code: 
  
  
  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
  
  
  UpperCAmelCase      :						str    =		logging.get_logger(__name__)
  
  UpperCAmelCase      :						Dict    =		{
      "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 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				Any 					=		"blenderbot"
  		UpperCamelCase    :				Tuple 					=		["past_key_values"]
  		UpperCamelCase    :				Optional[int] 					=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
  
  
  
  
  
  
  
  		def __init__(   self		,			A=80_08		,			A=1_28		,			A=2		,			A=1_02_40		,			A=32		,			A=24		,			A=1_02_40		,			A=32		,			A=0.0		,			A=0.0		,			A=True		,			A=True		,			A="gelu"		,			A=25_60		,			A=0.1		,			A=0.0		,			A=0.0		,			A=0.02		,			A=1		,			A=False		,			A=0		,			A=1		,			A=2		,			A=3		,			A=2		,			**A		,			)     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     vocab_size
  					lowerCamelCase								=     max_position_embeddings
  					lowerCamelCase								=     d_model
  					lowerCamelCase								=     encoder_ffn_dim
  					lowerCamelCase								=     encoder_layers
  					lowerCamelCase								=     encoder_attention_heads
  					lowerCamelCase								=     decoder_ffn_dim
  					lowerCamelCase								=     decoder_layers
  					lowerCamelCase								=     decoder_attention_heads
  					lowerCamelCase								=     dropout
  					lowerCamelCase								=     attention_dropout
  					lowerCamelCase								=     activation_dropout
  					lowerCamelCase								=     activation_function
  					lowerCamelCase								=     init_std
  					lowerCamelCase								=     encoder_layerdrop
  					lowerCamelCase								=     decoder_layerdrop
  					lowerCamelCase								=     use_cache
  					lowerCamelCase								=     encoder_layers
  					lowerCamelCase								=     scale_embedding  # scale factor will be sqrt(d_model) if True
  
  					super().__init__(
  					    pad_token_id=A		,			bos_token_id=A		,			eos_token_id=A		,			is_encoder_decoder=A		,			decoder_start_token_id=A		,			encoder_no_repeat_ngram_size=A		,			forced_eos_token_id=A		,			**A		,			)
  
  
  
  
  
  
  
  class 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		@property
  		def 				__A      (   self  )     ->   Mapping[str, Mapping[int, str]]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self.task in ["default", "seq2seq-lm"]:
  								lowerCamelCase								=     OrderedDict(
  								    [
  								        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
  								        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
  								    ]  )
  								if self.use_past:
  											lowerCamelCase								=     {0: """batch"""}
  											lowerCamelCase								=     {0: """batch""", 1: """past_decoder_sequence + sequence"""}
  								else:
  											lowerCamelCase								=     {0: """batch""", 1: """decoder_sequence"""}
  											lowerCamelCase								=     {0: """batch""", 1: """decoder_sequence"""}
  								if self.use_past:
  											self.fill_with_past_key_values_(A		,			direction="""inputs"""  )
  					elif self.task == "causal-lm":
  								lowerCamelCase								=     OrderedDict(
  								    [
  								        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
  								        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
  								    ]  )
  								if self.use_past:
  											lowerCamelCase		,		lowerCamelCase								=     self.num_layers
  											for i in range(A  ):
  														lowerCamelCase								=     {0: """batch""", 2: """past_sequence + sequence"""}
  														lowerCamelCase								=     {0: """batch""", 2: """past_sequence + sequence"""}
  					else:
  								lowerCamelCase								=     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 				__A      (   self  )     ->   Mapping[str, Mapping[int, str]]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self.task in ["default", "seq2seq-lm"]:
  								lowerCamelCase								=     super().outputs
  					else:
  								lowerCamelCase								=     super(A		,			self  ).outputs
  								if self.use_past:
  											lowerCamelCase		,		lowerCamelCase								=     self.num_layers
  											for i in range(A  ):
  														lowerCamelCase								=     {0: """batch""", 2: """past_sequence + sequence"""}
  														lowerCamelCase								=     {0: """batch""", 2: """past_sequence + sequence"""}
  					return common_outputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A = -1		,			A = -1		,			A = False		,			A = None		,			)     ->   Mapping[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  					    A		,			A		,			A		,			A		,			A  )
  					# Generate decoder inputs
  					lowerCamelCase								=     seq_length if not self.use_past else 1
  					lowerCamelCase								=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  					    A		,			A		,			A		,			A		,			A  )
  					lowerCamelCase								=     {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
  					lowerCamelCase								=     dict(**A		,			**A  )
  
  					if self.use_past:
  								if not is_torch_available():
  											raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""  )
  								else:
  											import torch
  								lowerCamelCase		,		lowerCamelCase								=     common_inputs["""input_ids"""].shape
  								lowerCamelCase								=     common_inputs["""decoder_input_ids"""].shape[1]
  								lowerCamelCase		,		lowerCamelCase								=     self.num_attention_heads
  								lowerCamelCase								=     (
  								    batch,
  								    num_encoder_attention_heads,
  								    encoder_seq_length,
  								    self._config.hidden_size // num_encoder_attention_heads,
  								)
  								lowerCamelCase								=     decoder_seq_length
  								lowerCamelCase								=     (
  								    batch,
  								    num_decoder_attention_heads,
  								    decoder_past_length,
  								    self._config.hidden_size // num_decoder_attention_heads,
  								)
  								lowerCamelCase								=     torch.cat(
  								    [common_inputs["""decoder_attention_mask"""], torch.ones(A		,			A  )]		,			dim=1  )
  								lowerCamelCase								=     []
  								lowerCamelCase		,		lowerCamelCase								=     self.num_layers
  
