

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


_SCREAMING_SNAKE_CASE          = logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE          = {
    '''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):
 _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=50_257     ,     _UpperCAmelCase=1_024     ,     _UpperCAmelCase=768     ,     _UpperCAmelCase=12     ,     _UpperCAmelCase=12     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase="gelu_new"     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=1E-5     ,     _UpperCAmelCase=0.0_2     ,     _UpperCAmelCase="cls_index"     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=50_256     ,     _UpperCAmelCase=50_256     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=False     ,     **_UpperCAmelCase     ,     ):
   __UpperCAmelCase  :     Optional[Any]		=    vocab_size
   __UpperCAmelCase  :     str		=    n_positions
   __UpperCAmelCase  :     Any		=    n_embd
   __UpperCAmelCase  :     Optional[int]		=    n_layer
   __UpperCAmelCase  :     Tuple		=    n_head
   __UpperCAmelCase  :     Any		=    n_inner
   __UpperCAmelCase  :     Tuple		=    activation_function
   __UpperCAmelCase  :     List[str]		=    resid_pdrop
   __UpperCAmelCase  :     Optional[Any]		=    embd_pdrop
   __UpperCAmelCase  :     List[str]		=    attn_pdrop
   __UpperCAmelCase  :     Any		=    layer_norm_epsilon
   __UpperCAmelCase  :     str		=    initializer_range
   __UpperCAmelCase  :     Optional[int]		=    summary_type
   __UpperCAmelCase  :     Optional[int]		=    summary_use_proj
   __UpperCAmelCase  :     Optional[Any]		=    summary_activation
   __UpperCAmelCase  :     Tuple		=    summary_first_dropout
   __UpperCAmelCase  :     Union[str, Any]		=    summary_proj_to_labels
   __UpperCAmelCase  :     Optional[int]		=    scale_attn_weights
   __UpperCAmelCase  :     Optional[Any]		=    use_cache
   __UpperCAmelCase  :     Tuple		=    scale_attn_by_inverse_layer_idx
   __UpperCAmelCase  :     Union[str, Any]		=    reorder_and_upcast_attn

   __UpperCAmelCase  :     List[str]		=    bos_token_id
   __UpperCAmelCase  :     Tuple		=    eos_token_id

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



class     lowerCamelCase_   ( _lowerCAmelCase):




 def __init__( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase = "default"     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = False     ,     ):
   super().__init__(_UpperCAmelCase     ,     task=_UpperCAmelCase     ,     patching_specs=_UpperCAmelCase     ,     use_past=_UpperCAmelCase       )
   if not getattr(self._config     ,     'pad_token_id'     ,     _UpperCAmelCase       ):
     # TODO: how to do that better?
     __UpperCAmelCase  :     Tuple		=    0




 @property
 def 						a					( self       ):
   __UpperCAmelCase  :     Tuple		=    OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
   if self.use_past:
     self.fill_with_past_key_values_(_UpperCAmelCase     ,     direction='inputs'       )
     __UpperCAmelCase  :     Tuple		=    {0: 'batch', 1: 'past_sequence + sequence'}
   else:
     __UpperCAmelCase  :     Union[str, Any]		=    {0: 'batch', 1: 'sequence'}

   return common_inputs




 @property
 def 						a					( self       ):
   return self._config.n_layer




 @property
 def 						a					( self       ):
   return self._config.n_head




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase = -1     ,     _UpperCAmelCase = -1     ,     _UpperCAmelCase = False     ,     _UpperCAmelCase = None     ,     ):
   __UpperCAmelCase  :     Tuple		=    super(_UpperCAmelCase     ,     self       ).generate_dummy_inputs(
       _UpperCAmelCase     ,     batch_size=_UpperCAmelCase     ,     seq_length=_UpperCAmelCase     ,     is_pair=_UpperCAmelCase     ,     framework=_UpperCAmelCase       )

   # We need to order the input in the way they appears in the forward()
   __UpperCAmelCase  :     Optional[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, __UpperCAmelCase  :     str		=    common_inputs['input_ids'].shape
       # Not using the same length for past_key_values
       __UpperCAmelCase  :     Optional[Any]		=    seqlen + 2
       __UpperCAmelCase  :     Tuple		=    (
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       __UpperCAmelCase  :     List[Any]		=    [
           (torch.zeros(_UpperCAmelCase       ), torch.zeros(_UpperCAmelCase       )) for _ in range(self.num_layers       )
       ]

