


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


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    '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 	A_ (	lowerCamelCase__   ):




						'''simple docstring'''


						__snake_case 	=       """blenderbot-small"""
						__snake_case 	=       ["""past_key_values"""]
						__snake_case 	=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




						def __init__(    self:	List[str]						,				a:	Dict=5_0265						,				a:	Tuple=512						,				a:	Dict=8						,				a:	Optional[int]=2048						,				a:	Optional[Any]=16						,				a:	List[str]=8						,				a:	Union[str, Any]=2048						,				a:	int=16						,				a:	Optional[int]=0.0						,				a:	Optional[int]=0.0						,				a:	int=True						,				a:	List[str]=True						,				a:	Union[str, Any]="gelu"						,				a:	Union[str, Any]=512						,				a:	List[str]=0.1						,				a:	List[Any]=0.0						,				a:	Tuple=0.0						,				a:	Tuple=0.0_2						,				a:	Dict=1						,				a:	str=False						,				a:	List[Any]=0						,				a:	Any=1						,				a:	int=2						,				a:	List[Any]=2						,				**a:	Optional[Any]						,				):
							__lowerCamelCase		:							Optional[int]		 =						vocab_size
							__lowerCamelCase		:							int		 =						max_position_embeddings
							__lowerCamelCase		:							Any		 =						d_model
							__lowerCamelCase		:							str		 =						encoder_ffn_dim
							__lowerCamelCase		:							List[Any]		 =						encoder_layers
							__lowerCamelCase		:							Dict		 =						encoder_attention_heads
							__lowerCamelCase		:							List[str]		 =						decoder_ffn_dim
							__lowerCamelCase		:							Optional[Any]		 =						decoder_layers
							__lowerCamelCase		:							str		 =						decoder_attention_heads
							__lowerCamelCase		:							List[str]		 =						dropout
							__lowerCamelCase		:							int		 =						attention_dropout
							__lowerCamelCase		:							Union[str, Any]		 =						activation_dropout
							__lowerCamelCase		:							Tuple		 =						activation_function
							__lowerCamelCase		:							Union[str, Any]		 =						init_std
							__lowerCamelCase		:							Dict		 =						encoder_layerdrop
							__lowerCamelCase		:							Dict		 =						decoder_layerdrop
							__lowerCamelCase		:							List[Any]		 =						use_cache
							__lowerCamelCase		:							Optional[Any]		 =						encoder_layers
							__lowerCamelCase		:							Optional[Any]		 =						scale_embedding  # scale factor will be sqrt(d_model) if True

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



class 	A_ (	lowerCamelCase__   ):




						'''simple docstring'''


						@property
						def 					_snake_case     (    self:	int    ):






							if self.task in ["default", "seq2seq-lm"]:
								__lowerCamelCase		:							Any		 =						OrderedDict(
								    [
								        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
								        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
								    ]    )

								if self.use_past:
									__lowerCamelCase		:							Tuple		 =						{0: '''batch'''}
									__lowerCamelCase		:							Dict		 =						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
									__lowerCamelCase		:							Any		 =						{0: '''batch''', 1: '''decoder_sequence'''}
									__lowerCamelCase		:							Optional[int]		 =						{0: '''batch''', 1: '''decoder_sequence'''}

								if self.use_past:
									self.fill_with_past_key_values_(__lowerCamelCase						,				direction='inputs'    )
							elif self.task == "causal-lm":
								# TODO: figure this case out.
								__lowerCamelCase		:							Union[str, Any]		 =						OrderedDict(
								    [
								        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
								        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
								    ]    )
								if self.use_past:
									__lowerCamelCase		:							Union[str, Any]		 =						self.num_layers
									for i in range(__lowerCamelCase    ):
										__lowerCamelCase		:							Optional[Any]		 =						{0: '''batch''', 2: '''past_sequence + sequence'''}
										__lowerCamelCase		:							Dict		 =						{0: '''batch''', 2: '''past_sequence + sequence'''}
							else:
								__lowerCamelCase		:							str		 =						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 					_snake_case     (    self:	int    ):






							if self.task in ["default", "seq2seq-lm"]:
								__lowerCamelCase		:							Dict		 =						super().outputs
							else:
								__lowerCamelCase		:							int		 =						super(__lowerCamelCase						,				self    ).outputs
								if self.use_past:
									__lowerCamelCase		:							Optional[int]		 =						self.num_layers
									for i in range(__lowerCamelCase    ):
										__lowerCamelCase		:							str		 =						{0: '''batch''', 2: '''past_sequence + sequence'''}
										__lowerCamelCase		:							Union[str, Any]		 =						{0: '''batch''', 2: '''past_sequence + sequence'''}
							return common_outputs




						def 					_snake_case     (    self:	List[str]						,				a:	Any						,				a:	Optional[int] = -1						,				a:	str = -1						,				a:	int = False						,				a:	Any = None						,				):
							__lowerCamelCase		:							Tuple		 =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase    )

