)

						for lyr in self.encoders:
									SCREAMING_SNAKE_CASE							=			lyr(a		,       a)[0]
						SCREAMING_SNAKE_CASE							=			self.layer_norm(a)

						return self.dropout_post(a), encoder_inputs_mask







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
    'distilbert-base-uncased-distilled-squad': (
        'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
    'distilbert-base-cased-distilled-squad': (
        'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
    'distilbert-base-multilingual-cased': (
        'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
    ),
    'distilbert-base-uncased-finetuned-sst-2-english': (
        'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
    ),
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """distilbert"""
  _a		     =    {
      """hidden_size""": """dim""",
      """num_attention_heads""": """n_heads""",
      """num_hidden_layers""": """n_layers""",
  }




  def __init__(							self				,  lowerCAmelCase=30_522				,  lowerCAmelCase=512				,  lowerCAmelCase=False				,  lowerCAmelCase=6				,  lowerCAmelCase=12				,  lowerCAmelCase=768				,  lowerCAmelCase=4 * 768				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.1				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.02				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.2				,  lowerCAmelCase=0				,  **lowerCAmelCase				,  )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=vocab_size
        _lowercase      			=max_position_embeddings
        _lowercase      			=sinusoidal_pos_embds
        _lowercase      			=n_layers
        _lowercase      			=n_heads
        _lowercase      			=dim
        _lowercase      			=hidden_dim
        _lowercase      			=dropout
        _lowercase      			=attention_dropout
        _lowercase      			=activation
        _lowercase      			=initializer_range
        _lowercase      			=qa_dropout
        _lowercase      			=seq_classif_dropout
        super().__init__(**lowerCAmelCase				,  pad_token_id=lowerCAmelCase   )





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  @property
  def     A__	(							self   )    ->					Mapping[str, Mapping[int, str]]:
        '''simple docstring'''






        if self.task == "multiple-choice":
              _lowercase      			={0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
              _lowercase      			={0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]   )


