

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_			  = {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_			  = ['''DebertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_			  = [
           '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DebertaForMaskedLM''',
           '''DebertaForQuestionAnswering''',
           '''DebertaForSequenceClassification''',
           '''DebertaForTokenClassification''',
           '''DebertaModel''',
           '''DebertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_			  = [
           '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFDebertaForMaskedLM''',
           '''TFDebertaForQuestionAnswering''',
           '''TFDebertaForSequenceClassification''',
           '''TFDebertaForTokenClassification''',
           '''TFDebertaModel''',
           '''TFDebertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

       snake_case_			  = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


snake_case				:    List[str]       				=     logging.get_logger(__name__)

snake_case				:    int       				=     {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'blenderbot-small'
 SCREAMING_SNAKE_CASE__      =       ['past_key_values']
 SCREAMING_SNAKE_CASE__      =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




 def __init__(		self					,  _lowerCamelCase=5_0265					,  _lowerCamelCase=512					,  _lowerCamelCase=8					,  _lowerCamelCase=2048					,  _lowerCamelCase=16					,  _lowerCamelCase=8					,  _lowerCamelCase=2048					,  _lowerCamelCase=16					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.0					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=512					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.0					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1					,  _lowerCamelCase=False					,  _lowerCamelCase=0					,  _lowerCamelCase=1					,  _lowerCamelCase=2					,  _lowerCamelCase=2					,  **_lowerCamelCase					,  ):
       a						:Dict 							=       vocab_size
       a						:Optional[Any] 							=       max_position_embeddings
       a						:str 							=       d_model
       a						:Any 							=       encoder_ffn_dim
       a						:Optional[int] 							=       encoder_layers
       a						:List[str] 							=       encoder_attention_heads
       a						:List[str] 							=       decoder_ffn_dim
       a						:Optional[int] 							=       decoder_layers
       a						:str 							=       decoder_attention_heads
       a						:List[str] 							=       dropout
       a						:Optional[int] 							=       attention_dropout
       a						:Dict 							=       activation_dropout
       a						:List[str] 							=       activation_function
       a						:List[Any] 							=       init_std
       a						:Optional[int] 							=       encoder_layerdrop
       a						:Tuple 							=       decoder_layerdrop
       a						:List[str] 							=       use_cache
       a						:int 							=       encoder_layers
       a						:Union[str, Any] 							=       scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=_lowerCamelCase					,  bos_token_id=_lowerCamelCase					,  eos_token_id=_lowerCamelCase					,  is_encoder_decoder=_lowerCamelCase					,  decoder_start_token_id=_lowerCamelCase					,  forced_eos_token_id=_lowerCamelCase					,  **_lowerCamelCase					,  )







class 		_snake_case  (      _snake_case							):




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Optional[Any] 							=       OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]    )

             if self.use_past:
                   a						:Union[str, Any] 							=       {0: '''batch'''}
                   a						:Tuple 							=       {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
             else:
                   a						:Optional[int] 							=       {0: '''batch''', 1: '''decoder_sequence'''}
                   a						:str 							=       {0: '''batch''', 1: '''decoder_sequence'''}

             if self.use_past:
                   self.fill_with_past_key_values_(_lowerCamelCase					,  direction='''inputs'''    )
       elif self.task == "causal-lm":
             # TODO: figure this case out.
             a						:Optional[int] 							=       OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]    )
             if self.use_past:
                   a							,				a						:str 							=       self.num_layers
                   for i in range(_lowerCamelCase    ):
                         a						:List[Any] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
                         a						:List[str] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
             a						:Optional[int] 							=       OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                     ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                 ]    )

       return common_inputs




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:List[Any] 							=       super().outputs
       else:
             a						:Union[str, Any] 							=       super(_lowerCamelCase					,  self    ).outputs
             if self.use_past:
                   a							,				a						:int 							=       self.num_layers
                   for i in range(_lowerCamelCase    ):
                         a						:int 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
                         a						:Optional[Any] 							=       {0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       a						:Tuple 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

       # Generate decoder inputs
       a						:Dict 							=       seq_length if not self.use_past else 1
       a						:Dict 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       a						:List[Any] 							=       {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       a						:List[str] 							=       dict(**_lowerCamelCase					,  **_lowerCamelCase    )

