

"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class     __a   :

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						('_front', '_back', '_len')

			@dataclass
			class     __a   :

						"""simple docstring"""

						SCREAMING_SNAKE_CASE_						     =						None
						SCREAMING_SNAKE_CASE_						     =						None
						SCREAMING_SNAKE_CASE_						     =						None



			class     __a   :

						"""simple docstring"""

						SCREAMING_SNAKE_CASE_						     =						('_cur',)






						def __init__(							self     :					Optional[int]						,  lowercase_     :					Deque._Node | None						):
											UpperCamelCase__		:    List[str]         =cur






						def __iter__(							self     :					List[str]						):
											return self






						def    _lowerCAmelCase			(							self     :					List[Any]						):
											if self._cur is None:
																# finished iterating
																raise StopIteration
											UpperCamelCase__		:    str         =self._cur.val
											UpperCamelCase__		:    Any         =self._cur.next_node

											return val






			def __init__(							self     :					str						,  lowercase_     :					Iterable[Any] | None = None						):
								UpperCamelCase__		:    Any         =None
								UpperCamelCase__		:    Any         =None
								UpperCamelCase__		:    int         =0

								if iterable is not None:
													# append every value to the deque
													for val in iterable:
																		self.append(lowercase_						)






			def    _lowerCAmelCase			(							self     :					str						,  lowercase_     :					Any						):
								UpperCamelCase__		:    Union[str, Any]         =self._Node(lowercase_						,  lowercase_						,  lowercase_						)
								if self.is_empty():
													# front = back
													UpperCamelCase__		:    Any         =node
													UpperCamelCase__		:    List[Any]         =1
								else:
													# connect nodes
													UpperCamelCase__		:    Any         =node
													UpperCamelCase__		:    Optional[Any]         =self._back
													UpperCamelCase__		:    str         =node  # assign new back to the new node

													self._len += 1

													# make sure there were no errors
													assert not self.is_empty(), "Error on appending value."






			def    _lowerCAmelCase			(							self     :					Optional[Any]						,  lowercase_     :					Any						):
								UpperCamelCase__		:    str         =self._Node(lowercase_						,  lowercase_						,  lowercase_						)
								if self.is_empty():
													# front = back
													UpperCamelCase__		:    str         =node
													UpperCamelCase__		:    Any         =1
								else:
													# connect nodes
													UpperCamelCase__		:    int         =self._front
													UpperCamelCase__		:    int         =node
													UpperCamelCase__		:    Any         =node  # assign new front to the new node

													self._len += 1

													# make sure there were no errors
													assert not self.is_empty(), "Error on appending value."






			def    _lowerCAmelCase			(							self     :					List[Any]						,  lowercase_     :					Iterable[Any]						):
								for val in iterable:
													self.append(lowercase_						)






			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					Iterable[Any]						):
								for val in iterable:
													self.appendleft(lowercase_						)






			def    _lowerCAmelCase			(							self     :					Optional[int]						):
								assert not self.is_empty(), "Deque is empty."

								UpperCamelCase__		:    List[Any]         =self._back
								UpperCamelCase__		:    Any         =self._back.prev_node  # set new back
								# drop the last node - python will deallocate memory automatically
								UpperCamelCase__		:    List[str]         =None

								self._len -= 1

								return topop.val






			def    _lowerCAmelCase			(							self     :					str						):
								assert not self.is_empty(), "Deque is empty."

								UpperCamelCase__		:    List[str]         =self._front
								UpperCamelCase__		:    List[Any]         =self._front.next_node  # set new front and drop the first node
								UpperCamelCase__		:    Dict         =None

								self._len -= 1

								return topop.val






			def    _lowerCAmelCase			(							self     :					Dict						):
								return self._front is None






			def __len__(							self     :					Any						):
								return self._len






			def __eq__(							self     :					Dict						,  lowercase_     :					object						):

								if not isinstance(lowercase_						,  lowercase_						):
													return NotImplemented

								UpperCamelCase__		:    Union[str, Any]         =self._front
								UpperCamelCase__		:    Union[str, Any]         =other._front

								# if the length of the dequeues are not the same, they are not equal
								if len(self						) != len(lowercase_						):
													return False

								while me is not None and oth is not None:
													# compare every value
													if me.val != oth.val:
																		return False
													UpperCamelCase__		:    Union[str, Any]         =me.next_node
													UpperCamelCase__		:    Union[str, Any]         =oth.next_node

								return True






			def __iter__(							self     :					Optional[int]						):
								return Deque._Iterator(self._front						)






			def __repr__(							self     :					Tuple						):
								UpperCamelCase__		:    List[Any]         =[]
								UpperCamelCase__		:    Optional[int]         =self._front
								while aux is not None:
													# append the values in a list to display
													values_list.append(aux.val						)
													UpperCamelCase__		:    Optional[Any]         =aux.next_node

								return f'''[{', '.join(repr(lowercase_						) for val in values_list						)}]'''


if __name__ == "__main__":
				import doctest

				doctest.testmod()