  								for _ in range(A  ):
  											common_inputs["past_key_values"].append(
  											    (
  											        torch.zeros(A  ),
  											        torch.zeros(A  ),
  											        torch.zeros(A  ),
  											        torch.zeros(A  ),
  											    )  )
  					return common_inputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A = -1		,			A = -1		,			A = False		,			A = None		,			)     ->   Mapping[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  					    A		,			A		,			A		,			A		,			A  )
  
  					if self.use_past:
  								if not is_torch_available():
  											raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""  )
  								else:
  											import torch
  								lowerCamelCase		,		lowerCamelCase								=     common_inputs["""input_ids"""].shape
  								lowerCamelCase								=     seqlen
  								lowerCamelCase		,		lowerCamelCase								=     self.num_layers
  								lowerCamelCase		,		lowerCamelCase								=     self.num_attention_heads
  								lowerCamelCase								=     (
  								    batch,
  								    num_encoder_attention_heads,
  								    past_key_values_length,
  								    self._config.hidden_size // num_encoder_attention_heads,
  								)
  								lowerCamelCase								=     common_inputs["""attention_mask"""].dtype
  								lowerCamelCase								=     torch.cat(
  								    [common_inputs["""attention_mask"""], torch.ones(A		,			A		,			dtype=A  )]		,			dim=1  )
  								lowerCamelCase								=     [
  								    (torch.zeros(A  ), torch.zeros(A  )) for _ in range(A  )
  								]
  					return common_inputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A = -1		,			A = -1		,			A = False		,			A = None		,			)     ->   Mapping[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     compute_effective_axis_dimension(
  					    A		,			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
  					lowerCamelCase								=     tokenizer.num_special_tokens_to_add(A  )
  					lowerCamelCase								=     compute_effective_axis_dimension(
  					    A		,			fixed_dimension=OnnxConfig.default_fixed_sequence		,			num_token_to_add=A  )
  
  					# Generate dummy inputs according to compute batch and sequence
  					lowerCamelCase								=     [""" """.join([tokenizer.unk_token]  ) * seq_length] * batch_size
  					lowerCamelCase								=     dict(tokenizer(A		,			return_tensors=A  )  )
  					return common_inputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A = -1		,			A = -1		,			A = False		,			A = None		,			)     ->   Mapping[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self.task in ["default", "seq2seq-lm"]:
  								lowerCamelCase								=     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
  								    A		,			batch_size=A		,			seq_length=A		,			is_pair=A		,			framework=A  )
  
  					elif self.task == "causal-lm":
  								lowerCamelCase								=     self._generate_dummy_inputs_for_causal_lm(
  								    A		,			batch_size=A		,			seq_length=A		,			is_pair=A		,			framework=A  )
  					else:
  								lowerCamelCase								=     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  								    A		,			batch_size=A		,			seq_length=A		,			is_pair=A		,			framework=A  )
  
  					return common_inputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A		,			A		,			A  )     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self.task in ["default", "seq2seq-lm"]:
  								lowerCamelCase								=     super()._flatten_past_key_values_(A		,			A		,			A		,			A  )
  					else:
  								lowerCamelCase								=     super(A		,			self  )._flatten_past_key_values_(
  								    A		,			A		,			A		,			A  )
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if direction not in ["inputs", "outputs"]:
  								raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given'  )
  
  					lowerCamelCase								=     """past_key_values""" if direction == """inputs""" else """present"""
  					lowerCamelCase		,		lowerCamelCase								=     self.num_layers
  
  					lowerCamelCase								=     """past_encoder_sequence"""
  					lowerCamelCase								=     """past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""
  
  					for i in range(A  ):
  								lowerCamelCase								=     {0: """batch""", 2: decoder_sequence}
  								lowerCamelCase								=     {0: """batch""", 2: decoder_sequence}
  								lowerCamelCase								=     {0: """batch""", 2: encoder_sequence}
  								lowerCamelCase								=     {0: """batch""", 2: encoder_sequence}
  
  
style_context: 
  
  
  import abc
  import copy
  import dataclasses
  from dataclasses import dataclass
  from typing import ClassVar, Dict, Type, TypeVar
  
  from ..features import Features
  
  
  UpperCAmelCase      :						List[Any]    =		TypeVar("T", bound="TaskTemplate")
  
  
  
  
  
  
  
  @dataclass(frozen=a_     )
  class 					__lowercase					(       abc.ABC     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				str
  		UpperCamelCase    :				ClassVar[Features]
  		UpperCamelCase    :				ClassVar[Features]
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A  )     ->   T:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return copy.deepcopy(self  )
  
  
  
  
  
  
  
  		@property
  		def 				__A      (   self  )     ->   Features:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return Features(**self.input_schema		,			**self.label_schema  )
  
  
  
  
  
  
  
  		@property
  		@abc.abstractmethod
  		def 				__A      (   self  )     ->   Dict[str, str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					raise NotImplementedError
  
  
  
  
  
  
  
  		@classmethod
  		def 				__A      (   cls		,			A  )     ->   T:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     {f.name for f in dataclasses.fields(cls  )}
  					return cls(**{k: v for k, v in template_dict.items() if k in field_names}  )
  
  