


from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)









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    )