							# Generate decoder inputs
							__lowerCamelCase		:							List[str]		 =						seq_length if not self.use_past else 1
							__lowerCamelCase		:							int		 =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase    )
							__lowerCamelCase		:							Optional[Any]		 =						{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
							__lowerCamelCase		:							Union[str, Any]		 =						dict(**__lowerCamelCase						,				**__lowerCamelCase    )

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

								__lowerCamelCase		:							Optional[int]		 =						torch.cat(
								    [common_inputs['decoder_attention_mask'], torch.ones(__lowerCamelCase						,				__lowerCamelCase    )]						,				dim=1    )

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

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




						def 					_snake_case     (    self:	List[Any]						,				a:	List[Any]						,				a:	Optional[Any] = -1						,				a:	str = -1						,				a:	List[str] = False						,				a:	Dict = None						,				):
							__lowerCamelCase		:							List[Any]		 =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase    )

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

								__lowerCamelCase		:							List[str]		 =						common_inputs['''attention_mask'''].dtype
								__lowerCamelCase		:							List[str]		 =						torch.cat(
								    [common_inputs['attention_mask'], torch.ones(__lowerCamelCase						,				__lowerCamelCase						,				dtype=__lowerCamelCase    )]						,				dim=1    )
								__lowerCamelCase		:							Tuple		 =						[
								    (torch.zeros(__lowerCamelCase    ), torch.zeros(__lowerCamelCase    )) for _ in range(__lowerCamelCase    )
								]
							return common_inputs




						def 					_snake_case     (    self:	Optional[Any]						,				a:	List[str]						,				a:	Union[str, Any] = -1						,				a:	Optional[int] = -1						,				a:	List[Any] = False						,				a:	int = None						,				):
							__lowerCamelCase		:							List[Any]		 =						compute_effective_axis_dimension(
							    __lowerCamelCase						,				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		:							Optional[Any]		 =						tokenizer.num_special_tokens_to_add(__lowerCamelCase    )
							__lowerCamelCase		:							Tuple		 =						compute_effective_axis_dimension(
							    __lowerCamelCase						,				fixed_dimension=OnnxConfig.default_fixed_sequence						,				num_token_to_add=__lowerCamelCase    )

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




						def 					_snake_case     (    self:	Union[str, Any]						,				a:	str						,				a:	Dict = -1						,				a:	str = -1						,				a:	Any = False						,				a:	Tuple = None						,				):






							if self.task in ["default", "seq2seq-lm"]:
								__lowerCamelCase		:							List[str]		 =						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
								    __lowerCamelCase						,				batch_size=__lowerCamelCase						,				seq_length=__lowerCamelCase						,				is_pair=__lowerCamelCase						,				framework=__lowerCamelCase    )

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

							return common_inputs




						def 					_snake_case     (    self:	Any						,				a:	Optional[Any]						,				a:	List[Any]						,				a:	str						,				a:	int    ):






							if self.task in ["default", "seq2seq-lm"]:
								__lowerCamelCase		:							Union[str, Any]		 =						super()._flatten_past_key_values_(__lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase    )
							else:
								__lowerCamelCase		:							Optional[int]		 =						super(__lowerCamelCase						,				self    )._flatten_past_key_values_(
								    __lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase						,				__lowerCamelCase    )





def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						len(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Any		 =						[[False] * (required_sum + 1) for _ in range(arr_len + 1						)]

 # for each arr value, a sum of zero(0) can be formed by not taking any element
 # hence True/1
 for i in range(arr_len + 1						):
  __lowerCamelCase		:							Dict		 =						True

 # sum is not zero and set is empty then false
 for i in range(1      ,	required_sum + 1						):
  __lowerCamelCase		:							Union[str, Any]		 =						False

 for i in range(1      ,	arr_len + 1						):
  for j in range(1      ,	required_sum + 1						):
   if arr[i - 1] > j:
    __lowerCamelCase		:							Tuple		 =						subset[i - 1][j]
   if arr[i - 1] <= j:
    __lowerCamelCase		:							List[Any]		 =						subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

 return subset[arr_len][required_sum]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