   __UpperCAmelCase  :     Optional[int]		=    common_inputs['attention_mask']
   if self.use_past:
     __UpperCAmelCase  :     List[str]		=    ordered_inputs['attention_mask'].dtype
     __UpperCAmelCase  :     int		=    torch.cat(
         [ordered_inputs['attention_mask'], torch.ones(_UpperCAmelCase     ,     _UpperCAmelCase     ,     dtype=_UpperCAmelCase       )]     ,     dim=1       )

   return ordered_inputs




 @property
 def 						a					( self       ):
   return 13








from typing import Mapping

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


_SCREAMING_SNAKE_CASE          = logging.get_logger(__name__)


class     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  '''mt5'''
 _lowerCAmelCase       		=  ['''past_key_values''']




 def __init__( self     ,     _UpperCAmelCase=250_112     ,     _UpperCAmelCase=512     ,     _UpperCAmelCase=64     ,     _UpperCAmelCase=1_024     ,     _UpperCAmelCase=8     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=6     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=128     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=1E-6     ,     _UpperCAmelCase=1.0     ,     _UpperCAmelCase="gated-gelu"     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase="T5Tokenizer"     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=1     ,     _UpperCAmelCase=0     ,     **_UpperCAmelCase     ,     ):
   super().__init__(
       is_encoder_decoder=_UpperCAmelCase     ,     tokenizer_class=_UpperCAmelCase     ,     tie_word_embeddings=_UpperCAmelCase     ,     pad_token_id=_UpperCAmelCase     ,     eos_token_id=_UpperCAmelCase     ,     decoder_start_token_id=_UpperCAmelCase     ,     **_UpperCAmelCase     ,     )
   __UpperCAmelCase  :     Dict		=    vocab_size
   __UpperCAmelCase  :     List[str]		=    d_model
   __UpperCAmelCase  :     str		=    d_kv
   __UpperCAmelCase  :     List[str]		=    d_ff
   __UpperCAmelCase  :     int		=    num_layers
   __UpperCAmelCase  :     List[Any]		=    (
       num_decoder_layers if num_decoder_layers is not None else self.num_layers
   )  # default = symmetry
   __UpperCAmelCase  :     List[Any]		=    num_heads
   __UpperCAmelCase  :     Tuple		=    relative_attention_num_buckets
   __UpperCAmelCase  :     Optional[int]		=    relative_attention_max_distance
   __UpperCAmelCase  :     Dict		=    dropout_rate
   __UpperCAmelCase  :     Union[str, Any]		=    layer_norm_epsilon
   __UpperCAmelCase  :     Tuple		=    initializer_factor
   __UpperCAmelCase  :     Union[str, Any]		=    feed_forward_proj
   __UpperCAmelCase  :     Optional[Any]		=    use_cache

   __UpperCAmelCase  :     Any		=    self.feed_forward_proj.split('-'       )
   __UpperCAmelCase  :     Dict		=    act_info[-1]
   __UpperCAmelCase  :     Any		=    act_info[0] == 'gated'

   if len(_UpperCAmelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCAmelCase       ) > 2:
     raise ValueError(
         F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
         'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
         '\'gated-gelu\' or \'relu\''       )

   # for backwards compatibility
   if feed_forward_proj == "gated-gelu":
     __UpperCAmelCase  :     List[str]		=    'gelu_new'




 @property
 def 						a					( self       ):
   return self.d_model




 @property
 def 						a					( self       ):
   return self.num_heads




 @property
 def 						a					( self       ):
   return self.num_layers



class     lowerCamelCase_   ( _lowerCAmelCase):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[int]		=    {
       'input_ids': {0: 'batch', 1: 'encoder_sequence'},
       'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
   }
   if self.use_past:
     __UpperCAmelCase  :     Optional[Any]		=    'past_encoder_sequence + sequence'
     __UpperCAmelCase  :     Tuple		=    {0: 'batch'}
     __UpperCAmelCase  :     List[Any]		=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
   else:
     __UpperCAmelCase  :     Tuple		=    {0: 'batch', 1: 'decoder_sequence'}
     __UpperCAmelCase  :     int		=    {0: 'batch', 1: 'decoder_sequence'}

   if self.use_past:
     self.fill_with_past_key_values_(_UpperCAmelCase     ,     direction='inputs'       )

   return common_inputs




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 						a					( self       ):
   return 13




 @property
 def 						a					( self       ):
   return 5E-4