"""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


_SCREAMING_SNAKE_CASE       :  Any        = logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE       :  Optional[int]        = {
    """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     __a   (      snake_case__    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						'blenderbot'
			SCREAMING_SNAKE_CASE_						     =						['past_key_values']
			SCREAMING_SNAKE_CASE_						     =						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






			def __init__(							self     :					Dict						,  lowercase_     :					Any=8008						,  lowercase_     :					Dict=128						,  lowercase_     :					Tuple=2						,  lowercase_     :					List[Any]=1_0240						,  lowercase_     :					Optional[int]=32						,  lowercase_     :					Optional[Any]=24						,  lowercase_     :					str=1_0240						,  lowercase_     :					Tuple=32						,  lowercase_     :					Optional[int]=0.0						,  lowercase_     :					Optional[int]=0.0						,  lowercase_     :					str=True						,  lowercase_     :					List[str]=True						,  lowercase_     :					Union[str, Any]="gelu"						,  lowercase_     :					List[str]=2560						,  lowercase_     :					Any=0.1						,  lowercase_     :					List[Any]=0.0						,  lowercase_     :					Any=0.0						,  lowercase_     :					Union[str, Any]=0.0_2						,  lowercase_     :					Any=1						,  lowercase_     :					Optional[int]=False						,  lowercase_     :					int=0						,  lowercase_     :					Optional[int]=1						,  lowercase_     :					Any=2						,  lowercase_     :					Any=3						,  lowercase_     :					Dict=2						,  **lowercase_     :					Optional[int]						,  ):
								UpperCamelCase__		:    int         =vocab_size
								UpperCamelCase__		:    List[Any]         =max_position_embeddings
								UpperCamelCase__		:    List[Any]         =d_model
								UpperCamelCase__		:    Union[str, Any]         =encoder_ffn_dim
								UpperCamelCase__		:    Optional[Any]         =encoder_layers
								UpperCamelCase__		:    List[str]         =encoder_attention_heads
								UpperCamelCase__		:    Optional[Any]         =decoder_ffn_dim
								UpperCamelCase__		:    Dict         =decoder_layers
								UpperCamelCase__		:    int         =decoder_attention_heads
								UpperCamelCase__		:    List[Any]         =dropout
								UpperCamelCase__		:    Optional[int]         =attention_dropout
								UpperCamelCase__		:    List[Any]         =activation_dropout
								UpperCamelCase__		:    Dict         =activation_function
								UpperCamelCase__		:    Optional[int]         =init_std
								UpperCamelCase__		:    Optional[int]         =encoder_layerdrop
								UpperCamelCase__		:    Tuple         =decoder_layerdrop
								UpperCamelCase__		:    List[str]         =use_cache
								UpperCamelCase__		:    int         =encoder_layers
								UpperCamelCase__		:    str         =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     __a   (      snake_case__    ):

			"""simple docstring"""

			@property
			def    _lowerCAmelCase			(							self     :					List[Any]						):
								if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase__		:    List[str]         =OrderedDict(
													    [
													        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
													        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
													    ]						)
													if self.use_past:
																		UpperCamelCase__		:    Any         ={0: '''batch'''}
																		UpperCamelCase__		:    Optional[Any]         ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
													else:
																		UpperCamelCase__		:    Optional[Any]         ={0: '''batch''', 1: '''decoder_sequence'''}
																		UpperCamelCase__		:    List[str]         ={0: '''batch''', 1: '''decoder_sequence'''}
													if self.use_past:
																		self.fill_with_past_key_values_(lowercase_						,  direction='''inputs'''						)
								elif self.task == "causal-lm":
													UpperCamelCase__		:    Optional[Any]         =OrderedDict(
													    [
													        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
													        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
													    ]						)
													if self.use_past:
																		UpperCamelCase__  ,			UpperCamelCase__		:    str         =self.num_layers
																		for i in range(lowercase_						):
																							UpperCamelCase__		:    Optional[int]         ={0: '''batch''', 2: '''past_sequence + sequence'''}
																							UpperCamelCase__		:    Dict         ={0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
													UpperCamelCase__		:    Any         =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    _lowerCAmelCase			(							self     :					Tuple						):
								if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase__		:    List[Any]         =super().outputs
								else:
													UpperCamelCase__		:    List[str]         =super(lowercase_						,  self						).outputs
													if self.use_past:
																		UpperCamelCase__  ,			UpperCamelCase__		:    int         =self.num_layers
																		for i in range(lowercase_						):
																							UpperCamelCase__		:    Dict         ={0: '''batch''', 2: '''past_sequence + sequence'''}
																							UpperCamelCase__		:    Dict         ={0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs






