code: 
  
  
  
  
  
  '''simple docstring'''
  
  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
  
  
  __lowerCAmelCase							:			str				=logging.get_logger(__name__)
  
  __lowerCAmelCase							:			Any				={
      "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
      # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
  }
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       """blenderbot"""
   __lowercase     	=       ["""past_key_values"""]
   __lowercase     	=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
   def __init__(       self   :Any		, lowercase_   :List[Any]=80_08		, lowercase_   :Optional[int]=1_28		, lowercase_   :Dict=2		, lowercase_   :Optional[Any]=1_02_40		, lowercase_   :Optional[int]=32		, lowercase_   :str=24		, lowercase_   :Dict=1_02_40		, lowercase_   :Optional[Any]=32		, lowercase_   :List[str]=0.0		, lowercase_   :Optional[Any]=0.0		, lowercase_   :Any=True		, lowercase_   :Any=True		, lowercase_   :List[Any]="gelu"		, lowercase_   :int=25_60		, lowercase_   :Dict=0.1		, lowercase_   :str=0.0		, lowercase_   :Optional[Any]=0.0		, lowercase_   :List[Any]=0.0_2		, lowercase_   :Optional[int]=1		, lowercase_   :Tuple=False		, lowercase_   :int=0		, lowercase_   :Any=1		, lowercase_   :Tuple=2		, lowercase_   :int=3		, lowercase_   :Union[str, Any]=2		, **lowercase_   :Dict		, )->							Optional[Any]:
       A__       =    vocab_size
       A__       =    max_position_embeddings
       A__       =    d_model
       A__       =    encoder_ffn_dim
       A__       =    encoder_layers
       A__       =    encoder_attention_heads
       A__       =    decoder_ffn_dim
       A__       =    decoder_layers
       A__       =    decoder_attention_heads
       A__       =    dropout
       A__       =    attention_dropout
       A__       =    activation_dropout
       A__       =    activation_function
       A__       =    init_std
       A__       =    encoder_layerdrop
       A__       =    decoder_layerdrop
       A__       =    use_cache
       A__       =    encoder_layers
       A__       =    scale_embedding  # scale factor will be sqrt(d_model) if True
  
       super().__init__(
           pad_token_id=lowercase_		, bos_token_id=lowercase_		, eos_token_id=lowercase_		, is_encoder_decoder=lowercase_		, decoder_start_token_id=lowercase_		, encoder_no_repeat_ngram_size=lowercase_		, forced_eos_token_id=lowercase_		, **lowercase_		, )
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   @property
   def   UpperCAmelCase_							(       self   :List[str]							)->							Mapping[str, Mapping[int, str]]:
       if self.task in ["default", "seq2seq-lm"]:
           A__       =    OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]							)
           if self.use_past:
               A__       =    {0: "batch"}
               A__       =    {0: "batch", 1: "past_decoder_sequence + sequence"}
           else:
               A__       =    {0: "batch", 1: "decoder_sequence"}
               A__       =    {0: "batch", 1: "decoder_sequence"}
           if self.use_past:
               self.fill_with_past_key_values_(lowercase_		, direction="inputs"							)
       elif self.task == "causal-lm":
           A__       =    OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]							)
           if self.use_past:
               A__,			A__       =    self.num_layers
               for i in range(lowercase_							):
                   A__       =    {0: "batch", 2: "past_sequence + sequence"}
                   A__       =    {0: "batch", 2: "past_sequence + sequence"}
       else:
           A__       =    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
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def   UpperCAmelCase_							(       self   :int							)->							Mapping[str, Mapping[int, str]]:
       if self.task in ["default", "seq2seq-lm"]:
           A__       =    super().outputs
       else:
           A__       =    super(lowercase_		, self							).outputs
           if self.use_past:
               A__,			A__       =    self.num_layers
               for i in range(lowercase_							):
                   A__       =    {0: "batch", 2: "past_sequence + sequence"}
                   A__       =    {0: "batch", 2: "past_sequence + sequence"}
       return common_outputs
   def   UpperCAmelCase_							(       self   :Optional[Any]		, lowercase_   :PreTrainedTokenizer		, lowercase_   :int = -1		, lowercase_   :int = -1		, lowercase_   :bool = False		, lowercase_   :Optional[TensorType] = None		, )->							Mapping[str, Any]:
       A__       =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_		, lowercase_		, lowercase_		, lowercase_		, lowercase_							)
       # Generate decoder inputs
       A__       =    seq_length if not self.use_past else 1
       A__       =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_		, lowercase_		, lowercase_		, lowercase_		, lowercase_							)
       A__       =    {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
       A__       =    dict(**lowercase_		, **lowercase_							)
  
       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__       =    common_inputs["input_ids"].shape
           A__       =    common_inputs["decoder_input_ids"].shape[1]
           A__,			A__       =    self.num_attention_heads
           A__       =    (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           A__       =    decoder_seq_length
           A__       =    (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )
           A__       =    torch.cat(
               [common_inputs["decoder_attention_mask"], torch.ones(lowercase_		, lowercase_							)]		, dim=1							)
           A__       =    []
           A__,			A__       =    self.num_layers
  
           for _ in range(lowercase_							):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(lowercase_							),
                       torch.zeros(lowercase_							),
                       torch.zeros(lowercase_							),
                       torch.zeros(lowercase_							),
                   )							)
       return common_inputs
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :PreTrainedTokenizer		, lowercase_   :int = -1		, lowercase_   :int = -1		, lowercase_   :bool = False		, lowercase_   :Optional[TensorType] = None		, )->							Mapping[str, Any]:
       A__       =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_		, lowercase_		, lowercase_		, lowercase_		, lowercase_							)
  
       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__       =    common_inputs["input_ids"].shape
           A__       =    seqlen
           A__,			A__       =    self.num_layers
           A__,			A__       =    self.num_attention_heads
           A__       =    (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           A__       =    common_inputs["attention_mask"].dtype
           A__       =    torch.cat(
               [common_inputs["attention_mask"], torch.ones(lowercase_		, lowercase_		, dtype=lowercase_							)]		, dim=1							)
           A__       =    [
               (torch.zeros(lowercase_							), torch.zeros(lowercase_							)) for _ in range(lowercase_							)
           ]
       return common_inputs
   def   UpperCAmelCase_							(       self   :str		, lowercase_   :PreTrainedTokenizer		, lowercase_   :int = -1		, lowercase_   :int = -1		, lowercase_   :bool = False		, lowercase_   :Optional[TensorType] = None		, )->							Mapping[str, Any]:
       # 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__       =    compute_effective_axis_dimension(
           lowercase_		, 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__       =    tokenizer.num_special_tokens_to_add(lowercase_							)
       A__       =    compute_effective_axis_dimension(
           lowercase_		, fixed_dimension=OnnxConfig.default_fixed_sequence		, num_token_to_add=lowercase_							)
  
       # Generate dummy inputs according to compute batch and sequence
       A__       =    [" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
       A__       =    dict(tokenizer(lowercase_		, return_tensors=lowercase_							)							)
       return common_inputs
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :PreTrainedTokenizer		, lowercase_   :int = -1		, lowercase_   :int = -1		, lowercase_   :bool = False		, lowercase_   :Optional[TensorType] = None		, )->							Mapping[str, Any]:
       if self.task in ["default", "seq2seq-lm"]:
           A__       =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               lowercase_		, batch_size=lowercase_		, seq_length=lowercase_		, is_pair=lowercase_		, framework=lowercase_							)
  
       elif self.task == "causal-lm":
           A__       =    self._generate_dummy_inputs_for_causal_lm(
               lowercase_		, batch_size=lowercase_		, seq_length=lowercase_		, is_pair=lowercase_		, framework=lowercase_							)
       else:
           A__       =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowercase_		, batch_size=lowercase_		, seq_length=lowercase_		, is_pair=lowercase_		, framework=lowercase_							)
  
       return common_inputs
   def   UpperCAmelCase_							(       self   :Any		, lowercase_   :str		, lowercase_   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :List[Any]							)->							Union[str, Any]:
       if self.task in ["default", "seq2seq-lm"]:
           A__       =    super()._flatten_past_key_values_(lowercase_		, lowercase_		, lowercase_		, lowercase_							)
       else:
           A__       =    super(lowercase_		, self							)._flatten_past_key_values_(
               lowercase_		, lowercase_		, lowercase_		, lowercase_							)
  
  
  
   def   UpperCAmelCase_							(       self   :str		, lowercase_   :Mapping[str, Mapping[int, str]]		, lowercase_   :str							)->							int:
       if direction not in ["inputs", "outputs"]:
           raise ValueError(F"direction must either be \"inputs\" or \"outputs\", but {direction} was given"							)
  
       A__       =    "past_key_values" if direction == "inputs" else "present"
       A__,			A__       =    self.num_layers
  
       A__       =    "past_encoder_sequence"
       A__       =    "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"
  
       for i in range(lowercase_							):
           A__       =    {0: "batch", 2: decoder_sequence}
           A__       =    {0: "batch", 2: decoder_sequence}
           A__       =    {0: "batch", 2: encoder_sequence}
           A__       =    {0: "batch", 2: encoder_sequence}
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :Any		, lowercase_   :Dict							)->							Any:
       A__       =    AutoTokenizer.from_pretrained(lowercase_							)
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :int							)->							Optional[int]:
  
       if isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    None
           for l in x:
               A__       =    self.tokenizer(
                   lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
               A__       =    torch.tensor([len(lowercase_							)]							)
               if d is not None:
                   for k in d.keys():
                       A__       =    torch.cat((d[k], t[k])		, 0							)
               else:
                   A__       =    t
  
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"							)							)
           A__       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"							)							)
  
       elif isinstance(lowercase_		, lowercase_							) and all(isinstance(_x		, lowercase_							) for _x in x							):
           A__       =    self.tokenizer(
               lowercase_		, padding="max_length"		, max_length=3_84		, truncation=lowercase_		, return_tensors="pt"		, )
  
       else:
           raise Exception(
               "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
               " strings` for supports are supported."							)
  
       return d
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :str		, lowercase_   :List[Any]		, lowercase_   :Dict=0.7_0							)->							int:
       A__       =    []
       for idx in range(len(W_query["input_ids"]							)							):
           A__       =    A__       =    range(p_start.shape[1]							)
  
           A__       =    []
           for start_id in start_indexes:
               for end_id in end_indexes:
                   if start_id < end_id:
                       output.append(
                           (
                               start_id,
                               end_id,
                               p_start[idx][start_id].item(),
                               p_end[idx][end_id].item(),
                           )							)
  
           output.sort(key=lambda lowercase_							: (tup[2] * tup[3])		, reverse=lowercase_							)
           A__       =    []
           for k in range(len(lowercase_							)							):
               if output[k][2] * output[k][3] >= thresh:
                   A__,			A__       =    output[k][0], output[k][1]
                   A__       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]							)
                   temp.append((decoded, output[k][2] * output[k][3])							)
  
           final_outputs.append(lowercase_							)
  
       return final_outputs
  
  
  
  