       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						:Optional[Any] 							=       common_inputs['''input_ids'''].shape
             a						:Tuple 							=       common_inputs['''decoder_input_ids'''].shape[1]
             a							,				a						:List[Any] 							=       self.num_attention_heads
             a						:List[Any] 							=       (
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             a						:int 							=       decoder_seq_length + 3
             a						:Union[str, Any] 							=       (
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )

             a						:Union[str, Any] 							=       torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase					,  _lowerCamelCase    )]					,  dim=1    )

             a						:List[Any] 							=       []
             # If the number of encoder and decoder layers are present in the model configuration, both are considered
             a							,				a						:Optional[int] 							=       self.num_layers
             a						:str 							=       min(_lowerCamelCase					,  _lowerCamelCase    )
             a						:str 							=       max(_lowerCamelCase					,  _lowerCamelCase    ) - min_num_layers
             a						:Tuple 							=       '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

             for _ in range(_lowerCamelCase    ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(_lowerCamelCase    ),
                           torch.zeros(_lowerCamelCase    ),
                           torch.zeros(_lowerCamelCase    ),
                           torch.zeros(_lowerCamelCase    ),
                       )    )
             # TODO: test this.
             a						:int 							=       encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
             for _ in range(_lowerCamelCase					,  _lowerCamelCase    ):
                   common_inputs["past_key_values"].append((torch.zeros(_lowerCamelCase    ), torch.zeros(_lowerCamelCase    ))    )
       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       a						:Tuple 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )

       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						:Dict 							=       common_inputs['''input_ids'''].shape
             # Not using the same length for past_key_values
             a						:Optional[int] 							=       seqlen + 2
             a							,				a						:Union[str, Any] 							=       self.num_layers
             a							,				a						:Optional[Any] 							=       self.num_attention_heads
             a						:str 							=       (
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )

             a						:Tuple 							=       common_inputs['''attention_mask'''].dtype
             a						:Any 							=       torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(_lowerCamelCase					,  _lowerCamelCase					,  dtype=_lowerCamelCase    )]					,  dim=1    )
             a						:Any 							=       [
                 (torch.zeros(_lowerCamelCase    ), torch.zeros(_lowerCamelCase    )) for _ in range(_lowerCamelCase    )
             ]
       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       a						:Optional[Any] 							=       compute_effective_axis_dimension(
           _lowerCamelCase					,  fixed_dimension=OnnxConfig.default_fixed_batch					,  num_token_to_add=0    )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       a						:Optional[int] 							=       tokenizer.num_special_tokens_to_add(_lowerCamelCase    )
       a						:Tuple 							=       compute_effective_axis_dimension(
           _lowerCamelCase					,  fixed_dimension=OnnxConfig.default_fixed_sequence					,  num_token_to_add=_lowerCamelCase    )

       # Generate dummy inputs according to compute batch and sequence
       a						:List[str] 							=       [''' '''.join([tokenizer.unk_token]    ) * seq_length] * batch_size
       a						:Dict 							=       dict(tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase    )    )
       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = -1					,  _lowerCamelCase = -1					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Tuple 							=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _lowerCamelCase					,  batch_size=_lowerCamelCase					,  seq_length=_lowerCamelCase					,  is_pair=_lowerCamelCase					,  framework=_lowerCamelCase    )

       elif self.task == "causal-lm":
             a						:Dict 							=       self._generate_dummy_inputs_for_causal_lm(
                 _lowerCamelCase					,  batch_size=_lowerCamelCase					,  seq_length=_lowerCamelCase					,  is_pair=_lowerCamelCase					,  framework=_lowerCamelCase    )
       else:
             a						:Dict 							=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _lowerCamelCase					,  batch_size=_lowerCamelCase					,  seq_length=_lowerCamelCase					,  is_pair=_lowerCamelCase					,  framework=_lowerCamelCase    )

       return common_inputs




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       if self.task in ["default", "seq2seq-lm"]:
             a						:Optional[int] 							=       super()._flatten_past_key_values_(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )
       else:
             a						:Any 							=       super(_lowerCamelCase					,  self    )._flatten_past_key_values_(
                 _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )





