
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_						:     Optional[Any]	       =							logging.get_logger(__name__)

UpperCamelCase_						:     List[str]	       =							{
    """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   __lowercase						(						__snake_case       ):
	_A       =   "blenderbot-small"
	_A       =   ["past_key_values"]
	_A       =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






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

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




class   __lowercase						(						__snake_case       ):






	@property
	def       _a(self				: List[Any]    ) ->			Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
													_lowercase       :	int           =   OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ]    )

													if self.use_past:
																			_lowercase       :	str           =   {0: "batch"}
																			_lowercase       :	int           =   {0: "batch", 1: "past_decoder_sequence + sequence"}
													else:
																			_lowercase       :	List[Any]           =   {0: "batch", 1: "decoder_sequence"}
																			_lowercase       :	Optional[Any]           =   {0: "batch", 1: "decoder_sequence"}

													if self.use_past:
																			self.fill_with_past_key_values_(snake_case      ,	direction="inputs"    )
							elif self.task == "causal-lm":
													# TODO: figure this case out.
													_lowercase       :	List[str]           =   OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ]    )
													if self.use_past:
																			_lowercase      ,    _lowercase       :	Dict           =   self.num_layers
																			for i in range(snake_case    ):
																									_lowercase       :	Tuple           =   {0: "batch", 2: "past_sequence + sequence"}
																									_lowercase       :	List[Any]           =   {0: "batch", 2: "past_sequence + sequence"}
							else:
													_lowercase       :	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       _a(self				: int    ) ->			Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
													_lowercase       :	Optional[Any]           =   super().outputs
							else:
													_lowercase       :	int           =   super(snake_case      ,	self    ).outputs
													if self.use_past:
																			_lowercase      ,    _lowercase       :	Tuple           =   self.num_layers
																			for i in range(snake_case    ):
																									_lowercase       :	Tuple           =   {0: "batch", 2: "past_sequence + sequence"}
																									_lowercase       :	List[str]           =   {0: "batch", 2: "past_sequence + sequence"}
							return common_outputs






	def       _a(self				: Union[str, Any]      ,	snake_case				: PreTrainedTokenizer      ,	snake_case				: int = -1      ,	snake_case				: int = -1      ,	snake_case				: bool = False      ,	snake_case				: Optional[TensorType] = None      ,	) ->			Mapping[str, Any]:
							_lowercase       :	str           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case    )

							# Generate decoder inputs
							_lowercase       :	Optional[Any]           =   seq_length if not self.use_past else 1
							_lowercase       :	Dict           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case    )
							_lowercase       :	int           =   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
							_lowercase       :	Optional[int]           =   dict(**snake_case      ,	**snake_case    )

							if self.use_past:
													if not is_torch_available():
																			raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."    )
													else:
																			import torch
													_lowercase      ,    _lowercase       :	Dict           =   common_inputs["input_ids"].shape
													_lowercase       :	int           =   common_inputs["decoder_input_ids"].shape[1]
													_lowercase      ,    _lowercase       :	Dict           =   self.num_attention_heads
													_lowercase       :	Any           =   (
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													_lowercase       :	Any           =   decoder_seq_length + 3
													_lowercase       :	Optional[Any]           =   (
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)

													_lowercase       :	Union[str, Any]           =   torch.cat(
													    [common_inputs["decoder_attention_mask"], torch.ones(snake_case      ,	snake_case    )]      ,	dim=1    )

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

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






	def       _a(self				: Union[str, Any]      ,	snake_case				: PreTrainedTokenizer      ,	snake_case				: int = -1      ,	snake_case				: int = -1      ,	snake_case				: bool = False      ,	snake_case				: Optional[TensorType] = None      ,	) ->			Mapping[str, Any]:
							_lowercase       :	List[str]           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case    )

							if self.use_past:
													if not is_torch_available():
																			raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."    )
													else:
																			import torch
													_lowercase      ,    _lowercase       :	Any           =   common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													_lowercase       :	List[str]           =   seqlen + 2
													_lowercase      ,    _lowercase       :	Tuple           =   self.num_layers
													_lowercase      ,    _lowercase       :	List[str]           =   self.num_attention_heads
													_lowercase       :	Union[str, Any]           =   (
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)

													_lowercase       :	Any           =   common_inputs["attention_mask"].dtype
													_lowercase       :	int           =   torch.cat(
													    [common_inputs["attention_mask"], torch.ones(snake_case      ,	snake_case      ,	dtype=snake_case    )]      ,	dim=1    )
													_lowercase       :	int           =   [
													    (torch.zeros(snake_case    ), torch.zeros(snake_case    )) for _ in range(snake_case    )
													]
							return common_inputs






	def       _a(self				: List[Any]      ,	snake_case				: PreTrainedTokenizer      ,	snake_case				: int = -1      ,	snake_case				: int = -1      ,	snake_case				: bool = False      ,	snake_case				: 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
							_lowercase       :	Dict           =   compute_effective_axis_dimension(
							    snake_case      ,	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
							_lowercase       :	List[Any]           =   tokenizer.num_special_tokens_to_add(snake_case    )
							_lowercase       :	int           =   compute_effective_axis_dimension(
							    snake_case      ,	fixed_dimension=OnnxConfig.default_fixed_sequence      ,	num_token_to_add=snake_case    )

							# Generate dummy inputs according to compute batch and sequence
							_lowercase       :	List[Any]           =   [" ".join([tokenizer.unk_token]    ) * seq_length] * batch_size
							_lowercase       :	List[str]           =   dict(tokenizer(snake_case      ,	return_tensors=snake_case    )    )
							return common_inputs






	def       _a(self				: Union[str, Any]      ,	snake_case				: PreTrainedTokenizer      ,	snake_case				: int = -1      ,	snake_case				: int = -1      ,	snake_case				: bool = False      ,	snake_case				: Optional[TensorType] = None      ,	) ->			Mapping[str, Any]:
							if self.task in ["default", "seq2seq-lm"]:
													_lowercase       :	Any           =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    snake_case      ,	batch_size=snake_case      ,	seq_length=snake_case      ,	is_pair=snake_case      ,	framework=snake_case    )

							elif self.task == "causal-lm":
													_lowercase       :	Optional[int]           =   self._generate_dummy_inputs_for_causal_lm(
													    snake_case      ,	batch_size=snake_case      ,	seq_length=snake_case      ,	is_pair=snake_case      ,	framework=snake_case    )
							else:
													_lowercase       :	List[str]           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    snake_case      ,	batch_size=snake_case      ,	seq_length=snake_case      ,	is_pair=snake_case      ,	framework=snake_case    )

							return common_inputs






	def       _a(self				: Optional[Any]      ,	snake_case				: str      ,	snake_case				: Union[str, Any]      ,	snake_case				: Any      ,	snake_case				: List[str]    ) ->			Optional[Any]:
							if self.task in ["default", "seq2seq-lm"]:
													_lowercase       :	List[str]           =   super()._flatten_past_key_values_(snake_case      ,	snake_case      ,	snake_case      ,	snake_case    )
							else:
													_lowercase       :	Tuple           =   super(snake_case      ,	self    )._flatten_past_key_values_(
													    snake_case      ,	snake_case      ,	snake_case      ,	snake_case    )






import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union




@dataclass
class   __lowercase						:
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   None
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   True
	_A       =   None
	_A       =   1
	_A       =   None
	_A       =   False
	_A       =   None
	_A       =   None






	def       _a(self				: str    ) ->			"DownloadConfig":
							return self.__class__(**{k: copy.deepcopy(snake_case    ) for k, v in self.__dict__.items()}    )




