



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    :						Any  =						logging.get_logger(__name__)

snake_case    :						Union[str, Any]  =						{
    '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 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    List[str]           =						'blenderbot-small'
					__A     :    List[str]           =						['past_key_values']
					__A     :    str           =						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







					def __init__(       self      :     int			,		_A      :     Optional[int]=5_0265			,		_A      :     Dict=512			,		_A      :     str=8			,		_A      :     Optional[Any]=2048			,		_A      :     Union[str, Any]=16			,		_A      :     Dict=8			,		_A      :     List[Any]=2048			,		_A      :     Union[str, Any]=16			,		_A      :     Union[str, Any]=0.0			,		_A      :     List[Any]=0.0			,		_A      :     Optional[Any]=True			,		_A      :     List[str]=True			,		_A      :     Union[str, Any]="gelu"			,		_A      :     Union[str, Any]=512			,		_A      :     List[str]=0.1			,		_A      :     Union[str, Any]=0.0			,		_A      :     str=0.0			,		_A      :     str=0.02			,		_A      :     Optional[Any]=1			,		_A      :     str=False			,		_A      :     Union[str, Any]=0			,		_A      :     List[str]=1			,		_A      :     Tuple=2			,		_A      :     Optional[Any]=2			,		**_A      :     Any			,		):
											A__	:							Union[str, Any]						    = vocab_size
											A__	:							List[str]						    = max_position_embeddings
											A__	:							str						    = d_model
											A__	:							str						    = encoder_ffn_dim
											A__	:							Any						    = encoder_layers
											A__	:							Dict						    = encoder_attention_heads
											A__	:							List[str]						    = decoder_ffn_dim
											A__	:							List[str]						    = decoder_layers
											A__	:							int						    = decoder_attention_heads
											A__	:							Tuple						    = dropout
											A__	:							int						    = attention_dropout
											A__	:							int						    = activation_dropout
											A__	:							Dict						    = activation_function
											A__	:							List[Any]						    = init_std
											A__	:							str						    = encoder_layerdrop
											A__	:							Tuple						    = decoder_layerdrop
											A__	:							Tuple						    = use_cache
											A__	:							Union[str, Any]						    = encoder_layers
											A__	:							List[str]						    = 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			,		forced_eos_token_id=_A			,		**_A			,		)




class 							lowerCAmelCase__  (	UpperCamelCase						):







					@property
					def  _lowercase			(       self      :     Union[str, Any]):
											if self.task in ["default", "seq2seq-lm"]:
																	A__	:							int						    = 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__	:							str						    = {0: "batch", 1: "past_decoder_sequence + sequence"}
																	else:
																							A__	:							Dict						    = {0: "batch", 1: "decoder_sequence"}
																							A__	:							Dict						    = {0: "batch", 1: "decoder_sequence"}

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







					def  _lowercase			(       self      :     List[Any]			,		_A      :     PreTrainedTokenizer			,		_A      :     int = -1			,		_A      :     int = -1			,		_A      :     bool = False			,		_A      :     Optional[TensorType] = None			,		):
											A__	:							List[str]						    = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    _A			,		_A			,		_A			,		_A			,		_A)

											# Generate decoder inputs
											A__	:							str						    = seq_length if not self.use_past else 1
											A__	:							int						    = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    _A			,		_A			,		_A			,		_A			,		_A)
											A__	:							str						    = {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
											A__	:							Tuple						    = 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
																	A__    ,       A__	:							Union[str, Any]						    = common_inputs["input_ids"].shape
																	A__	:							Optional[int]						    = common_inputs["decoder_input_ids"].shape[1]
																	A__    ,       A__	:							int						    = self.num_attention_heads
																	A__	:							Optional[int]						    = (
																	    batch,
																	    num_encoder_attention_heads,
																	    encoder_seq_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	A__	:							Any						    = decoder_seq_length + 3
																	A__	:							str						    = (
																	    batch,
																	    num_decoder_attention_heads,
																	    decoder_past_length,
																	    self._config.hidden_size // num_decoder_attention_heads,
																	)

																	A__	:							Dict						    = torch.cat(
																	    [common_inputs["decoder_attention_mask"], torch.ones(_A			,		_A)]			,		dim=1)

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

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







					def  _lowercase			(       self      :     int			,		_A      :     PreTrainedTokenizer			,		_A      :     int = -1			,		_A      :     int = -1			,		_A      :     bool = False			,		_A      :     Optional[TensorType] = None			,		):
											A__	:							List[str]						    = 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
																	A__    ,       A__	:							Tuple						    = common_inputs["input_ids"].shape
																	# Not using the same length for past_key_values
																	A__	:							Any						    = seqlen + 2
																	A__    ,       A__	:							str						    = self.num_layers
																	A__    ,       A__	:							Union[str, Any]						    = self.num_attention_heads
																	A__	:							Dict						    = (
																	    batch,
																	    num_encoder_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)

																	A__	:							List[Any]						    = common_inputs["attention_mask"].dtype
																	A__	:							Optional[Any]						    = torch.cat(
																	    [common_inputs["attention_mask"], torch.ones(_A			,		_A			,		dtype=_A)]			,		dim=1)
																	A__	:							Any						    = [
																	    (torch.zeros(_A), torch.zeros(_A)) for _ in range(_A)
																	]
											return common_inputs







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     PreTrainedTokenizer			,		_A      :     int = -1			,		_A      :     int = -1			,		_A      :     bool = False			,		_A      :     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
											A__	:							List[Any]						    = 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
											A__	:							Any						    = tokenizer.num_special_tokens_to_add(_A)
											A__	:							str						    = 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
											A__	:							Optional[int]						    = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
											A__	:							List[Any]						    = dict(tokenizer(_A			,		return_tensors=_A))
											return common_inputs







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




def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase = 0							,     __lowercase = 0       )		->		int:







						"""simple docstring"""

						A__	:							Tuple						    = right or len(__lowercase       ) - 1
						if left > right:
												return -1
						elif list_data[left] == key:
												return left
						elif list_data[right] == key:
												return right
						else:
												return search(__lowercase							,     __lowercase							,     left + 1							,     right - 1       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()