			def    _lowerCAmelCase			(							self     :					Optional[int]						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = None						,  ):
								UpperCamelCase__		:    List[Any]         =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
								# Generate decoder inputs
								UpperCamelCase__		:    List[Any]         =seq_length if not self.use_past else 1
								UpperCamelCase__		:    int         =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
								UpperCamelCase__		:    str         ={f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
								UpperCamelCase__		:    str         =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
													UpperCamelCase__  ,			UpperCamelCase__		:    Optional[int]         =common_inputs['''input_ids'''].shape
													UpperCamelCase__		:    Any         =common_inputs['''decoder_input_ids'''].shape[1]
													UpperCamelCase__  ,			UpperCamelCase__		:    Union[str, Any]         =self.num_attention_heads
													UpperCamelCase__		:    Any         =(
													    batch,
													    num_encoder_attention_heads,
													    encoder_seq_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													UpperCamelCase__		:    Tuple         =decoder_seq_length
													UpperCamelCase__		:    Optional[Any]         =(
													    batch,
													    num_decoder_attention_heads,
													    decoder_past_length,
													    self._config.hidden_size // num_decoder_attention_heads,
													)
													UpperCamelCase__		:    Union[str, Any]         =torch.cat(
													    [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_						,  lowercase_						)]						,  dim=1						)
													UpperCamelCase__		:    Any         =[]
													UpperCamelCase__  ,			UpperCamelCase__		:    Union[str, Any]         =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    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = None						,  ):
								UpperCamelCase__		:    str         =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
													UpperCamelCase__  ,			UpperCamelCase__		:    Tuple         =common_inputs['''input_ids'''].shape
													UpperCamelCase__		:    List[Any]         =seqlen
													UpperCamelCase__  ,			UpperCamelCase__		:    Any         =self.num_layers
													UpperCamelCase__  ,			UpperCamelCase__		:    Tuple         =self.num_attention_heads
													UpperCamelCase__		:    Optional[int]         =(
													    batch,
													    num_encoder_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // num_encoder_attention_heads,
													)
													UpperCamelCase__		:    Union[str, Any]         =common_inputs['''attention_mask'''].dtype
													UpperCamelCase__		:    int         =torch.cat(
													    [common_inputs['''attention_mask'''], torch.ones(lowercase_						,  lowercase_						,  dtype=lowercase_						)]						,  dim=1						)
													UpperCamelCase__		:    List[str]         =[
													    (torch.zeros(lowercase_						), torch.zeros(lowercase_						)) for _ in range(lowercase_						)
													]
								return common_inputs






			def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = 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
								UpperCamelCase__		:    str         =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
								UpperCamelCase__		:    Union[str, Any]         =tokenizer.num_special_tokens_to_add(lowercase_						)
								UpperCamelCase__		:    str         =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
								UpperCamelCase__		:    Union[str, Any]         =[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
								UpperCamelCase__		:    Tuple         =dict(tokenizer(lowercase_						,  return_tensors=lowercase_						)						)
								return common_inputs






			def    _lowerCAmelCase			(							self     :					int						,  lowercase_     :					PreTrainedTokenizer						,  lowercase_     :					int = -1						,  lowercase_     :					int = -1						,  lowercase_     :					bool = False						,  lowercase_     :					Optional[TensorType] = None						,  ):
								if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase__		:    Tuple         =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":
													UpperCamelCase__		:    Union[str, Any]         =self._generate_dummy_inputs_for_causal_lm(
													    lowercase_						,  batch_size=lowercase_						,  seq_length=lowercase_						,  is_pair=lowercase_						,  framework=lowercase_						)
								else:
													UpperCamelCase__		:    Optional[int]         =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    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					int						,  lowercase_     :					Union[str, Any]						,  lowercase_     :					Optional[Any]						,  lowercase_     :					List[Any]						):
								if self.task in ["default", "seq2seq-lm"]:
													UpperCamelCase__		:    Optional[int]         =super()._flatten_past_key_values_(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
								else:
													UpperCamelCase__		:    Optional[int]         =super(lowercase_						,  self						)._flatten_past_key_values_(
													    lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)






			def    _lowerCAmelCase			(							self     :					List[str]						,  lowercase_     :					Mapping[str, Mapping[int, str]]						,  lowercase_     :					str						):
								if direction not in ["inputs", "outputs"]:
													raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''						)

								UpperCamelCase__		:    Optional[int]         ='''past_key_values''' if direction == '''inputs''' else '''present'''
								UpperCamelCase__  ,			UpperCamelCase__		:    Optional[int]         =self.num_layers

								UpperCamelCase__		:    List[str]         ='''past_encoder_sequence'''
								UpperCamelCase__		:    Optional[int]         ='''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

								for i in range(lowercase_						):
													UpperCamelCase__		:    Tuple         ={0: '''batch''', 2: decoder_sequence}
													UpperCamelCase__		:    Union[str, Any]         ={0: '''batch''', 2: decoder_sequence}
													UpperCamelCase__		:    str         ={0: '''batch''', 2: encoder_sequence}
													UpperCamelCase__		:    List[Any]         ={0: '''batch''', 2: encoder_sequence}


